Hugging Face Trending Papers
Aug 6

Energy-Guided Flow Matching

Pixel-space generative models bypass lossy latent compression, yet necessitate joint learning of global structure and fine-grained details in a high-dimensional space. Standard flow matching interpolates noise toward a fixed clean-image endpoint, leaving the spectral evolution to be learned implicitly.

arXiv Computer Vision
Aug 28

High-Frequency First: A Two-Stage Approach for Improving Image INR

The paper proposes a two-stage training strategy for Implicit Neural Representations (INRs) that addresses spectral bias by using a neighbor-aware soft mask to emphasize high-frequency details early in training. In the first stage, the mask assigns higher weights to pixels with strong local variations, encouraging the network to focus on fine edges and textures. The second stage transitions to full-image training, and experiments show consistent improvements in reconstruction quality across existing INR methods.

By Sumit Kumar Dam, Mrityunjoy Gain, Eui-Nam Huh, Choong Seon Hong
arXiv AI
Aug 12

Flow Straight to Reality: Perceptually Consistent Flow Matching for Efficient Image Restoration

arXiv:2608. 10544v1 Announce Type: cross Abstract: Image restoration is fundamentally constrained by the tradeoff between distortion and perception: minimizing pixel-wise error yields over-smoothed results, whereas optimizing for perceptual realism often introduces structural deviations.

By Sangwoo Jo, Donggeun Ko, Jayeon Kang, Youngsang Kwak, Jaehwa Kwak, Sungjoon Choi