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AffineTok: Semantic Affine Consistency for Diffusion-Friendly Visual Tokenizer
Junqiu Yu, Pandeng Li, Yikai Wang +11
Visual tokenizers increasingly inject semantic supervision into latent spaces to make downstream diffusion easier. Yet how these semantics should be organized to facilitate denoisi…
MSAVBench: Towards Comprehensive and Reliable Evaluation of Multi-Shot Audio-Video Generation
Yujie Wei, Yujin Han, Zhekai Chen +20
Video generation is rapidly evolving from single-shot synthesis to complex multi-shot audio-video (MSAV) narratives to meet real-world demands. However, evaluating such frontier mo…
AIBench: Evaluating Visual-Logical Consistency in Academic Illustration Generation
Zhaohe Liao, Kaixun Jiang, Zhihang Liu +11
Although image generation has boosted various applications via its rapid evolution, whether the state-of-the-art models are able to produce ready-to-use academic illustrations for…
GenAgent: Scaling Text-to-Image Generation via Agentic Multimodal Reasoning
Kaixun Jiang, Yuzheng Wang, Junjie Zhou +6
We introduce GenAgent, unifying visual understanding and generation through an agentic multimodal model. Unlike unified models that face expensive training costs and understanding-…
ShowTable: Unlocking Creative Table Visualization with Collaborative Reflection and Refinement
Zhihang Liu, Xiaoyi Bao, Pandeng Li +7
While existing generation and unified models excel at general image generation, they struggle with tasks requiring deep reasoning, planning, and precise data-to-visual mapping abil…
RegionRAG: Region-level Retrieval-Augmented Generation for Visual Document Understanding
Yinglu Li, Zhiying Lu, Zhihang Liu +3
Multi-modal Retrieval-Augmented Generation (RAG) has become a critical method for empowering LLMs by leveraging candidate visual documents. However, current methods consider the en…
SpaceVLLM: Endowing Multimodal Large Language Model with Spatio-Temporal Video Grounding Capability
Jiankang Wang, Zhihan Zhang, Zhihang Liu +4
Multimodal large language models (MLLMs) have made remarkable progress in either temporal or spatial localization. However, they struggle to perform spatio-temporal video grounding…
Rethinking Video Tokenization: A Conditioned Diffusion-based Approach
Nianzu Yang, Pandeng Li, Liming Zhao +8
Existing video tokenizers typically use the traditional Variational Autoencoder (VAE) architecture for video compression and reconstruction. However, to achieve good performance, i…
Hybrid-Level Instruction Injection for Video Token Compression in Multi-modal Large Language Models
Zhihang Liu, Chen-Wei Xie, Pandeng Li +5
Recent Multi-modal Large Language Models (MLLMs) have been challenged by the computational overhead resulting from massive video frames, often alleviated through compression strate…
CAPability: A Comprehensive Visual Caption Benchmark for Evaluating Both Correctness and Thoroughness
Zhihang Liu, Chen-Wei Xie, Bin Wen +9
Visual captioning benchmarks have become outdated with the emergence of modern multimodal large language models (MLLMs), as the brief ground-truth sentences and traditional metrics…
Hallucination Mitigation Prompts Long-term Video Understanding
Yiwei Sun, Zhihang Liu, Chuanbin Liu +3
Recently, multimodal large language models have made significant advancements in video understanding tasks. However, their ability to understand unprocessed long videos is very lim…
Towards Balanced Alignment: Modal-Enhanced Semantic Modeling for Video Moment Retrieval
Zhihang Liu, Jun Li, Hongtao Xie +4
Video Moment Retrieval (VMR) aims to retrieve temporal segments in untrimmed videos corresponding to a given language query by constructing cross-modal alignment strategies. Howeve…