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20242026
most citedAdvancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models

1 citations · 1 across the 12 of their papers we have counts for

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13 papers

cs.CL2026

Calibration is the Bottleneck: An Action-Class Diagnostic of Multi-Turn Tool-Calling

Kangjia Zhao, Jiajun Li, Haozhan Shen +8

Multi-turn tool calling is a core evaluation scenario for large language model (LLM) agents. On public tool-calling benchmarks, open-weight models now approach or even surpass clos…

cs.CV2026

On-Policy Self-Distillation in Diffusion Models

Wei Zhou, Xiongwei Zhu, Lingdong Kong +14

Reinforcement learning can align diffusion models with human preferences and task-specific objectives, but endpoint rewards do not specify how an intermediate denoising prediction…

cs.CV2026

NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge

Aleksei Khalin, Egor Ershov, Artyom Panshin +46

This paper presents a review of the NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge. The objective of the competition was to merge a set of misaligned smartphone images…

cs.CV2026

NTIRE 2026 3D Restoration and Reconstruction in Real-world Adverse Conditions: RealX3D Challenge Results

Shuhong Liu, Chenyu Bao, Ziteng Cui +103

This paper presents a comprehensive review of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge, detailing the proposed methods and results. The challenge seeks to…

cs.CV2026

The Fourth Challenge on Image Super-Resolution (4) at NTIRE 2026: Benchmark Results and Method Overview

Zheng Chen, Kai Liu, Jingkai Wang +150

This paper presents the NTIRE 2026 image super-resolution (4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to r…

cs.CV2026

The Second Challenge on Real-World Face Restoration at NTIRE 2026: Methods and Results

Jingkai Wang, Jue Gong, Zheng Chen +50

This paper provides a review of the NTIRE 2026 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes. The challenge focuses on ge…

cs.CV2026

NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results

Xin Li, Jiachao Gong, Xijun Wang +75

This paper presents an overview of the NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models. This challenge utilizes a new short-form UGC (S-…

cs.CV2026

NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods

Jie Cai, Kangning Yang, Zhiyuan Li +50

In this paper, we review the NTIRE 2026 challenge on single-image reflection removal (SIRR) in the wild. SIRR is a fundamental task in image restoration. Despite progress in academ…

cs.CV2025

EditMGT: Unleashing Potentials of Masked Generative Transformers in Image Editing

Wei Chow, Linfeng Li, Lingdong Kong +13

Recent advances in diffusion models (DMs) have achieved exceptional visual quality in image editing tasks. However, the global denoising dynamics of DMs inherently conflate local e…

cs.CV2025

MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query

Wei Chow, Yuan Gao, Linfeng Li +15

Semantic retrieval is crucial for modern applications yet remains underexplored in current research. Existing datasets are limited to single languages, single images, or singular r…

cs.CL2025

Mixed-R1: Unified Reward Perspective For Reasoning Capability in Multimodal Large Language Models

Shilin Xu, Yanwei Li, Rui Yang +9

Recent works on large language models (LLMs) have successfully demonstrated the emergence of reasoning capabilities via reinforcement learning (RL). Although recent efforts leverag…

cs.CV2025

An Empirical Study of GPT-4o Image Generation Capabilities

Sixiang Chen, Jinbin Bai, Zhuoran Zhao +16

The landscape of image generation has rapidly evolved, from early GAN-based approaches to diffusion models and, most recently, to unified generative architectures that seek to brid…

cs.CV2024★ 1 cited

Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models

Wei Wang, Zhaowei Li, Qi Xu +7

Multi-modal large language models (MLLMs) have achieved remarkable success in fine-grained visual understanding across a range of tasks. However, they often encounter significant c…