Few-Shot Video Classification via Temporal Alignment (1906.11415v1)

Abstract: There is a growing interest in learning a model which could recognize novel classes with only a few labeled examples. In this paper, we propose Temporal Alignment Module (TAM), a novel few-shot learning framework that can learn to classify a previous unseen video. While most previous works neglect long-term temporal ordering information, our proposed model explicitly leverages the temporal ordering information in video data through temporal alignment. This leads to strong data-efficiency for few-shot learning. In concrete, TAM calculates the distance value of query video with respect to novel class proxies by averaging the per frame distances along its alignment path. We introduce continuous relaxation to TAM so the model can be learned in an end-to-end fashion to directly optimize the few-shot learning objective. We evaluate TAM on two challenging real-world datasets, Kinetics and Something-Something-V2, and show that our model leads to significant improvement of few-shot video classification over a wide range of competitive baselines.

• The paper introduces the Temporal Alignment Module (TAM), which leverages temporal ordering to improve few-shot video classification accuracy by up to 8% over existing baselines.
• TAM employs a continuous relaxation for end-to-end training, preserving long-term temporal dynamics and accommodating non-linear action speeds.
• Empirical results on Kinetics and Something-Something V2 highlight TAM's potential applicability to various sequence-to-sequence tasks beyond action recognition.

Few-Shot Video Classification via Temporal Alignment

The paper "Few-Shot Video Classification via Temporal Alignment" by Kaidi Cao et al. addresses the challenges posed by few-shot learning in video classification, particularly focusing on the temporal alignment of video data. The authors propose the Temporal Alignment Module (TAM), a novel approach that explicitly leverages temporal ordering information to improve few-shot learning performance.

In the few-shot learning context, the aim is to design models that can adapt to novel classes with a minimal number of labeled examples. Existing methods primarily focus on image data, but videos add complexity due to their temporal component. The paper argues that long-term temporal relations, often overlooked, are critical in understanding videos. Therefore, TAM is introduced to harness this temporal context by aligning sequences temporally, allowing for precise action recognition in novel scenarios.

The temporal alignment framework measures the alignment of a query video with class proxies by averaging per-frame distances along an alignment path. Crucially, TAM incorporates a continuous relaxation to facilitate an end-to-end trainable model. This continuous relaxation ensures differentiation, allowing optimization of the few-shot learning objective directly.

Empirical results demonstrate TAM’s effectiveness across two datasets: Kinetics and Something-Something V2. When tested on these datasets, TAM shows a significant improvement, surpassing existing baselines by around 8% at times. Such performance underscores the importance of considering temporal dynamics in few-shot video classification.

Specifically, TAM exhibits robustness in instances with non-linear temporal variations, such as differing action speeds. This robustness is achieved by maintaining the integrity of temporal order, which is often lost when temporal pooling methods like mean pooling are used. The authors note that traditional methods result in information loss and are not well-suited to few-shot learning.

From a broader perspective, TAM's approach to video sequence alignment has implications beyond action recognition. It can potentially be adapted for other sequence-to-sequence tasks where temporal fidelity is vital. However, future exploration might focus on reducing the computational overhead of alignment paths and improving efficiency further to handle larger video datasets effectively.

In conclusion, the Temporal Alignment Module leverages temporal structures intelligently to overcome the inherent challenges of few-shot video classification. The results indicate that explicitly modeling temporal order and interaction is a promising direction for improving video understanding, particularly in resource-constrained settings. Future research can build upon TAM to explore its integration with other video-based tasks and examine its potential for enhancing other domains that require temporal sequence alignment.