427 citations · 872 across the 51 of their papers we have counts for
51 papers
Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification
Hao Zheng, Runqi Wang, Jianzhuang Liu +1
The conventional few-shot classification aims at learning a model on a large labeled base dataset and rapidly adapting to a target dataset that is from the same distribution as the…
Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network
Yinglong Wang, Zhen Liu, Jianzhuang Liu +2
This paper presents a novel network structure with illumination-aware gamma correction and complete image modelling to solve the low-light image enhancement problem. Low-light envi…
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment
Runqi Wang, Hao Zheng, Xiaoyue Duan +5
Pre-trained vision-language models have inspired much research on few-shot learning. However, with only a few training images, there exist two crucial problems: (1) the visual feat…
AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions
Jiaming Guo, Xueyi Zou, Yuyi Chen +4
In recent years, videos and images in 720p (HD), 1080p (FHD) and 4K (UHD) resolution have become more popular for display devices such as TVs, mobile phones and VR. However, these…
AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning
Runqi Wang, Xiaoyue Duan, Guoliang Kang +5
Continual learning aims to enable a model to incrementally learn knowledge from sequentially arrived data. Previous works adopt the conventional classification architecture, which…
Controllable Mind Visual Diffusion Model
Bohan Zeng, Shanglin Li, Xuhui Liu +6
Brain signal visualization has emerged as an active research area, serving as a critical interface between the human visual system and computer vision models. Although diffusion mo…
Face Animation with an Attribute-Guided Diffusion Model
Bohan Zeng, Xuhui Liu, Sicheng Gao +4
Face animation has achieved much progress in computer vision. However, prevailing GAN-based methods suffer from unnatural distortions and artifacts due to sophisticated motion defo…
Implicit Diffusion Models for Continuous Super-Resolution
Sicheng Gao, Xuhui Liu, Bohan Zeng +6
Image super-resolution (SR) has attracted increasing attention due to its wide applications. However, current SR methods generally suffer from over-smoothing and artifacts, and mos…
Feature Calibration Network for Occluded Pedestrian Detection
Tianliang Zhang, Qixiang Ye, Baochang Zhang +3
Pedestrian detection in the wild remains a challenging problem especially for scenes containing serious occlusion. In this paper, we propose a novel feature learning method in the…
FNeVR: Neural Volume Rendering for Face Animation
Bohan Zeng, Boyu Liu, Hong Li +5
Face animation, one of the hottest topics in computer vision, has achieved a promising performance with the help of generative models. However, it remains a critical challenge to g…
Structure-Preserving Graph Representation Learning
Ruiyi Fang, Liangjian Wen, Zhao Kang +1
Though graph representation learning (GRL) has made significant progress, it is still a challenge to extract and embed the rich topological structure and feature information in an…
Removing Rain Streaks via Task Transfer Learning
Yinglong Wang, Chao Ma, Jianzhuang Liu
Due to the difficulty in collecting paired real-world training data, image deraining is currently dominated by supervised learning with synthesized data generated by e.g., Photosho…
Low-Light Video Enhancement with Synthetic Event Guidance
Lin Liu, Junfeng An, Jianzhuang Liu +6
Low-light video enhancement (LLVE) is an important yet challenging task with many applications such as photographing and autonomous driving. Unlike single image low-light enhanceme…
Anti-Retroactive Interference for Lifelong Learning
Runqi Wang, Yuxiang Bao, Baochang Zhang +3
Humans can continuously learn new knowledge. However, machine learning models suffer from drastic dropping in performance on previous tasks after learning new tasks. Cognitive scie…
Self-Supervision Can Be a Good Few-Shot Learner
Yuning Lu, Liangjian Wen, Jianzhuang Liu +2
Existing few-shot learning (FSL) methods rely on training with a large labeled dataset, which prevents them from leveraging abundant unlabeled data. From an information-theoretic p…
Prompt Distribution Learning
Yuning Lu, Jianzhuang Liu, Yonggang Zhang +2
We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks. Our method not only learns low-bias pr…
Differentiated Relevances Embedding for Group-based Referring Expression Comprehension
Fuhai Chen, Xuri Ge, Xiaoshuai Sun +4
The key of referring expression comprehension lies in capturing the cross-modal visual-linguistic relevance. Existing works typically model the cross-modal relevance in each image,…
SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers
Lin Liu, Shanxin Yuan, Jianzhuang Liu +3
We propose a novel zero-shot multi-frame image restoration method for removing unwanted obstruction elements (such as rains, snow, and moire patterns) that vary in successive frame…
IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization
Yunshan Zhong, Mingbao Lin, Gongrui Nan +4
Learning to synthesize data has emerged as a promising direction in zero-shot quantization (ZSQ), which represents neural networks by low-bit integer without accessing any of the r…
Prioritized Subnet Sampling for Resource-Adaptive Supernet Training
Bohong Chen, Mingbao Lin, Rongrong Ji +1
A resource-adaptive supernet adjusts its subnets for inference to fit the dynamically available resources. In this paper, we propose prioritized subnet sampling to train a resource…
Wavelet-Based Network For High Dynamic Range Imaging
Tianhong Dai, Wei Li, Xilei Cao +5
High dynamic range (HDR) imaging from multiple low dynamic range (LDR) images has been suffering from ghosting artifacts caused by scene and objects motion. Existing methods, such…
Training Compact CNNs for Image Classification using Dynamic-coded Filter Fusion
Mingbao Lin, Bohong Chen, Fei Chao +1
The mainstream approach for filter pruning is usually either to force a hard-coded importance estimation upon a computation-heavy pretrained model to select "important" filters, or…
Uformer: A General U-Shaped Transformer for Image Restoration
Zhendong Wang, Xiaodong Cun, Jianmin Bao +3
In this paper, we present Uformer, an effective and efficient Transformer-based architecture for image restoration, in which we build a hierarchical encoder-decoder network using t…
Towards Compact CNNs via Collaborative Compression
Yuchao Li, Shaohui Lin, Jianzhuang Liu +7
Channel pruning and tensor decomposition have received extensive attention in convolutional neural network compression. However, these two techniques are traditionally deployed in…