Temporal Context Modeling Network with Local-Global Complementary Architecture for Temporal Proposal Generation

Abstract

Temporal Action Proposal Generation (TAPG) is a promising but challenging task with a wide range of practical applications. Although state-of-the-art methods have made significant progress in TAPG, most ignore the impact of the temporal scales of action and lack the exploitation of effective boundary contexts. In this paper, we propose a simple but effective unified framework named Temporal Context Modeling Network (TCMNet) that generates temporal action proposals. TCMNet innovatively uses convolutional filters with different dilation rates to address the temporal scale issue. Specifically, TCMNet contains a BaseNet with dilated convolutions (DBNet), an Action Completeness Module (ACM), and a Temporal Boundary Generator (TBG). The DBNet aims to model temporal information. It handles input video features through different dilated convolutional layers and outputs a feature sequence as the input of ACM and TBG. The ACM aims to evaluate the confidence scores of densely distributed proposals. The TBG is designed to enrich the boundary context of an action instance. The TBG can generate action boundaries with high precision and high recall through a local–global complementary structure. We conduct comprehensive evaluations on two challenging video benchmarks: ActivityNet-1.3 and THUMOS14. Extensive experiments demonstrate the effectiveness of the proposed TCMNet on tasks of temporal action proposal generation and temporal action detection.

Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach

“…The [5] offer nine distributional models of semantic statistics as illustrated in Bound Encoding of the Aggregate Language Environment (BEAGLE) describe three additional models: (i) Constructed Semantics Model (CSM), (ii) Vectorspace Model, and (iii) Sparse Nonnegative Matrix Factorization (SpNMF). In addition, [6] detail how they adapted the Temporal Context Model (TCM) used in the research of memory into their own method of semantic modeling called the predictive Temporal Context Model (pTCM). There are two more categories that may be used to categorize semantic models; these are context phrase and contextual area.…”