37 citations · 89 across the 16 of their papers we have counts for
7 papers · 1 filter
AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation
Chaofan Ma, Yuhuan Yang, Chen Ju +3
Open-vocabulary semantic segmentation is a challenging task that requires segmenting novel object categories at inference time. Recent studies have explored vision-language pre-tra…
Audio-aware Query-enhanced Transformer for Audio-Visual Segmentation
Jinxiang Liu, Chen Ju, Chaofan Ma +3
The goal of the audio-visual segmentation (AVS) task is to segment the sounding objects in the video frames using audio cues. However, current fusion-based methods have the perform…
Multi-Modal Prototypes for Open-World Semantic Segmentation
Yuhuan Yang, Chaofan Ma, Chen Ju +4
In semantic segmentation, generalizing a visual system to both seen categories and novel categories at inference time has always been practically valuable yet challenging. To enabl…
Enhanced Multimodal Representation Learning with Cross-modal KD
Mengxi Chen, Linyu Xing, Yu Wang +1
This paper explores the tasks of leveraging auxiliary modalities which are only available at training to enhance multimodal representation learning through cross-modal Knowledge Di…
Annotation-free Audio-Visual Segmentation
Jinxiang Liu, Yu Wang, Chen Ju +3
The objective of Audio-Visual Segmentation (AVS) is to localise the sounding objects within visual scenes by accurately predicting pixel-wise segmentation masks. To tackle the task…
Controllable Mesh Generation Through Sparse Latent Point Diffusion Models
Zhaoyang Lyu, Jinyi Wang, Yuwei An +3
Mesh generation is of great value in various applications involving computer graphics and virtual content, yet designing generative models for meshes is challenging due to their ir…
On Transforming Reinforcement Learning by Transformer: The Development Trajectory
Shengchao Hu, Li Shen, Ya Zhang +2
Transformer, originally devised for natural language processing, has also attested significant success in computer vision. Thanks to its super expressive power, researchers are inv…