Top-down Neural Attention by Excitation Backprop (1608.00507v1)

Abstract: We aim to model the top-down attention of a Convolutional Neural Network (CNN) classifier for generating task-specific attention maps. Inspired by a top-down human visual attention model, we propose a new backpropagation scheme, called Excitation Backprop, to pass along top-down signals downwards in the network hierarchy via a probabilistic Winner-Take-All process. Furthermore, we introduce the concept of contrastive attention to make the top-down attention maps more discriminative. In experiments, we demonstrate the accuracy and generalizability of our method in weakly supervised localization tasks on the MS COCO, PASCAL VOC07 and ImageNet datasets. The usefulness of our method is further validated in the text-to-region association task. On the Flickr30k Entities dataset, we achieve promising performance in phrase localization by leveraging the top-down attention of a CNN model that has been trained on weakly labeled web images.

• The paper introduces Excitation Backprop, generating task-specific attention maps using a probabilistic Winner-Take-All framework.
• It refines attention by contrastively comparing target and non-target activations to improve feature discriminativeness.
• Empirical evaluations on standard benchmarks demonstrate improved weakly supervised localization and enhanced model interpretability.

Top-down Neural Attention by Excitation Backprop

The paper "Top-down Neural Attention by Excitation Backprop" by Jianming Zhang and collaborators presents an innovative method to model top-down attentional mechanisms in Convolutional Neural Networks (CNNs). This method is designed to generate task-specific attention maps through a novel backpropagation scheme named Excitation Backprop. The key contributions include a probabilistic Winner-Take-All (WTA) process, a technique to enhance discriminativeness through contrastive attention, and extensive evaluations on standard datasets demonstrating the method's efficacy.

Key Contributions

1. Probabilistic Winner-Take-All (WTA) Framework

Inspired by previous biological models of human visual attention, the paper formulates a probabilistic version of the WTA process. In this context, the method passes top-down signals in a top-down manner through the network layers to compute the Marginal Winning Probability (MWP) for each neuron. This approach contrasts with deterministic WTA, enabling the generation of more informative and nuanced attention maps.

2. Contrastive Top-Down Attention

To further refine the attention maps, the authors propose contrastive top-down attention, which improves discriminativeness by comparing target and non-target feature activations. This comparison is achieved by generating dual units in the network that represent alternative classes. Subtracting the non-target from the target MWP maps amplifies the unique features of the target class. This concept is validated through experiments showing significant performance gains, particularly in complex scenes with multiple objects.

3. Evaluation and Empirical Validation

The performance of Excitation Backprop is thoroughly validated using the MS COCO, PASCAL VOC07, ImageNet, and Flickr30k datasets. The evaluations encompass several aspects: weakly supervised object localization, dominant object localization, and text-to-region association. For weakly supervised localization, the method delivers superior results compared to current state-of-the-art approaches, particularly in complex multi-object scenes. The method's versatility is further demonstrated in text-to-region associations in the Flickr30k dataset, achieving competitive results without requiring explicit localization supervision.

Practical Implications

The proposed method has practical implications across various applications:

1. Weakly Supervised Object Localization: The MWP and contrastive MWP maps generated via Excitation Backprop yield fine-grained localization in complex images, showing improvements in cases with small or overlapping objects.
2. Interpretability of CNN Models: By visualizing the top-down attention mechanisms, the method provides insights into the internal workings of CNNs, beneficial for model transparency and debugging.
3. Scalability: Using the Stock6M dataset, the scalability factor is explored, indicating the method's robustness in handling large-scale weakly labeled datasets, which is useful for industrial applications in multimedia information retrieval.

Theoretical Implications and Future Work

The theoretical underpinnings of probabilistic WTA and Excitation Backprop contribute to a deeper understanding of neural attention mechanisms in artificial systems. Future work can extend these principles to other neural architectures beyond CNNs, such as graph neural networks or transformer-based models.

Furthermore, future research could delve into optimizing the computational aspects of Excitation Backprop, potentially integrating hardware acceleration techniques. Another promising avenue could involve exploring different strategies for contrastive signals, including leveraging more sophisticated LLMs for text-to-region associations.

In conclusion, the method of top-down neural attention by Excitation Backprop introduces a strong paradigm for generating interpretable and discriminative attention maps. Through extensive empirical validation, this approach proves to be generalizable and highly effective across various tasks, setting a new standard for attention mechanisms in neural networks.