Token Merging (ToMe) doubles the throughput of large Vision Transformers on images, video, and audio by merging similar tokens with a fast matching algorithm, incurring only 0.2-0.4% accuracy loss.
Cp-vit: Cascade vision transformer pruning via progressive sparsity prediction
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UNVERDICTED 4representative citing papers
TAPE applies temporal-aware token pruning with smoothing, reselection, and timestep scheduling to speed up video diffusion models while preserving visual fidelity and coherence.
STEP uses dynamic superpatch merging via dCTS and early token exits to cut token count by 2.5x and computational complexity by up to 4x on ViT-Large for high-res segmentation, with at most 2% accuracy drop and 40% tokens halted early.
AutoSculpt models DNNs as graphs, embeds pruning patterns, and uses deep reinforcement learning to reach up to 90% pruning and 18% better FLOPs reduction than baselines on ResNet, MobileNet, VGG, and Vision Transformers.
citing papers explorer
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Token Merging: Your ViT But Faster
Token Merging (ToMe) doubles the throughput of large Vision Transformers on images, video, and audio by merging similar tokens with a fast matching algorithm, incurring only 0.2-0.4% accuracy loss.
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Temporal Aware Pruning for Efficient Diffusion-based Video Generation
TAPE applies temporal-aware token pruning with smoothing, reselection, and timestep scheduling to speed up video diffusion models while preserving visual fidelity and coherence.
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Where Do Tokens Go? Understanding Pruning Behaviors in STEP at High Resolutions
STEP uses dynamic superpatch merging via dCTS and early token exits to cut token count by 2.5x and computational complexity by up to 4x on ViT-Large for high-res segmentation, with at most 2% accuracy drop and 40% tokens halted early.
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AutoSculpt: A Pattern-based Model Auto-pruning Framework Using Reinforcement Learning and Graph Learning
AutoSculpt models DNNs as graphs, embeds pruning patterns, and uses deep reinforcement learning to reach up to 90% pruning and 18% better FLOPs reduction than baselines on ResNet, MobileNet, VGG, and Vision Transformers.