This paper constructs RTP-Dataset (Rail Transit Passenger Dataset), a dataset specifically designed for detecting anomalous behavior in rail transit scenarios, and proposes a Scene Semantic Enhancement Multimodal Network (SSEM-Net), a network that integrates three modalities: text, skeleton, and RGB.
This article proposes ActNet, a novel deep convolutional neural network architecture that combines multi-scale feature learning with a focus-aware attention mechanism to address the problem of recognizing human actions from still images.
A Multi-modal Interaction Enhanced Segment Anything Model (MIE-SAM) that reconfigures SAM's image encoder into a weight-sharing dual-branch image encoders, and translates the fused features into the fine-grained saliency map in an entirely prompt-free, end-to-end manner.
Ze Li, Ying-Ying Zhang, Shuai Zhang et al.· Neural Networks· 0 citations
An interactive multimodal scene understanding framework based on frequency-domain dynamic routing and activation-region guidance, aiming to enhance multimodal feature representation for semantic segmentation and object detection, consistently outperforms existing approaches in image fusion, semantic segmentation, and o...
Large-scale vision-language pretrained models like CLIP, paired with parameter-efficient fine-tuning techniques, have emerged as promising solutions for image-to-video transfer in video action recognition. However, existing methods often prioritize strong supervised performance at the expense of transferability and gen...
Meng-Meng Wang, Ze-Yi Huang, Bo-Yuan Jiang et al.· IEEE Transactions on Pattern...· 0 citations
Existing Action Quality Assessment (AQA) methods have limitations, such as over-reliance on unimodal, insufficient long-term temporal modeling, and modality alignment biases in multimodal models. To address these issues, we propose TCFNet, an AQA approach from the perspective of multimodal fusion. Compared to previous...
