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VaME: Exploring Variational Latent Reasoning for Multimodal Embeddings
Peixi Wu, Mingzhou Jiang, Feipeng Ma +11
Universal multimodal retrieval requires compact embeddings that preserve task-relevant semantic information across diverse modalities. Prior works have incorporated latent reasonin…
Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding?
Yun Li, Biao Yang, Peixi Wu +5
Embeddings have emerged as a standard representational interface linking foundation models with downstream systems. Most embedding benchmarks assess representations through discrim…
LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck
Peixi Wu, Biao Yang, Feipeng Ma +7
Reasoning-driven universal multimodal embedding has advanced rapidly by introducing Chain-of-Thought (CoT) reasoning into the embedding pipeline. Despite the strong performance acr…
CREM: Compression-Driven Representation Enhancement for Multimodal Retrieval and Comprehension
Lihao Liu, Yan Wang, Biao Yang +10
Multimodal Large Language Models (MLLMs) have shown remarkable success in comprehension tasks such as visual description and visual question answering. However, their direct applic…
Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding
Da Li, Yuxiao Luo, Keping Bi +7
Multimodal Large Language Models advance multimodal representation learning by acquiring transferable semantic embeddings, thereby substantially enhancing performance across a rang…
Kwai Keye-VL 1.5 Technical Report
Biao Yang, Bin Wen, Boyang Ding +58
In recent years, the development of Large Language Models (LLMs) has significantly advanced, extending their capabilities to multimodal tasks through Multimodal Large Language Mode…
Kwai Keye-VL Technical Report
Kwai Keye Team, Biao Yang, Bin Wen +57
While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities on static images, they often fall short in comprehending dynamic, information-dense short-form vi…