Omni-Realm Benchmark (OmniBenchmark) is a diverse (21 semantic realm-wise datasets) and concise (realm-wise datasets have no concepts overlapping) benchmark for evaluating pre-trained model generalization across semantic super-concepts/realms, e.g. across mammals to aircraft.
28 PAPERS • 1 BENCHMARK
Games dataset containing 100,000 Gameplay Images of 175 Video Games across 10 Sports Genres - AMERICAN FOOTBALL, BASKETBALL, BIKE RACING, CAR RACING, FIGHTING, HOCKEY, SOCCER, TABLE TENNIS, TENNIS.
4 PAPERS • 2 BENCHMARKS
Causal Triplet is a causal representation learning benchmark featuring not only visually more complex scenes, but also two crucial desiderata commonly overlooked in previous works:
2 PAPERS • NO BENCHMARKS YET
SYNTH-PEDES is a large-scale person dataset with image-text pairs by far, which contains 312,321 identities, 4,791,711 images, and 12,138,157 textual descriptions.
Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. Identifying vessels that do not show up in conventional monitoring systems---known as ``dark vessels''---is key to managing and securing the health of marine environments. With the rise of satellite-based synthetic aperture radar (SAR) imaging and modern machine learning (ML), it is now possible to automate detection of dark vessels day or night, under all-weather conditions. SAR images, however, require a domain-specific treatment and are not widely accessible to the ML community. Maritime objects (vessels and offshore infrastructure) are relatively small and sparse, challenging traditional computer vision approaches. We present the largest labeled dataset for training ML models to detect and characterize vessels and ocean structures in SAR imagery. xView3-SAR consists of nearly 1,000 analysis-ready SAR images from the Sentinel-1 mission that are, on average, 29,400-by-24,400 pixels each.
2 PAPERS • 1 BENCHMARK
We introduce the trapped yeast cell (TYC) dataset, a novel dataset for understanding instance-level semantics and motions of cells in microstructures. We release $105$ dense annotated high-resolution brightfield microscopy images, including about $19$k instance masks. We also release $261$ curated video clips composed of $1293$ high-resolution microscopy images to facilitate unsupervised understanding of cell motions and morphology.
1 PAPER • NO BENCHMARKS YET