AnchorSeg uses ordered query banks of latent reasoning tokens plus a spatial anchor token and a Token-Mask Cycle Consistency loss to achieve 67.7% gIoU and 68.1% cIoU on the ReasonSeg benchmark.
Dat++: Spatially dynamic vision transformer with deformable attention.arXiv preprint arXiv:2309.01430
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C-GSPN scales 2D spatial propagation to foundation vision encoders via a fast CUDA kernel, compressed blocks, and two-stage distillation, matching ViT performance with 15% fewer parameters and 4x block speedup at 2K resolution.
ViT³ is a Test-Time Training vision model that achieves linear complexity, matches or exceeds other linear models like Mamba on classification, generation, detection and segmentation, and narrows the gap to standard vision Transformers.
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AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation
AnchorSeg uses ordered query banks of latent reasoning tokens plus a spatial anchor token and a Token-Mask Cycle Consistency loss to achieve 67.7% gIoU and 68.1% cIoU on the ReasonSeg benchmark.
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Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders
C-GSPN scales 2D spatial propagation to foundation vision encoders via a fast CUDA kernel, compressed blocks, and two-stage distillation, matching ViT performance with 15% fewer parameters and 4x block speedup at 2K resolution.
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ViT$^3$: Unlocking Test-Time Training in Vision
ViT³ is a Test-Time Training vision model that achieves linear complexity, matches or exceeds other linear models like Mamba on classification, generation, detection and segmentation, and narrows the gap to standard vision Transformers.