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ReChannel adapts pretrained diffusion transformers (e.g., FLUX-Klein) for dense prediction tasks by mapping tokens directly to pixel-space patches, achieving state-of-the-art results on trimap-free matting, KITTI depth, and referring segmentation with minimal additional parameters.
RaysUp is an ultra-lightweight, task-agnostic feature upsampling framework that uses geometry-aware ray domain techniques to reconstruct high-resolution features from low-resolution VFM outputs, achieving state-of-the-art performance with 84% fewer parameters than prior work and 7x faster inference.
ViT-Up introduces a task-agnostic feature upsampler for Vision Transformers that predicts features at arbitrary continuous image coordinates, enabling dense feature maps at any resolution and improving dense prediction and semantic correspondence benchmarks. It outperforms prior state-of-the-art upsamplers, with gains of up to +2.07 mIoU on Cityscapes and +4.17 [email protected] on SPair-71k.
Phase Marginalization is a post-hoc method that addresses phase-dependent instability in Vision Transformers by evaluating structured patch-grid phases and aggregating outputs. It improves segmentation, depth, and local matching over the canonical baseline with minimal extra cost.