About Me

I am a postdoctoral research fellow at CMU and a research scientist at MBZUAI. I currently co-lead the Causal Learning and Reasoning (CLeaR) Group with Prof. Kun Zhang . Prior to that, I received both my Ph.D. and B.S. degrees from Tsinghua University, advised by Prof. Jie Zhou and Prof. Jiwen Lu . My research interests include causality, representation learning, and computer vision. A central focus of my current work is to develop principled and practical methods for learning meaningful visual representations that support recognization, understanding, generation, and reasoning.

I’m currently on the academic job market! If you’re interested in my research and believe I could be a strong addition to your department, I’d be glad to connect. Please don’t hesitate to reach out by dropping me an e-mail guangyichen1994(at)gmail.com .

News

  • 2025-05: 3 papers on demonstating causal representation learning and causal lens in Diffusion model, LLM, and domain adaptation are accepted by ICML'2025.
  • 2025-04: I give talks at NTU and NUS to discuss causal representation in visual understaning [slides], thanks Prof. Dacheng Tao and Prof. Xiaokui Xiao for hosting me!
  • 2025-03: SmartCLIP is selected as Highlight by CVPR'2025.
  • 2025-02: IDOL is selected as Oral by ICLR'2025.
  • 2025-02: 1 paper on casaul representation learning and CLIP model is accepted by CVPR'2025.
  • 2025-01: 4 papers on casaul representation learning are accepted by ICLR'2025.
  • 2025-01: 1 paper on casaul representation learning is accepted by WWW'2025.
  • 2024-10: 3 papers on casaul representation learning and 1 paper on causal discovery are accepted by NeurIPS'2024.
  • 2024-09: 1 paper on understanding LLMs's representation is accepted by EMNLP'2024.
  • 2024-09: I give a talk at swarma(集智) about causal representation learning [slides].
  • 2024-07: 1 paper on efficient generation of LLMs is accepted by ECCV'2024.
  • 2024-07: Call for Papers for our Causal Representation Learning Workshop@NeurIPS 2024: Welcome to submit your paper at Openreview!
  • 2024-07: I give a talk at Pacific Causal Inference Conference (PCIC) about causal representation learning for video understanding [slides].
  • 2024-06: I co-orginize the Causal Representation Learning Workshop at ICDM 2024. See you at Abu Dhabi in Deceber.
  • 2024-06: I give a talk at Valse to discuss prompt learning and visual context [slides], thanks Prof. Tianfei Zhou for the invitation!
  • 2024-05: 2 papers on causal representation learning are accepted by ICML'2024.
  • 2024-02: 1 paper on video understanding is accepted by CVPR'2024.
  • 2024-01: 3 papers on visaul reasonsing and causality are accepted by ICLR'2024.
  • 2024-01: 1 paper on graph representation learning is accepted by WWW'2024.
  • 2023-12: 1 paper on prompt learning is accepted by AAAI'2024 as Oral.
  • 2023-09: 3 paper (1 Spotlight) on causal representation learning, domain adaptation, and trajectory prediction are accepted by NeurIPS'2023.
  • 2023-07: 1 paper on visual reasoning is accepted by ICCV'2023.
  • 2023-04: 1 paper on graph representation learning is accepted by ICML'2023.
  • 2023-03: Our paper using a causal perspective to understand MAE is selected as Highlight (top 2.5%) at CVPR'2023.
  • 2023-02: 3 papers on causality, trajectory prediction, and prompt learning are accepted by CVPR'2023.
  • 2023-01: 2 papers on prompt learning (PLOT as Spotlight) and video understanding are accepted by ICLR'2023.

    • Research Topics

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    Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer

    We present Tem-Adapter, a method that improves VQA by leveraging image-based knowledge and introducing temporal and semantic aligners.
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    Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction

    We propose to formualte the sampling process with Baysesian optimization to promote stochasitic human trajectory prediction in an unsupervised manner.
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    Understanding Masked Autoencoders via Hierarchical Latent Variable Models

    We propose a causal perspective to understand the underlying mechanism of MAE to identify latent variables in the hierarchical model.
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    GAIN: On the Generalization of Instructional Action Understanding

    We present a benchmark, named GAIN, to analyze the generalizability of instructional action understanding models.
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    Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion

    We propose a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion.
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    Spatial-Temporal Attention-aware Learning for Video-based Person Re-identification

