Deep learning models have demonstrated remarkable capabilities in learning
complex patterns and concepts from training data. However, recent findings
indicate that these models tend to rely heavily on simple and easily
discernible features present in the background of images rather than the main
concepts or objects they are intended to classify. This phenomenon poses a
challenge to image classifiers as the crucial elements of interest in images
may be overshadowed. In this paper, we propose a novel approach to address this
issue and improve the learning of main concepts by image classifiers. Our
central idea revolves around concurrently guiding the model's attention toward
the foreground during the classification task. By emphasizing the foreground,
which encapsulates the primary objects of interest, we aim to shift the focus
of the model away from the dominant influence of the background. To accomplish
this, we introduce a mechanism that encourages the model to allocate sufficient
attention to the foreground. We investigate various strategies, including
modifying the loss function or incorporating additional architectural
components, to enable the classifier to effectively capture the primary concept
within an image. Additionally, we explore the impact of different foreground
attention mechanisms on model performance and provide insights into their
effectiveness. Through extensive experimentation on benchmark datasets, we
demonstrate the efficacy of our proposed approach in improving the
classification accuracy of image classifiers. Our findings highlight the
importance of foreground attention in enhancing model understanding and
representation of the main concepts within images. The results of this study
contribute to advancing the field of image classification and provide valuable
insights for developing more robust and accurate deep-learning models.

本文针对深度学习模型在图像分类中忽略主体目标的问题，提出了一种基于前景引导的分类器学习方法，在损失函数和架构组件方面作出一些修改，加强分类器对主体目标的关注并在基准数据集上测试，结果表明该方法可以提高图像分类器的识别准确性。

缓解偏差：通过改进模型解释增强图像分类

Mitigating Bias: Enhancing Image Classification by Improving Model  Explanations

Temporal video grounding (TVG) aims to localize a target segment in a video
according to a given sentence query. Though respectable works have made decent
achievements in this task, they severely rely on abundant video-query paired
data, which is expensive and time-consuming to collect in real-world scenarios.
In this paper, we explore whether a video grounding model can be learned
without any paired annotations. To the best of our knowledge, this paper is the
first work trying to address TVG in an unsupervised setting. Considering there
is no paired supervision, we propose a novel Deep Semantic Clustering Network
(DSCNet) to leverage all semantic information from the whole query set to
compose the possible activity in each video for grounding. Specifically, we
first develop a language semantic mining module, which extracts implicit
semantic features from the whole query set. Then, these language semantic
features serve as the guidance to compose the activity in video via a
video-based semantic aggregation module. Finally, we utilize a foreground
attention branch to filter out the redundant background activities and refine
the grounding results. To validate the effectiveness of our DSCNet, we conduct
experiments on both ActivityNet Captions and Charades-STA datasets. The results
demonstrate that DSCNet achieves competitive performance, and even outperforms
most weakly-supervised approaches.

本文提出一种无监督学习的方法 Deep Semantic Clustering Network，通过语言语义挖掘、视频语义聚合和前景注意力等步骤，实现对 Temporal video grounding 的定位，取得了竞争性的性能表现。