Generative Uncertainty as a Self-supervised Signal for Semantic Similarity Learning

arXiv:2609.35341 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/nvidia-cosmos/cosmos-predict2

Terminology

• Consistency — 23× in this paper · explained in 3 episodes
• Semantic Similarity — 23× in this paper · explained in 2 episodes
• Alignment — 22× in this paper · explained in 1 episode
• Sparsity — 14× in this paper · explained in 4 episodes
• Embeddings — 9× in this paper · explained in 2 episodes
• Generative Model — 9× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 8× in this paper
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Feature Selection — 8× in this paper · explained in 4 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Diffusion Models — 7× in this paper · explained in 6 episodes
• Cosine Similarity — 6× in this paper · explained in 3 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Clustering — 5× in this paper · explained in 2 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Video Object Segmentation — 5× in this paper
• Downstream Tasks — 4× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Core — 2× in this paper · explained in 2 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Mean Squared Error — 2× in this paper
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Aleatoric Uncertainty — 1× in this paper · explained in 4 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 9 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Out-of-Distribution (OOD) Detection — 1× in this paper · explained in 2 episodes
• Quantifying Uncertainty — 1× in this paper · explained in 5 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Winter Conference on Applications of Computer Vision — 1× in this paper

Sources

• Generative Uncertainty in Diffusion Models
• VRAG: Region Attention Graphs for Content-Based Video Retrieval
• The 2017 DAVIS Challenge on Video Object Segmentation
• UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
• Wan: Open and Advanced Large-Scale Video Generative Models
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

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