3 citations · 4 across the 22 of their papers we have counts for
8 papers · 1 filter
Task-Oriented Visual Feature Compression via Residual Vector Quantization for Device-Edge Multimodal Inference
Luning Pang, Cheng Yuan, Jiawei Shao +2
Large multimodal models (LMMs) support diverse visual understanding and reasoning tasks but are often impractical to run entirely on resource-constrained devices. Device-edge co-in…
Privacy-Aware Camera 2.0 Technical Report
Huan Song, Shuyu Tian, Ting Long +5
With the increasing deployment of intelligent sensing technologies in highly sensitive environments such as restrooms and locker rooms, visual surveillance systems face a profound…
Boosting SAM for Cross-Domain Few-Shot Segmentation via Conditional Point Sparsification
Jiahao Nie, Yun Xing, Wenbin An +6
Motivated by the success of the Segment Anything Model (SAM) in promptable segmentation, recent studies leverage SAM to develop training-free solutions for few-shot segmentation, w…
Single-Pixel Vision-Language Model for Intrinsic Privacy-Preserving Behavioral Intelligence
Hongjun An, Yiliang Song, Jiawei Shao +2
Adverse social interactions, such as bullying, harassment, and other illicit activities, pose significant threats to individual well-being and public safety, leaving profound impac…
Multimodal Continual Learning with MLLMs from Multi-scenario Perspectives
Kai Jiang, Siqi Huang, Xiangyu Chen +4
Multimodal large language models (MLLMs) deployed on devices must adapt to continuously changing visual scenarios such as variations in background and perspective, to effectively p…
What You Have is What You Track: Adaptive and Robust Multimodal Tracking
Yuedong Tan, Jiawei Shao, Eduard Zamfir +7
Multimodal data is known to be helpful for visual tracking by improving robustness to appearance variations. However, sensor synchronization challenges often compromise data availa…
Conditional Video Generation for High-Efficiency Video Compression
Fangqiu Yi, Jingyu Xu, Jiawei Shao +2
Perceptual studies demonstrate that conditional diffusion models excel at reconstructing video content aligned with human visual perception. Building on this insight, we propose a…
VideoScan: Enabling Efficient Streaming Video Understanding via Frame-level Semantic Carriers
Ruanjun Li, Yuedong Tan, Yuanming Shi +1
This paper introduces VideoScan, an efficient vision-language model (VLM) inference framework designed for real-time video interaction that effectively comprehends and retains stre…