8 citations · 20 across the 25 of their papers we have counts for
10 papers · 1 filter
RSAgent: Learning to Reason and Act for Text-Guided Segmentation via Multi-Turn Tool Invocations
Xingqi He, Yujie Zhang, Shuyong Gao +6
Text-guided object segmentation requires both cross-modal reasoning and pixel grounding abilities. Most recent methods treat text-guided segmentation as one-shot grounding, where t…
Dynamic Semantic-Aware Correlation Modeling for UAV Tracking
Xinyu Zhou, Tongxin Pan, Lingyi Hong +5
UAV tracking can be widely applied in scenarios such as disaster rescue, environmental monitoring, and logistics transportation. However, existing UAV tracking methods predominantl…
LSVOS 2025 Challenge Report: Recent Advances in Complex Video Object Segmentation
Chang Liu, Henghui Ding, Kaining Ying +46
This report presents an overview of the 7th Large-scale Video Object Segmentation (LSVOS) Challenge held in conjunction with ICCV 2025. Besides the two traditional tracks of LSVOS…
LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops
Jiyuan Fu, Kaixun Jiang, Lingyi Hong +5
Multimodal Large Language Models (MLLMs) have shown great promise but require substantial computational resources during inference. Attackers can exploit this by inducing excessive…
CrossLMM: Decoupling Long Video Sequences from LMMs via Dual Cross-Attention Mechanisms
Shilin Yan, Jiaming Han, Joey Tsai +5
The advent of Large Multimodal Models (LMMs) has significantly enhanced Large Language Models (LLMs) to process and interpret diverse data modalities (e.g., image and video). Howev…
Scoring, Remember, and Reference: Catching Camouflaged Objects in Videos
Yuang Feng, Shuyong Gao, Fuzhen Yan +4
Video Camouflaged Object Detection (VCOD) aims to segment objects whose appearances closely resemble their surroundings, posing a challenging and emerging task. Existing vision mod…
MSVCOD:A Large-Scale Multi-Scene Dataset for Video Camouflage Object Detection
Shuyong Gao, Yu'ang Feng, Qishan Wang +5
Video Camouflaged Object Detection (VCOD) is a challenging task which aims to identify objects that seamlessly concealed within the background in videos. The dynamic properties of…
VideoPure: Diffusion-based Adversarial Purification for Video Recognition
Kaixun Jiang, Zhaoyu Chen, Jiyuan Fu +3
Recent work indicates that video recognition models are vulnerable to adversarial examples, posing a serious security risk to downstream applications. However, current research has…
P3S-Diffusion:A Selective Subject-driven Generation Framework via Point Supervision
Junjie Hu, Shuyong Gao, Lingyi Hong +4
Recent research in subject-driven generation increasingly emphasizes the importance of selective subject features. Nevertheless, accurately selecting the content in a given referen…
DeTrack: In-model Latent Denoising Learning for Visual Object Tracking
Xinyu Zhou, Jinglun Li, Lingyi Hong +4
Previous visual object tracking methods employ image-feature regression models or coordinate autoregression models for bounding box prediction. Image-feature regression methods hea…