5 accepted papers
This paper pays attention to open-vocabulary 3D object affordance grounding (OVAG), which aims to localize affordance regions on 3D objects by leveraging interaction images or textual instructions. Most existing methods treat interaction images as sources of external affordance knowledge and align t…
The latest progress in text-to-3D generative models makes it possible to generate high-quality 3D content. Recent text-to-3D large model have achieved remarkable breakthroughs in multi-view consistency. However, their effectiveness is often affected by inherent biases, resulting in sensitivity to de
This paper pays attention to Weakly Supervised Affordance Grounding (WSAG) task that aims to train model to identify affordance regions using human-object interaction images and egocentric images without the need for costly pixel-level annotations. Most existing methods usually consider the affordan…
To promote the deployment of scenario understanding in the real world, Open-Vocabulary Scene Graph Generation (OV-SGG) has attracted much attention recently, aiming to generalize beyond the limited number of relation categories labeled during training and detect those unseen relations during inferen…
The scene graph generation (SGG) task is designed to identify the predicates based on the subject-object pairs. However, existing datasets generally include two imbalance cases: one is the class imbalance from the predicted predicates and another is the context imbalance from the given subject-objec…