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Claim this profileWorks17 from public data
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- NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1)21
An overview of the NTIRE 2026 challenge is presented, establishing a novel benchmark exploring the ability of MLLMs to mimic human expert cognition in evaluating high-quality image pairs, and identifying the visually superior image within a high-quality pair.
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- NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: AI Flash Portrait (Track 3)19
A comprehensive overview of the NTIRE 2026 3rd Restore Any Image Model (RAIM) challenge, with a specific focus on Track 3: AI Flash Portrait, and comprehensively evaluates the proposed algorithms utilizing a hybrid evaluation system that integrates objective quantitative metrics with rigorous subjective assessment protocols.
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- UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessment19
This paper utilizes multimodal large language models (MLLMs) to generate high-quality text descriptions and uses the generated text for IAA as metadata to purify noisy IAA data to effectively adapt the pre-trained UniQA to downstream tasks.
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- Gamma: Toward Generic Image Assessment with Mixture of Assessment Experts7
This paper proposes a Mixture of Assessment Experts (MoAE) module, which employs shared and adaptive experts to dynamically learn common and specific knowledge for different datasets, respectively, and introduces a Scene-based Differential Prompt (SDP) strategy, which uses scene-specific prompts to provide prior knowledge and guidance during the learning process, further boosting adaptation for various scenes.
- Beyond Ground-Truth: Leveraging Image Quality Priors for Real-World Image Restoration6
A novel framework is proposed that introduces an Image Quality Prior (IQP)-extracted from pre-trained No-Reference Image Quality Assessment (NR-IQA) models-to guide the restoration process toward perceptually optimal outputs explicitly and serves as a generalizable quality-guided enhancement strategy for existing methods.
- Structure Matters: Revisiting Boundary Refinement in Video Object Segmentation5
A novel bOundary Amendment video object Segmentation method with Inherent Structure refinement, hereby named OASIS, is proposed to enhance segmentation accuracy and generate an object-level structure map and refine the representations by highlighting boundary features.
- DR.Experts: Differential Refinement of Distortion-Aware Experts for Blind Image Quality Assessment4
DR.Experts is introduced, a novel prior-driven BIQA framework designed to explicitly incorporate distortion priors, enabling a reliable quality assessment and demonstrating the superiority of DR.Experts over current methods.
- SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking4
This work constructs SA-SV, the largest surgical iVOS benchmark with instance-level spatio-temporal annotations (masklets) spanning eight procedure types, and proposes SAM2S, a foundation model enhancing SAM2 for S urgical iVOS through DiveMem, a trainable diverse memory mechanism for robust long-term tracking.
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- Tool-IQA: Augmenting Image Quality Assessment with Simple Tools1
The proposed Tool-IQA equip VLMs with simple yet effective view tools: a Magnifier to inspect local details, and a Gamma Corrector to uncover visibility and hidden artifacts, and introduces a batch-aware training strategy to reward tool interactions that can yield positive contributions rather than simply encouraging usage.
- UniSurgSAM: A Unified Promptable Model for Reliable Surgical Video Segmentation1
UniSurgSAM is presented, a unified PVOS model enabling reliable surgical video segmentation through visual, textual, or audio prompts and achieves state-of-the-art performance in real time across all prompt modalities and granularities, providing a practical foundation for computer-assisted surgery.
- SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video1
ResGo, a benchmark of laparoscopic frames annotated with Go Zone bounding boxes and clinician-authored rationales covering phase, exposure quality reasoning, next action and risk reminder, and SurGo-R1, a model optimized via RLHF with a multi-turn phase-then-go architecture where the model first identifies the surgical phase, then generates reasoning and Go Zone coordinates conditioned on that context.
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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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