arXiv:2610.34618v1 Announce Type: new Abstract: Recently, point-supervised temporal action localization has attracted increasing attention due to its low annotation cost (only one frame per action in...
arXiv:2602.05718v2 Announce Type: replace Abstract: Point-supervised Temporal Action Localization (PTAL) adopts a lightly frame-annotated paradigm (\textit{i.e.}, labeling only a single frame per act...
arXiv:2602.01257v2 Announce Type: replace Abstract: Recently, point-supervised temporal action localization has gained significant attention for its effective balance between labeling costs and local...
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.
The paper introduces FailureSpot, a label‑efficient method for detecting failures at the timestamp level in vision‑language‑action (VLA) policies. It first generates weak supervision from unlabeled VLA action chunks by identifying abnormal patterns, then employs active learning to annotate only the most uncertain trajectories. Experiments on multiple VLA policies demonstrate improved performance for both timestamp‑level and trajectory‑level failure detection.
The paper introduces NAVA-WAM, a method that learns action priors directly from observation-only videos to pretrain action policies for robots. It uses a two-stage training process: first, it pretrains on videos with future‑video flow‑matching supervision to learn action‑relevant priors, and second, it fine‑tunes with action‑labeled demonstrations for robot control. Experiments show that NAVA‑WAM outperforms previous methods in both in‑distribution and out‑of‑distribution scenarios, achieving strong action‑label efficiency and real‑robot generalization.
arXiv:2608.29980v1 Announce Type: new Abstract: Unsupervised action segmentation is a challenging task. It involves finding action categories and boundaries in videos without labels. Existing Optimal...
arXiv:2610.19880v1 Announce Type: new Abstract: Unsupervised temporal action localization (UTAL) aims to identify action categories and localize their temporal boundaries without annotation supervisi...
arXiv:2609.38714v1 Announce Type: new Abstract: We describe our winning entry to the Waymo Open Dataset 2D Video Panoptic Segmentation Challenge. The task asks for a semantic class at every pixel of...
arXiv:2610.19526v1 Announce Type: new Abstract: Temporal action detection in long untrimmed videos suffers from inaccurate localization and low-quality proposals, especially with sparse annotations a...
The paper introduces PSEE, a method for progressive sensor event expansion that generates pseudo action segments from point-supervised labels in wearable sensor streams. By leveraging semantic activations, sensor-specific transition evidence, and adaptive temporal ownership, PSEE produces high‑quality pseudo boundaries that can train standard temporal action localization detectors without altering their inference. Experiments on four inertial‑sensing benchmarks show that PSEE outperforms adapted point‑supervised baselines, works with various detectors, and remains robust to different point sampling strategies.
arXiv:2610.31845v1 Announce Type: cross Abstract: Scaling policy learning requires broad experience and reliable supervision. Robot demonstrations provide high-quality trajectory supervision but are...