arXiv Computer Vision
FIS-OT: Feature-Induced Optimal Transport for Unsupervised Action Segmentation
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See the Change, Keep the Flow: Unsupervised Action Segmentation via Spectral-Temporal Representation Learning
arXiv:2608.29611v1 Announce Type: new Abstract: Unsupervised action segmentation aims to discover latent action categories and their temporal organization without action annotations. Optimal transpor...
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D-CLOT: Double Closed Loop Optimal Transport for Unsupervised Action Segmentation
arXiv:2608. 05877v1 Announce Type: cross Abstract: Optimal transport (OT) has emerged as an effective framework for unsupervised action segmentation.
arXiv Computer Vision
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Skeleton-based Zero-Shot Spatio-Temporal Action Localization via Weakly-Supervised Pretraining
The paper introduces Skeleton-Language feature Pooling Switching, a weakly‑supervised vision‑language pretraining strategy for skeleton‑based zero‑shot spatio‑temporal action localization. It replaces video‑level pooling with instance‑level feature computation during inference, enabling the model to estimate unseen actions without costly annotations. Additionally, Scene‑Mixed Discriminative Contrastive Learning is proposed to separate actions at the instance level within mixed scenes using a MIL framework, and experiments on four public datasets confirm the method’s effectiveness.
arXiv Computer Vision
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Exploring the Temporal Consistency for Point-Level Weakly-Supervised Temporal Action Localization
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arXiv Computer Vision
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Seeing as Humans Do: Learning from Motion to Segment Anything Without Supervision
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Hugging Face Trending Papers
Jul 13
Temporal Feature Distillation for Label-Efficient Precise Event Spotting in Sports Videos
Precise Event Spotting (PES) requires distinguishing visually similar yet semantically distinct adjacent frames, making it fundamentally different from image classification and coarse action recognition. Although self-distillation methods such as DINO have shown strong representation learning ability in images, we find that directly applying them to PES is ineffective: without supervised guidance, subtle but crucial motion cues are often suppressed as noise, leading to representations that are insensitive to precise event boundaries.