Efficient Video Class-Incremental Learning by Slightly Shifting New Classes to Remember Old Classes

Concetti Chiave

SNRO, a novel framework for video class-incremental learning, slightly shifts the features of new classes during their training stage to greatly improve the performance of old classes, while consuming the same memory as existing methods.

Sintesi

The authors propose a novel framework called SNRO for video class-incremental learning. SNRO consists of two key components:

1. Examples Sparse:

   • Sparse Extract: SNRO decimates the videos of old classes at a lower sample rate, storing a larger memory set under the same memory consumption.
   • Frame Alignment: SNRO uses interpolation to align the sparse frames with the network input, reducing the spatio-temporal information of the video representation.

2. Early Break:

   • SNRO terminates the training at a small epoch during the incremental training stage, preventing the model from over-stretching to the newly seen classes.

By slightly dropping the performance of the current task, SNRO greatly improves the performance of previous tasks, effectively alleviating the catastrophic forgetting of old classes. Experiments on UCF101, HMDB51, and UESTC-MMEA-CL datasets demonstrate the effectiveness of SNRO compared to state-of-the-art methods.

Statistiche

Recent video class-incremental learning usually excessively pursues the accuracy of the newly seen classes and relies on memory sets to mitigate catastrophic forgetting of the old classes.
Limited storage only allows storing a few representative videos.

Citazioni

"SNRO significantly alleviates the catastrophic forgetting of old classes at the cost of slightly drop the performance of the current new classes, thereby improving the overall recognition accuracy."
"Examples Sparse ensures we build larger memory sets consuming the same space as TCD. And using F/2 frames to represent a video contains less spatio-temporal information than using F frames, it effectively prevents the network from over-stretching to high-semantic spaces, which allows preserving more low semantic features in future incremental tasks."
"Early Break effectively prevents the tendency of over-fit to new classes, achieving a 0.73% CNN improvement with the same memory set construction method."

Approfondimenti chiave tratti da

Slightly Shift New Classes to Remember Old Classes for Video Class-Incremental Learning

by Jian Jiao,Yu... alle arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00901.pdf

Domande più approfondite

How can the proposed SNRO framework be extended to other domains beyond video action recognition, such as image classification or natural language processing?

The SNRO framework's core principles of Examples Sparse and Early Break can be adapted to domains beyond video action recognition. In image classification, Examples Sparse could involve storing key image patches instead of full frames to reduce memory consumption while maintaining essential information. Early Break could be applied by monitoring validation loss and terminating training when overfitting is detected. For natural language processing, Examples Sparse might involve storing key phrases or word embeddings instead of full sentences, optimizing memory usage. Early Break could be implemented by monitoring perplexity scores during language model training and stopping when performance plateaus or deteriorates.

What are the potential drawbacks or limitations of the Examples Sparse and Early Break strategies, and how could they be further improved?

One potential drawback of Examples Sparse is the risk of losing critical information by sampling fewer frames or data points. To mitigate this, a dynamic sampling strategy could be implemented, where the sampling rate adjusts based on the complexity of the data or the model's learning progress. Early Break's limitation lies in its reliance on a predefined threshold, which may not always align with the model's learning dynamics. Improvements could involve adaptive threshold setting based on validation metrics or incorporating reinforcement learning techniques to dynamically adjust the stopping criteria based on model performance.

How might the SNRO framework be adapted to handle more complex or diverse incremental learning scenarios, such as when the task boundaries are not clearly defined or when the class distributions change over time?

In scenarios with ambiguous task boundaries or evolving class distributions, the SNRO framework could incorporate online clustering techniques to adapt memory sets dynamically. By clustering incoming data points and updating memory sets based on cluster centroids, the model can adapt to shifting class distributions. Additionally, a meta-learning approach could be integrated to learn task boundaries or class transitions implicitly, allowing the model to adjust its learning strategy based on the data characteristics. Continuous learning mechanisms like elastic weight consolidation could also be combined with SNRO to handle non-stationary environments effectively.