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789 dataset results for Images AND English

MS COCO (Microsoft Common Objects in Context)

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.

10,128 PAPERS • 92 BENCHMARKS

Tiny ImageNet

Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images and 50 test images.

939 PAPERS • 8 BENCHMARKS

Flickr30k

The Flickr30k dataset contains 31,000 images collected from Flickr, together with 5 reference sentences provided by human annotators.

728 PAPERS • 9 BENCHMARKS

CIFAR-10C

Common corruptions dataset for CIFAR10

414 PAPERS • NO BENCHMARKS YET

CASIA-WebFace

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.

381 PAPERS • 2 BENCHMARKS

3DPW

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.

339 PAPERS • 5 BENCHMARKS

Conceptual Captions

Automatic image captioning is the task of producing a natural-language utterance (usually a sentence) that correctly reflects the visual content of an image. Up to this point, the resource most used for this task was the MS-COCO dataset, containing around 120,000 images and 5-way image-caption annotations (produced by paid annotators).

311 PAPERS • 2 BENCHMARKS

DRIVE (Digital Retinal Images for Vessel Extraction)

The Digital Retinal Images for Vessel Extraction (DRIVE) dataset is a dataset for retinal vessel segmentation. It consists of a total of JPEG 40 color fundus images; including 7 abnormal pathology cases. The images were obtained from a diabetic retinopathy screening program in the Netherlands. The images were acquired using Canon CR5 non-mydriatic 3CCD camera with FOV equals to 45 degrees. Each image resolution is 584*565 pixels with eight bits per color channel (3 channels).

274 PAPERS • 2 BENCHMARKS

OK-VQA (Outside Knowledge Visual Question Answering)

Outside Knowledge Visual Question Answering (OK-VQA) includes more than 14,000 questions that require external knowledge to answer.

258 PAPERS • 2 BENCHMARKS

EMNIST (Extended MNIST)

EMNIST (extended MNIST) has 4 times more data than MNIST. It is a set of handwritten digits with a 28 x 28 format.

234 PAPERS • 9 BENCHMARKS

ICDAR 2013

The ICDAR 2013 dataset consists of 229 training images and 233 testing images, with word-level annotations provided. It is the standard benchmark dataset for evaluating near-horizontal text detection.

229 PAPERS • 3 BENCHMARKS

GOT-10k (Generic Object Tracking Benchmark)

The GOT-10k dataset contains more than 10,000 video segments of real-world moving objects and over 1.5 million manually labelled bounding boxes. The dataset contains more than 560 classes of real-world moving objects and 80+ classes of motion patterns.

203 PAPERS • 2 BENCHMARKS

MIMIC-CXR

MIMIC-CXR from Massachusetts Institute of Technology presents 371,920 chest X-rays associated with 227,943 imaging studies from 65,079 patients. The studies were performed at Beth Israel Deaconess Medical Center in Boston, MA.

164 PAPERS • 2 BENCHMARKS

VCR (Visual Commonsense Reasoning)

Visual Commonsense Reasoning (VCR) is a large-scale dataset for cognition-level visual understanding. Given a challenging question about an image, machines need to present two sub-tasks: answer correctly and provide a rationale justifying its answer. The VCR dataset contains over 212K (training), 26K (validation) and 25K (testing) questions, answers and rationales derived from 110K movie scenes.

158 PAPERS • 13 BENCHMARKS

CMU-MOSEI

CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) is the largest dataset of sentence level sentiment analysis and emotion recognition in online videos. CMU-MOSEI contains more than 65 hours of annotated video from more than 1000 speakers and 250 topics.

153 PAPERS • 2 BENCHMARKS

VisDial (Visual Dialog)

Visual Dialog (VisDial) dataset contains human annotated questions based on images of MS COCO dataset. This dataset was developed by pairing two subjects on Amazon Mechanical Turk to chat about an image. One person was assigned the job of a ‘questioner’ and the other person acted as an ‘answerer’. The questioner sees only the text description of an image (i.e., an image caption from MS COCO dataset) and the original image remains hidden to the questioner. Their task is to ask questions about this hidden image to “imagine the scene better”. The answerer sees the image, caption and answers the questions asked by the questioner. The two of them can continue the conversation by asking and answering questions for 10 rounds at max.

144 PAPERS • 6 BENCHMARKS

Total-Text

Total-Text is a text detection dataset that consists of 1,555 images with a variety of text types including horizontal, multi-oriented, and curved text instances. The training split and testing split have 1,255 images and 300 images, respectively.

