Micro-Expression is subtle facial movements that reveal hidden emotions, but their fleeting and involuntary nature poses significant challenges for detection. This paper introduces a novel approach addressing two critical tasks in Micro-Expression analysis: spotting and Recognize. We integrate the VideoMAE V2 framework with a temporal information adapter and multi-scale feature fusion to enhance the performance of Micro-Expression Spotting-then-Recognize. Our method leverages the temporal information adapter to capture local temporal context within video frames, improving feature extraction efficiency. Additionally, we construct a multi-scale image pyramid to capture a range of motion features, from broad movements to subtle details. By combining these multi-scale features, our approach strengthens the model's capabilities in Micro-Expression Spotting-then-Recognize. Our method effectively addresses issues related to environmental variations, involuntary facial movements, and dataset imbalance, leading to improved accuracy in Micro-Expression Spotting-then-Recognize.

Visual Spatial Description (VSD) is an emerging image-to-text task which aims at generating descriptions of the spatial relationships between given objects in an image. In this paper, we apply Retrieval-Augmented Generation (RAG) technology in guiding Multimodal Large Language Models (MLLMs) for the task of VSD, complemented by an Adaptive Hallucination Corrector, and further fine-tuning them to bolster semantic understanding and overall model efficacy. We found that our approach demonstrated higher accuracy and fewer hallucination errors in both spatial relationship classification and visual language description tasks within the VSD task, achieving state-of-the-art results.