SWT: Self-Supervised Video Object Segmentation via Sliding, Wavelet and Transportation

arXiv:2609.31725 · cs.CV, cs.AI · Submitted 2026-09-22 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/machine928/SWT

Terminology

• Optimal Transport — 52× in this paper · explained in 5 episodes
• Wavelet Transform — 33× in this paper · explained in 2 episodes
• Video Object Segmentation — 30× in this paper
• Ablation — 10× in this paper · explained in 1 episode
• Consistency — 10× in this paper · explained in 3 episodes
• Contrastive Learning — 8× in this paper · explained in 5 episodes
• Ablation Studies — 5× in this paper · explained in 1 episode
• Accuracy — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Self-Supervised Learning — 4× in this paper · explained in 7 episodes
• Temporal consistency — 4× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Clustering — 3× in this paper · explained in 2 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Optical Flow — 3× in this paper · explained in 2 episodes
• Activation Function — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Adaptability — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• SWT on the Video Instance Parsing — 1× in this paper

Sources

• Segment and Track Anything
• Dynamic in Static: Hybrid Visual Correspondence for Self-Supervised Video Object Segmentation
• The 2017 DAVIS Challenge on Video Object Segmentation
• Uni-ViGU: Towards Unified Video Generation and Understanding via A Diffusion-Based Video Generator
• Uni-cot: Towards Unified Chain-of-Thought Reasoning Across Text and Vision
• SoulX-FlashTalk: Real-Time Infinite Streaming of Audio-Driven Avatars via Self-Correcting Bidirectional Distillation
• Self-Supervised Learning with Swin Transformers
• SoulX-FlashHead: Oracle-guided Generation of Infinite Real-time Streaming Talking Heads
• MuseTalk: Real-Time High-Fidelity Video Dubbing via Spatio-Temporal Sampling
• AnyTalker: Scaling Multi-Person Talking Video Generation with Interactivity Refinement

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