query stringlengths 17 161 | keyphrase_query stringlengths 3 85 | year int64 2.01k 2.02k | negative_cands list | positive_cands list | abstracts list |
|---|---|---|---|---|---|
I want to implement a real-time action detection system. | action detection video | 2,017 | [
"NAB",
"G3D",
"ESAD",
"BAR",
"SoccerDB"
] | [
"UCF101",
"COCO"
] | [
{
"dkey": "UCF101",
"dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). T... |
We propose a deep learning based framework for image relighting. It consists of a generator network which | image relighting images | 2,019 | [
"AMASS",
"Places",
"GoPro",
"UNSW-NB15"
] | [
"CARLA",
"KITTI"
] | [
{
"dkey": "CARLA",
"dval": "CARLA (CAR Learning to Act) is an open simulator for urban driving, developed as an open-source layer over Unreal Engine 4. Technically, it operates similarly to, as an open source layer over Unreal Engine 4 that provides sensors in the form of RGB cameras (with customizable posi... |
I want to select sentences to support my answers for the multi-hop questions. | multi-hop question answering text | 2,019 | [
"WikiHop",
"CommonsenseQA",
"HybridQA",
"GYAFC",
"BiPaR",
"QNLI",
"QED"
] | [
"ARC",
"MultiRC"
] | [
{
"dkey": "ARC",
"dval": "The AI2’s Reasoning Challenge (ARC) dataset is a multiple-choice question-answering dataset, containing questions from science exams from grade 3 to grade 9. The dataset is split in two partitions: Easy and Challenge, where the latter partition contains the more difficult questions... |
This paper proposes a novel self-guiding LSTM (sg-LSTM) image caption | image captioning images text | 2,019 | [
"Bengali Hate Speech",
"Weibo NER",
"nocaps",
"AOLP",
"MSU-MFSD",
"MVSEC"
] | [
"COCO",
"Flickr30k"
] | [
{
"dkey": "COCO",
"dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K imag... |
I want to train a model to find the referred object within the image according to the natural | natural language object retrieval images paragraph-level | 2,017 | [
"SNIPS",
"COVERAGE",
"ConvAI2",
"Image and Video Advertisements",
"Market-1501",
"CLEVR-Hans"
] | [
"COCO",
"ReferItGame"
] | [
{
"dkey": "COCO",
"dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K imag... |
This is the source code of my paper. | language modeling | 2,019 | [
"CommonsenseQA",
"SNIPS",
"PHINC",
"ConvAI2",
"CONCODE"
] | [
"WebText",
"WikiText-103"
] | [
{
"dkey": "WebText",
"dval": "WebText is an internal OpenAI corpus created by scraping web pages with emphasis on\ndocument quality. The authors scraped all outbound links from\nReddit which received at least 3\nkarma. The authors used the approach as a heuristic indicator for\nwhether other users found the... |
We propose an end-to-end model for cross-lingual transfer learning for question answering. We | question answering text | 2,019 | [
"iVQA",
"ReQA",
"EXAMS",
"XQA",
"XQuAD"
] | [
"DRCD",
"NewsQA",
"SQuAD"
] | [
{
"dkey": "DRCD",
"dval": "Delta Reading Comprehension Dataset (DRCD) is an open domain traditional Chinese machine reading comprehension (MRC) dataset. This dataset aimed to be a standard Chinese machine reading comprehension dataset, which can be a source dataset in transfer learning. The dataset contains... |
The proposed model can learn to disentangle appearance and geometric information from image and video sequences in | image/video editing | 2,018 | [
"Moving MNIST",
"irc-disentanglement",
"REDS",
"3DMatch",
"ABC Dataset",
"MAFL"
] | [
"CIFAR-10",
"CelebA"
] | [
{
"dkey": "CIFAR-10",
"dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck),... |
A novel cascaded CNN scheme for accurate face landmark localization. | face landmark localization images | 2,018 | [
"WFLW",
"AFLW2000-3D",
"UTKFace",
"LS3D-W",
"LaPa"
] | [
"Helen",
"AFW"
] | [
{
"dkey": "Helen",
"dval": "The HELEN dataset is composed of 2330 face images of 400×400 pixels with labeled facial components generated through manually-annotated contours along eyes, eyebrows, nose, lips and jawline."
},
{
"dkey": "AFW",
"dval": "AFW (Annotated Faces in the Wild) is a face det... |
We propose a unified model that combines the strengths of two well-established deformable model approaches to the face alignment | face alignment images | 2,015 | [
"iFakeFaceDB",
"PANDORA",
"MaskedFace-Net",
"SpeakingFaces",
"EPIC-KITCHENS-100",
"Scan2CAD"
] | [
"AFW",
"LFPW"
] | [
{
"dkey": "AFW",
"dval": "AFW (Annotated Faces in the Wild) is a face detection dataset that contains 205 images with 468 faces. Each face image is labeled with at most 6 landmarks with visibility labels, as well as a bounding box."
