Spatial understanding has been a challenging task for existing Multi-modal Large Language Models (MLLMs). Previous methods leverage large-scale MLLM finetuning to enhance MLLM’s spatial understanding ability. In this paper, we present a data-efficient approach. We propose a LLM agent system with strong and advanced spa...
Hsiang-Wei Huang, Junbin Lu, Pyong-Kun Kim et al.· Conference on Multimedia Inf...· 0 citations
Answering questions accurately and efficiently in embodied scenarios presents significant challenges due to limited computational and memory resources for Vision Language Model (VLM) inference. Existing methods adopt visual search key frame retrieval method to select critical question-related key frames for VLM input....
Hsiang-Wei Huang, Fu-Chen Chen, Li-Wu Tsao et al.· 0 citations
Experiments on video temporal grounding and highlight detection benchmarks show that TEMPURA substantially improves strong base VLMs across model families and scales, confirming that combining event-level reasoning with fine-grained temporal segmentation is an effective recipe for video temporal understanding.
This work presents LeFlow, which learns a reusable latent trajectory prior operating directly in the latent dynamics space from the world model, and argues that latent world models should support not only prediction but reusable planning priors.
Hsiang-Wei Huang, Jian-Xu Shangguan, Junbin Lu et al.· 5 citations
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