arXiv AI
Aug 7
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.
See the Change, Keep the Flow: Unsupervised Action Segmentation via Spectral-Temporal Representation Learning
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arXiv Computer Vision
Sep 1
FIS-OT: Feature-Induced Optimal Transport for Unsupervised Action Segmentation
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 Computer Vision
Sep 16
Exploring the Temporal Consistency for Point-Level Weakly-Supervised Temporal Action Localization
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 Machine Learning
Aug 4
DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition
arXiv:2505. 20894v2 Announce Type: replace Abstract: Despite recognized limitations in modeling long-range temporal dependencies, Human Activity Recognition (HAR) has traditionally relied on a sliding window approach to segment labeled datasets.
arXiv Machine Learning
Jun 19
FlexLAM: Resolving the Bottleneck Trade-off in Latent Action Learning
arXiv:2606. 19408v1 Announce Type: new Abstract: Latent actions provide a compact interface between action-free video and downstream decision-making, yet existing Latent Action Models (LAMs) force every transition through a fixed-capacity bottleneck.
Hugging Face Trending Papers
Jun 8
Vision-Language Guided Hyperspectral Object Tracking via Semantics Fusion and Contextual Template Updating
Hyperspectral object tracking (HOT) leverages the rich spectral information provided by hyperspectral videos (HSVs), offering substantial potential for object tracking. However, efficiently extracting and exploiting spectral information from redundant spectral bands remains a fundamental challenge, which severely limits model generalization and tracking performance.
arXiv Computer Vision
Aug 31
VidParse: Online Parsing of Egocentric Procedures Like a Pro
arXiv:2608.27562v1 Announce Type: new Abstract: Translating continuous, noisy egocentric video streams into discrete, temporally ordered action steps is fraught with visual challenges. Heavy ego-moti...
arXiv Computer Vision
Sep 22
Action-Slot: Structured Action-Centric Representation Learning for Multi-Agent Atomic Activity Understanding
The paper introduces Action‑Slot, a structured action‑centric representation learning framework for multi‑agent atomic activity understanding. It reformulates slot attention into activity‑aligned slots, parallel spatio‑temporal updates, and background regularization to disentangle concurrent, asynchronous activities directly from raw video. Additionally, an attention‑difference pseudo‑mask method enables weakly supervised localization, and a new synthetic dataset, TACO, provides balanced atomic activity coverage with pixel‑level annotations.
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.
arXiv AI
Jul 7
FM-ChangeNet: Learning Change through Pathwise Feature Transport
arXiv:2607. 04750v1 Announce Type: new Abstract: We present FM-ChangeNet, a pathwise-supervised framework for change detection that reformulates bi-temporal reasoning as continuous transport in feature space rather than static endpoint comparison.
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.
Hugging Face Trending Papers
Jun 11
OR-Action: Multi-Role Video Understanding with Fine-Grained Actions
Fine-grained understanding of operating room (OR) activity could enable workflow-aware assistance, yet remains difficult due to clutter, occlusions, and limited sensing. The prevailing approach to model this environment is scene graphs as an interpretable representation of OR interactions.