论文《Attention-Guided Contrastive Role Representations for Multi-agent Reinforcement Learning》来自ICLR 2024。这篇论文提出 Attention-guided COntrastive Role representation learning for MARL(ACORM),通过注意力机制学习一种更加紧凑的智能体角色表示,使得具有相似角色的智能体能够通过知识转移获得更高的学习效率,并且...
ACORM(Attention-guided COntrastive Role Representations learning for MARL)是一种新颖的多智能体强化学习框架,通过学习和利用角色表征来促进代理间的行为异质性、知识转移和熟练协调。 关键组件 1. 角色表征对比学习 将角色表征学习形式化为互信息最大化; 使用带有 InfoNCE 损失的对比学习来优化角色表征; 定期聚类智能...
此外,the finer relationship is established by the attention between the processed feature map Spam and the generated FG segmentation mask Proi, which can be formulated as Spam ⊗ Proi。详细信息将在下一节中进行调查。 3.3. Attention-guided Modules 考虑到 FG things与 BG stuff之间的互补关系, 我...
In this paper, we introduce an Attention-guided multi-Modal and multi-Scale Fusion (AMSF) module to simultaneously sample complementary local features scattered in multi-modal and multi-scale layers, and adaptively aggregate them with fine-grained attention to fully exploit different modalities for ...
几篇论文实现代码:《Attention-Guided Hierarchical Structure Aggregation for Image Matting》(CVPR 2020) GitHub:http://t.cn/A6zS3oi3 《Blurry Video Frame Interpolation》(CVPR 2020) GitHub:http://t.c...
Attention-guided generator 上面已经介绍了整个pipeline,这里就再来看他们的生成器。 最终的s'由两部分组成,转换到T域的前景和原图s的背景。 Attention network在公式1中起到了非常重要的作用,如果Attention map Sa为全1,相当于Attention 了整个图像,那么整个工作就和CycleGAN一样了,如果Sa为全0那么所有生成的图像都会...
We also design a novel loss term to train the attention weights, which drastically boosts the video matting performance. Besides, we show how to effectively solve the trimap generation problem by fine-tuning a state-of-the-art video object segmentation network with a sparse set of user-...
This model consists of a model of attention-guided organized perception of object segments on Markov random fields and a model of learning object categories based on a probabilistic latent component analysis. In attention guided organized perception, concurrent figure-ground segmentation is performed on ...
Unsupervised Attention-guided Image-to-Image Translation论文笔记
Unsupervised Attention-guided Image-to-Image Translation 目前的无监督Image-to-Image Translation很难在不改变背景或场景中多个对象的情况下将注意力集中在单个对象上。作者提出了一种方法来解决这个问题。 Introduction 目前的包括CycleGAN在内的许多无监督Image-to-Image Translation方法都无法只关注特定的场景对象,如下...