4 citations · 6 across the 10 of their papers we have counts for
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cs.CV2024★ 1 cited
MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval
Junjie Zhou, Zheng Liu, Ze Liu +6
Despite the rapidly growing demand for multimodal retrieval, progress in this field remains severely constrained by a lack of training data. In this paper, we introduce MegaPairs,…
cs.LG2024★ 4 cited
SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking
Xingrun Xing, Boyan Gao, Zheng Zhang +5
Recent advancements in large language models (LLMs) with billions of parameters have improved performance in various applications, but their inference processes demand significant…
cs.IR2024
VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval
Junjie Zhou, Zheng Liu, Shitao Xiao +2
Multi-modal retrieval becomes increasingly popular in practice. However, the existing retrievers are mostly text-oriented, which lack the capability to process visual information.…