Summary of Hpe-cogvlm: Advancing Vision Language Models with a Head Pose Grounding Task, by Yu Tian et al.

HPE-CogVLM: Advancing Vision Language Models with a Head Pose Grounding Task

by Yu Tian, Tianqi Shao, Tsukasa Demizu, Xuyang Wu, Hsin-Tai Wu

First submitted to arxiv on: 4 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary A novel framework for improving head pose estimation (HPE) accuracy is proposed by leveraging Vision Language Models (VLMs). The CogVLM VLM can analyze entire images and focus on specific objects through attention mechanisms. However, direct fine-tuning of the VLM for HPE fails to achieve desirable accuracy, while some model merging methods improve accuracy but produce blended invalid response formats. To integrate HPE capability into CogVLM effectively, a novel LoRA layer-based model merging method is developed. This merging approach applies a high cosine similarity threshold and a winner-takes-all layer selection strategy, aligning attention to the HPE task while preserving original object detection knowledge. The proposed framework, HPE-CogVLM, achieves a 31.5% reduction in Mean Absolute Error over the current state-of-the-art CNN model, 6DRepNet, in cross-dataset evaluation.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper tries to make computers better at recognizing how people tilt their heads. Right now, these computers aren’t very good at this task because they need a lot of information about the head’s shape and position. The researchers found that using special computer models called Vision Language Models can help with this problem. They developed a new way to use these models to make the computers better at recognizing head tilts. This new method is able to accurately recognize how people tilt their heads, which could be useful in many areas such as medicine or robotics.