activity
20222026
most citedObject Segmentation by Mining Cross-Modal Semantics

1 citations · 1 across the 10 of their papers we have counts for

collaborators

13 papers

cs.CV2026

TRaM-VSR: Importance-Aware Token Routing and Merging for One-Step Diffusion Video Super-Resolution

Sicheng Gao, Zhuyun Zhou, Yixuan Liu +3

Video super-resolution (VSR) using large-scale Diffusion Transformer (DiT) priors achieves exceptional perceptual quality but is often impractical due to the quadratic computationa…

cs.CV2026

The RealDefocus Benchmark for Defocus Deblurring

Tim Seizinger, Zhuyun Zhou, Radu Timofte

Single-Image Defocus Deblurring (SIDD) aims to recover an all-in-focus image from a single defocused observation, but rigorous and reproducible evaluation remains challenging due t…

cs.CV2026

The First Controllable Bokeh Rendering Challenge at NTIRE 2026

Tim Seizinger, Florin-Alexandru Vasluianu, Jeffrey Chen +23

This study presents the outcomes of the first Controllable Bokeh Rendering Challenge at NTIRE and highlights the most effective submitted methodologies. In total, 44 participants r…

cs.CV2026

NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results

Jiebin Yan, Chenyu Tu, Weixia Zhang +8

This paper presents a comprehensive review of the NITRE 2026 Efficient Low Light Image Enhancement (E-LLIE) Challenge, highlighting the proposed solutions and final outcomes. This…

cs.CV2026

NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results

Wenbin Zou, Tianyi Liu, Kejun Wu +37

This paper reports on the NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration (BSCVR). The challenge aims to advance research on recovering visually coherent videos from…

cs.CV2025

MIORe & VAR-MIORe: Benchmarks to Push the Boundaries of Restoration

George Ciubotariu, Zhuyun Zhou, Zongwei Wu +1

We introduce MIORe and VAR-MIORe, two novel multi-task datasets that address critical limitations in current motion restoration benchmarks. Designed with high-frame-rate (1000 FPS)…

cs.CV2025

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results

George Ciubotariu, Florin-Alexandru Vasluianu, Zhuyun Zhou +44

This paper presents a comprehensive review of the AIM 2025 High FPS Non-Uniform Motion Deblurring Challenge, highlighting the proposed solutions and final results. The objective of…

cs.CV2025

NTIRE 2025 Image Shadow Removal Challenge Report

Florin-Alexandru Vasluianu, Tim Seizinger, Zhuyun Zhou +79

This work examines the findings of the NTIRE 2025 Shadow Removal Challenge. A total of 306 participants have registered, with 17 teams successfully submitting their solutions durin…

cs.CV2024

Steering Prediction via a Multi-Sensor System for Autonomous Racing

Zhuyun Zhou, Zongwei Wu, Florian Bolli +5

Autonomous racing has rapidly gained research attention. Traditionally, racing cars rely on 2D LiDAR as their primary visual system. In this work, we explore the integration of an…

cs.CV2024

XTrack: Multimodal Training Boosts RGB-X Video Object Trackers

Yuedong Tan, Zongwei Wu, Yuqian Fu +7

Multimodal sensing has proven valuable for visual tracking, as different sensor types offer unique strengths in handling one specific challenging scene where object appearance vari…

cs.CV2023★ 1 cited

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.…

cs.CV2023

Event-Free Moving Object Segmentation from Moving Ego Vehicle

Zhuyun Zhou, Zongwei Wu, Danda Pani Paudel +5

Moving object segmentation (MOS) in dynamic scenes is an important, challenging, but under-explored research topic for autonomous driving, especially for sequences obtained from mo…

cs.CV2022

RGB-Event Fusion for Moving Object Detection in Autonomous Driving

Zhuyun Zhou, Zongwei Wu, Rémi Boutteau +3

Moving Object Detection (MOD) is a critical vision task for successfully achieving safe autonomous driving. Despite plausible results of deep learning methods, most existing approa…