Search Results for author:
Found 36 papers, 23 papers with code
NTIRE 2025 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper presents the NTIRE 2025 image super-resolution ($\times$4) challenge, one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025.
NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results
This paper provides a review of the NTIRE 2025 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes.
Any Image Restoration via Efficient Spatial-Frequency Degradation Adaptation
Restoring any degraded image efficiently via just one model has become increasingly significant and impactful, especially with the proliferation of mobile devices.
NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images.
The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR).
ContextFormer: Redefining Efficiency in Semantic Segmentation
We propose ContextFormer, a hybrid framework leveraging the strengths of CNNs and ViTs in the bottleneck to balance efficiency, accuracy, and robustness for real-time semantic segmentation.
ReCap: Better Gaussian Relighting with Cross-Environment Captures
Accurate 3D objects relighting in diverse unseen environments is crucial for realistic virtual object placement.
INRetouch: Context Aware Implicit Neural Representation for Photography Retouching
We develop a context-aware Implicit Neural Representation that learns to apply edits adaptively based on image content and context, and is capable of learning from a single example.
Complexity Experts are Task-Discriminative Learners for Any Image Restoration
To address this, we introduce ``complexity experts" -- flexible expert blocks with varying computational complexity and receptive fields.
Ranked #2 on
Blind All-in-One Image Restoration
on 5-Degradations
Enhanced Super-Resolution Training via Mimicked Alignment for Real-World Scenes
In this study, we propose a novel plug-and-play module designed to mitigate these misalignment issues by aligning LR inputs with HR images during training.
Steering Prediction via a Multi-Sensor System for Autonomous Racing
Our goal is to fuse the 2D LiDAR data with event data in an end-to-end learning framework for steering prediction, which is crucial for autonomous racing.
Restore Anything Model via Efficient Degradation Adaptation
With the proliferation of mobile devices, the need for an efficient model to restore any degraded image has become increasingly significant and impactful.
Ranked #5 on
5-Degradation Blind All-in-One Image Restoration
on 5-Degradation Blind All-in-One Image Restoration
5-Degradation Blind All-in-One Image Restoration
Benchmarking
+2
XTrack: Multimodal Training Boosts RGB-X Video Object Trackers
Technically, we achieve this by routing samples from one modality to the expert of the others, within a mixture-of-experts framework designed for multimodal video object tracking.
Efficient Degradation-aware Any Image Restoration
Reconstructing missing details from degraded low-quality inputs poses a significant challenge.
Ranked #4 on
5-Degradation Blind All-in-One Image Restoration
on 5-Degradation Blind All-in-One Image Restoration
5-Degradation Blind All-in-One Image Restoration
Blind All-in-One Image Restoration
+1
MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems.
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
Event-Based Eye Tracking. AIS 2024 Challenge Survey
This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Towards Image Ambient Lighting Normalization
However, existing works often simplify this task within the context of shadow removal, limiting the light sources to one and oversimplifying the scene, thus excluding complex self-shadows and restricting surface classes to smooth ones.
PASTA: Towards Flexible and Efficient HDR Imaging Via Progressively Aggregated Spatio-Temporal Alignment
Leveraging Transformer attention has led to great advancements in HDR deghosting.
Rethinking Few-shot 3D Point Cloud Semantic Segmentation
The former arises from non-uniform point sampling, allowing models to distinguish the density disparities between foreground and background for easier segmentation.
Few-shot 3D Point Cloud Semantic Segmentation
Segmentation
+1
See More Details: Efficient Image Super-Resolution by Experts Mining
Subsequently, the model delves into the subtleties of rank choice by leveraging a mixture of low-rank experts.
Single-Model and Any-Modality for Video Object Tracking
In practice, most existing RGB trackers learn a single set of parameters to use them across datasets and applications.
Ranked #29 on
Rgb-T Tracking
on LasHeR
Temporal-aware Hierarchical Mask Classification for Video Semantic Segmentation
Modern approaches have proved the huge potential of addressing semantic segmentation as a mask classification task which is widely used in instance-level segmentation.
NPF-200: A Multi-Modal Eye Fixation Dataset and Method for Non-Photorealistic Videos
Non-photorealistic videos are in demand with the wave of the metaverse, but lack of sufficient research studies.
Alignment-free HDR Deghosting with Semantics Consistent Transformer
The spatial attention aims to deal with the intra-image correlation to model the dynamic motion, while the channel attention enables the inter-image intertwining to enhance the semantic consistency across frames.
Event-Free Moving Object Segmentation from Moving Ego Vehicle
Subsequently, we devise EmoFormer, a novel network able to exploit the event data.
CamDiff: Camouflage Image Augmentation via Diffusion Model
Specifically, we leverage the latent diffusion model to synthesize salient objects in camouflaged scenes, while using the zero-shot image classification ability of the Contrastive Language-Image Pre-training (CLIP) model to prevent synthesis failures and ensure the synthesized object aligns with the input prompt.
HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness
In this paper, from a new perspective, we propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection.
Source-free Depth for Object Pop-out
In this work, we adapt such depth inference models for object segmentation using the objects' "pop-out" prior in 3D.
RGB-Event Fusion for Moving Object Detection in Autonomous Driving
Moving Object Detection (MOD) is a critical vision task for successfully achieving safe autonomous driving.
Ranked #3 on
Object Detection
on DSEC
Robust RGB-D Fusion for Saliency Detection
In this work, we aim for RGB-D saliency detection that is robust to the low-quality depths which primarily appear in two forms: inaccuracy due to noise and the misalignment to RGB.
Depth-Adapted CNNs for RGB-D Semantic Segmentation
Recent RGB-D semantic segmentation has motivated research interest thanks to the accessibility of complementary modalities from the input side.
Ranked #52 on
Semantic Segmentation
on NYU Depth v2
Modality-Guided Subnetwork for Salient Object Detection
Recent RGBD-based models for saliency detection have attracted research attention.
Depth-Adapted CNN for RGB-D cameras
These filters compute the spatial coherence by weighting the photometric information on a fixed neighborhood without taking into account the geometric information.
