Extracting class activation maps (CAM) is arguably the most standard step of
generating pseudo masks for weakly-supervised semantic segmentation (WSSS).
Yet, we find that the crux of the unsatisfactory pseudo masks is the binary
cross-entropy loss (BCE) widely used in CAM. Specifically, due to the
sum-over-class pooling nature of BCE, each pixel in CAM may be responsive to
multiple classes co-occurring in the same receptive field. As a result, given a
class, its hot CAM pixels may wrongly invade the area belonging to other
classes, or the non-hot ones may be actually a part of the class. To this end,
we introduce an embarrassingly simple yet surprisingly effective method:
Reactivating the converged CAM with BCE by using softmax cross-entropy loss
(SCE), dubbed \textbf{ReCAM}. Given an image, we use CAM to extract the feature
pixels of each single class, and use them with the class label to learn another
fully-connected layer (after the backbone) with SCE. Once converged, we extract
ReCAM in the same way as in CAM. Thanks to the contrastive nature of SCE, the
pixel response is disentangled into different classes and hence less mask
ambiguity is expected. The evaluation on both PASCAL VOC and MS~COCO shows that
ReCAM not only generates high-quality masks, but also supports plug-and-play in
any CAM variant with little overhead.

本文介绍了一种称为 ReCAM 的弱监督语义分割方法，通过将 softmax 交叉熵损失函数用于已收敛的 class activation map 从而生成高质量的掩膜。

弱监督语义分割的类别激活图

Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation

The ubiquity of implicit feedback makes them the default choice to build
online recommender systems. While the large volume of implicit feedback
alleviates the data sparsity issue, the downside is that they are not as clean
in reflecting the actual satisfaction of users. For example, in E-commerce, a
large portion of clicks do not translate to purchases, and many purchases end
up with negative reviews. As such, it is of critical importance to account for
the inevitable noises in implicit feedback for recommender training. However,
little work on recommendation has taken the noisy nature of implicit feedback
into consideration.
In this work, we explore the central theme of denoising implicit feedback for
recommender training. We find serious negative impacts of noisy implicit
feedback,i.e., fitting the noisy data prevents the recommender from learning
the actual user preference. Our target is to identify and prune noisy
interactions, so as to improve the quality of recommender training. By
observing the process of normal recommender training, we find that noisy
feedback typically has large loss values in the early stages. Inspired by this
observation, we propose a new training strategy namedAdaptive Denoising
Training(ADT), which adaptively prunes noisy interactions during training.
Specifically, we devise two paradigms for adaptive loss formulation: Truncated
Loss that discards the large-loss samples with a dynamic threshold in each
iteration; and reweighted Loss that adaptively lowers the weight of large-loss
samples. We instantiate the two paradigms on the widely used binary
cross-entropy loss and test the proposed ADT strategies on three representative
recommenders. Extensive experiments on three benchmarks demonstrate that ADT
significantly improves the quality of recommendation over normal training.

研究探讨了去噪隐式反馈机制对于推荐系统训练的影响和重要性，提出了一种自适应去噪训练策略（ADT），通过在训练过程中适当地剪枝噪声交互以提高推荐系统的训练质量。实验结果表明，ADT 显著改善了推荐质量。

Denoising Implicit Feedback for Recommendation

Text-image cross-modal retrieval is a challenging task in the field of
language and vision. Most previous approaches independently embed images and
sentences into a joint embedding space and compare their similarities. However,
previous approaches rarely explore the interactions between images and
sentences before calculating similarities in the joint space. Intuitively, when
matching between images and sentences, human beings would alternatively attend
to regions in images and words in sentences, and select the most salient
information considering the interaction between both modalities. In this paper,
we propose Cross-modal Adaptive Message Passing (CAMP), which adaptively
controls the information flow for message passing across modalities. Our
approach not only takes comprehensive and fine-grained cross-modal interactions
into account, but also properly handles negative pairs and irrelevant
information with an adaptive gating scheme. Moreover, instead of conventional
joint embedding approaches for text-image matching, we infer the matching score
based on the fused features, and propose a hardest negative binary
cross-entropy loss for training. Results on COCO and Flickr30k significantly
surpass state-of-the-art methods, demonstrating the effectiveness of our
approach.

本文提出了一种跨模态自适应信息传递模型（Cross-modal Adaptive Message Passing, CAMP），通过自适应地控制跨模态信息的流动，全面且精细地考虑跨模态交互，使用硬负样本二元交叉熵损失函数进行训练，在 COCO 和 Flickr30k 上显著超过最先进方法，显示出了我们方法的有效性。