},
{
"dkey": "LFPW",
"dval": "The Labeled Face Parts in-the-Wil... |
We report the results of our replication study on BERT pretraining. Our best model outperforms every published | language model pretraining text | 2,019 | [
"GSL",
"THEODORE",
"ReCAM",
"BDD100K",
"Horne 2017 Fake News Data"
] | [
"QNLI",
"MRPC",
"RACE",
"GLUE",
"SQuAD"
] | [
{
"dkey": "QNLI",
"dval": "The QNLI (Question-answering NLI) dataset is a Natural Language Inference dataset automatically derived from the Stanford Question Answering Dataset v1.1 (SQuAD). SQuAD v1.1 consists of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) co... |
We present a simple and effective model for learning general purpose sentence representations. Our model uses a single | natural language inference text | 2,017 | [
"GLUE",
"SuperGLUE",
"Fluent Speech Commands",
"BDD100K"
] | [
"SNLI",
"MultiNLI"
] | [
{
"dkey": "SNLI",
"dval": "The SNLI dataset (Stanford Natural Language Inference) consists of 570k sentence-pairs manually labeled as entailment, contradiction, and neutral. Premises are image captions from Flickr30k, while hypotheses were generated by crowd-sourced annotators who were shown a premise and a... |
This paper proposes a new multiple-choice reading comprehension (MCRC) model which performs | multiple-choice reading comprehension text paragraph-level | 2,019 | [
"DREAM",
"DROP",
"CosmosQA",
"OneStopQA",
"C3",
"VisualMRC"
] | [
"RACE",
"SQuAD"
] | [
{
"dkey": "RACE",
"dval": "The ReAding Comprehension dataset from Examinations (RACE) dataset is a machine reading comprehension dataset consisting of 27,933 passages and 97,867 questions from English exams, targeting Chinese students aged 12-18. RACE consists of two subsets, RACE-M and RACE-H, from middle ... |
I have been reading about blood vessel segmentation and tried to reproduce the results. | retinal blood vessel segmentation images | 2,017 | [
"IntrA",
"COCO-Tasks",
"ORVS",
"SUN3D",
"ROSE"
] | [
"STARE",
"DRIVE"
] | [
{
"dkey": "STARE",
"dval": "The STARE (Structured Analysis of the Retina) dataset is a dataset for retinal vessel segmentation. It contains 20 equal-sized (700×605) color fundus images. For each image, two groups of annotations are provided.."
},
{
"dkey": "DRIVE",
"dval": "The Digital Retinal I... |
I want to build a model to automatically determine whether an image is acceptable for diagnosis. | fundus image quality classification images | 2,018 | [
"ACDC",
"SemEval 2014 Task 4 Sub Task 2",
"QNLI",
"Image and Video Advertisements",
"IntrA",
"Violin"
] | [
"STARE",
"DRIVE"
] | [
{
"dkey": "STARE",
"dval": "The STARE (Structured Analysis of the Retina) dataset is a dataset for retinal vessel segmentation. It contains 20 equal-sized (700×605) color fundus images. For each image, two groups of annotations are provided.."
},
{
"dkey": "DRIVE",
"dval": "The Digital Retinal I... |
We propose an end-to-end framework to reconstruct the 3D scene from | semantic reconstruction indoor scenes images | 2,020 | [
"DIPS",
"MLe2e",
"E2E",
"DeeperForensics-1.0",
"THEODORE",
"DDD20"
] | [
"COCO",
"Pix3D"
] | [
{
"dkey": "COCO",
"dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K imag... |
We investigate the effectiveness of different pre-trained language models for Question Answering (QA) on four | question answering text | 2,019 | [
"How2QA",
"TweetQA",
"SQuAD-shifts",
"PAQ",
"TVQA"
] | [
"CoQA",
"SQuAD"
] | [
{
"dkey": "CoQA",
"dval": "CoQA is a large-scale dataset for building Conversational Question Answering systems. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation.\n\nCoQA contains 1... |
I want to use a supervised model to recognize activities from low-resolution videos. | extreme low-resolution activity recognition images | 2,019 | [
"TinyVIRAT",
"DAiSEE",
"DIV2K",
"UCF-Crime",
"MPII Cooking 2 Dataset",
"Composable activities dataset",
"FaceForensics"
] | [
"UCF101",
"HMDB51"
] | [
{
"dkey": "UCF101",
"dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). T... |
I want to learn an action recognition model from trimmed videos. | action recognition videos | 2,019 | [
"Kinetics-600",
"Kinetics",
"AViD",
"DISFA",
"JHMDB",
"MTL-AQA",
"EPIC-KITCHENS-100"
] | [
"UCF101",
"ActivityNet"
] | [
{
"dkey": "UCF101",
"dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). T... |
I want to train a supervised model that is robust to adversarial perturbations. | adversarial robustness image classification | 2,018 | [
"ImageNet-P",
"NYU-VP",
"eQASC",
"SNIPS",
"DailyDialog++",
"APRICOT",
"Clothing1M"
] | [
"ImageNet",
"CIFAR-10"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
We propose a simple yet effective approach to exploit the available dense depth | 3d semantic labeling images dense depth maps outdoor street scenes | 2,018 | [
"DocBank",
"IMDB-BINARY",
"REDDIT-BINARY",
"Localized Narratives",
"SBU Captions Dataset",
"Shiny dataset"
] | [
"SYNTHIA",
"Cityscapes"
] | [
{
"dkey": "SYNTHIA",
"dval": "The SYNTHIA dataset is a synthetic dataset that consists of 9400 multi-viewpoint photo-realistic frames rendered from a virtual city and comes with pixel-level semantic annotations for 13 classes. Each frame has resolution of 1280 × 960."
