Publications (27)
AD^2-Bench: A Hierarchical CoT Benchmark for MLLM in Autonomous Driving under Adverse Conditions
Zhaoyang Wei, Chenhui Qiang, Bowen Jiang +3
Chain-of-Thought (CoT) reasoning has emerged as a powerful approach to enhance the structured, multi-step decision-making capabilities of Multi-Modal Large Models (MLLMs), is parti…
Boosting Segment Anything Model Towards Open-Vocabulary Learning
Xumeng Han, Longhui Wei, Xuehui Yu +6
The recent Segment Anything Model (SAM) has emerged as a new paradigmatic vision foundation model, showcasing potent zero-shot generalization and flexible prompting. Despite SAM fi…
Object Localization under Single Coarse Point Supervision
Xuehui Yu, Pengfei Chen, Di Wu +6
Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotatio…
Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards
Xuehui Yu, Fucheng Cai, Meiyi Wang +2
Inference-time guided sampling steers state-of-the-art diffusion and flow models without fine-tuning by interpreting the generation process as a controllable trajectory. This provi…
Rethinking Sampling Strategies for Unsupervised Person Re-identification
Xumeng Han, Xuehui Yu, Guorong Li +5
Unsupervised person re-identification (re-ID) remains a challenging task. While extensive research has focused on the framework design and loss function, this paper shows that samp…
P2P-Loc: Point to Point Tiny Person Localization
Xuehui Yu, Di Wu, Qixiang Ye +2
Bounding-box annotation form has been the most frequently used method for visual object localization tasks. However, bounding-box annotation relies on a large amount of precisely a…
ClickTrack: Towards Real-time Interactive Single Object Tracking
Kuiran Wang, Xuehui Yu, Wenwen Yu +5
Single object tracking(SOT) relies on precise object bounding box initialization. In this paper, we reconsidered the deficiencies in the current approaches to initializing single o…
Causal prompting model-based offline reinforcement learning
Xuehui Yu, Yi Guan, Rujia Shen +3
Model-based offline Reinforcement Learning (RL) allows agents to fully utilise pre-collected datasets without requiring additional or unethical explorations. However, applying mode…
Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement
Guangqian Guo, Aixi Ren, Yong Guo +6
Segment Anything Models (SAMs), known for their exceptional zero-shot segmentation performance, have garnered significant attention in the research community. Nevertheless, their p…
ReLayout: Integrating Relation Reasoning for Content-aware Layout Generation with Multi-modal Large Language Models
Jiaxu Tian, Xuehui Yu, Yaoxing Wang +3
Content-aware layout aims to arrange design elements appropriately on a given canvas to convey information effectively. Recently, the trend for this task has been to leverage large…
Segment Any-Quality Images with Generative Latent Space Enhancement
Guangqian Guo, Yong Guo, Xuehui Yu +3
Despite their success, Segment Anything Models (SAMs) experience significant performance drops on severely degraded, low-quality images, limiting their effectiveness in real-world…
Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes
Di Wu, Pengfei Chen, Xuehui Yu +3
Object detection via inaccurate bounding boxes supervision has boosted a broad interest due to the expensive high-quality annotation data or the occasional inevitability of low ann…
SAPNet++: Evolving Point-Prompted Instance Segmentation with Semantic and Spatial Awareness
Zhaoyang Wei, Xumeng Han, Xuehui Yu +4
Single-point annotation is increasingly prominent in visual tasks for labeling cost reduction. However, it challenges tasks requiring high precision, such as the point-prompted ins…
SM+: Refined Scale Match for Tiny Person Detection
Nan Jiang, Xuehui Yu, Xiaoke Peng +2
Detecting tiny objects ( e.g., less than 20 x 20 pixels) in large-scale images is an important yet open problem. Modern CNN-based detectors are challenged by the scale mismatch bet…
P2Object: Single Point Supervised Object Detection and Instance Segmentation
Pengfei Chen, Xuehui Yu, Xumeng Han +5
Object recognition using single-point supervision has attracted increasing attention recently. However, the performance gap compared with fully-supervised algorithms remains large.…
Causal Coupled Mechanisms: A Control Method with Cooperation and Competition for Complex System
Xuehui Yu, Jingchi Jiang, Xinmiao Yu +2
Complex systems are ubiquitous in the real world and tend to have complicated and poorly understood dynamics. For their control issues, the challenge is to guarantee accuracy, robu…
P2RBox: Point Prompt Oriented Object Detection with SAM
Guangming Cao, Xuehui Yu, Wenwen Yu +5
Single-point annotation in oriented object detection of remote sensing scenarios is gaining increasing attention due to its cost-effectiveness. However, due to the granularity ambi…
CPR++: Object Localization via Single Coarse Point Supervision
Xuehui Yu, Pengfei Chen, Kuiran Wang +5
Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotatio…
Effective Fusion Factor in FPN for Tiny Object Detection
Yuqi Gong, Xuehui Yu, Yao Ding +3
FPN-based detectors have made significant progress in general object detection, e.g., MS COCO and PASCAL VOC. However, these detectors fail in certain application scenarios, e.g.,…
P2Seg: Pointly-supervised Segmentation via Mutual Distillation
Zipeng Wang, Xuehui Yu, Xumeng Han +4
Point-level Supervised Instance Segmentation (PSIS) aims to enhance the applicability and scalability of instance segmentation by utilizing low-cost yet instance-informative annota…
Scale Match for Tiny Person Detection
Xuehui Yu, Yuqi Gong, Nan Jiang +2
Visual object detection has achieved unprecedented ad-vance with the rise of deep convolutional neural networks.However, detecting tiny objects (for example tiny per-sons less than…
Point-to-Box Network for Accurate Object Detection via Single Point Supervision
Pengfei Chen, Xuehui Yu, Xumeng Han +7
Object detection using single point supervision has received increasing attention over the years. However, the performance gap between point supervised object detection (PSOD) and…
Skill-aware Mutual Information Optimisation for Generalisation in Reinforcement Learning
Xuehui Yu, Mhairi Dunion, Xin Li +1
Meta-Reinforcement Learning (Meta-RL) agents can struggle to operate across tasks with varying environmental features that require different optimal skills (i.e., different modes o…
Semantic-aware SAM for Point-Prompted Instance Segmentation
Zhaoyang Wei, Pengfei Chen, Xuehui Yu +3
Single-point annotation in visual tasks, with the goal of minimizing labelling costs, is becoming increasingly prominent in research. Recently, visual foundation models, such as Se…
The 1st Tiny Object Detection Challenge:Methods and Results
Xuehui Yu, Zhenjun Han, Yuqi Gong +22
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a…
Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking
Nan Jiang, Kuiran Wang, Xiaoke Peng +7
Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce and recreation. With this, monitoring the operation status of UAVs is crucially important. In this work,…
SAM-CP: Marrying SAM with Composable Prompts for Versatile Segmentation
Pengfei Chen, Lingxi Xie, Xinyue Huo +5
The Segment Anything model (SAM) has shown a generalized ability to group image pixels into patches, but applying it to semantic-aware segmentation still faces major challenges. Th…