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Works68 from public data
- Single-Model and Any-Modality for Video Object Tracking172
This work introduces Un-Track, a Unified Tracker of a single set of parameters for any modality, which surpasses both SOTA unified trackers and modality-specific counterparts, validating the effectiveness and practicality.
- HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness158
This paper proposes a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection, and uses a granularity-based attention scheme to strengthen the discriminatory power of RGB and depth features separately and introduces a unified cross dual-attention module for multi-modal and multi-level fusion in a coarse-to-fine manner.
- Source-free Depth for Object Pop-out142
This work adapts depth inference models for object segmentation using the objects’ "pop-out" prior in 3D to adapt the inferred depth maps such that objects can be localized using only 3D information.
- Complexity Experts are Task-Discriminative Learners for Any Image Restoration128
The proposed MoCE-IR model outperforms state-of-the-art methods, affirming its efficiency and practical applicability and introducing “complexity experts” – flexible expert blocks with varying computational complexity and receptive fields.
- 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.
- NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results85
This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.
- NTIRE 2024 Challenge on Image Super-Resolution (×4): Methods and Results83
This paper reviews the NTIRE 2024 challenge on image super-resolution (×4), highlighting the solutions proposed and the outcomes obtained, and offers a comprehensive overview of current trends in this field.
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- See More Details: Efficient Image Super-Resolution by Experts Mining51
This work introduces SeemoRe, an efficient SR model employing expert mining, strategically incorporates experts at different levels, adopting a collaborative methodology, allowing the model to achieve an optimal performance with minimal computational costs in efficient settings.
- Rethinking Few-shot 3D Point Cloud Semantic Segmentation50
This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS), with a focus on two significant is-sues in the state-of-the-art: foreground leakage and sparse point distribution, and proposes a novel FS-PCS model based on correlation optimization, referred to as Correlation Optimization Segmentation (COSeg).
- Robust RGB-D Fusion for Saliency Detection48
This work proposes a robust RGB-D fusion method that benefits from layer-wise, and trident spatial, attention mechanisms, and demonstrates that the proposed fusion method performs consistently better than the state-of-the-art fusion alternatives.
- 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.
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- The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report39
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques.
- Event-Based Eye Tracking. AIS 2024 Challenge Survey38
This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge and examines novel and diverse methods from the submitted factsheets to advance future event-based eye tracking research.
- NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results37
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images to establish a new and powerful benchmark for the task of removing raindrops under varying lighting and focus conditions.
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- NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results29
This paper thoroughly evaluates the state-of-the-art advancements in LLIE, showcasing the significant progress.
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- CamDiff: Camouflage Image Augmentation via Diffusion Model28
CamDiff is introduced, a novel approach inspired by AI-Generated Content that overcomes the scarcity of multi-pattern training images and significantly enhances COD baselines' training and testing phases, emphasizing robustness across diverse domains.
- The Eleventh NTIRE 2026 Efficient Super-Resolution Challenge Report27
This paper reviews the NTIRE 2026 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results and gauges the state-of-the-art results for efficient single-image super-resolution.
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- NTIRE 2025 Challenge on Image Super-Resolution ($\times 4$): Methods and Results23
The NTIRE 2025 image super-resolution ($\times 4$) challenge, one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025, aims to recover high-resolution images from low-resolution counterparts generated through bicubic downsampling with a $\times 4$ scaling factor.
- Depth-Adapted CNN for RGB-D Cameras23
This paper tackles the problem of improving the classical RGB CNN methods by using the depth information provided by the RGB-D cameras by proposing a novel and generic procedure to articulate both photometric and geometric information in CNN architecture.
- 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.
- What You Have is What You Track: Adaptive and Robust Multimodal Tracking22
This paper proposes a flexible framework for robust multimodal tracking through a novel Heterogeneous Mixture-of-Experts fusion mechanism with adaptive complexity, coupled with a video-level masking strategy that ensures both temporal consistency and spatial completeness - critical for effective video tracking.
- The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results21
This report presents a detailed overview of the NTIRE 2026 CD-FSOD Challenge, including a summary of the submitted approaches and an analysis of the final results across all participating teams.
- NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results21
An overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images demonstrates the growing progress in this challenging task.
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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-Based Eye Tracking. 2025 Event-Based Vision Workshop17
This survey serves as a review for the 2025 Event-Based Eye Tracking Challenge organized as part of the 2025 CVPR event-based vision workshop and summarizes the innovative methods from teams rank the top in the challenge.
