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- Dominique Ginhac BSuggested from co-authorship
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Works18 from public data
- RGB-Event Fusion for Moving Object Detection in Autonomous Driving102
RENet is a novel RGB-Event fusion Network that jointly exploits the two complementary modalities to achieve more robust MOD under challenging scenarios for autonomous driving and performs significantly better than the state-of-the-art RGB- event fusion alternatives.
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- Object Segmentation by Mining Cross-Modal Semantics47
A novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features, with the aim of controlling the modal contribution based on relative entropy.
- 42
- XTrack: Multimodal Training Boosts RGB-X Video Object Trackers39
This work proposes a mixture-of-experts framework designed for multimodal video object tracking, and implements a classifier with weak loss tasked with distinguishing between modalities, which shows the benefit of the proposed method for RGB-X tracker during inference, with an average precision improvement over the current SOTA.
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- Ntire 2025 Image Shadow Removal Challenge Report22
This work examines the findings of the NTIRE 2025 Shadow Removal Challenge, with 17 teams successfully submitting their solutions during the final evaluation phase, and one focusing on reconstruction fidelity and the other on visual perception through a user study.
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- NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results19
The NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration provides a common benchmark for evaluating restoration methods under realistic corruption settings and provides useful insights for future research on robust video restoration under practical bitstream corruption.
- Event-Free Moving Object Segmentation from Moving Ego Vehicle13
This work introduces a novel large-scale dataset called DSEC-MOS for moving object segmentation from moving ego vehicles, which is the first of its kind, and devise EmoFormer, a novel network able to exploit the event data, which relies only on event data for training but does not require event input during inference.
- MIORe & VAR-MIORe: Benchmarks to Push the Boundaries of Restoration4
MIORe and VAR-MIORe are introduced, two novel multi-task datasets that address critical limitations in current motion restoration benchmarks and provide high-resolution, scalable ground truths that challenge existing algorithms under both controlled and adverse conditions.
- AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results3
This paper thoroughly evaluates the state-of-the-art advances in high-FPS single image motion deblurring, showcasing the significant progress in the field, while leveraging samples of the novel dataset, MIORe, that introduces challenging examples of movement patterns.
- Steering Prediction via a Multi-Sensor System for Autonomous Racing3
This work explores the integration of an event camera with the existing system to provide enhanced temporal information and introduces a new fusion learning policy to guide the fusion process, enhancing robustness against misalignment.
- NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results–
A systematic evaluation of recent methods for E-LLIE is provided, offering a comprehensive overview of state-of-the-art progress and demonstrating significant improvements in both performance and efficiency.
- The RealDefocus Benchmark for Defocus Deblurring–
RealDefocus, a benchmark derived from the real-world RealBokeh dataset originally proposed for Bokeh Rendering, is built on, providing paired defocused inputs and sharp ground truth images, predefined training/validation/test splits, and a unified evaluation framework for comparing image restoration and neural rendering approaches.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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