태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Given a demonstration video V and a target image I, the goal of video-to-image affordance grounding predict an affordance heatmap over the target image according to the hand-interacted region in the video, accompanied by the affordance action (e.g., press, turn).
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The OPRA Dataset was introduced in Demo2Vec: Reasoning Object Affordances From Online Videos (CVPR'18) for reasoning object affordances from online demonstration videos. It contains 11,505 demonstration clips and 2,512 object images scraped from 6 popular YouTube product review channels along with the corresponding affordance annotations. More details can be found on our https://sites.google.com/view/demo2vec/.
From Grounded Human-Object Interaction Hotspots from Video (ICCV'19): We collect annotations for interaction keypoints on EPIC Kitchens in order to quantitatively evaluate our method in parallel to the OPRA dataset (where annotations are available). We note that these annotations are collected purely for evaluation, and are not used for training our model. We select the 20 most frequent verbs, and select 31 nouns that afford these interactions.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.