},
{
"dkey": "Cityscapes",
... |
A novel hybrid convolutional and transformer model, WaLDORf, that achieves state-of-the- | nlu text | 2,019 | [
"BraTS 2017",
"THEODORE",
"Glint360K",
"GTEA",
"PG-19",
"LibriSpeech",
"Multi-PIE"
] | [
"QNLI",
"GLUE",
"SQuAD"
] | [
{
"dkey": "QNLI",
"dval": "The QNLI (Question-answering NLI) dataset is a Natural Language Inference dataset automatically derived from the Stanford Question Answering Dataset v1.1 (SQuAD). SQuAD v1.1 consists of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) co... |
Visual question answering (VQA) is an important task in the field of computer | visual question answering images natural language | 2,016 | [
"VizWiz",
"ST-VQA",
"VQA-E",
"TDIUC"
] | [
"DBpedia",
"COCO",
"DAQUAR"
] | [
{
"dkey": "DBpedia",
"dval": "DBpedia (from \"DB\" for \"database\") is a project aiming to extract structured content from the information created in the Wikipedia project. DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other related datas... |
I want to build an effective tracking model based on a simple tracking framework. | tracking image sequences | 2,019 | [
"SNIPS",
"ProPara",
"Frames Dataset",
"PoseTrack"
] | [
"Penn Treebank",
"OTB"
] | [
{
"dkey": "Penn Treebank",
"dval": "The English Penn Treebank (PTB) corpus, and in particular the section of the corpus corresponding to the articles of Wall Street Journal (WSJ), is one of the most known and used corpus for the evaluation of models for sequence labelling. The task consists of annotating ea... |
I want to use distant supervision to extract evidence sentences from reference documents for MRC tasks. | machine reading comprehension text paragraph-level | 2,019 | [
"DocRED",
"Delicious",
"ELI5",
"Melinda",
"DWIE",
"FOBIE"
] | [
"RACE",
"SearchQA",
"MultiNLI"
] | [
{
"dkey": "RACE",
"dval": "The ReAding Comprehension dataset from Examinations (RACE) dataset is a machine reading comprehension dataset consisting of 27,933 passages and 97,867 questions from English exams, targeting Chinese students aged 12-18. RACE consists of two subsets, RACE-M and RACE-H, from middle ... |
I want to train a classifier to classify objects in images. | object classification images | 2,018 | [
"GYAFC",
"UCF101",
"Chinese Classifier",
"Food-101",
"SNIPS",
"StreetStyle"
] | [
"ImageNet",
"CelebA"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
I want to use a video inpainting model to inpaint the frames of | video frame inpainting | 2,020 | [
"FVI",
"DeepFashion",
"DTD",
"OpenEDS",
"SNIPS",
"FaceForensics"
] | [
"UCF101",
"HMDB51"
] | [
{
"dkey": "UCF101",
"dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). T... |
An end-to-end hierarchical action recognition architecture. | action recognition video | 2,017 | [
"EPIC-KITCHENS-55",
"CCPD",
"E2E",
"PixelHelp",
"MLe2e",
"RCTW-17",
"DDD20"
] | [
"ImageNet",
"HMDB51"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
I'm training a question answering system on the SQuAD dataset. | long-form question answering text paragraph-level | 2,019 | [
"Spoken-SQuAD",
"SQuAD",
"SQuAD-shifts",
"MultiReQA",
"TweetQA",
"QNLI"
] | [
"ELI5",
"WikiSum"
] | [
{
"dkey": "ELI5",
"dval": "ELI5 is a dataset for long-form question answering. It contains 270K complex, diverse questions that require explanatory multi-sentence answers. Web search results are used as evidence documents to answer each question.\n\nELI5 is also a task in Dodecadialogue."
},
{
"dkey... |
A novel CNN architecture for face detection. The main contribution is a new loss layer for CNNs, which | face detection image | 2,016 | [
"MMED",
"THEODORE",
"AFLW2000-3D",
"MSU-MFSD",
"CNN/Daily Mail",
"ReCoRD",
"MLSUM"
] | [
"COFW",
"AFLW"
] | [
{
"dkey": "COFW",
"dval": "The Caltech Occluded Faces in the Wild (COFW) dataset is designed to present faces in real-world conditions. Faces show large variations in shape and occlusions due to differences in pose, expression, use of accessories such as sunglasses and hats and interactions with objects (e.... |
The proposed attention-based adversarial defense framework consists of a two-stage pipeline. The first stage is designed | adversarial defense images | 2,018 | [
"AnimalWeb",
"Raindrop",
"DramaQA",
"ECSSD",
"Fakeddit",
"WinoGrande",
"Spoken-SQuAD"
] | [
"ImageNet",
"CIFAR-10"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
I want to use a CNN-based tracking model. | visual tracking video | 2,019 | [
"SNIPS",
"LAG",
"AFLW2000-3D",
"DiCOVA",
"ConvAI2"
] | [
"OTB",
"VOT2017"
] | [
{
"dkey": "OTB",
"dval": "Object Tracking Benchmark (OTB) is a visual tracking benchmark that is widely used to evaluate the performance of a visual tracking algorithm. The dataset contains a total of 100 sequences and each is annotated frame-by-frame with bounding boxes and 11 challenge attributes. OTB-201... |
Instance mask projection is an end-to-end trainable operator that projects instance | semantic segmentation images top-view grid map sequences autonomous driving | 2,019 | [
"WikiReading",
"KnowledgeNet",
"THEODORE",
"PKU-MMD",
"LSHTC",
"SOBA",
"ISBDA"
] | [
"COCO",
"Cityscapes"
] | [
{
"dkey": "COCO",
"dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K imag... |
I want to train a model to answer questions from text. | question answering text | 2,018 | [
"TextVQA",
"RecipeQA",
"CommonsenseQA",
"BREAK",
"TrecQA",
"Spoken-SQuAD"
] | [
"WebQuestions",
"SQuAD",
"TriviaQA"
] | [
{
"dkey": "WebQuestions",
"dval": "The WebQuestions dataset is a question answering dataset using Freebase as the knowledge base and contains 6,642 question-answer pairs. It was created by crawling questions through the Google Suggest API, and then obtaining answers using Amazon Mechanical Turk. The origina... |
3D object recognition is an important component of many vision and robotics systems. | 3d object recognition voxels pixels | 2,016 | [
"OCID",
"3DNet",
"Flightmare Simulator",
"HoME"
] | [
"ImageNet",
"ModelNet"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
I want to detect facial landmark and components simultaneously. | landmark-region-based facial detection images | 2,019 | [
"AFLW2000-3D",
"300-VW",
"LS3D-W",
"AFLW",
"AffectNet"
] | [
"Helen",
"AFW"
] | [
{
"dkey": "Helen",
"dval": "The HELEN dataset is composed of 2330 face images of 400×400 pixels with labeled facial components generated through manually-annotated contours along eyes, eyebrows, nose, lips and jawline."
