keyframe
See&Trek: Training-Free Spatial Prompting for Multimodal Large Language Model
We introduce SEE&TREK, the first training-free prompting framework tailored to enhance the spatial understanding of Multimodal Large Language Models (MLLMS) under vision-only constraints. While prior efforts have incorporated modalities like depth or point clouds to improve spatial reasoning, purely visualspatial understanding remains underexplored.
See&Trek: Training-Free Spatial Prompting for Multimodal Large Language Model
We introduce See&Trek, the first training-free prompting framework tailored to enhance the spatial understanding of Multimodal Large Language Models (MLLMs) under vision-only constraints. While prior efforts have incorporated modalities like depth or point clouds to improve spatial reasoning, purely visual-spatial understanding remains underexplored.
Overleaf Example
Recent studies have shown promising results on utilizing large pre-trained image-language models for video question answering.
Appendix for EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought Y ao Mu
This involves introducing specific prompts such as "in
