← Back to all news
arXiv Computer Vision By Jiaxi Yin, Ge Wang, Han Ding, Fei Wang

PSEE: Progressive Sensor Event Expansion for Point-Supervised Temporal Action Localization

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
Aug 27

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.

By Koshiro Nagano, Fumiaki Sato, Ryo Hachiuma, Kazuki Tsutsukawa, Taiki Sekii
ragmultimodal
More like this →
arXiv AI
Sep 18

PointEvent: Rethinking Event-based Tiny Object Detection via Serialized Motion Evidence Accumulation

PointEvent introduces serialized motion evidence accumulation for event-based tiny object detection, treating motion continuity as an ordered evidence propagation process. The method organizes event streams into locality‑preserving spatiotemporal paths and chronology‑preserving temporal paths, alternating serialized scans across complementary orders to consolidate fragmented motion evidence. A lightweight event‑wise state‑space framework with a high‑resolution event branch and compact context modulation achieves state‑of‑the‑art performance with the fewest parameters and fastest inference among compared methods.

By Zongze Wu, Baofeng Jia, Weiqi Yan, Jingyuan Zhang, Yu Zang, Xiaoyu Chen, Jing Han
computer-visionbenchmarks
More like this →