FairFace is a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino. Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups.
165 PAPERS • 1 BENCHMARK
GazeFollow is a large-scale dataset annotated with the location of where people in images are looking. It uses several major datasets that contain people as a source of images: 1, 548 images from SUN, 33, 790 images from MS COCO, 9, 135 images from Actions 40, 7, 791 images from PASCAL, 508 images from the ImageNet detection challenge and 198, 097 images from the Places dataset. This concatenation results in a challenging and large image collection of people performing diverse activities in many everyday scenarios.
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The SentiCap dataset contains several thousand images with captions with positive and negative sentiments. These sentimental captions are constructed by the authors by re-writing factual descriptions. In total there are 2000+ sentimental captions.
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The TrajNet Challenge represents a large multi-scenario forecasting benchmark. The challenge consists on predicting 3161 human trajectories, observing for each trajectory 8 consecutive ground-truth values (3.2 seconds) i.e., t−7,t−6,…,t, in world plane coordinates (the so-called world plane Human-Human protocol) and forecasting the following 12 (4.8 seconds), i.e., t+1,…,t+12. The 8-12-value protocol is consistent with the most trajectory forecasting approaches, usually focused on the 5-dataset ETH-univ + ETH-hotel + UCY-zara01 + UCY-zara02 + UCY-univ. Trajnet extends substantially the 5-dataset scenario by diversifying the training data, thus stressing the flexibility and generalization one approach has to exhibit when it comes to unseen scenery/situations. In fact, TrajNet is a superset of diverse datasets that requires to train on four families of trajectories, namely 1) BIWI Hotel (orthogonal bird’s eye flight view, moving people), 2) Crowds UCY (3 datasets, tilted bird’s eye view
12 PAPERS • 2 BENCHMARKS
BLVD is a large scale 5D semantics dataset collected by the Visual Cognitive Computing and Intelligent Vehicles Lab. This dataset contains 654 high-resolution video clips owing 120k frames extracted from Changshu, Jiangsu Province, China, where the Intelligent Vehicle Proving Center of China (IVPCC) is located. The frame rate is 10fps/sec for RGB data and 3D point cloud. The dataset contains fully annotated frames which yield 249,129 3D annotations, 4,902 independent individuals for tracking with the length of overall 214,922 points, 6,004 valid fragments for 5D interactive event recognition, and 4,900 individuals for 5D intention prediction. These tasks are contained in four kinds of scenarios depending on the object density (low and high) and light conditions (daytime and nighttime).
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Atari-HEAD is a dataset of human actions and eye movements recorded while playing Atari videos games. For every game frame, its corresponding image frame, the human keystroke action, the reaction time to make that action, the gaze positions, and immediate reward returned by the environment were recorded. The gaze data was recorded using an EyeLink 1000 eye tracker at 1000Hz. The human subjects are amateur players who are familiar with the games. The human subjects were only allowed to play for 15 minutes and were required to rest for at least 15 minutes before the next trial. Data was collected from 4 subjects, 16 games, 175 15-minute trials, and a total of 2.97 million frames/demonstrations.
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The Flick Cropping Dataset consists of high quality cropping and pairwise ranking annotations used to evaluate the performance of automatic image cropping approaches.
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CUHK Image Cropping is a dataset for image cropping. The photos are of varying aesthetic quality and span a variety of image categories, including animal, architecture, human, landscape, night, plant and man-made objects. Each image is manually cropped by three expert photographers (graduate students in art whose primary medium is photography) to form three training sets. There are 1,000 photos in the dataset.
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A View From Somewhere (AVFS)—a dataset of 638,180 face similarity judgments over 4,921 faces. Each judgment corresponds to the odd-one-out (i.e., least similar) face in a triplet of faces and is accompanied by both the identifier and demographic attributes of the annotator who made the judgment.
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