Yixing Chen

Yu Jiang, Yixing Chen, Xingyang Li

Motion synthesis plays a vital role in various fields of artificial intelligence. Among the various conditions of motion generation, text can describe motion details elaborately and is easy to acquire, making text-to-motion(T2M) generation important. State-of-the-art T2M techniques mainly leverage diffusion models to generate motions with text prompts as guidance, tackling the many-to-many nature of T2M tasks. However, existing T2M approaches face challenges, given the gap between the natural language domain and the physical domain, making it difficult to generate motions fully consistent with the texts. We leverage kinematic phrases(KP), an intermediate representation that bridges these two modalities, to solve this. Our proposed method, KETA, decomposes the given text into several decomposed texts via a language model. It trains an aligner to align decomposed texts with the KP segments extracted from the generated motions. Thus, it's possible to restrict the behaviors for diffusion-based T2M models. During the training stage, we deploy the text-KP alignment loss as an auxiliary goal to supervise the models. During the inference stage, we refine our generated motions for multiple rounds in our decoder structure, where we compute the text-KP distance as the guidance signal in each new round. Experiments demonstrate that KETA achieves up to 1.19x, 2.34x better R precision and FID value on both backbones of the base model, motion diffusion model. Compared to a wide range of T2M generation models. KETA achieves either the best or the second-best performance.

Wenchang Li, John P. Lalor, Yixing Chen et al.

Sentiment analysis is integral to understanding the voice of the customer and informing businesses' strategic decisions. Conventional sentiment analysis involves three separate tasks: aspect-category detection, aspect-category sentiment analysis, and rating prediction. However, independently tackling these tasks can overlook their interdependencies and often requires expensive, fine-grained annotations. This paper introduces unified sentiment analysis, a novel learning paradigm that integrates the three aforementioned tasks into a coherent framework. To achieve this, we propose the Distantly Supervised Pyramid Network (DSPN), which employs a pyramid structure to capture sentiment at word, aspect, and document levels in a hierarchical manner. Evaluations on multi-aspect review datasets in English and Chinese show that DSPN, using only star rating labels for supervision, demonstrates significant efficiency advantages while performing comparably well to a variety of benchmark models. Additionally, DSPN's pyramid structure enables the interpretability of its outputs. Our findings validate DSPN's effectiveness and efficiency, establishing a robust, resource-efficient, unified framework for sentiment analysis.