activity
20242026
collaborators

7 papers

cs.CV2026

Audio-Visual Segmentation via Depth-Guided Collaborative Modeling

Zhaojin Fu, Yuyang Hong, Qi Yang +4

Audio-Visual Segmentation (AVS) is a fundamental task in multimodal perception that performs pixel-level segmentation of sounding objects in videos by leveraging both visual and au…

cs.CV2026

WikiSeeker: Rethinking the Role of Vision-Language Models in Knowledge-Based Visual Question Answering

Yingjian Zhu, Xinming Wang, Kun Ding +3

Multi-modal Retrieval-Augmented Generation (RAG) has emerged as a highly effective paradigm for Knowledge-Based Visual Question Answering (KB-VQA). Despite recent advancements, pre…

cs.CV2026

SeaVIS: Sound-Enhanced Association for Online Audio-Visual Instance Segmentation

Yingjian Zhu, Ying Wang, Yuyang Hong +5

Recently, an audio-visual instance segmentation (AVIS) task has been introduced, aiming to identify, segment and track individual sounding instances in videos. However, prevailing…

cs.MM2025

Taming Modality Entanglement in Continual Audio-Visual Segmentation

Yuyang Hong, Qi Yang, Tao Zhang +5

Recently, significant progress has been made in multi-modal continual learning, aiming to learn new tasks sequentially in multi-modal settings while preserving performance on previ…

cs.CV2025

AddressVLM: Cross-view Alignment Tuning for Image Address Localization using Large Vision-Language Models

Shixiong Xu, Chenghao Zhang, Lubin Fan +5

Large visual language models (LVLMs) have demonstrated impressive performance in coarse-grained geo-localization at the country or city level, but they struggle with fine-grained s…

cs.CV2024

Reusable Architecture Growth for Continual Stereo Matching

Chenghao Zhang, Gaofeng Meng, Bin Fan +4

The remarkable performance of recent stereo depth estimation models benefits from the successful use of convolutional neural networks to regress dense disparity. Akin to most tasks…

cs.CV2024

Defying Imbalanced Forgetting in Class Incremental Learning

Shixiong Xu, Gaofeng Meng, Xing Nie +3

We observe a high level of imbalance in the accuracy of different classes in the same old task for the first time. This intriguing phenomenon, discovered in replay-based Class Incr…