3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization

By Sanath Narayan, Hisham Cholakkal, Fahad Shahbaz Khan, Ling Shao. 2019

174 citations recorded by the source corpus.

Temporal action localization is a challenging computer vision problem with numerous real-world applications. Most existing methods require laborious frame-level supervision to train action localization models. In this work, we propose a framework, called 3C-Net, which only requires video-level supervision (weak supervision) in the form of action category labels and the corresponding count. We introduce a novel formulation to learn discriminative action features with enhanced localization capabilities. Our joint formulation has three terms: a classification term to ensure the separability of learned action features, an adapted multi-label center loss term to enhance the action feature discriminability and a counting loss term to delineate adjacent action sequences, leading to improved localization. Comprehensive experiments are performed on two challenging benchmarks: THUMOS14 and ActivityNet 1.2. Our approach sets a new state-of-the-art for weakly-supervised temporal action localization on both datasets. On the THUMOS14 dataset, the proposed method achieves an absolute gain of 4.6% in terms of mean average precision (mAP), compared to the state-of-the-art. Source code is available at https://github.com/naraysa/3c-net.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100 (2020, background): “Previous weakly-supervised approaches utilised video-level or transcript supervision, where the set [64,65,66,67,68,69] or sequence [70,71,72,73,74,75] of actions in the video are used in training, without temporal bounds.”
ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos (2020, unknown): “o 50.9% when using TSN backbone and increasing the skip ratio to ActivityNet v1.2 Approach Detection mAP@ Spotting mAP 0.5 0.75 0.95 Avg W-TALC [40] 37.0 14.6 - 18.0 - SSN-SW [31] - - - 18.1 - 3C-Net [41] 37.2 23.7 9.2 21.7 - FPTADC [42] 37.6 21.8 2.4 21.9 - SSN-TAG [31] 39.2 25.3 5.4 25.9 55.4 BSN ”