3 citations · 14 across the 43 of their papers we have counts for
18 papers · 1 filter
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection
Xin Chen, Liujuan Cao, Shengchuan Zhang +2
Due to the scarcity and unpredictable nature of defect samples, industrial anomaly detection (IAD) predominantly employs unsupervised learning. However, all unsupervised IAD method…
UniVST: A Unified Framework for Training-free Localized Video Style Transfer
Quanjian Song, Mingbao Lin, Wengyi Zhan +3
This paper presents UniVST, a unified framework for localized video style transfer based on diffusion models. It operates without the need for training, offering a distinct advanta…
Few-Shot Image Quality Assessment via Adaptation of Vision-Language Models
Xudong Li, Zihao Huang, Yan Zhang +5
Image Quality Assessment (IQA) remains an unresolved challenge in computer vision due to complex distortions, diverse image content, and limited data availability. Existing Blind I…
CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection
Xunfa Lai, Zhiyu Yang, Jie Hu +6
Existing camouflaged object detection~(COD) methods depend heavily on large-scale pixel-level annotations.However, acquiring such annotations is laborious due to the inherent camou…
Advancing Multimodal Large Language Models with Quantization-Aware Scale Learning for Efficient Adaptation
Jingjing Xie, Yuxin Zhang, Mingbao Lin +2
This paper presents the first study to explore the potential of parameter quantization for multimodal large language models to alleviate the significant resource constraint encount…
HRSAM: Efficient Interactive Segmentation in High-Resolution Images
You Huang, Wenbin Lai, Jiayi Ji +3
The Segment Anything Model (SAM) has advanced interactive segmentation but is limited by the high computational cost on high-resolution images. This requires downsampling to meet G…
HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection
Liujuan Cao, Jianghang Lin, Zebo Hong +4
Most WSOD methods rely on traditional object proposals to generate candidate regions and are confronted with unstable training, which easily gets stuck in a poor local optimum. In…
Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text
Xinyang Li, Zhangyu Lai, Linning Xu +5
Recent advancements in 3D generation have leveraged synthetic datasets with ground truth 3D assets and predefined cameras. However, the potential of adopting real-world datasets, w…
Depth-Guided Semi-Supervised Instance Segmentation
Xin Chen, Jie Hu, Xiawu Zheng +3
Semi-Supervised Instance Segmentation (SSIS) aims to leverage an amount of unlabeled data during training. Previous frameworks primarily utilized the RGB information of unlabeled i…
UniPTS: A Unified Framework for Proficient Post-Training Sparsity
Jingjing Xie, Yuxin Zhang, Mingbao Lin +3
Post-training Sparsity (PTS) is a recently emerged avenue that chases efficient network sparsity with limited data in need. Existing PTS methods, however, undergo significant perfo…
FocSAM: Delving Deeply into Focused Objects in Segmenting Anything
You Huang, Zongyu Lan, Liujuan Cao +4
The Segment Anything Model (SAM) marks a notable milestone in segmentation models, highlighted by its robust zero-shot capabilities and ability to handle diverse prompts. SAM follo…
Multi-Modal Prompt Learning on Blind Image Quality Assessment
Wensheng Pan, Timin Gao, Yan Zhang +10
Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Currently, leveraging seman…
Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode Multi-view Latent Diffusion
Xinyang Li, Zhangyu Lai, Linning Xu +5
We present Dual3D, a novel text-to-3D generation framework that generates high-quality 3D assets from texts in only minute.The key component is a dual-mode multi-view latent di…
GOI: Find 3D Gaussians of Interest with an Optimizable Open-vocabulary Semantic-space Hyperplane
Yansong Qu, Shaohui Dai, Xinyang Li +4
3D open-vocabulary scene understanding, crucial for advancing augmented reality and robotic applications, involves interpreting and locating specific regions within a 3D space as d…
Cantor: Inspiring Multimodal Chain-of-Thought of MLLM
Timin Gao, Peixian Chen, Mengdan Zhang +8
With the advent of large language models(LLMs) enhanced by the chain-of-thought(CoT) methodology, visual reasoning problem is usually decomposed into manageable sub-tasks and tackl…
CutDiffusion: A Simple, Fast, Cheap, and Strong Diffusion Extrapolation Method
Mingbao Lin, Zhihang Lin, Wengyi Zhan +2
Transforming large pre-trained low-resolution diffusion models to cater to higher-resolution demands, i.e., diffusion extrapolation, significantly improves diffusion adaptability.…
NeRF-DetS: Enhanced Adaptive Spatial-wise Sampling and View-wise Fusion Strategies for NeRF-based Indoor Multi-view 3D Object Detection
Chi Huang, Xinyang Li, Yansong Qu +4
In indoor scenes, the diverse distribution of object locations and scales makes the visual 3D perception task a big challenge. Previous works (e.g, NeRF-Det) have demonstrated that…
DMAD: Dual Memory Bank for Real-World Anomaly Detection
Jianlong Hu, Xu Chen, Zhenye Gan +7
Training a unified model is considered to be more suitable for practical industrial anomaly detection scenarios due to its generalization ability and storage efficiency. However, t…