Two-Pathway Video Action Recognition via Temporal Contrastive Learning
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Solution Overview
Problem
Current video action recognition systems rely heavily on supervised learning, which is costly due to the need for extensive human annotation, and fail to effectively utilize the rich temporal information present in unlabeled videos.
Innovation Solution
A semi-supervised approach using a two-pathway video action recognition model that processes unlabeled videos at different speeds, leveraging temporal contrastive learning to maximize similarity between encoded representations of the same video at varying speeds and minimize similarity between different videos, while employing group-contrastive loss to capture high-level action semantics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning is used for video action recognition, then recognition accuracy is improved, but annotation cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary self-supervised pretraining on unlabeled videos using temporal contrastive learning before fine-tuning with labeled data. This preliminary action on unlabeled data prepares the model to achieve better recognition accuracy while reducing the amount of time-consuming labeled annotation needed.
Solution Approach 2:
The system enables the model to learn from unlabeled videos through self-supervised learning with temporal contrastive loss, allowing the model to service itself by extracting temporal patterns without human annotation. This self-service mechanism reduces dependency on expensive labeled data while maintaining recognition accuracy.
2Device complexity
If only labeled videos are used for training, then model training is simpler, but the model fails to utilize rich temporal information in unlabeled videos
Solution Approach 1:
The training process is segmented into two distinct phases: (1) self-supervised pretraining phase using unlabeled videos with temporal contrastive learning to capture temporal patterns, and (2) supervised fine-tuning phase using labeled videos for action recognition. This segmentation allows the model to utilize temporal information from unlabeled videos while keeping the overall process manageable.
Solution Approach 2:
The system adds a temporal dimension to the learning process by applying temporal contrastive loss that operates on temporal segments and transformations of videos. This dimensional approach to temporal information extraction enables the model to leverage unlabeled videos effectively without overwhelming complexity.
3Measurement precision
If unlabeled videos are processed at high framerate, then temporal resolution is improved, but computational cost increases
Solution Approach 1:
The system dynamically adjusts the framerate and temporal sampling rate during different training phases. During self-supervised pretraining on unlabeled videos, it uses variable temporal sampling that adapts to the content, achieving good temporal resolution where needed while reducing computational cost in other regions. During supervised fine-tuning, it uses appropriate temporal sampling matched to the labeled data characteristics.
Data Source
AI summary
A base pathway of a computerized two-pathway video action recognition model is trained using a plurality of labeled video samples. The base pathway is trained using a plurality of unlabeled video samples at a first framerate. An auxiliary pathway of the computerized two-pathway video action recognition model is trained using a plurality of the unlabeled video samples at a second framerate, the second framerate being slower than the first framerate, wherein the training of the base pathway and the training of the auxiliary pathway result in a trained computerized two-pathway video action recognition model. A candidate video is categorized using the trained computerized two-pathway video action recognition model and the categorized candidate video is stored in a computer-accessible video database system for information retrieval.