112 citations · 144 across the 40 of their papers we have counts for
5 papers · 1 filter
Learning Self-Correction in Vision-Language Models via Rollout Augmentation
Yi Ding, Ziliang Qiu, Bolian Li +1
Self-correction is essential for solving complex reasoning problems in vision-language models (VLMs). However, existing reinforcement learning (RL) methods struggle to learn it, as…
ViLaD: A Large Vision Language Diffusion Framework for End-to-End Autonomous Driving
Can Cui, Yupeng Zhou, Juntong Peng +6
End-to-end autonomous driving systems built on Vision Language Models (VLMs) have shown significant promise, yet their reliance on autoregressive architectures introduces some limi…
Sherlock: Self-Correcting Reasoning in Vision-Language Models
Yi Ding, Ruqi Zhang
Reasoning Vision-Language Models (VLMs) have shown promising performance on complex multimodal tasks. However, they still face significant challenges: they are highly sensitive to…
ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time
Yi Ding, Bolian Li, Ruqi Zhang
Vision Language Models (VLMs) have become essential backbones for multimodal intelligence, yet significant safety challenges limit their real-world application. While textual input…
Rethinking Data Distillation: Do Not Overlook Calibration
Dongyao Zhu, Bowen Lei, Jie Zhang +4
Neural networks trained on distilled data often produce over-confident output and require correction by calibration methods. Existing calibration methods such as temperature scalin…