In this article, we explore the concept of slot attention in the context of visual representation learning. We examine how iterative slot attention enables the disentanglement module to progressively learn distinct information, such as actions and scenes, from raw image data. By attending to encoded features as keys and values, the slots learn to represent different aspects of the input data, leading to improved representation learning.
Effect of Softmax Normalization Axis
We analyze the effect of softmax normalization along the slot-axis versus the conventional key-axis normalization. Applying softmax normalization along the slot-axis results in a gain of 5.5 points in the harmonic mean, indicating that slot attention significantly contributes to disentangled representation learning.
Effect of Mask Extraction Method
We also investigate the impact of using a learned segmentation method, such as SegFormer, to extract masks for slot attention. This approach yields a further improvement of 1.9 points in the harmonic mean, demonstrating the effectiveness of learning masks for improved representation learning.
Conclusion
In summary, slot attention enables the disentanglement module to learn distinct information from raw image data, leading to improved representation learning. By iteratively attending to encoded features as keys and values, the slots progressively learn to represent different aspects of the input data, resulting in better performance in human activity understanding tasks. The findings of this study demonstrate the importance of slot attention in demystifying visual representations and highlight its potential applications in various computer vision domains.
