67 citations · 254 across the 27 of their papers we have counts for
27 papers
From Calibration to Refinement: Seeking Certainty via Probabilistic Evidence Propagation for Noisy-Label Person Re-Identification
Xin Yuan, Zhiyong Zhang, Xin Xu +2
With the increasing demand for robust person Re-ID in unconstrained environments, learning from datasets with noisy labels and sparse per-identity samples remains a critical challe…
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network
Wenjie Li, Juncheng Li, Guangwei Gao +4
Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs. However, existing methods result in the loss…
Meta Transferring for Deblurring
Po-Sheng Liu, Fu-Jen Tsai, Yan-Tsung Peng +3
Most previous deblurring methods were built with a generic model trained on blurred images and their sharp counterparts. However, these approaches might have sub-optimal deblurring…
A heterogeneous group CNN for image super-resolution
Chunwei Tian, Yanning Zhang, Wangmeng Zuo +3
Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image super-resolution (SR)…
Magic ELF: Image Deraining Meets Association Learning and Transformer
Kui Jiang, Zhongyuan Wang, Chen Chen +3
Convolutional neural network (CNN) and Transformer have achieved great success in multimedia applications. However, little effort has been made to effectively and efficiently harmo…
Image Super-resolution with An Enhanced Group Convolutional Neural Network
Chunwei Tian, Yixuan Yuan, Shichao Zhang +3
CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image supe…
Stripformer: Strip Transformer for Fast Image Deblurring
Fu-Jen Tsai, Yan-Tsung Peng, Yen-Yu Lin +2
Images taken in dynamic scenes may contain unwanted motion blur, which significantly degrades visual quality. Such blur causes short- and long-range region-specific smoothing artif…
Both Style and Fog Matter: Cumulative Domain Adaptation for Semantic Foggy Scene Understanding
Xianzheng Ma, Zhixiang Wang, Yacheng Zhan +4
Although considerable progress has been made in semantic scene understanding under clear weather, it is still a tough problem under adverse weather conditions, such as dense fog, d…
Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment
Sadid Sahami, Gene Cheung, Chia-Wen Lin
We study the problem of efficiently summarizing a short video into several keyframes, leveraging recent progress in fast graph sampling. Specifically, we first construct a similari…
Carrying out CNN Channel Pruning in a White Box
Yuxin Zhang, Mingbao Lin, Chia-Wen Lin +5
Channel Pruning has been long studied to compress CNNs, which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which…
Asymmetric CNN for image super-resolution
Chunwei Tian, Yong Xu, Wangmeng Zuo +2
Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. According to nature of different applications, designing appropria…
Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement
Kui Jiang, Zhongyuan Wang, Zheng Wang +4
Low-light image enhancement aims to improve an image's visibility while keeping its visual naturalness. Different from existing methods tending to accomplish the relighting task di…
SiMaN: Sign-to-Magnitude Network Binarization
Mingbao Lin, Rongrong Ji, Zihan Xu +4
Binary neural networks (BNNs) have attracted broad research interest due to their efficient storage and computational ability. Nevertheless, a significant challenge of BNNs lies in…
Dual-Level Collaborative Transformer for Image Captioning
Yunpeng Luo, Jiayi Ji, Xiaoshuai Sun +5
Descriptive region features extracted by object detection networks have played an important role in the recent advancements of image captioning. However, they are still criticized…
Image Inpainting Guided by Coherence Priors of Semantics and Textures
Liang Liao, Jing Xiao, Zheng Wang +2
Existing inpainting methods have achieved promising performance in recovering defected images of specific scenes. However, filling holes involving multiple semantic categories rema…
Rotated Binary Neural Network
Mingbao Lin, Rongrong Ji, Zihan Xu +5
Binary Neural Network (BNN) shows its predominance in reducing the complexity of deep neural networks. However, it suffers severe performance degradation. One of the major impedime…
Graph Signal Processing for Geometric Data and Beyond: Theory and Applications
Wei Hu, Jiahao Pang, Xianming Liu +3
Geometric data acquired from real-world scenes, e.g., 2D depth images, 3D point clouds, and 4D dynamic point clouds, have found a wide range of applications including immersive tel…
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach
Xian Zhong, Cheng Gu, Wenxin Huang +3
Few-shot learning is a challenging problem that has attracted more and more attention recently since abundant training samples are difficult to obtain in practical applications. Me…
Lightweight image super-resolution with enhanced CNN
Chunwei Tian, Ruibin Zhuge, Zhihao Wu +4
Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR). However, their excessive amo…
Designing and Training of A Dual CNN for Image Denoising
Chunwei Tian, Yong Xu, Wangmeng Zuo +3
Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a comple…
Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes
Liang Liao, Jing Xiao, Zheng Wang +2
Completing a corrupted image with correct structures and reasonable textures for a mixed scene remains an elusive challenge. Since the missing hole in a mixed scene of a corrupted…
From IC Layout to Die Photo: A CNN-Based Data-Driven Approach
Hao-Chiang Shao, Chao-Yi Peng, Jun-Rei Wu +4
We propose a deep learning-based data-driven framework consisting of two convolutional neural networks: i) LithoNet that predicts the shape deformations on a circuit due to IC fabr…
A Real-time Global Inference Network for One-stage Referring Expression Comprehension
Yiyi Zhou, Rongrong Ji, Gen Luo +5
Referring Expression Comprehension (REC) is an emerging research spot in computer vision, which refers to detecting the target region in an image given an text description. Most ex…
Deep Learning on Image Denoising: An overview
Chunwei Tian, Lunke Fei, Wenxian Zheng +3
Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods deali…