},
{
"dkey": "AFW",
"dval": "AFW (Annotated Faces in the Wild) is a face det... |
I would like to implement the Neural Architecture Search (NAS) approach and apply it | neural architecture search | 2,019 | [
"NAS-Bench-201",
"NAS-Bench-101",
"NATS-Bench",
"NAS-Bench-1Shot1",
"30MQA"
] | [
"ImageNet",
"Caltech-101",
"CIFAR-10"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
Multi-task learning for face attribute recognition. | multi-attribute face recognition images | 2,018 | [
"PETA",
"SUN Attribute",
"PA-HMDB51",
"CompCars",
"IJB-A",
"HVU"
] | [
"Adience",
"CelebA"
] | [
{
"dkey": "Adience",
"dval": "The Adience dataset, published in 2014, contains 26,580 photos across 2,284 subjects with a binary gender label and one label from eight different age groups, partitioned into five splits. The key principle of the data set is to capture the images as close to real world conditi... |
A deep learning approach for direct regression of [DATASET] human body poses from single images, which | human pose estimation images | 2,019 | [
"Deep Fashion3D",
"ExPose",
"EgoDexter",
"JTA",
"PoseTrack"
] | [
"3DPW",
"COCO"
] | [
{
"dkey": "3DPW",
"dval": "The 3D Poses in the Wild dataset is the first dataset in the wild with accurate 3D poses for evaluation. While other datasets outdoors exist, they are all restricted to a small recording volume. 3DPW is the first one that includes video footage taken from a moving phone camera.\n\... |
A facial expression recognition system with a variant of evolutionary firefly algorithm for feature optimization. | facial expression recognition images | 2,016 | [
"SFEW",
"BP4D",
"AffectNet",
"RAF-DB",
"Oulu-CASIA",
"FRGC"
] | [
"MMI",
"JAFFE"
] | [
{
"dkey": "MMI",
"dval": "The MMI Facial Expression Database consists of over 2900 videos and high-resolution still images of 75 subjects. It is fully annotated for the presence of AUs in videos (event coding), and partially coded on frame-level, indicating for each frame whether an AU is in either the neut... |
We propose a novel and simple method to train GANs. As an analysis, we show that the limited support problem | gan images | 2,019 | [
"BDD100K",
"UASOL",
"Localized Narratives",
"SuperGLUE",
"THEODORE",
"MMDB"
] | [
"CIFAR-10",
"CelebA"
] | [
{
"dkey": "CIFAR-10",
"dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck),... |
I want to train a supervised model for image inpainting. | image inpainting | 2,018 | [
"FVI",
"SNIPS",
"ConvAI2",
"I-HAZE",
"CLUECorpus2020",
"FaceForensics"
] | [
"Places",
"CelebA"
] | [
{
"dkey": "Places",
"dval": "The Places dataset is proposed for scene recognition and contains more than 2.5 million images covering more than 205 scene categories with more than 5,000 images per category."
},
{
"dkey": "CelebA",
"dval": "CelebFaces Attributes dataset contains 202,599 face image... |
I want to train a supervised image recognition model. | image recognition images | 2,019 | [
"SNIPS",
"ConvAI2",
"Libri-Light",
"EPIC-KITCHENS-100",
"I-HAZE",
"CLUECorpus2020",
"Stanford Cars"
] | [
"DTD",
"COCO"
] | [
{
"dkey": "DTD",
"dval": "The Describable Textures Dataset (DTD) contains 5640 texture images in the wild. They are annotated with human-centric attributes inspired by the perceptual properties of textures."
},
{
"dkey": "COCO",
"dval": "The MS COCO (Microsoft Common Objects in Context) dataset ... |
I am training a model that classifies images into 1000 classes. | image classification images | 2,019 | [
"ConvAI2",
"FaceForensics",
"ShapeNet",
"FSDnoisy18k",
"CommonsenseQA",
"FSS-1000",
"GYAFC"
] | [
"ImageNet",
"MPII",
"CIFAR-10"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
In this paper, we show how state-of-the-art neural networks can learn to quantify | abstract quantification video | 2,017 | [
"LogiQA",
"THEODORE",
"E2E",
"ANLI",
"SemArt",
"RFW",
"SGD"
] | [
"ImageNet",
"COCO"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
I want to build a robust defense system for adversarial attacks. | adversarial defense images | 2,020 | [
"Oxford5k",
"PHM2017",
"UNSW-NB15",
"APRICOT",
"ECSSD",
"DailyDialog++"
] | [
"ImageNet",
"CIFAR-10"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
I'm trying to understand the effects of various pretraining objectives on the learned | function word comprehension | 2,019 | [
"ExtremeWeather",
"SuperGLUE",
"ProofWriter",
"CosmosQA",
"CHALET",
"3D Ken Burns Dataset"
] | [
"COCO",
"GLUE",
"WikiText-103"
] | [
{
"dkey": "COCO",
"dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K imag... |
I am a newbie of deep learning. I want to learn a simple unified neural | person re-identification images | 2,019 | [
"Flightmare Simulator",
"ConvAI2",
"SNIPS",
"dMelodies"
] | [
"VehicleID",
"CUHK03"
] | [
{
"dkey": "VehicleID",
"dval": "The “VehicleID” dataset contains CARS captured during the daytime by multiple real-world surveillance cameras distributed in a small city in China. There are 26,267 vehicles (221,763 images in total) in the entire dataset. Each image is attached with an id label corresponding... |
We propose an end-to-end approach for video object segmentation. We integrate the | instance level video object segmentation | 2,018 | [
"THEODORE",
"DeeperForensics-1.0",
"PHOENIX14T",
"iVQA",
"MEDIA"
] | [
"DAVIS",
"DAVIS 2016"
] | [
{
"dkey": "DAVIS",
"dval": "The Densely Annotation Video Segmentation dataset (DAVIS) is a high quality and high resolution densely annotated video segmentation dataset under two resolutions, 480p and 1080p. There are 50 video sequences with 3455 densely annotated frames in pixel level. 30 videos with 2079 ... |
I want to train a CNN model for face recognition. | face recognition images | 2,019 | [
"SNIPS",
"Glint360K",
"ConvAI2",
"FDDB",
"AFLW2000-3D"
] | [
"CASIA-WebFace",
"MegaFace"
] | [
{
"dkey": "CASIA-WebFace",
"dval": "The CASIA-WebFace dataset is used for face verification and face identification tasks. The dataset contains 494,414 face images of 10,575 real identities collected from the web."
