5 accepted papers
Multimodal sentiment analysis (MSA) aims to predict human sentiments by integrating signals from different modalities such as text, video, and audio. However, raw multimodal sequences often suffer from semantic inconsistencies--exhibiting redundancy or conflicts within and across modalities--which h…
We propose a method for audio-visual knowledge distillation. Existing methods typically distill from the latent embeddings or outputs. The former requires matching feature dimensions, if not the same architecture, between teacher and student models while the latter supports any teacher-student pairi…
Graph Convolutional Networks (GCNs) have been widely used to model the high-order dynamic dependencies for skeleton-based action recognition. Most existing approaches do not explicitly embed the high-order spatio-temporal importance to joints’ spatial connection topology and intensity, and they do n…
Top-k multi-label learning, which returns the top-k predicted labels from an input, has many practical applications such as image annotation, document analysis, and web search engine. However, the vulnerabilities of such algorithms with regards to dedicated adversarial perturbation attacks have not…
We develop a robust multi-scale structure-aware neural network for human pose estimation. This method improves the recent deep conv-deconv hourglass models with four key improvements: (1) multi-scale supervision to strengthen contextual feature learning in matching body keypoints by combining featur…