- Modality-Guided Subnetwork for Salient Object Detection17
A novel fusion design named modality-guided subnetwork (MGSnet) is presented, taking the inner workings of depth-prediction networks into account, and proposing to estimate the pseudo-geometry maps from RGB input — essentially mimicking the multi-modality input.
- ReCap: Better Gaussian Relighting with Cross-Environment Captures16
This work presents ReCap, treating cross-environment captures as multi-task target to provide the missing supervision that cuts through the entanglement, and jointly optimizes multiple lighting representations that share a common set of material attributes.
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- Depth-Adapted CNNs for RGB-D Semantic Segmentation15
This paper proposes a novel framework to incorporate the depth information in the RGB convolutional neural network (CNN), termed Z-ACN (Depth-Adapted CNN), which generates a 2D depth-adapted offset which is fully constrained by low-level features to guide the feature extraction on RGB images.
- 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.
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- Efficient Degradation-aware Any Image Restoration12
Extensive comparisons demonstrate that the DaAIR outperforms both state-of-the-art All-in-One models and degradation-specific counterparts, affirming the efficacy and practicality of this efficient restorer integrating holistic and specific learning within a unified model.
- Temporal-aware Hierarchical Mask Classification for Video Semantic Segmentation10
A novel solution THE-Mask for VSS is presented, which introduces temporal-aware hierarchical object queries for the first time and achieves state-of-the-art performance on the latest challenging VSS benchmark VSPW without bells and whistles.
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- Make Your Own Sprites6
A data-driven pixelization method that can produce sharp and crisp cell effects with controllable cell sizes by overcomes the limitation of existing learning-based methods in cell size control by introducing a reference pixel art to explicitly regularize the cell structure.
- ContextFormer: Redefining Efficiency in Semantic Segmentation5
This work proposes 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, setting a new benchmark for efficiency and performance.
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- INRetouch: Context Aware Implicit Neural Representation for Photography Retouching4
A novel retouch transfer approach that learns from professional edits through before-after image pairs, enabling precise replication of complex editing operations and a context-aware Implicit Neural Representation that learns to apply edits adaptively based on image content and context.
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- Restore Anything Model via Efficient Degradation Adaptation4
A unified manner to achieve joint embedding is proposed by leveraging the inherent similarities across various degradations for efficient and comprehensive restoration by leveraging the inherent similarities across various degradations.
- PASTA: Towards Flexible and Efficient HDR Imaging Via Progressively Aggregated Spatio-Temporal Alignment4
PASTA, a novel Progressively Aggregated Spatio-Temporal Alignment framework for HDR deghosting achieves effectiveness and efficiency by harnessing hierarchical representation during feature distanglement and substantially boosts computational speed and optimizes the HDR imaging workflow.
- DiTVR: Zero-Shot Diffusion Transformer for Video Restoration3
DiTVR is introduced, a zero shot video restoration framework that couples a diffusion transformer with trajectory aware attention and a wavelet guided, flow consistent sampler, demonstrating superior temporal consistency and detail preservation while remaining robust to flow noise and occlusions.
- 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.
- OLF: RGB-D adaptive late fusion for robust 6D pose estimation3
This paper proposes a novel fusion design that adaptively merges RGB-D cues that outperforms existing methods, particularly in low-quality depth input scenarios and conducts extensive experiments on the 6D pose estimation benchmark.
- NPF-200: A Multi-Modal Eye Fixation Dataset and Method for Non-Photorealistic Videos3
This work aims to take a step forward to understand how humans perceive non-photorealistic videos with eye fixation (i.e., saliency detection), which is critical for enhancing media production, artistic design, and game user experience.
- Layover or Direct Flight: Rethinking Audio-Guided Image Segmentation2
This work focuses on object grounding, i.e., localizing an object of interest in a visual scene based on verbal human instructions based on verbal human instructions, and demonstrates that direct grounding from audio is not only feasible but, in some cases, even outperforms transcription-based methods, especially in terms of robustness to linguistic variability.
- Reference‐based Screentone Transfer via Pattern Correspondence and Regularization2
This work proposes a novel data‐driven method aiming to transfer the screentone pattern from a reference manga image through a 1D stylegram, and encodes the sceentone style through a1D sty Telegram.
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- 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.
- Adverse-to-the-eXtreme Panoptic Segmentation: URVIS 2026 Study and Benchmark–
This report summarizes the challenge setting and benchmark results, analyse the performance of the submitted methods, and discusses current progress and remaining challenges for robust multimodal panoptic segmentation in adverse-to-extreme weather.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. 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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