activity
20212026
most citedCHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned Normalization

1 citations · 2 across the 10 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2026

Self-Evolving Spatial Reasoning in Vision Language Models via Geometric Logic Consistency

Junming Liu, Yuqi Li, Yifei Sun +6

Vision-Language Models (VLMs) have made striking progress, yet their spatial reasoning remains fragile. Models that answer an original input correctly can still fail under valid tr…

cs.CV2025

Artemis: Structured Visual Reasoning for Perception Policy Learning

Wei Tang, Yanpeng Sun, Shan Zhang +6

Recent reinforcement-learning frameworks for visual perception policy usually incorporate intermediate reasoning chains expressed in natural language. Empirical observations indica…

cs.CV2025★ 1 cited

Math Blind: Failures in Diagram Understanding Undermine Reasoning in MLLMs

Yanpeng Sun, Shan Zhang, Wei Tang +5

Diagrams represent a form of visual language that encodes abstract concepts and relationships through structured symbols and their spatial arrangements. Unlike natural images, they…

cs.CV2025

Open Eyes, Then Reason: Fine-grained Visual Mathematical Understanding in MLLMs

Shan Zhang, Aotian Chen, Yanpeng Sun +6

Current multimodal large language models (MLLMs) often underperform on mathematical problem-solving tasks that require fine-grained visual understanding. The limitation is largely…

cs.CV2021

Manifold Learning Benefits GANs

Yao Ni, Piotr Koniusz, Richard Hartley +1

In this paper, we improve Generative Adversarial Networks by incorporating a manifold learning step into the discriminator. We consider locality-constrained linear and subspace-bas…

cs.CV2021

Meta-Learning for Multi-Label Few-Shot Classification

Christian Simon, Piotr Koniusz, Mehrtash Harandi

Even with the luxury of having abundant data, multi-label classification is widely known to be a challenging task to address. This work targets the problem of multi-label meta-lear…