25 citations · 70 across the 14 of their papers we have counts for
14 papers
Prompt-Free Conditional Diffusion for Multi-object Image Augmentation
Haoyu Wang, Lei Zhang, Wei Wei +2
Diffusion models has underpinned much recent advances of dataset augmentation in various computer vision tasks. However, when involving generating multi-object images as real scena…
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning
Fei Zhou, Peng Wang, Lei Zhang +5
Meta-learning offers a promising avenue for few-shot learning (FSL), enabling models to glean a generalizable feature embedding through episodic training on synthetic FSL tasks in…
Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Models
Weijie Chen, Haoyu Wang, Shicai Yang +6
We do not pursue a novel method in this paper, but aim to study if a modern text-to-image diffusion model can tailor any task-adaptive image classifier across domains and categorie…
Glocal Energy-based Learning for Few-Shot Open-Set Recognition
Haoyu Wang, Guansong Pang, Peng Wang +3
Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few…
Mind the Gap: Polishing Pseudo labels for Accurate Semi-supervised Object Detection
Lei Zhang, Yuxuan Sun, Wei Wei
Exploiting pseudo labels (e.g., categories and bounding boxes) of unannotated objects produced by a teacher detector have underpinned much of recent progress in semi-supervised obj…
E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information Flow
Zhiqiang Lang, Chongxing Song, Lei Zhang +1
Binary neural network (BNN) provides a promising solution to deploy parameter-intensive deep single image super-resolution (SISR) models onto real devices with limited storage and…
NTIRE 2021 Multi-modal Aerial View Object Classification Challenge
Jerrick Liu, Nathan Inkawhich, Oliver Nina +30
In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR. This challenge is comp…
NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image
Boaz Arad, Radu Timofte, Ohad Ben-Shahar +4
This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. As…
AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results
Kai Zhang, Shuhang Gu, Radu Timofte +26
This paper reviews the AIM 2019 challenge on constrained example-based single image super-resolution with focus on proposed solutions and results. The challenge had 3 tracks. Takin…
Towards Effective Deep Embedding for Zero-Shot Learning
Lei Zhang, Peng Wang, Lingqiao Liu +4
Zero-shot learning (ZSL) can be formulated as a cross-domain matching problem: after being projected into a joint embedding space, a visual sample will match against all candidate…
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network
Lei Zhang, Peng Wang, Chunhua Shen +4
Deep neural networks have achieved remarkable success in single image super-resolution (SISR). The computing and memory requirements of these methods have hindered their applicatio…
Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN
Yiqi Yan, Lei Zhang, Jun Li +2
Different from traditional hyperspectral super-resolution approaches that focus on improving the spatial resolution, spectral super-resolution aims at producing a high-resolution h…
Beyond Low Rank: A Data-Adaptive Tensor Completion Method
Lei Zhang, Wei Wei, Qinfeng Shi +3
Low rank tensor representation underpins much of recent progress in tensor completion. In real applications, however, this approach is confronted with two challenging problems, nam…
When Unsupervised Domain Adaptation Meets Tensor Representations
Hao Lu, Lei Zhang, Zhiguo Cao +4
Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another. It is thus of great practical importa…