ViT-B/16 (AI model): news timeline & CVEs

TT-VidT: Decoupling the Temporal Axis for Efficient Motion-Centric Video Pretraining

TT-VidT pairs a DINOv3 spatial ViT with a temporal layer, leading motion benchmarks at lower FLOPs.

TT-VidT separates a DINOv3-initialized ViT-B/16 spatial path from a compact Temporal Transfer Layer trained with Diff Compression to reconstruct target frames from a first-frame appearance anchor and motion tokens. A matched 24-configuration study at roughly 170–190M scale uses sim1.7M OpenVid and Moments-in-Time v2 clips for eight epochs. TT-VidT leads Jester, Something-Something V2, ARID, and Diving48, improving 54–121% over the strongest non-TT row while using 48% fewer encoder FLOPs than DisMo and 55% fewer than VideoMAE or V-JEPA2.

Hugging Face daily papersSep 26, 20:00 UTC · 6d agoAI research

Weight Pair Encoding: Inducing a Smaller Grammar in Neural Network Weights

WeightPE finetunes int8 weights so Re-Pair grammars shrink to 0.38–0.43x of QAT, costing about 1–2 accuracy points.

Weight Pair Encoding finetunes int8 network weights so a lossy Re-Pair compressor finds a smaller grammar, using a straight-through estimator under a global L2 budget. On MLP weights of ViT-B/16 and ViT-L/16 finetuned on CIFAR-10, grammars are 0.43x and 0.38x the size of an equivalent int8 QAT run, costing 1.9 and 1.1 accuracy points. The same trend appears with LZ78 and SEQUITUR, compressors the networks were not trained against. The authors call grammar size a new explicit training objective for weights.

arXiv cs.AI / cs.LG / cs.CLSep 25, 17:29 UTC · 7d agoAI research

Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision

A reinforcement-learning black-box attack fools vision models with fewer queries than earlier methods.

The paper proposes RIBA, a black-box adversarial attack that borrows reinforcement-learning ideas to optimize perturbations against non-differentiable computer-vision models. Compared with prior attacks, it reports 25.4% fewer median queries to attack a ResNet-18 on CIFAR-10 and 22.5% fewer to fool a ViT-B/16 on ImageNet. The authors also say RIBA can match white-box attack performance against an adversarially trained model. No real-world incident is described.

arXiv cs.CRSep 21, 08:14 UTC · 12d agoAI safety & security

ViT-B/16

Timeline3 articles

Appears with

TT-VidT: Decoupling the Temporal Axis for Efficient Motion-Centric Video Pretraining

Weight Pair Encoding: Inducing a Smaller Grammar in Neural Network Weights

Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision