3 citations · 5 across the 16 of their papers we have counts for
18 papers
GO-PRE: Goal-Oriented Next-Best-View Selection via Predictive Rendering Entropy for Active 3D Reconstruction
Yan Song, Zhihao Li, Chenglong Li +3
Active 3D reconstruction relies on active view selection to maximize reconstruction fidelity under limited capture budgets. However, most existing methods rely on surrogate signals…
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…
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…
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…
X-Prompt: Multi-modal Visual Prompt for Video Object Segmentation
Pinxue Guo, Wanyun Li, Hao Huang +7
Multi-modal Video Object Segmentation (VOS), including RGB-Thermal, RGB-Depth, and RGB-Event, has garnered attention due to its capability to address challenging scenarios where tr…
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…
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…
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning
Lingyi Hong, Shilin Yan, Renrui Zhang +8
Visual object tracking aims to localize the target object of each frame based on its initial appearance in the first frame. Depending on the input modility, tracking tasks can be d…
OneVOS: Unifying Video Object Segmentation with All-in-One Transformer Framework
Wanyun Li, Pinxue Guo, Xinyu Zhou +5
Contemporary Video Object Segmentation (VOS) approaches typically consist stages of feature extraction, matching, memory management, and multiple objects aggregation. Recent advanc…
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…
Plug-and-Play Feature Generation for Few-Shot Medical Image Classification
Qianyu Guo, Huifang Du, Xing Jia +4
Few-shot learning (FSL) presents immense potential in enhancing model generalization and practicality for medical image classification with limited training data; however, it still…