Chenxun Deng
Ph.D. Student, Institute of Automation, Chinese Academy of Sciences
I work on computer vision and machine learning, with a focus on long-tailed recognition,
diffusion models, wildlife visual understanding, and multi-animal tracking.
My recent research explores how prior knowledge, generative modeling, and expert specialization
can improve recognition under imbalanced data and complex real-world conditions.
Research Interests
Computer Vision
Long-Tailed Learning
Diffusion Models
Wildlife Recognition
Multi-Animal Tracking
Vision-Language Modeling
About
My research centers on robust visual understanding under challenging data conditions,
especially long-tailed distributions, limited supervision, and complex biological or ecological scenes.
I am also interested in the interaction between generative modeling and recognition,
particularly how diffusion models and language-guided priors can support discriminative tasks.
Selected Publications
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PGMM: Prior Guided Multi-expert Model for Long-tailed Classification
Pattern Recognition, 2026
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ChatDiff: A ChatGPT-based diffusion model for long-tailed classification
Neural Networks, 2025
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SAVIOR: Assessing volume alignment quality for serial section electron microscopy images using large vision-language model
Neurocomputing, 2026
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DeLoCo: Decoupled location context-guided framework for wildlife species classification using camera trap images
Ecological Informatics, 2025
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SDNet: A self-supervised bird recognition method based on large language models and diffusion models for improving long-term bird monitoring
Avian Research, 2026