355 citations · 2.7k across the 325 of their papers we have counts for
22 papers · 2 filters
Single-Model and Any-Modality for Video Object Tracking
Zongwei Wu, Jilai Zheng, Xiangxuan Ren +5
In the realm of video object tracking, auxiliary modalities such as depth, thermal, or event data have emerged as valuable assets to complement the RGB trackers. In practice, most…
Deep Equilibrium Diffusion Restoration with Parallel Sampling
Jiezhang Cao, Yue Shi, Kai Zhang +3
Diffusion model-based image restoration (IR) aims to use diffusion models to recover high-quality (HQ) images from degraded images, achieving promising performance. Due to the inhe…
MoVideo: Motion-Aware Video Generation with Diffusion Models
Jingyun Liang, Yuchen Fan, Kai Zhang +3
While recent years have witnessed great progress on using diffusion models for video generation, most of them are simple extensions of image generation frameworks, which fail to ex…
A Study of Forward-Forward Algorithm for Self-Supervised Learning
Jonas Brenig, Radu Timofte
Self-supervised representation learning has seen remarkable progress in the last few years, with some of the recent methods being able to learn useful image representations without…
When Super-Resolution Meets Camouflaged Object Detection: A Comparison Study
Juan Wen, Shupeng Cheng, Peng Xu +4
Super Resolution (SR) and Camouflaged Object Detection (COD) are two hot topics in computer vision with various joint applications. For instance, low-resolution surveillance images…
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
Xiaohong Liu, Xiongkuo Min, Wei Sun +69
This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement W…
mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs
Gregor Geigle, Abhay Jain, Radu Timofte +1
Modular vision-language models (Vision-LLMs) align pretrained image encoders with (frozen) large language models (LLMs) and post-hoc condition LLMs to `understand' the image input.…
NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement
Marcos V. Conde, Javier Vazquez-Corral, Michael S. Brown +1
3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipelin…
Denoising Diffusion Models for Plug-and-Play Image Restoration
Yuanzhi Zhu, Kai Zhang, Jingyun Liang +4
Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and interpretable method for solving various inverse problems by utilizing any off-the-shelf denoiser…
Alignment-free HDR Deghosting with Semantics Consistent Transformer
Steven Tel, Zongwei Wu, Yulun Zhang +4
High dynamic range (HDR) imaging aims to retrieve information from multiple low-dynamic range inputs to generate realistic output. The essence is to leverage the contextual informa…
Equivariant Multi-Modality Image Fusion
Zixiang Zhao, Haowen Bai, Jiangshe Zhang +6
Multi-modality image fusion is a technique that combines information from different sensors or modalities, enabling the fused image to retain complementary features from each modal…
Object Segmentation by Mining Cross-Modal Semantics
Zongwei Wu, Jingjing Wang, Zhuyun Zhou +5
Multi-sensor clues have shown promise for object segmentation, but inherent noise in each sensor, as well as the calibration error in practice, may bias the segmentation accuracy.…
StyleGenes: Discrete and Efficient Latent Distributions for GANs
Evangelos Ntavelis, Mohamad Shahbazi, Iason Kastanis +3
We propose a discrete latent distribution for Generative Adversarial Networks (GANs). Instead of drawing latent vectors from a continuous prior, we sample from a finite set of lear…
NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results
Yingqian Wang, Longguang Wang, Zhengyu Liang +3
In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradati…
Single Image Depth Prediction Made Better: A Multivariate Gaussian Take
Ce Liu, Suryansh Kumar, Shuhang Gu +2
Neural-network-based single image depth prediction (SIDP) is a challenging task where the goal is to predict the scene's per-pixel depth at test time. Since the problem, by definit…
Graph Transformer GANs for Graph-Constrained House Generation
Hao Tang, Zhenyu Zhang, Humphrey Shi +5
We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained h…
Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution
Zixiang Zhao, Jiangshe Zhang, Xiang Gu +5
Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing, aims to upsample low-resolution (LR) depth maps with additional information involved in hi…
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
Zixiang Zhao, Haowen Bai, Yuanzhi Zhu +7
Multi-modality image fusion aims to combine different modalities to produce fused images that retain the complementary features of each modality, such as functional highlights and…
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement
Yuanhao Cai, Hao Bian, Jing Lin +3
When enhancing low-light images, many deep learning algorithms are based on the Retinex theory. However, the Retinex model does not consider the corruptions hidden in the dark or i…
Efficient and Explicit Modelling of Image Hierarchies for Image Restoration
Yawei Li, Yuchen Fan, Xiaoyu Xiang +4
The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration. To achieve tha…
VA-DepthNet: A Variational Approach to Single Image Depth Prediction
Ce Liu, Suryansh Kumar, Shuhang Gu +2
We introduce VA-DepthNet, a simple, effective, and accurate deep neural network approach for the single-image depth prediction (SIDP) problem. The proposed approach advocates using…
Audio-Visual Efficient Conformer for Robust Speech Recognition
Maxime Burchi, Radu Timofte
End-to-end Automatic Speech Recognition (ASR) systems based on neural networks have seen large improvements in recent years. The availability of large scale hand-labeled datasets a…