75 citations · 127 across the 11 of their papers we have counts for
8 papers · 1 filter
A Visual Dependence-Aware Framework for Multimodal Unsupervised Continual Post-Training
Kaichen Li, Zhilin Zhu, Jianhao Huang +7
In this paper, we explore a novel task of Multimodal Unsupervised Continual Post-Training (MU-CPT), enabling deployed MLLMs to continually evolve from streaming unlabeled data. Exi…
BARE: Towards Bias-Aware and Reasoning-Enhanced One-Tower Visual Grounding
Hongbing Li, Linhui Xiao, Zihan Zhao +4
Visual Grounding (VG), which aims to locate a specific region referred to by expressions, is a fundamental yet challenging task in the multimodal understanding fields. While recent…
RGBT-GroundBench: Visual Grounding Beyond RGB in Complex Real-World Scenarios
Tianyi Zhao, Jiawen Xi, Linhui Xiao +4
Visual grounding (VG) localizes target objects in an image from natural-language expressions. In real-world perception, RGB cues often degrade under low illumination and adverse we…
Toward Visual Grounding: A Survey
Linhui Xiao, Xiaoshan Yang, Xiangyuan Lan +2
Visual Grounding, also known as Referring Expression Comprehension and Phrase Grounding, aims to ground the specific region(s) within the image(s) based on the given expression tex…
OneRef: Unified One-tower Expression Grounding and Segmentation with Mask Referring Modeling
Linhui Xiao, Xiaoshan Yang, Fang Peng +2
Constrained by the separate encoding of vision and language, existing grounding and referring segmentation works heavily rely on bulky Transformer-based fusion en-/decoders and a v…
HiVG: Hierarchical Multimodal Fine-grained Modulation for Visual Grounding
Linhui Xiao, Xiaoshan Yang, Fang Peng +2
Visual grounding, which aims to ground a visual region via natural language, is a task that heavily relies on cross-modal alignment. Existing works utilized uni-modal pre-trained m…
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding
Linhui Xiao, Xiaoshan Yang, Fang Peng +3
Visual Grounding (VG) is a crucial topic in the field of vision and language, which involves locating a specific region described by expressions within an image. To reduce the reli…
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification
Fang Peng, Xiaoshan Yang, Linhui Xiao +2
Although significant progress has been made in few-shot learning, most of existing few-shot image classification methods require supervised pre-training on a large amount of sample…