activity
20242026
most citedPEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised Learning

4 citations · 4 across the 5 of their papers we have counts for

collaborators

11 papers

cs.AI2026

When Efficiency Becomes Fragility: Exploiting Dynamic Routing Vulnerabilities in Adaptive UAV Tracking

Shaofeng Liang, Runwei Guan, Wenshuo Chen +7

Resource constraints on UAV platforms have driven a paradigm shift in aerial tracking, from pursuing performance toward balancing accuracy with efficiency. Adaptive Transformer Tra…

cs.CV2026

MemoGen: Can Past Experience Improve Future Text-to-Image Generation?

Wenshuo Chen, Kuimou Yu, Bowen Tian +10

Modern text-to-image models have achieved strong visual synthesis, yet remain unreliable when prompts require implicit visual constraints, relational reasoning, or external knowled…

cs.AI2026

AnyEdit++: Adaptive Long-Form Knowledge Editing via Bayesian Surprise

Bowen Tian, Caixue He, Jiemin Wu +4

Editing complex, long-form knowledge in Large Language Models remains a significant challenge due to the difficulty of maintaining generation coherence. Existing autoregressive met…

cs.IR2026

HyBIRD: Hyperbolic Bridge Retrieval and Diagnosis for Methodology Inspiration Retrieval

Yang Yang, Boyun Xu, Hao Fu +6

Methodology Inspiration Retrieval (MIR) asks a system to retrieve prior papers whose methods can inspire a new research proposal. Unlike general scientific retrieval, the central c…

cs.CV20264 cited

PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised Learning

Bowen Tian, Songning Lai, Lujundong Li +4

Fine-grained image classification has witnessed significant advancements with the advent of deep learning and computer vision technologies. However, the scarcity of detailed annota…

cs.CL2026

ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding

Zirui Wang, Yusen Hou, Shaofeng Liang +4

The explosive growth of large language models (LLMs) has created a heterogeneous and poorly documented ecosystem, making systematic model comparison increasingly important for prov…