198 citations · 688 across the 92 of their papers we have counts for
27 papers · 1 filter
Density Matters: Improved Core-set for Active Domain Adaptive Segmentation
Shizhan Liu, Zhengkai Jiang, Yuxi Li +3
Active domain adaptation has emerged as a solution to balance the expensive annotation cost and the performance of trained models in semantic segmentation. However, existing works…
DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection
Haoyang He, Jiangning Zhang, Hongxu Chen +6
Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection. The exceptional image reconstruction capabilities of recently popular diffusion models have…
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection
Jiangning Zhang, Xuhai Chen, Yabiao Wang +5
This work studies a challenging and practical issue known as multi-class unsupervised anomaly detection (MUAD). This problem requires only normal images for training while simultan…
A discovery of Two Slow Pulsars with FAST: "Ronin" from the Globular Cluster M15
Dengke Zhou, Pei Wang, Di Li +26
Globular clusters harbor numerous millisecond pulsars, but long-period pulsars ( ms) are rarely found. In this study, we employed a fast folding algorithm to analyze…
AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model
Teng Hu, Jiangning Zhang, Ran Yi +5
Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. Althoug…
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection
Jiangning Zhang, Haoyang He, Xuhai Chen +5
Large Multimodal Model (LMM) GPT-4V(ision) endows GPT-4 with visual grounding capabilities, making it possible to handle certain tasks through the Visual Question Answering (VQA) p…
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection
Xuhai Chen, Jiangning Zhang, Guanzhong Tian +5
This paper considers zero-shot Anomaly Detection (AD), performing AD without reference images of the test objects. We propose a framework called CLIP-AD to leverage the zero-shot c…
Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Region
Teng Hu, Ran Yi, Haokun Zhu +5
Stroke-based rendering aims to recreate an image with a set of strokes. Most existing methods render complex images using an uniform-block-dividing strategy, which leads to boundar…
Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption
Teng Hu, Jiangning Zhang, Liang Liu +7
Training a generative model with limited number of samples is a challenging task. Current methods primarily rely on few-shot model adaption to train the network. However, in scenar…
Toward High Quality Facial Representation Learning
Yue Wang, Jinlong Peng, Jiangning Zhang +4
Face analysis tasks have a wide range of applications, but the universal facial representation has only been explored in a few works. In this paper, we explore high-performance pre…
X-ray hardening preceding the onset of SGR 1935+2154's radio pulsar phase
Pei Wang, Jian Li, Long Ji +32
Magnetars are neutron stars with extremely strong magnetic fields, frequently powering high-energy activity in X-rays. Pulsed radio emission following some X-ray outbursts have bee…
PVG: Progressive Vision Graph for Vision Recognition
Jiafu Wu, Jian Li, Jiangning Zhang +4
Convolution-based and Transformer-based vision backbone networks process images into the grid or sequence structures, respectively, which are inflexible for capturing irregular obj…
RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation
Ke Fan, Changan Wang, Yabiao Wang +3
Glass-like objects are widespread in daily life but remain intractable to be segmented for most existing methods. The transparent property makes it difficult to be distinguished fr…
Align, Perturb and Decouple: Toward Better Leverage of Difference Information for RSI Change Detection
Supeng Wang, Yuxi Li, Ming Xie +4
Change detection is a widely adopted technique in remote sense imagery (RSI) analysis in the discovery of long-term geomorphic evolution. To highlight the areas of semantic changes…
Dual Path Transformer with Partition Attention
Zhengkai Jiang, Liang Liu, Jiangning Zhang +3
This paper introduces a novel attention mechanism, called dual attention, which is both efficient and effective. The dual attention mechanism consists of two parallel components: l…
Learning Global-aware Kernel for Image Harmonization
Xintian Shen, Jiangning Zhang, Jun Chen +5
Image harmonization aims to solve the visual inconsistency problem in composited images by adaptively adjusting the foreground pixels with the background as references. Existing me…
Transavs: End-To-End Audio-Visual Segmentation With Transformer
Yuhang Ling, Yuxi Li, Zhenye Gan +3
Audio-Visual Segmentation (AVS) is a challenging task, which aims to segment sounding objects in video frames by exploring audio signals. Generally AVS faces two key challenges: (1…
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution
Xuhai Chen, Jiangning Zhang, Chao Xu +3
Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant. However, the blur involved in real applications are usually space-v…
MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection
Liang Liu, Boshen Zhang, Jiangning Zhang +6
Scale variation across object instances remains a key challenge in object detection task. Despite the remarkable progress made by modern detection models, this challenge is particu…
Calibrated Teacher for Sparsely Annotated Object Detection
Haohan Wang, Liang Liu, Boshen Zhang +6
Fully supervised object detection requires training images in which all instances are annotated. This is actually impractical due to the high labor and time costs and the unavoidab…
Iterative Few-shot Semantic Segmentation from Image Label Text
Haohan Wang, Liang Liu, Wuhao Zhang +5
Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images. Most previous methods rely on the pixel-level label of…
Multimodal Industrial Anomaly Detection via Hybrid Fusion
Yue Wang, Jinlong Peng, Jiangning Zhang +3
2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched f…
Learning from Noisy Labels with Decoupled Meta Label Purifier
Yuanpeng Tu, Boshen Zhang, Yuxi Li +5
Training deep neural networks(DNN) with noisy labels is challenging since DNN can easily memorize inaccurate labels, leading to poor generalization ability. Recently, the meta-lear…
Learning with Noisy labels via Self-supervised Adversarial Noisy Masking
Yuanpeng Tu, Boshen Zhang, Yuxi Li +6
Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithm…