To tackle this issue, we make an early effort to study temporal action localization from the perspective of multi-modality feature learning, based on the observation that different actions exhibit specific preferences to appearance or motion modality.
Based on the exemplar-consultation mechanism, the long-term dependencies can be captured by regarding historical frames as exemplars, while the category-level modeling can be achieved by regarding representative frames from a category as exemplars.
Ranked #3 on Online Action Detection on TVSeries
Weakly supervised temporal action localization aims at learning the instance-level action pattern from the video-level labels, where a significant challenge is action-context confusion.
Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency.
no code implementations • 17 Feb 2021 • Le Yang, Gabriele Vajente, Mariana Fazio, Alena Ananyeva, GariLynn Billingsley, Ashot Markosyan, Riccardo Bassiri, Kiran Prasai, Martin M. Fejer, Carmen S. Menoni
Herein, we show the atomic arrangement of strong network forming GeO2 glass is modified at medium range (< 2 nm) through vapor deposition at elevated temperatures.
Due to the need to store the intermediate activations for back-propagation, end-to-end (E2E) training of deep networks usually suffers from high GPUs memory footprint.
As InfoPro loss is difficult to compute in its original form, we derive a feasible upper bound as a surrogate optimization objective, yielding a simple but effective algorithm.
Full-reference (FR) point cloud quality assessment (PCQA) has achieved impressive progress in recent years.
The accuracy of deep convolutional neural networks (CNNs) generally improves when fueled with high resolution images.
Internal friction in oxide thin films imposes a critical limitation to the sensitivity and stability of ultra-high finesse optical cavities for gravitational wave detectors.
To address this problem, this paper proposes a novel anchor-free action localization module that assists action localization by temporal points.
The existing methods can be categorized into two localization-by-classification pipelines, i. e., the pre-classification pipeline and the post-classification pipeline.
In this report, we introduce the Winner method for HACS Temporal Action Localization Challenge 2019.
The ensemble Kalman filter reduces the computational complexity required to obtain predictions with Gaussian processes preserving the accuracy level of these predictions.
In this paper, we formulate this problem as a Markov Decision Process, where agents are learned to segment object regions under a deep reinforcement learning framework.
Object segmentation in weakly labelled videos is an interesting yet challenging task, which aims at learning to perform category-specific video object segmentation by only using video-level tags.