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.
10,336 PAPERS • 93 BENCHMARKS
LVIS is a dataset for long tail instance segmentation. It has annotations for over 1000 object categories in 164k images.
449 PAPERS • 14 BENCHMARKS
Objects365 is a large-scale object detection dataset, Objects365, which has 365 object categories over 600K training images. More than 10 million, high-quality bounding boxes are manually labeled through a three-step, carefully designed annotation pipeline. It is the largest object detection dataset (with full annotation) so far and establishes a more challenging benchmark for the community.
138 PAPERS • 3 BENCHMARKS
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Vision-language modeling has enabled open-vocabulary tasks where predictions can be queried using any text prompt in a zero-shot manner. Existing open-vocabulary tasks focus on object classes, whereas research on object attributes is limited due to the lack of a reliable attribute-focused evaluation benchmark. This paper introduces the Open-Vocabulary Attribute Detection (OVAD) task and the corresponding OVAD benchmark. The objective of the novel task and benchmark is to probe object-level attribute information learned by vision-language models. To this end, we created a clean and densely annotated test set covering 117 attribute classes on the 80 object classes of MS COCO. It includes positive and negative annotations, which enables open-vocabulary evaluation. Overall, the benchmark consists of 1.4 million annotations. For reference, we provide a first baseline method for open-vocabulary attribute detection. Moreover, we demonstrate the benchmark's value by studying the attribute dete
13 PAPERS • 3 BENCHMARKS
Description Detection Dataset ($D^3$, /dikju:b/) is an attempt at creating a next-generation object detection dataset. Unlike traditional detection datasets, the class names of the objects are no longer simple nouns or noun phrases, but rather complex and descriptive, such as a dog not being held by a leash. For each image in the dataset, any object that matches the description is annotated. The dataset provides annotations such as bounding boxes and finely crafted instance masks.It comprises of 422 well-designed descriptions and 24,282 positive object-description pairs.
8 PAPERS • 1 BENCHMARK