6 citations · 24 across the 55 of their papers we have counts for
7 papers · 1 filter
ArrowGEV: Grounding Events in Video via Learning the Arrow of Time
Fangxu Yu, Ziyao Lu, Liqiang Niu +2
Grounding events in videos serves as a fundamental capability in video analysis. While Vision Language Models (VLMs) are increasingly employed for this task, existing approaches pr…
LaCo: Efficient Layer-wise Compression of Visual Tokens for Multimodal Large Language Models
Juntao Liu, Liqiang Niu, Wenchao Chen +2
Existing visual token compression methods for Multimodal Large Language Models (MLLMs) predominantly operate as post-encoder modules, limiting their potential for efficiency gains.…
Continuous Visual Autoregressive Generation via Score Maximization
Chenze Shao, Fandong Meng, Jie Zhou
Conventional wisdom suggests that autoregressive models are used to process discrete data. When applied to continuous modalities such as visual data, Visual AutoRegressive modeling…
D2C: Unlocking the Potential of Continuous Autoregressive Image Generation with Discrete Tokens
Panpan Wang, Liqiang Niu, Fandong Meng +3
In the domain of image generation, latent-based generative models occupy a dominant status; however, these models rely heavily on image tokenizer. To meet modeling requirements, au…
LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning
Zhibin Lan, Liqiang Niu, Fandong Meng +2
Universal multimodal embedding models play a critical role in tasks such as interleaved image-text retrieval, multimodal RAG, and multimodal clustering. However, our empirical resu…
AVG-LLaVA: An Efficient Large Multimodal Model with Adaptive Visual Granularity
Zhibin Lan, Liqiang Niu, Fandong Meng +3
Recently, large multimodal models (LMMs) have achieved significant advancements. When dealing with high-resolution images, dominant LMMs typically divide them into multiple local i…
MaskMamba: A Hybrid Mamba-Transformer Model for Masked Image Generation
Wenchao Chen, Liqiang Niu, Ziyao Lu +2
Image generation models have encountered challenges related to scalability and quadratic complexity, primarily due to the reliance on Transformer-based backbones. In this study, we…