1 citations · 1 across the 10 of their papers we have counts for
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UME-R1: Exploring Reasoning-Driven Generative Multimodal Embeddings
Zhibin Lan, Liqiang Niu, Fandong Meng +2
The remarkable success of multimodal large language models (MLLMs) has driven advances in multimodal embeddings, yet existing models remain inherently discriminative, limiting thei…
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.…
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…