activity
20242026
collaborators

8 papers

cs.CV2026

Learning Taxonomic Trees with Hierarchical Representation Regularization for Large Multimodal Models

Hulingxiao He, Zhi Tan, Yuxin Peng

Taxonomies provide key information about the semantic relationships between concepts and the inherent organization of vision and language. Despite their impressive capabilities, la…

cs.CV2026

AesFormer: Transform Everyday Photos into Beautiful Memories

Tianxiang Du, Hulingxiao He, Yuxin Peng

In everyday photography, aesthetically appealing moments are often captured with structural flaws (e.g., composition, camera viewpoint, or pose) that existing retouching and portra…

cs.CV2026

Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning

Hulingxiao He, Zijun Geng, Yuxin Peng

Any entity in the visual world can be hierarchically grouped based on shared characteristics and mapped to fine-grained sub-categories. While Multi-modal Large Language Models (MLL…

cs.CV2026

Taxonomy-Aware Representation Alignment for Hierarchical Visual Recognition with Large Multimodal Models

Hulingxiao He, Zhi Tan, Yuxin Peng

A high-performing, general-purpose visual understanding model should map visual inputs to a taxonomic tree of labels, identify novel categories beyond the training set for which fe…

cs.CV2026

Venus: Benchmarking and Empowering Multimodal Large Language Models for Aesthetic Guidance and Cropping

Tianxiang Du, Hulingxiao He, Yuxin Peng

The widespread use of smartphones has made photography ubiquitous, yet a clear gap remains between ordinary users and professional photographers, who can identify aesthetic issues…

cs.CV2025

Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models

Hulingxiao He, Geng Li, Zijun Geng +2

Multi-modal large language models (MLLMs) have shown remarkable abilities in various visual understanding tasks. However, MLLMs still struggle with fine-grained visual recognition…