arXiv subjects

Jianlin Peng

Publications and source records attributed to Jianlin Peng.

2 recordsLinked to original sources

arXiv2024

MInD: Improving Multimodal Sentiment Analysis via Multimodal Information Disentanglement

Learning effective joint representations has been a central task in multi-modal sentiment analysis. Previous works addressing this task focus on exploring sophisticated fusion techniques to enhance performance. However, the inherent heterogeneity of distinct modalities remains a core problem that brings challenges in fusing and coordinating the multi-modal signals at both the representational level and the informational level, impeding the full exploitation of multi-modal information. To address this problem, we propose the Multi-modal Information Disentanglement (MInD) method, which decomposes the multi-modal inputs into modality-invariant and modality-specific components through a shared encoder and multiple private encoders. Furthermore, by explicitly training generated noise in an adversarial manner, MInD is able to isolate uninformativeness, thus improves the learned representations. Therefore, the proposed disentangled decomposition allows for a fusion process that is simpler than alternative methods and results in improved performance. Experimental evaluations conducted on representative benchmark datasets demonstrate MInD's effectiveness in both multi-modal emotion recognition and multi-modal humor detection tasks. Code will be released upon acceptance of the paper.

cs.MM↗

arXiv2021

IIP-Transformer: Intra-Inter-Part Transformer for Skeleton-Based Action Recognition

Recently, Transformer-based networks have shown great promise on skeleton-based action recognition tasks. The ability to capture global and local dependencies is the key to success while it also brings quadratic computation and memory cost. Another problem is that previous studies mainly focus on the relationships among individual joints, which often suffers from the noisy skeleton joints introduced by the noisy inputs of sensors or inaccurate estimations. To address the above issues, we propose a novel Transformer-based network (IIP-Transformer). Instead of exploiting interactions among individual joints, our IIP-Transformer incorporates body joints and parts interactions simultaneously and thus can capture both joint-level (intra-part) and part-level (inter-part) dependencies efficiently and effectively. From the data aspect, we introduce a part-level skeleton data encoding that significantly reduces the computational complexity and is more robust to joint-level skeleton noise. Besides, a new part-level data augmentation is proposed to improve the performance of the model. On two large-scale datasets, NTU-RGB+D 60 and NTU RGB+D 120, the proposed IIP-Transformer achieves the-state-of-art performance with more than 8x less computational complexity than DSTA-Net, which is the SOTA Transformer-based method.

cs.CV↗