LEARNet: A Learning Entropy-Aware Representation Network for Educational Video Understanding
Chitrakala S, Nivedha V V, Niranjana S R
TL;DR
LEARNet is a new framework that efficiently identifies key instructional content in educational videos by focusing on high-information frames.
Contribution
LEARNet introduces an entropy-aware architecture combining Temporal Information Bottleneck and Spatial–Semantic Decoder for educational video understanding.
Findings
LEARNet reduces visual redundancy by 70.2% while maintaining high annotation accuracy (F1 = 0.89, mAP@50 = 0.88).
The framework enables the creation of EVUD-2M, a large-scale benchmark with multi-level semantic labels for educational videos.
Abstract
Educational videos contain long periods of visual redundancy, where only a few frames convey meaningful instructional information. Conventional video models, which are designed for dynamic scenes, often fail to capture these subtle pedagogical transitions. We introduce LEARNet, an entropy-aware framework that models educational video understanding as the extraction of high-information instructional content from low-entropy visual streams. LEARNet combines a Temporal Information Bottleneck (TIB) for selecting pedagogically significant keyframes with a Spatial–Semantic Decoder (SSD) that produces fine-grained annotations refined through a proposed Relational Consistency Verification Network (RCVN). This architecture enables the construction of EVUD-2M, a large-scale benchmark with multi-level semantic labels for diverse instructional formats. LEARNet achieves substantial redundancy…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Video Analysis and Summarization · Human Pose and Action Recognition