142 PAPERS • 2 BENCHMARKS

ScienceQA (Science Question Answering)

Science Question Answering (ScienceQA) is a new benchmark that consists of 21,208 multimodal multiple choice questions with diverse science topics and annotations of their answers with corresponding lectures and explanations. Out of the questions in ScienceQA, 10,332 (48.7%) have an image context, 10,220 (48.2%) have a text context, and 6,532 (30.8%) have both. Most questions are annotated with grounded lectures (83.9%) and detailed explanations (90.5%). The lecture and explanation provide general external knowledge and specific reasons, respectively, for arriving at the correct answer. To the best of our knowledge, ScienceQA is the first large-scale multimodal dataset that annotates lectures and explanations for the answers.

137 PAPERS • 1 BENCHMARK

SID (See-in-the-Dark)

The See-in-the-Dark (SID) dataset contains 5094 raw short-exposure images, each with a corresponding long-exposure reference image. Images were captured using two cameras: Sony α7SII and Fujifilm X-T2.

129 PAPERS • 3 BENCHMARKS

SumMe

The SumMe dataset is a video summarization dataset consisting of 25 videos, each annotated with at least 15 human summaries (390 in total).

124 PAPERS • 3 BENCHMARKS

MSRA-TD500 (MSRA Text Detection 500 Database)

The MSRA-TD500 dataset is a text detection dataset that contains 300 training images and 200 test images. Text regions are arbitrarily orientated and annotated at sentence level. Different from the other datasets, it contains both English and Chinese text.

118 PAPERS • 1 BENCHMARK

SHAPES (Swarm Heuristics based Adaptive and Penalized Estimation of Splines)

SHAPES is a dataset of synthetic images designed to benchmark systems for understanding of spatial and logical relations among multiple objects. The dataset consists of complex questions about arrangements of colored shapes. The questions are built around compositions of concepts and relations, e.g. Is there a red shape above a circle? or Is a red shape blue?. Questions contain between two and four attributes, object types, or relationships. There are 244 questions and 15,616 images in total, with all questions having a yes and no answer (and corresponding supporting image). This eliminates the risk of learning biases.

111 PAPERS • 1 BENCHMARK

COD10K (Camouflaged/Concealed Object Detection)

Sensory ecologists have found that this s background matching camouflage strategy works by deceiving the visual perceptual system of the observer. Naturally, addressing concealed object detection (COD) requires a significant amount of visual perception knowledge. Understanding COD has not only scientific value in itself, but it also important for applications in many fundamental fields, such as computer vision (e.g., for search-and-rescue work, or rare species discovery), medicine (e.g., polyp segmentation, lung infection segmentation), agriculture (e.g., locust detection to prevent invasion), and art (e.g., recreational art). The high intrinsic similarities between the targets and non-targets make COD far more challenging than traditional object segmentation/detection. Although it has gained increased attention recently, studies on COD still remain scarce, mainly due to the lack of a sufficiently large dataset and a standard benchmark like Pascal-VOC, ImageNet, MS-COCO, ADE20K, and DA

109 PAPERS • 2 BENCHMARKS

PASCAL VOC 2012 test

SCC Data Set

109 PAPERS • 3 BENCHMARKS

CC12M (Conceptual 12M)

Conceptual 12M (CC12M) is a dataset with 12 million image-text pairs specifically meant to be used for vision-and-language pre-training.

104 PAPERS • NO BENCHMARKS YET

HO-3D

A hand-object interaction dataset with 3D pose annotations of hand and object. The dataset contains 66,034 training images and 11,524 test images from a total of 68 sequences. The sequences are captured in multi-camera and single-camera setups and contain 10 different subjects manipulating 10 different objects from YCB dataset. The annotations are automatically obtained using an optimization algorithm. The hand pose annotations for the test set are withheld and the accuracy of the algorithms on the test set can be evaluated with standard metrics using the CodaLab challenge submission(see project page). The object pose annotations for the test and train set are provided along with the dataset.

