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cs.CV2026
Why Does RL Generalize Better Than SFT? A Data-Centric Perspective on VLM Post-Training
Aojun Lu, Tao Feng, Hangjie Yuan +2
The adaptation of large-scale Vision-Language Models (VLMs) through post-training reveals a pronounced generalization gap: models fine-tuned with Reinforcement Learning (RL) consis…
cs.CV2024
Revisiting Long-Tailed Learning: Insights from an Architectural Perspective
Yuhan Pan, Yanan Sun, Wei Gong
Long-Tailed (LT) recognition has been widely studied to tackle the challenge of imbalanced data distributions in real-world applications. However, the design of neural architecture…
cs.CV2024
Towards Accurate and Robust Architectures via Neural Architecture Search
Yuwei Ou, Yuqi Feng, Yanan Sun
To defend deep neural networks from adversarial attacks, adversarial training has been drawing increasing attention for its effectiveness. However, the accuracy and robustness resu…