Deep Reinforced Attention Learning for Quality-Aware Visual Recognition

• URL: http://arxiv.org/abs/2007.06156v2
• Date: Tue, 30 Aug 2022 17:45:52 GMT
• Title: Deep Reinforced Attention Learning for Quality-Aware Visual Recognition
• Authors: Duo Li, Qifeng Chen
• Abstract summary: We build upon the weakly-supervised generation mechanism of intermediate attention maps in any convolutional neural networks. We introduce a meta critic network to evaluate the quality of attention maps in the main network.
• Score: 73.15276998621582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we build upon the weakly-supervised generation mechanism of intermediate attention maps in any convolutional neural networks and disclose the effectiveness of attention modules more straightforwardly to fully exploit their potential. Given an existing neural network equipped with arbitrary attention modules, we introduce a meta critic network to evaluate the quality of attention maps in the main network. Due to the discreteness of our designed reward, the proposed learning method is arranged in a reinforcement learning setting, where the attention actors and recurrent critics are alternately optimized to provide instant critique and revision for the temporary attention representation, hence coined as Deep REinforced Attention Learning (DREAL). It could be applied universally to network architectures with different types of attention modules and promotes their expressive ability by maximizing the relative gain of the final recognition performance arising from each individual attention module, as demonstrated by extensive experiments on both category and instance recognition benchmarks.

Related papers

• Switchable Self-attention Module [3.8992324495848356]
  We propose a self-attention module SEM. Based on the input information of the attention module and alternative attention operators, SEM can automatically decide to select and integrate attention operators to compute attention maps. The effectiveness of SEM is demonstrated by extensive experiments on widely used benchmark datasets and popular self-attention networks.
  arXiv  Detail & Related papers  (2022-09-13T01:19:38Z)
• Alignment Attention by Matching Key and Query Distributions [48.93793773929006]
  This paper introduces alignment attention that explicitly encourages self-attention to match the distributions of the key and query within each head. It is simple to convert any models with self-attention, including pre-trained ones, to the proposed alignment attention. On a variety of language understanding tasks, we show the effectiveness of our method in accuracy, uncertainty estimation, generalization across domains, and robustness to adversarial attacks.
  arXiv  Detail & Related papers  (2021-10-25T00:54:57Z)
• Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification [101.49122450005869]
  We present a counterfactual attention learning method to learn more effective attention based on causal inference. Specifically, we analyze the effect of the learned visual attention on network prediction. We evaluate our method on a wide range of fine-grained recognition tasks.
  arXiv  Detail & Related papers  (2021-08-19T14:53:40Z)
• Bayesian Attention Belief Networks [59.183311769616466]
  Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks. This paper introduces Bayesian attention belief networks, which construct a decoder network by modeling unnormalized attention weights. We show that our method outperforms deterministic attention and state-of-the-art attention in accuracy, uncertainty estimation, generalization across domains, and adversarial attacks.
  arXiv  Detail & Related papers  (2021-06-09T17:46:22Z)
• Reinforced Attention for Few-Shot Learning and Beyond [37.840203408724996]
  Few-shot learning aims to correctly recognize query samples from unseen classes given a limited number of support samples. We equip the backbone network with an attention agent, which is trained by reinforcement learning. Experiments show that our embedding network has the capability to progressively generate a more discriminative representation.
  arXiv  Detail & Related papers  (2021-04-09T05:01:15Z)
• Variational Structured Attention Networks for Deep Visual Representation Learning [49.80498066480928]
  We propose a unified deep framework to jointly learn both spatial attention maps and channel attention in a principled manner. Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework. We implement the inference rules within the neural network, thus allowing for end-to-end learning of the probabilistic and the CNN front-end parameters.
  arXiv  Detail & Related papers  (2021-03-05T07:37:24Z)
• Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization [95.73898853032865]
  We present a new domain generalization framework that learns how to generalize across domains simultaneously. We demonstrate the effectiveness of our approach on two standard object recognition benchmarks.
  arXiv  Detail & Related papers  (2020-07-18T03:12:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.