99 PAPERS • 2 BENCHMARKS

VIST (Visual Storytelling)

The Visual Storytelling Dataset (VIST) consists of 210,819 unique photos and 50,000 stories. The images were collected from albums on Flickr. The albums included 10 to 50 images and all the images in an album are taken in a 48-hour span. The stories were created by workers on Amazon Mechanical Turk, where the workers were instructed to choose five images from the album and write a story about them. Every story has five sentences, and every sentence is paired with its appropriate image. The dataset is split into 3 subsets, a training set (80%), a validation set (10%) and a test set (10%). All the words and interpunction signs in the stories are separated by a space character and all the location names are replaced with the word location. All the names of people are replaced with the words male or female depending on the gender of the person.

98 PAPERS • 2 BENCHMARKS

Cholec80 (Surgical Workflow Dataset)

Cholec80 is an endoscopic video dataset containing 80 videos of cholecystectomy surgeries performed by 13 surgeons. The videos are captured at 25 fps and downsampled to 1 fps for processing. The whole dataset is labeled with the phase and tool presence annotations. The phases have been defined by a senior surgeon in Strasbourg hospital, France. Since the tools are sometimes hardly visible in the images and thus difficult to be recognized visually, a tool is defined as present in an image if at least half of the tool tip is visible.

96 PAPERS • 2 BENCHMARKS

RVL-CDIP

The RVL-CDIP dataset consists of scanned document images belonging to 16 classes such as letter, form, email, resume, memo, etc. The dataset has 320,000 training, 40,000 validation and 40,000 test images. The images are characterized by low quality, noise, and low resolution, typically 100 dpi.

96 PAPERS • 3 BENCHMARKS

CAMO (Camouflaged Object)

Camouflaged Object (CAMO) dataset specifically designed for the task of camouflaged object segmentation. We focus on two categories, i.e., naturally camouflaged objects and artificially camouflaged objects, which usually correspond to animals and humans in the real world, respectively. Camouflaged object images consists of 1250 images (1000 images for the training set and 250 images for the testing set). Non-camouflaged object images are collected from the MS-COCO dataset (1000 images for the training set and 250 images for the testing set). CAMO has objectness mask ground-truth.

92 PAPERS • 2 BENCHMARKS

MM-Vet

MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

89 PAPERS • 1 BENCHMARK

COCO-Text

The COCO-Text dataset is a dataset for text detection and recognition. It is based on the MS COCO dataset, which contains images of complex everyday scenes. The COCO-Text dataset contains non-text images, legible text images and illegible text images. In total there are 22184 training images and 7026 validation images with at least one instance of legible text.

79 PAPERS • 2 BENCHMARKS

VQG (Visual Question Generation)

VQG is a collection of datasets for visual question generation. VQG questions were collected by crowdsourcing the task on Amazon Mechanical Turk (AMT). The authors provided details on the prompt and the specific instructions for all the crowdsourcing tasks in this paper in the supplementary material. The prompt was successful at capturing nonliteral questions. Images were taken from the MSCOCO dataset.

77 PAPERS • 1 BENCHMARK

NLVR (Natural Language Visual Reasoningnatural language for visual reasoning)

NLVR contains 92,244 pairs of human-written English sentences grounded in synthetic images. Because the images are synthetically generated, this dataset can be used for semantic parsing.

76 PAPERS • 3 BENCHMARKS

GuessWhat?!

GuessWhat?! is a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images.

73 PAPERS • NO BENCHMARKS YET

ST-VQA (Scene Text Visual Question Answering)

ST-VQA aims to highlight the importance of exploiting high-level semantic information present in images as textual cues in the VQA process.

72 PAPERS • NO BENCHMARKS YET

VQA-RAD (Visual Question Answering in Radiology)

VQA-RAD consists of 3,515 question–answer pairs on 315 radiology images.

68 PAPERS • 1 BENCHMARK

DanceTrack

A large-scale multi-object tracking dataset for human tracking in occlusion, frequent crossover, uniform appearance and diverse body gestures. It is proposed to emphasize the importance of motion analysis in multi-object tracking instead of mainly appearance-matching-based diagram.

62 PAPERS • 1 BENCHMARK

MSP-IMPROV (MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception)

We present the MSP-IMPROV corpus, a multimodal emotional database, where the goal is to have control over lexical content and emotion while also promoting naturalness in the recordings. Studies on emotion perception often require stimuli with fixed lexical content, but that convey different emotions. These stimuli can also serve as an instrument to understand how emotion modulates speech at the phoneme level, in a manner that controls for coarticulation. Such audiovisual data are not easily available from natural recordings. A common solution is to record actors reading sentences that portray different emotions, which may not produce natural behaviors. We propose an alternative approach in which we define hypothetical scenarios for each sentence that are carefully designed to elicit a particular emotion. Two actors improvise these emotion-specific situations, leading them to utter contextualized, non-read renditions of sentences that have fixed lexical content and convey different emot

61 PAPERS • 2 BENCHMARKS

PlantVillage

The PlantVillage dataset consists of 54303 healthy and unhealthy leaf images divided into 38 categories by species and disease.

