W-TALC: Weakly-supervised Temporal Activity Localization and Classification (1807.10418v3)

Abstract: Most activity localization methods in the literature suffer from the burden of frame-wise annotation requirement. Learning from weak labels may be a potential solution towards reducing such manual labeling effort. Recent years have witnessed a substantial influx of tagged videos on the Internet, which can serve as a rich source of weakly-supervised training data. Specifically, the correlations between videos with similar tags can be utilized to temporally localize the activities. Towards this goal, we present W-TALC, a Weakly-supervised Temporal Activity Localization and Classification framework using only video-level labels. The proposed network can be divided into two sub-networks, namely the Two-Stream based feature extractor network and a weakly-supervised module, which we learn by optimizing two complimentary loss functions. Qualitative and quantitative results on two challenging datasets - Thumos14 and ActivityNet1.2, demonstrate that the proposed method is able to detect activities at a fine granularity and achieve better performance than current state-of-the-art methods.

• The paper’s primary contribution is introducing W-TALC, a framework that localizes and classifies activities using only video-level annotations.
• It employs a dual loss strategy with Multiple Instance Learning Loss and Co-Activity Similarity Loss to enhance activity localization on benchmarks.
• W-TALC balances detection granularity and computational efficiency, enabling scalable analysis on large, weakly-labeled video datasets.

Analysis of "W-TALC: Weakly-supervised Temporal Activity Localization and Classification"

The paper, "W-TALC: Weakly-supervised Temporal Activity Localization and Classification," presents a framework for temporal activity localization and classification using weak supervision, specifically leveraging video-level annotations. The research focuses on reducing the dependency on frame-wise activity labels, which are labor-intensive to obtain. The proposed method distinguishes itself by solely using video-level labels for both training and inference, thereby facilitating scalable video analysis.

Framework and Methodology

1. Weakly-supervised Temporal Activity Localization and Classification Framework

The core contribution of this paper lies in its novel framework named W-TALC, which includes two primary components:

• Feature Extraction Network: Utilizes pre-trained models like UntrimmedNet and I3D. These networks extract temporal features through RGB and optical flow streams.
• Weakly-supervised Module: Comprises trainable, task-specific layers employing two complementary loss functions.

2. Loss Functions

The authors propose two types of loss functions integral to optimizing the network:

• Multiple Instance Learning Loss (MILL): This loss function pools maximum activation instances over temporal dimensions, aiding in localizing activities without strong annotations.
• Co-Activity Similarity Loss (CASL): Introduces relational constraints between video pairs with similar labels. CASL contributes significantly to refined localization by checking consistency in activity-specific regions across video pairs.

Results and Contributions

The paper's empirical evaluation on two challenging datasets—Thumos14 and ActivityNet1.2—highlights W-TALC's efficacy. Notably, W-TALC achieves superior performance compared to state-of-the-art methods, especially under weak supervision constraints. For instance, on the Thumos14 dataset, the framework demonstrates a significant improvement in mAP across various IoU thresholds, affirming the benefit of incorporating CASL along with MILL.

A critical advantage of W-TALC is the balance between high detection granularity and computational efficiency, managed through strategic video sampling that maintains manageable input lengths during training.

Implications and Future Directions

The primary theoretical implication of this work is the demonstration that weak label information, when structured properly with relational constraints like CASL, can lead to effective temporal segmentation and classification. This is particularly significant in environments where obtaining detailed annotations is impractical.

Practically, W-TALC expands the potential for leveraging large-scale, weakly-labeled video repositories, enabling scalable activity analysis for applications such as video surveillance, content-based retrieval, and event detection in multimedia.

The paper opens avenues for further exploration in several directions:

• Integrating weakly-supervised learning frameworks with naturally sparse datasets.
• Extending the CASL concept to other multimodal learning environments, potentially exploring cross-modal similarities.
• Adapting the framework to real-time processing scenarios, where computational overhead remains a constraint.

In conclusion, the research delineates a significant step toward making temporal activity analysis more accessible and feasible, advocating the practical utility of weak supervision through innovative learning strategies.