    We propose a spatial-temporal attention to jointly discover the salient clues in both spatial and temporal domain.
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    Probabilistic Temporal Modeling for Unintentional Action Localization

    We propose a probabilistic framework with dense predictions to allow the uncertainty of annotations for unintentional action localization.
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    Unintentional Action Localization via Counterfactual Examples

    We propose to disentangle the effects of content and intention clues by building a counterfactual video pool and learning hidden causal processes contrastively.
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    FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment

    We construct a new fine-grained dataset for the explainable action quality assessment, named FineDiving.
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    Human Trajectory Prediction via Counterfactual Analysis

    We propose a counterfactual analysis method for human trajectory prediction to investigate the causality between the predictions and inputs, and alleviate the negative effects brought by environment bias.
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    Personalized Trajectory Prediction via Distribution Discrimination

    We present a distribution discrimination (DisDis) method to predict personalized motion patterns by distinguishing the potential distributions in a self-supervised manner.
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    Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification

    We propose to learn the attention with counterfactual causality, which provides a tool to measure the attention quality and a powerful supervisory signal to guide the learning process.
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    Person Re-identification via Attention Pyramid

    We propose attention pyramid networks by the "split-attend-merge-stack" principle to jointly learn the attentions under different scales.
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    Temporal Coherence or Temporal Motion: Which is More Critical for Video-based Person Re-identification?

    We show temporal coherence plays a more critical role than temporal motion for video-based person ReID and develop an adversarial feature augmentation to highlight temporal coherence.
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    Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification

    We propose to adaptively and progressively mine credible training samples to avoid the damage from the noise of predicted pseudo labels for unsupervised domain adaptation person ReID.
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    Self-Critical Attention Learning for Person Re-Identification

    We present a self-critical attention learning method which applies a critic module to examine and supervise the attention model.
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    Prompt Learning with Optimal Transport for Vision-Language Models

    we propose to learn multiple comprehensive prompts with optimal transport to adapt the pretrained vision-laguage model.
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    Adversarial Alignment for Source Free Object Detection

    Adversarial Alignment proposes a variance-based criterion of detection to build pseudo supervisions for source free adaptative object detection.
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    DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting

    DenseCLIP is a new framework for dense prediction by implicitly and explicitly adapting the pre-trained knowledge from CLIP.
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    Temporally Disentangled Representation Learning

    A framework to recover time-delayed latent causal variables and identify their relations from sequential data under stationary environments or different distribution shifts.
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    Partial Disentanglement for Domain Adaptation

    A framework with partial identifiablity to minimize unnecessary influences of domain shift with minimal changes of causal mechanisms.

    Publications

    Some selected recent publications. Please see Google Scholar for details.

    Honors and Awards

  • Jiang Zhen Scholarship, Tsinghua University, 2020
  • 2nd place in Semi-Supervised Recognition Challenge at FGVC7, CVPR, 2020
  • Samsung Scholarship, 2019
  • Academic Excellence Scholarship, Tsinghua University, 2015
  • Tsinghua Scholarship, Tsinghua University, 2014
  • National Encouragement Scholarship, Ministry of Education of P.R. China, 2014
  • National Encouragement Scholarship, Ministry of Education of P.R. China, 2013

    • Teaching

  • TA Analog Electronic Technology Foundation, Tsinghua University, 2018.
  • TA Analog Electronic Technology Foundation, Tsinghua University, 2019.
  • TA Numerical Analysis and Algorithm, Tsinghua University, 2019.
  • TA Probabilistic and Statistical Inference (ML703), MBZUAI，2021, Fall.
  • TA Probabilistic and Statistical Inference (ML703), MBZUAI，2022, (Spring, Fall).
  • TA Advanced Probabilistic and Statistical Inference (ML803), MBZUAI，2023, (Spring, Fall).
  • TA Advanced Probabilistic and Statistical Inference (ML803), MBZUAI，2024, Spring.

    • Academic Services

  • Publicity Chair: for CLeaR 2023.
  • Co-organizer:
    • The Third Workshop on Human Identification in Multimedia at ICME 2019 – [Website]
    • The Workshop on Causal Representation Learning at NeurIPS 2024 – [Website]
    • The Workshop on Causal Representation Learning at ICDM 2024 – [Website]
  • Conference Reviewer / Program Committee Member: CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, AAAI, and so on.
  • Journal Reviewer: TPAMI, TIP, IJCV, TNNLS, TMM, TCSVT, and so on.