37 citations · 103 across the 18 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…
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…
Multi-modal Prompting for Low-Shot Temporal Action Localization
Chen Ju, Zeqian Li, Peisen Zhao +5
In this paper, we consider the problem of temporal action localization under low-shot (zero-shot & few-shot) scenario, with the goal of detecting and classifying the action instanc…
DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery
Chaofan Ma, Yuhuan Yang, Chen Ju +5
Learning from a large corpus of data, pre-trained models have achieved impressive progress nowadays. As popular generative pre-training, diffusion models capture both low-level vis…
Constraint and Union for Partially-Supervised Temporal Sentence Grounding
Chen Ju, Haicheng Wang, Jinxiang Liu +5
Temporal sentence grounding aims to detect the event timestamps described by the natural language query from given untrimmed videos. The existing fully-supervised setting achieves…