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20232026
most citedA Closer Look at Few-shot Classification Again

16 citations · 33 across the 17 of their papers we have counts for

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5 papers · 1 filter

cs.CV2026

Structure-aware Prompt Adaptation from Seen to Unseen for Open-Vocabulary Compositional Zero-Shot Learning

Yihang Duan, Jiong Wang, Pengpeng Zeng +5

The goal of Open-Vocabulary Compositional Zero-Shot Learning (OV-CZSL) is to recognize attribute-object compositions in the open-vocabulary setting, where compositions of both seen…

cs.CV2026

TIMI: Training-Free Image-to-3D Multi-Instance Generation with Spatial Fidelity

Xiao Cai, Pengpeng Zeng, Ji Zhang +3

Precise spatial fidelity in Image-to-3D multi-instance generation is critical for downstream real-world applications. Recent work attempts to address this by fine-tuning pre-traine…

cs.LG2026

Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols

Xu Luo, Ji Zhang, Lianli Gao +2

Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.However, there lacks a unified, rigorous evaluation protocol that is bot…

cs.RO2026

Beyond the Majority: Long-tail Imitation Learning for Robotic Manipulation

Junhong Zhu, Ji Zhang, Jingkuan Song +2

While generalist robot policies hold significant promise for learning diverse manipulation skills through imitation, their performance is often hindered by the long-tail distributi…

cs.RO2026

Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation

Kaipeng Fang, Weiqing Liang, Yuyang Li +5

Real-robot demonstrations are prohibitively expensive, while simulation data and real-world human demonstrations are both scalable but each leaves a distinct gap: simulation suffer…