},
{
"dkey": "MegaFace",
"dval": "MegaFace was a publicly available dataset which... |
I want to train a supervised model for general human action recognition from video. | general human action recognition video | 2,017 | [
"Kinetics",
"Kinetics-600",
"EPIC-KITCHENS-100",
"AViD",
"HAA500"
] | [
"UCF101",
"HMDB51"
] | [
{
"dkey": "UCF101",
"dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). T... |
I want to translate between 2D image views and | image 3d shape translation images shapes | 2,020 | [
"3DMatch",
"Dayton",
"MTNT",
"SNIPS",
"UAVA"
] | [
"ShapeNet",
"Pix3D"
] | [
{
"dkey": "ShapeNet",
"dval": "ShapeNet is a large scale repository for 3D CAD models developed by researchers from Stanford University, Princeton University and the Toyota Technological Institute at Chicago, USA. The repository contains over 300M models with 220,000 classified into 3,135 classes arranged u... |
We introduce an efficient, online and fully convolutional network (FCN) to handle video object segmentation | video object segmentation | 2,017 | [
"THEODORE",
"BSDS500",
"NVGesture",
"BraTS 2017"
] | [
"DAVIS",
"COCO"
] | [
{
"dkey": "DAVIS",
"dval": "The Densely Annotation Video Segmentation dataset (DAVIS) is a high quality and high resolution densely annotated video segmentation dataset under two resolutions, 480p and 1080p. There are 50 video sequences with 3455 densely annotated frames in pixel level. 30 videos with 2079 ... |
I want to build an end-to-end high-performance person re-identification system | person re-identification images | 2,019 | [
"MLe2e",
"Airport",
"E2E",
"ACDC"
] | [
"Market-1501",
"CUHK03"
] | [
{
"dkey": "Market-1501",
"dval": "Market-1501 is a large-scale public benchmark dataset for person re-identification. It contains 1501 identities which are captured by six different cameras, and 32,668 pedestrian image bounding-boxes obtained using the Deformable Part Models pedestrian detector. Each person... |
A deep multi-task learning framework for face attribute estimation. | face attribute estimation image | 2,018 | [
"OmniArt",
"WFLW",
"NYU-VP",
"RVL-CDIP",
"K2HPD",
"IJB-A"
] | [
"MORPH",
"CelebA"
] | [
{
"dkey": "MORPH",
"dval": "MORPH is a facial age estimation dataset, which contains 55,134 facial images of 13,617 subjects ranging from 16 to 77 years old."
},
{
"dkey": "CelebA",
"dval": "CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, e... |
I want to design a network with minimal computation cost and high performance. | neural architecture optimization images | 2,019 | [
"COVERAGE",
"ESAD",
"IBC",
"SNIPS",
"SUN360",
"I-HAZE",
"Wiki-CS"
] | [
"ImageNet",
"CIFAR-10"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
A self-similarity grouping approach to unsupervised domain adaptation in person re-identification | unsupervised domain adaptation person re-identification images | 2,019 | [
"Libri-Adapt",
"Airport",
"Partial-iLIDS",
"CUHK02",
"SYSU-MM01"
] | [
"DukeMTMC-reID",
"Market-1501"
] | [
{
"dkey": "DukeMTMC-reID",
"dval": "The DukeMTMC-reID (Duke Multi-Tracking Multi-Camera ReIDentification) dataset is a subset of the DukeMTMC for image-based person re-ID. The dataset is created from high-resolution videos from 8 different cameras. It is one of the largest pedestrian image datasets wherein ... |
I want to introduce a new collective decision strategy for open set recognition. | open set recognition video | 2,018 | [
"ConvAI2",
"BDD100K",
"SNIPS",
"OpenEDS",
"Hollywood 3D dataset",
"VoxForge",
"HotpotQA"
] | [
"Letter",
"USPS"
] | [
{
"dkey": "Letter",
"dval": "Letter Recognition Data Set is a handwritten digit dataset. The task is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. The character images were based on 20 different fonts and each lette... |
A novel adversarial multi-source domain aggregation | domain adaptation semantic segmentation images gta synthia cityscapes bdds | 2,019 | [
"Cell",
"DailyDialog++",
"PEC",
"WinoGrande",
"ImageCLEF-DA",
"PACS"
] | [
"ImageNet",
"Cityscapes"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
We propose a simple but effective approach for transferring supervision from neighboring examples to improve multi-label | multi-label classification images | 2,018 | [
"SuperGLUE",
"TableBank",
"DocBank",
"BraTS 2017",
"SICK",
"SBU Captions Dataset"
] | [
"COCO",
"Flickr30k"
] | [
{
"dkey": "COCO",
"dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K imag... |
I want to train a supervised model for face alignment. | face alignment images | 2,018 | [
"FaceForensics",
"SNIPS",
"iFakeFaceDB",
"ConvAI2",
"Sentence Compression",
"WFLW"
] | [
"COFW",
"AFW",
"AFLW"
] | [
{
"dkey": "COFW",
"dval": "The Caltech Occluded Faces in the Wild (COFW) dataset is designed to present faces in real-world conditions. Faces show large variations in shape and occlusions due to differences in pose, expression, use of accessories such as sunglasses and hats and interactions with objects (e.... |
I want to improve video action recognition accuracy with spatial-temporal attention and regularizers. | video action recognition | 2,019 | [
"FineGym",
"Charades",
"UCFRep",
"TAPOS",
"SoccerDB",
"HAA500",
"TUM Kitchen"
] | [
"ImageNet",
"UCF101",
"HMDB51"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
A generative adversarial network that models moving objects in images without any supervision whatsoever. | moving object detection images | 2,019 | [
"ISTD",
"Raindrop",
"FDF",
"TAO"
] | [
"FBMS",
"DAVIS"
