most citedRetrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook

6 citations · 7 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2025

Loomis Painter: Reconstructing the Painting Process

Markus Pobitzer, Chang Liu, Chenyi Zhuang +3

Step-by-step painting tutorials are vital for learning artistic techniques, but existing video resources (e.g., YouTube) lack interactivity and personalization. While recent genera…

cs.CV2025

SceneSplat++: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting

Mengjiao Ma, Qi Ma, Yue Li +10

3D Gaussian Splatting (3DGS) serves as a highly performant and efficient encoding of scene geometry, appearance, and semantics. Moreover, grounding language in 3D scenes has proven…

cs.CV20256 cited

Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook

Xu Zheng, Ziqiao Weng, Yuanhuiyi Lyu +7

Retrieval-augmented generation (RAG) has emerged as a pivotal technique in artificial intelligence (AI), particularly in enhancing the capabilities of large language models (LLMs)…

cs.CV2025

Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration

Yawei Li, Bin Ren, Jingyun Liang +5

While vision transformers achieve significant breakthroughs in various image restoration (IR) tasks, it is still challenging to efficiently scale them across multiple types of degr…

cs.CV2025

SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language Pretraining

Yue Li, Qi Ma, Runyi Yang +10

Recognizing arbitrary or previously unseen categories is essential for comprehensive real-world 3D scene understanding. Currently, all existing methods rely on 2D or textual modali…

cs.CV20241 cited

Hierarchical Information Flow for Generalized Efficient Image Restoration

Yawei Li, Bin Ren, Jingyun Liang +5

While vision transformers show promise in numerous image restoration (IR) tasks, the challenge remains in efficiently generalizing and scaling up a model for multiple IR tasks. To…