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Fusing event data with global illumination unlocks significantly better low-light image enhancement, outperforming prior art by up to 1.24dB PSNR and 0.069 SSIM.
Unified benchmarks reveal the state-of-the-art in simultaneously addressing multiple real-world image degradations like blur, low-light, and rain.
Linear transport flows between degraded and clean image domains enable fast, adaptable image restoration that outperforms existing methods in distortion-perception balance.
EQA agents can now handle dynamic, human-populated scenes better thanks to a training-free method that selectively remembers only the most informative visual evidence.
Forget generic CoT: Embed-RL uses reinforcement learning to generate reasoning traces that are explicitly optimized for multimodal embedding tasks, leading to significant performance gains.
