Different objects in the same scene are more or less related to each other, but only a limited
number of these relationships are noteworthy. Inspired by Detection Transformer, which …

Abstract Current Scene Graph Generation (SGG) methods explore contextual information to
predict relationships among entity pairs. However, due to the diverse visual appearance of …

Deep learning techniques have led to remarkable breakthroughs in the field of object
detection and have spawned a lot of scene-understanding tasks in recent years. Scene …

Abstract Scene Graph Generation (SGG) remains a challenging visual understanding task
due to its compositional property. Most previous works adopt a bottom-up two-stage or a …

Abstract Scene Graph Generation (SGG) aims to extract< subject, predicate, object>
relationships in images for vision understanding. Although recent works have made steady …

Object detection has achieved a huge breakthrough with deep neural networks and massive
annotated data. However, current detection methods cannot be directly transferred to the …

Image-text retrieval is a fundamental task to bridge vision and language by exploiting
various strategies to fine-grained alignment between regions and words. This is still tough …

Abstract Scene Graph Generation (SGG) is a challenging task of detecting objects and
predicting relationships between objects. After DETR was developed one-stage SGG …

Panoptic Scene Graph (PSG) is a challenging task in Scene Graph Generation (SGG) that
aims to create a more comprehensive scene graph representation using panoptic …

Pretrained vision-language models, such as CLIP, have demonstrated strong generalization
capabilities, making them promising tools in the realm of zero-shot visual recognition. Visual …