6 citations · 11 across the 26 of their papers we have counts for
29 papers
Beyond Token Importance: Preserving Spatial Scaffolds for Efficient Vision-Language-Action Inference
Jiayu Chen, Shuyong Gao, Jingkai Jia +6
Existing VLA pruning strategies primarily select individual visual tokens according to task-level semantic relevance, while overlooking the spatial information required for robotic…
VLA-Hijack: A Transferable Patch Attack against Vision-Language-Action Models via Visual Proprioception Hijacking
Jiyuan Fu, Kaixun Jiang, Jingkai Jia +7
While Vision-Language-Action (VLA) models have emerged as powerful generalist policies, their severe vulnerability to adversarial patches significantly hinders their deployment in…
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…
Collaborative Reconstruction and Repair for Multi-class Industrial Anomaly Detection
Qishan Wang, Haofeng Wang, Shuyong Gao +5
Industrial anomaly detection is a challenging open-set task that aims to identify unknown anomalous patterns deviating from normal data distribution. To avoid the significant memor…
Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory
Yuxuan Lin, Hanjing Yan, Xuan Tong +6
Few-shot multimodal industrial anomaly detection is a critical yet underexplored task, offering the ability to quickly adapt to complex industrial scenarios. In few-shot settings,…
Search is All You Need for Few-shot Anomaly Detection
Qishan Wang, Jia Guo, Shuyong Gao +5
Few-shot anomaly detection (FSAD) has emerged as a crucial yet challenging task in industrial inspection, where normal distribution modeling must be accomplished with only a few no…
HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection Dataset
Qishan Wang, Shuyong Gao, Junjie Hu +4
Multi-class Unsupervised Anomaly Detection algorithms (MUAD) are receiving increasing attention due to their relatively low deployment costs and improved training efficiency. 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…
AnimatePainter: A Self-Supervised Rendering Framework for Reconstructing Painting Process
Junjie Hu, Shuyong Gao, Qianyu Guo +4
Humans can intuitively decompose an image into a sequence of strokes to create a painting, yet existing methods for generating drawing processes are limited to specific data types…
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…
A Holistically Point-guided Text Framework for Weakly-Supervised Camouflaged Object Detection
Tsui Qin Mok, Shuyong Gao, Haozhe Xing +3
Weakly-Supervised Camouflaged Object Detection (WSCOD) has gained popularity for its promise to train models with weak labels to segment objects that visually blend into their surr…
Boosting Salient Object Detection with Knowledge Distillated from Large Foundation Models
Miaoyang He, Shuyong Gao, Tsui Qin Mok +2
Salient Object Detection (SOD) aims to identify and segment prominent regions within a scene. Traditional models rely on manually annotated pseudo labels with precise pixel-level a…
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…
General Compression Framework for Efficient Transformer Object Tracking
Lingyi Hong, Jinglun Li, Xinyu Zhou +10
Previous works have attempted to improve tracking efficiency through lightweight architecture design or knowledge distillation from teacher models to compact student trackers. Howe…
All rivers run into the sea: Unified Modality Brain-like Emotional Central Mechanism
Xinji Mai, Junxiong Lin, Haoran Wang +10
In the field of affective computing, fully leveraging information from a variety of sensory modalities is essential for the comprehensive understanding and processing of human emot…
PG-Attack: A Precision-Guided Adversarial Attack Framework Against Vision Foundation Models for Autonomous Driving
Jiyuan Fu, Zhaoyu Chen, Kaixun Jiang +3
Vision foundation models are increasingly employed in autonomous driving systems due to their advanced capabilities. However, these models are susceptible to adversarial attacks, p…
Hi-EF: Benchmarking Emotion Forecasting in Human-interaction
Haoran Wang, Xinji Mai, Zeng Tao +6
Affective Forecasting is an psychology task that involves predicting an individual's future emotional responses, often hampered by reliance on external factors leading to inaccurac…
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution
Junxiong Lin, Zeng Tao, Xuan Tong +10
The problem of blind image super-resolution aims to recover high-resolution (HR) images from low-resolution (LR) images with unknown degradation modes. Most existing methods model…
D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition
Haoran Wang, Xinji Mai, Zeng Tao +10
The contemporary state-of-the-art of Dynamic Facial Expression Recognition (DFER) technology facilitates remarkable progress by deriving emotional mappings of facial expressions fr…
LVOS: A Benchmark for Large-scale Long-term Video Object Segmentation
Lingyi Hong, Zhongying Liu, Wenchao Chen +9
Video object segmentation (VOS) aims to distinguish and track target objects in a video. Despite the excellent performance achieved by off-the-shell VOS models, existing VOS benchm…
ClickVOS: Click Video Object Segmentation
Pinxue Guo, Lingyi Hong, Xinyu Zhou +7
Video Object Segmentation (VOS) task aims to segment objects in videos. However, previous settings either require time-consuming manual masks of target objects at the first frame d…
Improving Adversarial Transferability of Vision-Language Pre-training Models through Collaborative Multimodal Interaction
Jiyuan Fu, Zhaoyu Chen, Kaixun Jiang +4
Despite the substantial advancements in Vision-Language Pre-training (VLP) models, their susceptibility to adversarial attacks poses a significant challenge. Existing work rarely s…
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation
Lingyi Hong, Wei Zhang, Shuyong Gao +2
Unsupervised video object segmentation (UVOS) aims at detecting the primary objects in a given video sequence without any human interposing. Most existing methods rely on two-strea…
Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context
Yicheng Song, Shuyong Gao, Haozhe Xing +3
Unsupervised salient object detection aims to detect salient objects without using supervision signals eliminating the tedious task of manually labeling salient objects. To improve…