2 citations · 2 across the 11 of their papers we have counts for
13 papers
Drone-PUR: From Visual Clue Discovery to Understanding and Reasoning in UAV Scenes
Jiahe Wu, Bing Cao, Xingyue Guo +5
The reasoning capabilities of vision-language models (VLMs) can be limited by incomplete or inaccurate visual observations in the general domain. In unmanned aerial vehicle (UAV) s…
RGBX-R1: Visual Modality Chain-of-Thought Guided Reinforcement Learning for Multimodal Grounding
Jiahe Wu, Bing Cao, Qilong Wang +3
Multimodal Large Language Models (MLLM) are primarily pre-trained on the RGB modality, thereby limiting their performance on other modalities, such as infrared, depth, and event da…
AM-Net: Adaptively Aligned Multi-Scale Moment for Few-Shot Action Recognition
Zilin Gao, Qilong Wang, Bingbing Zhang +2
Thanks to capability to alleviate the cost of large-scale annotation, few-shot action recognition (FSAR) has attracted increased attention of researchers in recent years. Existing…
Constrained Prompt Enhancement for Improving Zero-Shot Generalization of Vision-Language Models
Xiaojie Yin, Qilong Wang, Qinghua Hu
Vision-language models (VLMs) pre-trained on web-scale data exhibit promising zero-shot generalization but often suffer from semantic misalignment due to domain gaps between pre-tr…
DALIP: Distribution Alignment-based Language-Image Pre-Training for Domain-Specific Data
Junjie Wu, Jiangtao Xie, Zhaolin Zhang +4
Recently, Contrastive Language-Image Pre-training (CLIP) has shown promising performance in domain-specific data (e.g., biology), and has attracted increasing research attention. E…
CoE: Chain-of-Explanation via Automatic Visual Concept Circuit Description and Polysemanticity Quantification
Wenlong Yu, Qilong Wang, Chuang Liu +2
Explainability is a critical factor influencing the wide deployment of deep vision models (DVMs). Concept-based post-hoc explanation methods can provide both global and local insig…
Asymmetric Reinforcing against Multi-modal Representation Bias
Xiyuan Gao, Bing Cao, Pengfei Zhu +2
The strength of multimodal learning lies in its ability to integrate information from various sources, providing rich and comprehensive insights. However, in real-world scenarios,…
: Synonymous Semantic Space for Improving Zero-Shot Generalization of Vision-Language Models
Xiaojie Yin, Qilong Wang, Bing Cao +1
Recently, many studies have been conducted to enhance the zero-shot generalization ability of vision-language models (e.g., CLIP) by addressing the semantic misalignment between im…
Test-Time Dynamic Image Fusion
Bing Cao, Yinan Xia, Yi Ding +2
The inherent challenge of image fusion lies in capturing the correlation of multi-source images and comprehensively integrating effective information from different sources. Most e…
Conditional Controllable Image Fusion
Bing Cao, Xingxin Xu, Pengfei Zhu +2
Image fusion aims to integrate complementary information from multiple input images acquired through various sources to synthesize a new fused image. Existing methods usually emplo…
TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition
Yilong Wang, Zilin Gao, Qilong Wang +3
Going beyond few-shot action recognition (FSAR), cross-domain FSAR (CDFSAR) has attracted recent research interests by solving the domain gap lying in source-to-target transfer lea…
Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark
Bing Cao, Quanhao Lu, Jiekang Feng +3
The dynamic imbalance of the fore-background is a major challenge in video object counting, which is usually caused by the sparsity of target objects. This remains understudied in…
Visible and Clear: Finding Tiny Objects in Difference Map
Bing Cao, Haiyu Yao, Pengfei Zhu +1
Tiny object detection is one of the key challenges in the field of object detection. The performance of most generic detectors dramatically decreases in tiny object detection tasks…