8 papers · 1 filter
HDRAgent: An Agentic Framework for Multi-Exposure HDR Imaging
Weiyu Zhou, Tao Hu, Yijian Wang +3
Most existing multi-exposure HDR methods follow a fixed feed-forward reconstruction paradigm, making them prone to ghosting artifacts in complex dynamic scenes. To address this iss…
NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1)
Guanyi Qin, Jie Liang, Bingbing Zhang +50
In this paper, we present an overview of the NTIRE 2026 challenge on the 3rd Restore Any Image Model in the Wild, specifically focusing on Track 1: Professional Image Quality Asses…
Redefining Quality Criteria and Distance-Aware Score Modeling for Image Editing Assessment
Xinjie Zhang, Qiang Li, Xiaowen Ma +3
Recent advances in image editing have heightened the need for reliable Image Editing Quality Assessment (IEQA). Unlike traditional methods, IEQA requires complex reasoning over mul…
NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Multi-Exposure Image Fusion in Dynamic Scenes (Track 2)
Lishen Qu, Yao Liu, Jie Liang +32
This paper presents NTIRE 2026, the 3rd Restore Any Image Model (RAIM) challenge on multi-exposure image fusion in dynamic scenes. We introduce a benchmark that targets a practical…
TRIO: Token Reduction via Inference-Objective Guidance for Efficient Vision-Language Models
Haokui Zhang, Congyang Ou, Dawei Yan +5
Recently, reducing redundant visual tokens in vision-language models (VLMs) to accelerate VLM inference has emerged as a hot topic. However, most existing methods rely on heuristic…
TPGDiff: Hierarchical Triple-Prior Guided Diffusion for Image Restoration
Yanjie Tu, Qingsen Yan, Axi Niu +1
All-in-one image restoration aims to address diverse degradation types using a single unified model. Existing methods typically rely on degradation priors to guide restoration, yet…
Filter, Correlate, Compress: Training-Free Token Reduction for MLLM Acceleration
Yuhang Han, Xuyang Liu, Zihan Zhang +6
The quadratic complexity of Multimodal Large Language Models (MLLMs) with respect to context length poses significant computational and memory challenges, hindering their real-worl…
TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings
Dawei Yan, Pengcheng Li, Yang Li +7
Currently, inspired by the success of vision-language models (VLMs), an increasing number of researchers are focusing on improving VLMs and have achieved promising results. However…