Yingjie Chen

I am an AI Researcher at Alibaba TongYi Lab. I graduated with Ph.D. degree in the school of Computer Science from Peking University in 2024 and B.S. degree from Peking University in 2019. My research focuses on Computer Vision and Affective Computing. At Alibaba, I'm working on generative AI models, especially cotrollable video generation.

We are now recruiting for Summer Internships, and positions for Research Interns (RI) are continuously open for applications. Welcome to contact me with your CV and research statement!

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Publications (* denotes equal contribution)

We inspect the keyframe-based semi-supervised AU intensity estimation problem and identify the spurious correlation problem as the main challenge. To this end, we propose Trend-Aware Supervision to raise intra-trend and inter-trend awareness during training to learn invariant AU-specifc features.

We inspect face clustering problem and find existing methods failed to identify hard clusters—yielding significantly low recall for small or sparse clusters. To mitigate the issue of small clusters, we introduce NDDe based on the diffusion of neighborhood densities.

We observe that there are three kinds of inherent relations among AUs, which can be treated as strong prior knowledge, and pursuing the consistency of such knowledge is the key to learning subject-consistent representations. To this end, we propose a supervised hierarchical contrastive learning method (SupHCL) for AU recognition to pursue knowledge consistency among different facial images and different AUs.

We formulate subject variant problem in AU recognition using an AU causal diagram to explain the whys and wherefores. Based on our causal diagram, we propose a plug-in causal intervention module, CIS, which could be inserted into advanced AU recognition models for removing the effect caused by confounder Subject.

Considering that context is essential to resolve ambiguity in human visual system, modeling context within or among facial images emerges as a promising approach for AU recognition task. To this end, we propose CaFGraph, a novel context-aware facial multi-graph that can model both morphological & muscular-based region-level local context and region-level temporal context.

The dynamic process of facial muscle movement, as the core feature of AU, is yet ignored and rarely exploited by prior studies. Based on such observation, we propose Flow Supervised Module (FSM) to explicitly capture the dynamic facial movement in the form of Flow and use the learned Flow to provide supervision signals for the detection model during the training stage effectively and efficiently.

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