Tell Me Where to Look: Guided Attention Inference Network (1802.10171v1)

Abstract: Weakly supervised learning with only coarse labels can obtain visual explanations of deep neural network such as attention maps by back-propagating gradients. These attention maps are then available as priors for tasks such as object localization and semantic segmentation. In one common framework we address three shortcomings of previous approaches in modeling such attention maps: We (1) first time make attention maps an explicit and natural component of the end-to-end training, (2) provide self-guidance directly on these maps by exploring supervision form the network itself to improve them, and (3) seamlessly bridge the gap between using weak and extra supervision if available. Despite its simplicity, experiments on the semantic segmentation task demonstrate the effectiveness of our methods. We clearly surpass the state-of-the-art on Pascal VOC 2012 val. and test set. Besides, the proposed framework provides a way not only explaining the focus of the learner but also feeding back with direct guidance towards specific tasks. Under mild assumptions our method can also be understood as a plug-in to existing weakly supervised learners to improve their generalization performance.

• The paper presents an end-to-end trainable framework that integrates attention maps directly into the learning objective.
• The study leverages a self-guidance mechanism to expand focus beyond the most discriminative regions for richer object representations.
• The method achieves significant segmentation improvements, with GAIN and its extension reaching up to 62.1% mIoU on the PASCAL VOC dataset.

Guided Attention Inference Network for Weakly Supervised Learning

The paper "Tell Me Where to Look: Guided Attention Inference Network" introduces a novel approach for enhancing attention maps within weakly supervised learning frameworks. The authors propose the Guided Attention Inference Network (GAIN), which aims to improve the quality of attention maps used for visual explanations in deep neural networks.

Weakly supervised learning, using only coarse labels, has been a focal point in overcoming the scarcity of labeled data for tasks such as object localization and semantic segmentation. Conventional methods utilize back-propagating gradients to generate attention maps. However, these methods often produce attention maps that cover only the most discriminative parts of an object, potentially limiting their utility as priors for more detailed tasks.

Key Contributions

1. End-to-End Trainable Attention Maps: GAIN integrates attention maps as a seamless part of the network's end-to-end training. Rather than treating attention maps as a by-product, they are embedded into the learning objective.
2. Self-Guidance Mechanism: The approach introduces a self-guidance technique where the network leverages its own predictions to refine attention maps effectively. This mechanism encourages the network to broaden its attention beyond mere discriminative regions, leading to more comprehensive representations of target objects.
3. Bridging Weak and Strong Supervision: GAIN also presents an extension, GAIN$_{ext}$, where additional supervision, such as pixel-level labels, can be incorporated when available. This flexibility enhances segmentation performance without relying solely on image-level labels.

Experimental Results

The empirical evaluation on the PASCAL VOC 2012 dataset reveals that GAIN significantly outperforms existing methods. Specifically, GAIN achieves a mean Intersection-over-Union (mIoU) of 55.3% on the validation set and 56.8% on the test set. When enhanced with additional pixel-level supervision, GAIN$_{ext}$ reaches an mIoU of 60.5% and 62.1% for the validation and test sets, respectively. These results demonstrate the framework’s effectiveness compared to state-of-the-art techniques under weak supervision settings.

Implications and Future Directions

The implications of this research are both theoretical and practical. Theoretically, GAIN challenges the conventional use of attention maps, proposing a more integrated approach to refine these maps during the training process. Practically, the method enhances tasks such as semantic segmentation by providing more accurate object localization priors.

Future work could explore the application of GAIN to other high-level vision tasks beyond classification, investigating how this attention mechanism could improve regression and multi-modal learning tasks. A further area of interest might be the integration of these methods with neural architectures using Transformer-like models to evaluate the impact of guided attention on sequential and temporal data tasks.

Overall, the Guided Attention Inference Network offers a compelling advancement in weakly supervised learning, providing a robust methodology to derive more descriptive and task-relevant attention maps through an innovative training strategy.