An adaptive delta-prioritization algorithm using cosine distance and Hamming-drift thresholds improves embedding distortion by 4.8-7.2% and next-token perplexity by 2.1-6.3% over periodic keyframing at matched low bitrates for tokenized driving world models.
Fabian Mentzer, George D Toderici, Michael Tschannen, and Eirikur Agustsson
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Introduces a benchmark for VLMs on compressed images and a universal adaptor to improve performance across codecs and bitrates.
Gaussian primitives compress 3D Taylor-Green vortex flows at ratios over 1000x while preserving velocity but degrading enstrophy, with anisotropic extensions recovering small-scale vortical structures better than baseline or other variants.
HCFSSNet uses convolutional layers plus a Vision Frequency State Space block with omni-directional scanning and frequency reweighting to reach competitive rate-distortion performance in learned image compression.
citing papers explorer
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Network-Efficient World Model Token Streaming
An adaptive delta-prioritization algorithm using cosine distance and Hamming-drift thresholds improves embedding distortion by 4.8-7.2% and next-token perplexity by 2.1-6.3% over periodic keyframing at matched low bitrates for tokenized driving world models.
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Benchmarking and Enhancing VLM for Compressed Image Understanding
Introduces a benchmark for VLMs on compressed images and a universal adaptor to improve performance across codecs and bitrates.
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A Compact Hybrid Convolution--Frequency State Space Network for Learned Image Compression
HCFSSNet uses convolutional layers plus a Vision Frequency State Space block with omni-directional scanning and frequency reweighting to reach competitive rate-distortion performance in learned image compression.