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20232026
most citedGenerative Multi-Modal Knowledge Retrieval with Large Language Models

2 citations · 4 across the 13 of their papers we have counts for

collaborators

14 papers

cs.CV2026

RefCaptioner: Multi-Reference Image-Grounded Video Captioning

Tengfei Liu, Yang Shi, Yuran Wang +16

Existing video captioning models generate natural descriptions of video content but cannot explicitly ground local visual elements to multiple reference images. We introduce multi-…

cs.CV2026

TIR-Agent: Training an Explorative and Efficient Agent for Image Restoration

Guoli Jia, Yisheng Zhang, Haote Hu +11

Vision-language agents that orchestrate specialized tools for image restoration (IR) have emerged as a promising method, yet most existing frameworks operate in a training-free man…

cs.CV2026

RL-RIG: A Generative Spatial Reasoner via Intrinsic Reflection

Tianyu Wang, Zhiyuan Ma, Qian Wang +3

Recent advancements in image generation have achieved impressive results in producing high-quality images. However, existing image generation models still generally struggle with a…

cs.CV2025

Emotion-Director: Bridging Affective Shortcut in Emotion-Oriented Image Generation

Guoli Jia, Junyao Hu, Xinwei Long +5

Image generation based on diffusion models has demonstrated impressive capability, motivating exploration into diverse and specialized applications. Owing to the importance of emot…

cs.CV2025

MARS2 2025 Challenge on Multimodal Reasoning: Datasets, Methods, Results, Discussion, and Outlook

Peng Xu, Shengwu Xiong, Jiajun Zhang +125

This paper reviews the MARS2 2025 Challenge on Multimodal Reasoning. We aim to bring together different approaches in multimodal machine learning and LLMs via a large benchmark. We…

cs.LG2025

Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models

Hao Zhang, Bo Huang, Zhenjia Li +6

Large Language Models (LLMs) have transformed both everyday life and scientific research. However, adapting LLMs from general-purpose models to specialized tasks remains challengin…

cs.CV2025

AdsQA: Towards Advertisement Video Understanding

Xinwei Long, Kai Tian, Peng Xu +10

Large language models (LLMs) have taken a great step towards AGI. Meanwhile, an increasing number of domain-specific problems such as math and programming boost these general-purpo…

cs.CL2025★ 1 cited

SSRL: Self-Search Reinforcement Learning

Yuchen Fan, Kaiyan Zhang, Heng Zhou +15

We investigate the potential of large language models (LLMs) to serve as efficient simulators for agentic search tasks in reinforcement learning (RL), thereby reducing dependence o…

cs.CL2025

ReviewRL: Towards Automated Scientific Review with RL

Sihang Zeng, Kai Tian, Kaiyan Zhang +9

Peer review is essential for scientific progress but faces growing challenges due to increasing submission volumes and reviewer fatigue. Existing automated review approaches strugg…

cs.CV2025

LENS: Multi-level Evaluation of Multimodal Reasoning with Large Language Models

Ruilin Yao, Bo Zhang, Jirui Huang +18

Multimodal Large Language Models (MLLMs) have achieved significant advances in integrating visual and linguistic information, yet their ability to reason about complex and real-wor…

cs.LG2025

Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models

Xingtai Lv, Youbang Sun, Kaiyan Zhang +8

State Space Models (SSMs) have emerged as a promising alternative to the popular transformer-based models and have been increasingly gaining attention. Compared to transformers, SS…

cs.CV2025

Retrieval-Augmented Visual Question Answering via Built-in Autoregressive Search Engines

Xinwei Long, Zhiyuan Ma, Ermo Hua +3

Retrieval-augmented generation (RAG) has emerged to address the knowledge-intensive visual question answering (VQA) task. Current methods mainly employ separate retrieval and gener…

cs.IR2024★ 2 cited

Generative Multi-Modal Knowledge Retrieval with Large Language Models

Xinwei Long, Jiali Zeng, Fandong Meng +4

Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications. However, existing methods face challenges in terms of…

cs.CL2023★ 1 cited

CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model

Kaiyan Zhang, Ning Ding, Biqing Qi +3

Instruction tuning has recently been recognized as an effective way of aligning Large Language Models (LLMs) to enhance their generalization ability across various tasks. However,…