Orthogonal Temporal Interpolation for Zero-Shot Video Recognition

TL;DR

This paper introduces an orthogonal temporal interpolation method for zero-shot video recognition that improves the use of temporal features in vision-language models, leading to better recognition accuracy on unseen categories.

Contribution

It proposes a novel orthogonal temporal interpolation module and matching loss to enhance temporal feature learning in zero-shot video recognition, outperforming previous methods.

Findings

OTI outperforms state-of-the-art on Kinetics-600, UCF101, HMDB51

Orthogonal temporal features improve zero-shot recognition accuracy

Refined spatial-temporal features are more effective than spatial-only features

Abstract

Zero-shot video recognition (ZSVR) is a task that aims to recognize video categories that have not been seen during the model training process. Recently, vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability for ZSVR. To make VLMs applicable to the video domain, existing methods often use an additional temporal learning module after the image-level encoder to learn the temporal relationships among video frames. Unfortunately, for video from unseen categories, we observe an abnormal phenomenon where the model that uses spatial-temporal feature performs much worse than the model that removes temporal learning module and uses only spatial feature. We conjecture that improper temporal modeling on video disrupts the spatial feature of the video. To verify our hypothesis, we propose Feature Factorization to retain the orthogonal temporal feature of the video and use interpolation to construct refined spatial-temporal feature. The model using appropriately refined spatial-temporal feature performs better than the one using only spatial feature, which verifies the effectiveness of the orthogonal temporal feature for the ZSVR task. Therefore, an Orthogonal Temporal Interpolation module is designed to learn a better refined spatial-temporal video feature during training. Additionally, a Matching Loss is introduced to improve the quality of the orthogonal temporal feature. We propose a model called OTI for ZSVR by employing orthogonal temporal interpolation and the matching loss based on VLMs. The ZSVR accuracies on popular video datasets (i.e., Kinetics-600, UCF101 and HMDB51) show that OTI outperforms the previous state-of-the-art method by a clear margin.