Junke Wang

Explicitly aligning video captions with atomic audiovisual events unlocks substantial gains in fine-grained temporal and attributional accuracy, outperforming unstructured generation by over 9% on event-centric metrics.

Visual perception agents can achieve significant performance gains by actively seeking information beyond a single glance, outperforming static models by up to 30 points on challenging benchmarks.

Diffusion models no longer need to simultaneously plan and paint; inserting discrete "visual thought" tokens between VLMs and DiTs decouples high-level semantic reasoning from pixel synthesis for tighter alignment and direct intermediate control.

LingBot-VA 2.0 achieves few-shot generalization in complex robot manipulation tasks, outperforming traditional video generative models.

TRCGL-Net achieves a remarkable tail-class mAP of 0.4904, setting a new benchmark for rare disease recognition in chest X-ray classification.

ThinkingVLA achieves a breakthrough in robotic manipulation by seamlessly interleaving visual and textual reasoning, leading to superior performance in long-horizon tasks.

Semantic visual-action tokenization in RepWAM significantly enhances robotic manipulation performance, outperforming traditional reconstruction-based approaches.