The Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset is the most widely-used dataset for fine-grained visual categorization task. It contains 11,788 images of 200 subcategories belonging to birds, 5,994 for training and 5,794 for testing. Each image has detailed annotations: 1 subcategory label, 15 part locations, 312 binary attributes and 1 bounding box. The textual information comes from Reed et al.. They expand the CUB-200-2011 dataset by collecting fine-grained natural language descriptions. Ten single-sentence descriptions are collected for each image. The natural language descriptions are collected through the Amazon Mechanical Turk (AMT) platform, and are required at least 10 words, without any information of subcategories and actions. Source: Fine-grained Visual-textual Representation Learning Image Source: http://www.vision.caltech.edu/visipedia/CUB-200-2011.html
The Caltech101 dataset contains images from 101 object categories (e.g., “helicopter”, “elephant” and “chair” etc.) and a background category that contains the images not from the 101 object categories. For each object category, there are about 40 to 800 images, while most classes have about 50 images. The resolution of the image is roughly about 300×200 pixels. Source: Simple and Efficient Learning using Privileged Information
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 person as 0° (front), 45°, 90° (right), 135°, and 180° (back). Source: PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification Image Source: Qin et al
SPair-71k contains 70,958 image pairs with diverse variations in viewpoint and scale. Compared to previous datasets, it is significantly larger in number and contains more accurate and richer annotations. Source: SPair-71k: A Large-scale Benchmark for Semantic Correspondence Image Source: http://cvlab.postech.ac.kr/research/SPair-71k/
AP-10K is the first large-scale benchmark for general animal pose estimation, to facilitate the research in animal pose estimation. AP-10K consists of 10,015 images collected and filtered from 23 animal families and 60 species following the taxonomic rank and high-quality keypoint annotations labeled and checked manually. Source: AP-10K: A Benchmark for Animal Pose Estimation in the Wild Image source: https://arxiv.org/pdf/2108.12617v1.pdf
A evaluation benchmark ZEB for image matching by merging 8 real-world datasets and 4 simulated datasets with diverse image resolutions, scene conditions and view points.
The PhotoSynth (PS) dataset for patch matching consists of a total of 30 scenes with 25 scenes for training and 5 scenes for validation. The different image pairs are captured in different illumination conditions, at different scales and with different viewpoints. Source: https://arxiv.org/abs/1801.01466 Image Source: https://github.com/rmitra/PS-Dataset
Audiovisual Moments in Time (AVMIT) is a large-scale dataset of audiovisual action events. The dataset includes the annotation of 57,177 audiovisual videos from the Moments in Time dataset, each independently evaluated by 3 of 11 trained participants. Each annotation pertains to whether the labelled audiovisual action event is present and whether it is the most prominent feature of the video. The dataset also provides a curated test set of 960 videos across 16 classes, suitable for comparative experiments involving computational models and human participants, specifically when addressing research questions where audiovisual correspondence is of critical importance.
The DispScenes dataset was created to address the specific problem of disparate image matching. The image pairs in all the datasets exhibit high levels of variation in illumination and viewpoint and also contain instances of occlusion. The DispScenes dataset provides manual ground truth keypoint correspondences for all images. Source: Matching Disparate Image Pairs Using Shape-Aware ConvNets