] | [
{
"dkey": "FBMS",
"dval": "The Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59) is an extension of the BMS dataset with 33 additional video sequences. A total of 720 frames is annotated. It has pixel-accurate segmentation annotations of moving objects. FBMS-59 comes with a split into a training set a... |
We propose a novel unsupervised method for learning human activity representations in minutes-long videos. In VideoGraph | human activity representation videos paragraph-level | 2,019 | [
"EPIC-KITCHENS-100",
"Watch-n-Patch",
"Icentia11K",
"MIT Traffic",
"AVA"
] | [
"Charades",
"Breakfast"
] | [
{
"dkey": "Charades",
"dval": "The Charades dataset is composed of 9,848 videos of daily indoors activities with an average length of 30 seconds, involving interactions with 46 objects classes in 15 types of indoor scenes and containing a vocabulary of 30 verbs leading to 157 action classes. Each video in t... |
I want to study the effect of training on multiple languages, and improve upon the state-of | multilingual nli text | 2,020 | [
"Dialogue State Tracking Challenge",
"SNIPS",
"LibriSpeech",
"SuperGLUE",
"DailyDialog++"
] | [
"MultiNLI",
"GLUE"
] | [
{
"dkey": "MultiNLI",
"dval": "The Multi-Genre Natural Language Inference (MultiNLI) dataset has 433K sentence pairs. Its size and mode of collection are modeled closely like SNLI. MultiNLI offers ten distinct genres (Face-to-face, Telephone, 9/11, Travel, Letters, Oxford University Press, Slate, Verbatim, ... |
A data-driven approach for estimating hand-object manipulations from RGB images. | hand-object manipulations rgb images | 2,019 | [
"YCBInEOAT Dataset",
"EgoHands",
"InterHand2.6M",
"SynthHands",
"NYUv2",
"ContactPose"
] | [
"HIC",
"ShapeNet"
] | [
{
"dkey": "HIC",
"dval": "The Hands in action dataset (HIC) dataset has RGB-D sequences of hands interacting with objects."
},
{
"dkey": "ShapeNet",
"dval": "ShapeNet is a large scale repository for 3D CAD models developed by researchers from Stanford University, Princeton University and the Toy... |
I want to use CNN to process 3D data. | 3d data processing | 2,015 | [
"AFLW2000-3D",
"SNIPS",
"UAVA",
"NewsQA"
] | [
"UCF101",
"CIFAR-10"
] | [
{
"dkey": "UCF101",
"dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). T... |
RMSNorm for deep neural networks. | image classification images | 2,019 | [
"UNITOPATHO",
"GoPro",
"COVIDx",
"COWC",
"UNSW-NB15",
"PROTEINS",
"IPN Hand"
] | [
"COCO",
"CIFAR-10"
] | [
{
"dkey": "COCO",
"dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K imag... |
We conduct the first adversarial attacks on pose-estimation networks. We explore different architectures and | adversarial attacks human pose estimation images | 2,019 | [
"UNSW-NB15",
"FDF",
"LSP",
"MSU-MFSD",
"APRICOT",
"Make3D"
] | [
"MPII",
"COCO"
] | [
{
"dkey": "MPII",
"dval": "The MPII Human Pose Dataset for single person pose estimation is composed of about 25K images of which 15K are training samples, 3K are validation samples and 7K are testing samples (which labels are withheld by the authors). The images are taken from YouTube videos covering 410 d... |
We propose to learn a deep convolutional neural network from a small dataset. The main novelty is the incorporation | image classification | 2,015 | [
"GoPro",
"COVIDx",
"Places",
"Birdsnap",
"COWC"
] | [
"ImageNet",
"CIFAR-10"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
I want to train a model for image-text matching, which can be used | image-text matching images sentences | 2,018 | [
"FaceForensics",
"SNIPS",
"Market-1501",
"ConvAI2"
] | [
"ImageNet",
"Flickr30k"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
I want to create a dataset to evaluate different methods for visual navigation, such as frame- | visual navigation images events | 2,016 | [
"TUM monoVO",
"ACDC",
"Newspaper Navigator",
"YouCook",
"Virtual KITTI"
] | [
"Middlebury",
"KITTI"
] | [
{
"dkey": "Middlebury",
"dval": "The Middlebury Stereo dataset consists of high-resolution stereo sequences with complex geometry and pixel-accurate ground-truth disparity data. The ground-truth disparities are acquired using a novel technique that employs structured lighting and does not require the calibr... |
I want to scan the trojaned model to check if it is trojaned. | trojan detection images | 2,019 | [
"SNIPS",
"UAVA",
"Scan2CAD",
"SIZER",
"ConvAI2",
"People Snapshot Dataset"
] | [
"ImageNet",
"CIFAR-10"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
Multi-expert Gender Classification on Age Group (MGA). | age gender classification image | 2,018 | [
"BanglaLekha-Isolated",
"aGender",
"FairFace",
"FFHQ-Aging",
"Celeb-DF"
] | [
"Adience",
"MORPH"
] | [
{
"dkey": "Adience",
"dval": "The Adience dataset, published in 2014, contains 26,580 photos across 2,284 subjects with a binary gender label and one label from eight different age groups, partitioned into five splits. The key principle of the data set is to capture the images as close to real world conditi... |
I want to train a semi-supervised model for image classification from labeled and unlabeled samples. | image classification images | 2,019 | [
"EMBER",
"VoxPopuli",
"DCASE 2018 Task 4",
"PanNuke",
"Fakeddit"
] | [
"ImageNet",
"CIFAR-10"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
A comprehensive survey of recent advances in object detection from RGB images | rgb-d object detection images depth maps | 2,019 | [
"CLOTH",
"LAMBADA",
"YAGO",
"FAT",
"THEODORE",
