DINO (AI model): news timeline & CVEs

AI agents build 3D scenes from photos but have no idea if they got it right

LEGO-Anything has coding agents write Blender scenes from one photo, but they cannot reliably judge geometric accuracy.

LEGO-Anything, called Image-to-Code, has a coding agent iteratively write and revise an executable Blender program from a single photo so the scene can be edited and queried. LEGO-Bench scores 208 images from 104 simulator scenes and 443 assets on validity, geometric accuracy, and visual similarity. GPT-6 Astra reached 53.4 percent indoors and 39.6 percent outdoors, versus about 15 percent for weaker GPT setups; extra reasoning lifted Astra from 32.3 to 61.8 percent on an office subset. Models judged geometry near chance, while the training-free LEGO-Plugin, which substitutes measurements for self-assessment, improved weaker agents by up to 62.7 percentage points.

The Decoder08:31 UTC · 2h agoAI research1

SemanTok: Predictable Semantic Tokens for Efficient Autoregressive Video Generation

SemanTok aligns video tokens with frozen DINO features so short prefixes carry clip semantics.

SemanTok is a flexible video tokenizer that feeds frozen DINO features into its encoder and reconstructs them from each retained token prefix with lightweight heads. A 201M-parameter SemanTok autoregressive model matches or beats a VideoFlexTok model 3.4 times larger, and larger SemanTok models further improve fidelity. Short prefixes are cheaper to predict and produce better generation quality, leaving pixel detail to later tokens. Semantic alignment holds on out-of-distribution classes and at every decoder noise level, including pure noise.

Hugging Face daily papersSep 29, 20:00 UTC · 3d agoAI research

Harnessing CLIP and DINO: An Uncertainty-Aware Cascaded Fusion Network for Generalizable Deepfake Image Detection

UCF-Net fuses CLIP and DINO features with entropy-based uncertainty weighting to improve generalizable deepfake image detection across generators.

Researchers propose UCF-Net, an uncertainty-aware cascaded fusion network that combines CLIP's language-aligned semantic priors with DINO's self-supervised visual-structure priors for deepfake detection. It aggregates hierarchical features across transformer depths via layer-wise expert modules and performs weighted fusion driven by entropy-derived uncertainty. The authors consolidate public deepfake datasets into a unified benchmark of roughly 4 million images plus a cross-generator set of over 8,000 faces from eight recent generators, where UCF-Net achieves the best mean AUC among evaluated methods, though zero-shot transfer remains challenging.

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

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AI agents build 3D scenes from photos but have no idea if they got it right

SemanTok: Predictable Semantic Tokens for Efficient Autoregressive Video Generation

Harnessing CLIP and DINO: An Uncertainty-Aware Cascaded Fusion Network for Generalizable Deepfake Image Detection