태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
3D object captioning involves generating a natural language description of an object, given its point cloud representation.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
Objaverse is a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category. Source: Objaverse: A Universe of Annotated 3D Objects Image Source: https://objaverse.allenai.org/
TUMTraffic-VideoQA is a novel dataset designed to understand spatiotemporal video in complex roadside traffic scenarios. The dataset comprises 1,000 videos, featuring 85,000 multiple-choice QA pairs, 2,300 object captioning, and 5,700 object grounding annotations, encompassing diverse real-world conditions such as adverse weather and traffic anomalies. By incorporating tuple-based spatiotemporal object expressions, TUMTraffic-VideoQA unifies three essential tasks—multiple-choice video question answering, referred object captioning, and spatiotemporal object grounding—within a cohesive evaluation framework.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.