"Kitchen Scenes"
] | [
"LFSD",
"Caltech-101",
"KITTI",
"ModelNet"
] | [
{
"dkey": "LFSD",
"dval": "The Light Field Saliency Database (LFSD) contains 100 light fields with 360×360 spatial resolution. A rough focal stack and an all-focus image are provided for each light field. The images in this dataset usually have one salient foreground object and a background with good color ... |
We discuss the literature on instance retrieval from images and present a comprehensive survey of state-of-the- | instance retrieval images | 2,018 | [
"GSL",
"THEODORE",
"LogiQA",
"SCUT-CTW1500",
"CTC",
"DOTA"
] | [
"ImageNet",
"Oxford5k"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
I am planning to use a supervised approach for graph classification. | graph classification graphs | 2,019 | [
"ConvAI2",
"arXiv",
"CSPubSum",
"Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison"
] | [
"ENZYMES",
"PROTEINS"
] | [
{
"dkey": "ENZYMES",
"dval": "ENZYMES is a dataset of 600 protein tertiary structures obtained from the BRENDA enzyme database. The ENZYMES dataset contains 6 enzymes."
},
{
"dkey": "PROTEINS",
"dval": "PROTEINS is a dataset of proteins that are classified as enzymes or non-enzymes. Nodes repres... |
I want to learn a universal image embedding to measure the semantic similarity of two images. | universal image embedding images | 2,020 | [
"SNIPS",
"SICK",
"COVERAGE",
"Flickr Audio Caption Corpus",
"Spot-the-diff"
] | [
"ImageNet",
"VehicleID"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
I want to train a model for sentence encoding. | natural language inference | 2,017 | [
"ConvAI2",
"SNIPS",
"Discovery Dataset",
"GYAFC",
"SentEval"
] | [
"SNLI",
"MultiNLI"
] | [
{
"dkey": "SNLI",
"dval": "The SNLI dataset (Stanford Natural Language Inference) consists of 570k sentence-pairs manually labeled as entailment, contradiction, and neutral. Premises are image captions from Flickr30k, while hypotheses were generated by crowd-sourced annotators who were shown a premise and a... |
We propose a novel whole-to-part network and a part-to-whole network for the | image classification | 2,018 | [
"UNITOPATHO",
"UMDFaces",
"MuPoTS-3D",
"Business Scene Dialogue",
"Localized Narratives"
] | [
"ImageNet",
"CIFAR-10"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
I want to train a fully supervised model for interactive instance segmentation from images. | interactive instance segmentation images | 2,018 | [
"CryoNuSeg",
"ACDC",
"Virtual KITTI",
"RobotPush",
"SNIPS"
] | [
"COCO",
"SBD"
] | [
{
"dkey": "COCO",
"dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K imag... |
The model is a fully convolutional network with a total of 44,852,11 | object recognition images | 2,020 | [
"COCO-Text",
"Helen",
"Stanford Cars",
"Decagon",
"BSDS500",
"Total-Text"
] | [
"ImageNet",
"CIFAR-10"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
A semi-supervised approach for anomaly detection, which leverages the unlabelled data to better learn the feature | anomaly detection text | 2,019 | [
"VoxPopuli",
"Thyroid",
"DAD",
"DCASE 2018 Task 4",
"NAB",
"ExtremeWeather"
] | [
"ImageNet",
"CIFAR-10"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
The task of video description is the generation of a natural language sentence to describe the content of a given | video description | 2,018 | [
"Violin",
"ProPara",
"VATEX",
"SNLI",
"DiDeMo",
"ToTTo",
"SWAG"
] | [
"COCO",
"MSVD",
"ActivityNet",
"LSMDC",
"CLEVR"
] | [
{
"dkey": "COCO",
"dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K imag... |
I want to train a supervised model for person re-identification in the wild | person re-identification wild video | 2,017 | [
"SYSU-MM01",
"ATRW",
"Airport",
"VeRi-Wild",
"Partial-iLIDS",
"DukeMTMC-reID"
] | [
"KITTI",
"Market-1501"
] | [
{
"dkey": "KITTI",
"dval": "KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RG... |
I want to use unsupervised representation learning to learn useful feature representations for high-level recognition problems. | unsupervised representation learning images | 2,017 | [
"Icentia11K",
"CC100",
"VoxPopuli",
"PTC",
"REDDIT-12K",
"AtariARI",
"Email-EU"
] | [
"ImageNet",
"UCF101"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
I want to train a model for semantic segmentation from images. | semantic segmentation image | 2,016 | [
"SNIPS",
"ConvAI2",
"SemanticKITTI",
"Cata7",
"Swiss3DCities"
] | [
"COCO",
"Cityscapes"
] | [
{
"dkey": "COCO",
"dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K imag... |
We propose a new algorithm for human gait recognition using a computer vision technique called image recognition. In this | human gait recognition images | 2,020 | [
"BDD100K",
"Hollywood 3D dataset",
"USF",
"OCID"
] | [
"MPII",
"COCO"
] | [
{
"dkey": "MPII",
"dval": "The MPII Human Pose Dataset for single person pose estimation is composed of about 25K images of which 15K are training samples, 3K are validation samples and 7K are testing samples (which labels are withheld by the authors). The images are taken from YouTube videos covering 410 d... |
I want to train a face detector. | face detection image | 2,017 | [
"SNIPS",
"WildDeepfake",
"SCUT-CTW1500",
"AFLW2000-3D",
"ConvAI2"
] | [
"AFW",
"AFLW"
] | [
{
"dkey": "AFW",
"dval": "AFW (Annotated Faces in the Wild) is a face detection dataset that contains 205 images with 468 faces. Each face image is labeled with at most 6 landmarks with visibility labels, as well as a bounding box."
