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cs.CV2024★ 1 cited
Right this way: Can VLMs Guide Us to See More to Answer Questions?
Li Liu, Diji Yang, Sijia Zhong +4
In question-answering scenarios, humans can assess whether the available information is sufficient and seek additional information if necessary, rather than providing a forced answ…
cs.CV2024
Dual-Model Distillation for Efficient Action Classification with Hybrid Edge-Cloud Solution
Timothy Wei, Hsien Xin Peng, Elaine Xu +3
As Artificial Intelligence models, such as Large Video-Language models (VLMs), grow in size, their deployment in real-world applications becomes increasingly challenging due to har…
cs.CL2024
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues
Diji Yang, Jinmeng Rao, Kezhen Chen +4
Although the Retrieval-Augmented Generation (RAG) paradigms can use external knowledge to enhance and ground the outputs of Large Language Models (LLMs) to mitigate generative hall…