Weakly supervised temporal action localization is a newly emerging yet widely studied topic in recent years. The existing methods can be categorized into two localization-by-classification pipelines, i.e., the pre-classification pipeline and the post-classification pipeline. The pre-classification pipeline first performs classification on each video snippet, and then, aggregates the snippet-level classification scores to obtain the video-level classification score. In contrast, the post-classification pipeline aggregates the snippet-level features first and then predicts the video-level classification score based on the aggregated feature. Although the classifiers in these two pipelines are used in different ways, the role they play is exactly the same-to classify the given features to identify the corresponding action categories. To this end, an ideal classifier can make both pipelines work. This inspires us to simultaneously learn these two pipelines in a unified framework to obtain an effective classifier. Specifically, in the proposed learning framework, we implement two parallel network streams to model the two localization-by-classification pipelines simultaneously and make the two network streams share the same classifier. This achieves the novel Equivalent Classification Mapping (ECM) mechanism. Moreover, we discover that an ideal classifier may possess two characteristics: 1) the frame-level classification scores obtained from the pre-classification stream and the feature aggregation weights in the post-classification stream should be consistent; and 2) the classification results of these two streams should be identical. Based on these two characteristics, we further introduce a weight-transition module and an equivalent training strategy into the proposed learning framework, which assists to thoroughly mine the equivalence mechanism. Comprehensive experiments are conducted on three benchmarks and ECM achieves accurate action localization results.

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. To overcome this challenge, one recent work builds an action-click supervision framework. It requires similar annotation costs but can steadily improve the localization performance when compared to the conventional weakly supervised methods. In this paper, by revealing that the performance bottleneck of the existing approaches mainly comes from the background errors, we find that a stronger action localizer can be trained with labels on the background video frames rather than those on the action frames. To this end, we convert the action-click supervision to the background-click supervision and develop a novel method, called BackTAL. Specifically, BackTAL implements two-fold modeling on the background video frames, i.e., the position modeling and the feature modeling. In position modeling, we not only conduct supervised learning on the annotated video frames but also design a score separation module to enlarge the score differences between the potential action frames and backgrounds. In feature modeling, we propose an affinity module to measure frame-specific similarities among neighboring frames and dynamically attend to informative neighbors when calculating temporal convolution. Extensive experiments on three benchmarks are conducted, which demonstrate the high performance of the established BackTAL and the rationality of the proposed background-click supervision.

Weakly supervised object detection (WSOD) is an interesting yet challenging task in the computer vision community. The core is to discover the image regions that contain the complete object instances under the image-level supervision. Existing works usually solve this problem via a proposal selection strategy, which selects the most discriminative box regions from the weakly labeled training images. However, these regions usually only contain the discriminative object parts rather than the complete object instances. To address this problem, this article proposes to learn a searching-agent to gradually mine desirable object regions under a region searching paradigm, where we formulate the searching process as a Markov decision process and learn the searching-agent under a deep reinforcement learning framework. To learn such a searching-agent under the weak supervision, we extract the pseudo-complete object regions and the corresponding local discriminative object parts and introduce the obtained pseudo-target-part training pairs into the reinforcement learning process of the search-agent. This learning strategy has twofold advantages: 1) it can mimic the searching process to reveal complete object regions from a certain discriminative part of the object under the weak supervision and 2) it will not suffer from the learning difficulty arise from the long-action sequence that happens when searching from the entire image range. Comprehensive experiments on benchmark data sets demonstrate that by integrating the learned searching-agent with the existing WSOD method, we can achieve better performance than the other state-of-the-art and baseline methods.