},
{
"dkey": "AFLW",
"dval": "The Annotated Facial Landmarks in... |
The goal of fashion alignment is to detect the positions of key points defined on fashion items. With | fashion landmark detection images | 2,016 | [
"Fashion-Gen",
"OBP",
"Fashion-MNIST",
"Fashion IQ",
"KITTI-trajectory-prediction",
"Fashion 144K"
] | [
"DeepFashion",
"LSP"
] | [
{
"dkey": "DeepFashion",
"dval": "DeepFashion is a dataset containing around 800K diverse fashion images with their rich annotations (46 categories, 1,000 descriptive attributes, bounding boxes and landmark information) ranging from well-posed product images to real-world-like consumer photos."
},
{
... |
Domain adaptation for digit recognition. | domain adaptation images paragraph-level | 2,020 | [
"Libri-Adapt",
"EMNIST",
"MNIST-M",
"VisDA-2017"
] | [
"ImageNet",
"Cityscapes"
] | [
{
"dkey": "ImageNet",
"dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released data... |
Learning a discriminative representation for person re-identification. | person re-identification images | 2,017 | [
"DukeMTMC-reID",
"CUHK-PEDES",
"VeRi-776",
"Airport",
"Partial-iLIDS"
] | [
"VIPeR",
"CUHK03"
] | [
{
"dkey": "VIPeR",
"dval": "The Viewpoint Invariant Pedestrian Recognition (VIPeR) dataset includes 632 people and two outdoor cameras under different viewpoints and light conditions. Each person has one image per camera and each image has been scaled to be 128×48 pixels. It provides the pose angle of each ... |
In this paper, we introduce a new pretraining task, which forces language models to incorporate knowledge | fact completion text | 2,019 | [
"GSL",
"KLEJ",
"DuoRC",
"SuperGLUE"
] | [
"SearchQA",
"SQuAD",
"TriviaQA"
] | [
{
"dkey": "SearchQA",
"dval": "SearchQA was built using an in-production, commercial search engine. It closely reflects the full pipeline of a (hypothetical) general question-answering system, which consists of information retrieval and answer synthesis."
},
{
"dkey": "SQuAD",
"dval": "The Stanf... |
We propose two hierarchical Gaussian descriptors for person re-identification, and show that these descriptors outperform | person re-identification images | 2,017 | [
"PRID2011",
"3DMatch",
"CUHK02",
"Airport",
"Partial-iLIDS",
"SYSU-MM01"
] | [
"VIPeR",
"Market-1501"
] | [
{
"dkey": "VIPeR",
"dval": "The Viewpoint Invariant Pedestrian Recognition (VIPeR) dataset includes 632 people and two outdoor cameras under different viewpoints and light conditions. Each person has one image per camera and each image has been scaled to be 128×48 pixels. It provides the pose angle of each ... |
I have built a robust framework for action recognition and prediction. | action recognition prediction video | 2,018 | [
"G3D",
"DISFA",
"UCFRep",
"Stanford Dogs",
"TinyVIRAT",
"PHM2017"
] | [
"UCF101",
"HMDB51"
] | [
{
"dkey": "UCF101",
"dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). T... |
I propose an adaptive fusion strategy to exploit the complementary properties of deep and shallow features for tracking. | generic object tracking image | 2,018 | [
"DiCOVA",
"ASNQ",
"WFLW",
"HIGGS Data Set"
] | [
"VOT2017",
"VOT2016"
] | [
{
"dkey": "VOT2017",
"dval": "VOT2017 is a Visual Object Tracking dataset for different tasks that contains 60 short sequences annotated with 6 different attributes."
},
{
"dkey": "VOT2016",
"dval": "VOT2016 is a video dataset for visual object tracking. It contains 60 video clips and 21,646 cor... |
I want to train a model for panoramic road scene object detection. | object detection panoramic images | 2,019 | [
"IDD",
"SYNTHIA-AL",
"COCO-Tasks",
"T-LESS"
] | [
"COCO",
"KITTI"
] | [
{
"dkey": "COCO",
"dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K imag... |
I want to train a model to localize instrument playing in music videos. | weakly supervised instrument-playing action localization video | 2,018 | [
"MusicNet",
"UCF101",
"RWC",
"SNIPS",
"Countix",
"FAIR-Play"
] | [
"AudioSet",
"YouTube-8M"
] | [
{
"dkey": "AudioSet",
"dval": "Audioset is an audio event dataset, which consists of over 2M human-annotated 10-second video clips. These clips are collected from YouTube, therefore many of which are in poor-quality and contain multiple sound-sources. A hierarchical ontology of 632 event classes is employed... |
We present a Transformer based multi-lingual sentence encoder model that learns cross-lingual representations for | cross-lingual retrieval text | 2,019 | [
"CC100",
"NCLS",
"REFreSD",
"WikiAnn",
"EXAMS",
"CLIRMatrix",
"XTREME"
] | [
"SNLI",
"SentEval",
"SQuAD"
] | [
{
"dkey": "SNLI",
"dval": "The SNLI dataset (Stanford Natural Language Inference) consists of 570k sentence-pairs manually labeled as entailment, contradiction, and neutral. Premises are image captions from Flickr30k, while hypotheses were generated by crowd-sourced annotators who were shown a premise and a... |
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