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cs.CV2026

Taming I2V models for Image HOI Editing: A Cognitive Benchmark and Agentic Self-Correcting Framework

Jiayi Gao, Qingchao Chen, Yuxin Peng +1

Current image editing methods excel at static attributes but fail at complex Human-Object Interactions (HOI), a critical challenge unaddressed by existing benchmarks that conflate…

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

DiCoBench: Benchmarking Multi-Image Fine-Grained Perception via Differential and Commonality Visual Cues

Geng Li, Yuxin Peng

Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive fine-grained perception capabilities. However, existing benchmarks predominantly rely o…

cs.CV2026

Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: From Evaluation to Diagnosis

Hong-Tao Yu, Chen-Wei Xie, Yuxin Peng +2

Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated remarkable multimodal perception and reasoning capabilities. While numerous benchmarks have evaluated…

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

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

Learning to Generate via Understanding: Understanding-Driven Intrinsic Rewarding for Unified Multimodal Models

Jiadong Pan, Liang Li, Yuxin Peng +6

Recently, unified multimodal models (UMMs) have made remarkable progress in integrating visual understanding and generation, demonstrating strong potential for complex text-to-imag…

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.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.CV2025

Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: A Comprehensive Evaluation

Hong-Tao Yu, Yuxin Peng, Serge Belongie +1

Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated remarkable multimodal perception capabilities, garnering significant attention. While numerous evaluat…