35 citations · 152 across the 32 of their papers we have counts for
50 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…