1 citations · 3 across the 10 of their papers we have counts for
13 papers
Anchor and Adapt: Asymmetric Prompt Adaptation for Few-Shot Industrial Anomaly Detection
Mengyang Zhao, Teng Fu, Haiyang Yu +3
In few-shot industrial anomaly detection, the few normal target images provide no direct defect supervision, making anomaly prompts difficult to learn from these samples alone. Som…
DocCogito: Aligning Layout Cognition and Step-Level Grounded Reasoning for Document Understanding
Yuchuan Wu, Minghan Zhuo, Teng Fu +3
Document understanding with multimodal large language models (MLLMs) requires not only accurate answers but also explicit, evidence-grounded reasoning, especially in high-stakes sc…
OmniPT: Unleashing the Potential of Large Vision Language Models for Pedestrian Tracking and Understanding
Teng Fu, Mengyang Zhao, Ke Niu +2
LVLMs have been shown to perform excellently in image-level tasks such as VQA and caption. However, in many instance-level tasks, such as visual grounding and object detection, LVL…
From Intent to Execution: Multimodal Chain-of-Thought Reinforcement Learning for Precise CAD Code Generation
Ke Niu, Haiyang Yu, Zhuofan Chen +4
Computer-Aided Design (CAD) plays a vital role in engineering and manufacturing, yet current CAD workflows require extensive domain expertise and manual modeling effort. Recent adv…
Interpretable Oracle Bone Script Decipherment through Radical and Pictographic Analysis with LVLMs
Kaixin Peng, Mengyang Zhao, Haiyang Yu +2
As the oldest mature writing system, Oracle Bone Script (OBS) has long posed significant challenges for archaeological decipherment due to its rarity, abstractness, and pictographi…
IADGPT: Unified LVLM for Few-Shot Industrial Anomaly Detection, Localization, and Reasoning via In-Context Learning
Mengyang Zhao, Teng Fu, Haiyang Yu +2
Few-Shot Industrial Anomaly Detection (FS-IAD) has important applications in automating industrial quality inspection. Recently, some FS-IAD methods based on Large Vision-Language…
CrowdTrack: A Benchmark for Difficult Multiple Pedestrian Tracking in Real Scenarios
Teng Fu, Yuwen Chen, Zhuofan Chen +3
Multi-object tracking is a classic field in computer vision. Among them, pedestrian tracking has extremely high application value and has become the most popular research category.…
CReFT-CAD: Boosting Orthographic Projection Reasoning for CAD via Reinforcement Fine-Tuning
Ke Niu, Zhuofan Chen, Haiyang Yu +5
Computer-Aided Design (CAD) plays a pivotal role in industrial manufacturing. Orthographic projection reasoning underpins the entire CAD workflow, encompassing design, manufacturin…
EVE: Towards End-to-End Video Subtitle Extraction with Vision-Language Models
Haiyang Yu, Mengyang Zhao, Jinghui Lu +8
Video subtitles play a crucial role in short videos and movies, as they not only help models better understand video content but also support applications such as video translation…
ChatReID: Open-ended Interactive Person Retrieval via Hierarchical Progressive Tuning for Vision Language Models
Ke Niu, Haiyang Yu, Mengyang Zhao +6
Person re-identification (Re-ID) is a crucial task in computer vision, aiming to recognize individuals across non-overlapping camera views. While recent advanced vision-language mo…
EAFormer: Scene Text Segmentation with Edge-Aware Transformers
Haiyang Yu, Teng Fu, Bin Li +1
Scene text segmentation aims at cropping texts from scene images, which is usually used to help generative models edit or remove texts. The existing text segmentation methods tend…
Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-Training
Ke Niu, Haiyang Yu, Xuelin Qian +3
Existing person re-identification (Re-ID) methods principally deploy the ImageNet-1K dataset for model initialization, which inevitably results in sub-optimal situations due to the…
DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions
Teng Fu, Xiaocong Wang, Haiyang Yu +3
Multiple object tracking (MOT) tends to become more challenging when severe occlusions occur. In this paper, we analyze the limitations of traditional Convolutional Neural Network-…