Abstract
Class activation map (CAM) has been widely used to highlight image regions that contribute to class predictions. Despite its simplicity and computational efficiency, CAM often struggles to identify discriminative regions that distinguish visually similar fine-grained classes. Prior efforts address this limitation by introducing more sophisticated explanation processes, but at the cost of extra complexity. In this paper, we propose Finer-CAM, a method that retains CAM's efficiency while achieving precise localization of discriminative regions. Our key insight is that the deficiency of CAM lies not in "how" it explains, but in "what" it explains}. Specifically, previous methods attempt to identify all cues contributing to the target class's logit value, which inadvertently also activates regions predictive of visually similar classes. By explicitly comparing the target class with similar classes and spotting their differences, Finer-CAM suppresses features shared with other classes and emphasizes the unique, discriminative details of the target class. Finer-CAM is easy to implement, compatible with various CAM methods, and can be extended to multi-modal models for accurate localization of specific concepts. Additionally, Finer-CAM allows adjustable comparison strength, enabling users to selectively highlight coarse object contours or fine discriminative details. Quantitatively, we show that masking out the top 5% of activated pixels by Finer-CAM results in a larger relative confidence drop compared to baselines. The source code and demo are available at this https URL.
Abstract (translated)
类激活图（Class Activation Map，CAM）广泛用于突出对类别预测有贡献的图像区域。尽管其简单性和计算效率较高，但CAM在识别区分视觉相似的细粒度类别中的判别性区域时常常力不从心。先前的努力通过引入更复杂的解释过程来解决这一限制，但这会增加额外的复杂性。本文中，我们提出了一种名为Finer-CAM的方法，该方法保留了CAM的效率，同时实现了对判别性区域的精确定位。我们的关键见解在于，CAM的不足并不在于它是如何进行解释的，而是在于它所解释的内容。具体而言，先前的方法试图识别所有有助于目标类别logit值的线索，这无意中激活了一些预测视觉相似类别的区域。 通过明确地将目标类别与类似类别进行比较，并找出它们之间的差异，Finer-CAM抑制了与其他类别共享的特征，并强调了目标类别独有的、具有判别性的细节。Finer-CAM易于实现，兼容各种CAM方法，并可以扩展到多模态模型中以准确定位特定概念。此外，Finer-CAM允许调整比较强度，使用户能够选择性地突出粗略的对象轮廓或精细的鉴别性细节。 从定量上看，我们展示了通过Finer-CAM屏蔽激活像素的前5%，相比基线方法会导致相对置信度更大的下降。源代码和演示可在以下链接找到：[请在此处插入URL]。
URL
https://arxiv.org/abs/2501.11309