61 PAPERS • 1 BENCHMARK

LIP (Look into Person)

The LIP (Look into Person) dataset is a large-scale dataset focusing on semantic understanding of a person. It contains 50,000 images with elaborated pixel-wise annotations of 19 semantic human part labels and 2D human poses with 16 key points. The images are collected from real-world scenarios and the subjects appear with challenging poses and view, heavy occlusions, various appearances and low resolution.

59 PAPERS • 1 BENCHMARK

Winoground

Winoground is a dataset for evaluating the ability of vision and language models to conduct visio-linguistic compositional reasoning. Given two images and two captions, the goal is to match them correctly -- but crucially, both captions contain a completely identical set of words, only in a different order. The dataset was carefully hand-curated by expert annotators and is labeled with a rich set of fine-grained tags to assist in analyzing model performance.

56 PAPERS • 1 BENCHMARK

UBFC-rPPG (Univ. Bourgogne Franche-Comté Remote PhotoPlethysmoGraphy)

We introduce here a new database called UBFC-rPPG (stands for Univ. Bourgogne Franche-Comté Remote PhotoPlethysmoGraphy) comprising two datasets that are focused specifically on rPPG analysis. The UBFC-RPPG database was created using a custom C++ application for video acquisition with a simple low cost webcam (Logitech C920 HD Pro) at 30fps with a resolution of 640x480 in uncompressed 8-bit RGB format. A CMS50E transmissive pulse oximeter was used to obtain the ground truth PPG data comprising the PPG waveform as well as the PPG heart rates. During the recording, the subject sits in front of the camera (about 1m away from the camera) with his/her face visible. All experiments are conducted indoors with a varying amount of sunlight and indoor illumination. The link to download the complete video dataset is available on request. A basic Matlab implementation can also be provided to read ground truth data acquired with a pulse oximeter.

55 PAPERS • 1 BENCHMARK

EmoryNLP

EmoryNLP comprises 97 episodes, 897 scenes, and 12,606 utterances, where each utterance is annotated with one of the seven emotions borrowed from the six primary emotions in the Willcox (1982)’s feeling wheel, sad, mad, scared, powerful, peaceful, joyful, and a default emotion of neutral.

52 PAPERS • 1 BENCHMARK

Localized Narratives

We propose Localized Narratives, a new form of multimodal image annotations connecting vision and language. We ask annotators to describe an image with their voice while simultaneously hovering their mouse over the region they are describing. Since the voice and the mouse pointer are synchronized, we can localize every single word in the description. This dense visual grounding takes the form of a mouse trace segment per word and is unique to our data. We annotated 849k images with Localized Narratives: the whole COCO, Flickr30k, and ADE20K datasets, and 671k images of Open Images, all of which we make publicly available. We provide an extensive analysis of these annotations showing they are diverse, accurate, and efficient to produce. We also demonstrate their utility on the application of controlled image captioning.

52 PAPERS • 5 BENCHMARKS

SEED-Bench

SEED-Bench consists of 19K multiple choice questions with accurate human annotations (~6 larger than existing benchmarks), which spans 12 evaluation dimensions including the comprehension of both the image and video modality.

52 PAPERS • NO BENCHMARKS YET

ICDAR 2003

The ICDAR2003 dataset is a dataset for scene text recognition. It contains 507 natural scene images (including 258 training images and 249 test images) in total. The images are annotated at character level. Characters and words can be cropped from the images.

51 PAPERS • 1 BENCHMARK

LLVIP (A Visible-infrared Paired Dataset for Low-light Vision)

Visible-infrared Paired Dataset for Low-light Vision 30976 images (15488 pairs) 24 dark scenes, 2 daytime scenes Support for image-to-image translation (visible to infrared, or infrared to visible), visible and infrared image fusion, low-light pedestrian detection, and infrared pedestrian detection (The original image and video pairs (before registration) of LLVIP are also released!)

49 PAPERS • 6 BENCHMARKS

Datasets

789 dataset results for Images AND English