natural language supervision

It is difficult to construct a data collection including all possible combinations of human actions and interacting objects due to the combinatorial nature of human-object interactions (HOI). In this work, we aim to develop a transferable HOI detector for unseen interactions. Existing HOI detectors often treat interactions as discrete labels and learn a classifier according to a predetermined category space. This is inherently inapt for detecting unseen interactions which are out of the predefined categories. Conversely, we treat independent HOI labels as the natural language supervision of interactions and embed them into a joint visual-and-text space to capture their correlations. More specifically, we propose a new HOI visual encoder to detect the interacting humans and objects, and map them to a joint feature space to perform interaction recognition. Our visual encoder is instantiated as a Vision Transformer with new learnable HOI tokens and a sequence parser to generate unique HOI predictions. It distills and leverages the transferable knowledge from the pretrained CLIP model to perform the zero-shot interaction detection. Experiments on two datasets, SWIG-HOI and HICO-DET, validate that our proposed method can achieve a notable mAP improvement on detecting both seen and unseen HOIs. Our code is available at https://github.com/scwangdyd/promting_hoi.

This paper considers the problem of open-vocabulary semantic segmentation (OVS), that aims to segment objects of arbitrary classes beyond a pre-defined, closed-set categories. The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor, which only exploits web-crawled image-text pairs for pre-training without using any mask annotations. OVSegmentor assembles the image pixels into a set of learnable group tokens via a slot-attention based binding module, then aligns the group tokens to corresponding caption embeddings. Second, we propose two proxy tasks for training, namely masked entity completion and cross-image mask consistency. The former aims to infer all masked entities in the caption given group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities. The latter enforces consistent mask predictions between images that contain shared entities, encouraging the model to learn visual invariance. Third, we construct CC4M dataset for pre-training by filtering CC12M with frequently appeared entities, which significantly improves training efficiency. Fourth, we perform zero-shot transfer on four benchmark datasets, PASCAL VOC, PASCAL Context, COCO Object, and ADE20K. OVSegmentor achieves superior results over state-of-the-art approaches on PASCAL VOC using only 3% data (4M vs 134M) for pre-training.