activity
20242026
most citedMultimodality Invariant Learning for Multimedia-Based New Item Recommendation

29 citations · 31 across the 14 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2026

Mask 2D-3D: Adaptive Dual-Masked Autoencoder Network for Image-to-Point Cloud Registration

Zhixin Cheng, Jiacheng Deng, Xiaotian Yin +3

Detection-free methods for image-to-point cloud registration are prone to erroneous correspondences caused by domain and modality discrepancies, limited sensitivity of feature extr…

cs.CV2026

Acoustically Grounded Cost Learning for Open-Vocabulary Audio-Visual Semantic Segmentation

Tianrui Hui, Shaofei Huang, Qisong Han +6

Open-Vocabulary Audio-Visual Semantic Segmentation (OV-AVSS) aims to perform pixel-level segmentation of sound-emitting objects from an open set of categories. The previous method…

cs.CV2025

GT-Mean Loss: A Simple Yet Effective Solution for Brightness Mismatch in Low-Light Image Enhancement

Jingxi Liao, Shijie Hao, Richang Hong +1

Low-light image enhancement (LLIE) aims to improve the visual quality of images captured under poor lighting conditions. In supervised LLIE research, there exists a significant yet…

cs.CV2025

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer

Shaofei Huang, Rui Ling, Tianrui Hui +6

Audio-Visual Segmentation (AVS) aims to segment sound-producing objects in video frames based on the associated audio signal. Prevailing AVS methods typically adopt an audio-centri…

cs.CV2025

Domain Generalization for Face Anti-spoofing via Content-aware Composite Prompt Engineering

Jiabao Guo, Ajian Liu, Yunfeng Diao +5

The challenge of Domain Generalization (DG) in Face Anti-Spoofing (FAS) is the significant interference of domain-specific signals on subtle spoofing clues. Recently, some CLIP-bas…

cs.CV2025

EgoBlind: Towards Egocentric Visual Assistance for the Blind

Junbin Xiao, Nanxin Huang, Hao Qiu +5

We present EgoBlind, the first egocentric VideoQA dataset collected from blind individuals to evaluate the assistive capabilities of contemporary multimodal large language models (…