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- 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.
- 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.
- GEditBench v2: A Human-Aligned Benchmark for General Image Editing11
GEditBench v2 is introduced, a comprehensive benchmark with 1,200 real-world user queries spanning 23 tasks, including a dedicated open-set category for unconstrained, out-of-distribution editing instructions beyond predefined tasks, and PVC-Judge is proposed, an open-source pairwise assessment model for visual consistency, trained via two novel region-decoupled preference data synthesis pipelines.
- RSGround-R1: Rethinking Remote Sensing Visual Grounding through Spatial Reasoning8
To mitigate incoherent localization behaviors across rollouts, this work introduces a spatial consistency guided optimization scheme that dynamically adjusts policy updates based on their spatial coherence, ensuring stable and robust convergence.
- A Frozen Pixel-Space Diffusion Model Can Guide Itself with Its Own Samples3
This work attaches a lightweight prediction head to an intermediate layer, keep the backbone frozen, and use the discrepancy between the intermediate and final predictions as a self-guidance direction during sampling to train a frozen, pretrained pixel diffusion model that can guide itself.
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- EvoVid: Temporal-Centric Self-Evolution for Video Large Language Models2
This work proposes a temporal-centric self-evolving framework that enables Video-LLMs to improve directly from raw, unannotated videos, and introduces two complementary temporal-centric rewards: a temporal-aware Questioner reward that encourages temporally dependent question generation through temporal perturbation sensitivity, and a temporal-grounded Solver reward that provides automatic temporal supervision via inherent video segment localization.
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- WBCAtt+: Fine-grained pixel-level morphological annotations for white blood cell images–
A novel dataset of WBC images densely annotated with 11 morphological attributes and five pixel-level cell components, which is the first to provide comprehensive annotations for WBC images and provides baseline models for attribute recognition and semantic segmentation.
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- Phy-CoSF: Physics-Guided Continuous Spectral Fields Reconstruction and Super-Resolution for Snapshot Compressive Imaging–
Phy-CoSF is proposed, which synergizes deep unfolding networks with implicit neural representations, establishing a new paradigm for continuous spectral reconstruction and super-resolution in CASSI, enabling the synthesis of high-fidelity HSIs at arbitrary target wavelengths.
- RealisticDreamer: Guidance Score Distillation for Few-shot Gaussian Splatting–
This work introduces an unified guidance form to correct the noise prediction result of VDM, incorporating both a depth warp guidance based on real depth maps and a guidance based on semantic image features, ensuring that the score update direction from VDM aligns with the correct camera pose and accurate geometry.
- The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results–
The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025, aimed to bridge the gap between the theoretical promise of Disentangled Representation Learning and its application in realistic scenarios, moving beyond synthetic benchmarks.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. 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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