# Look at here : Utilizing supervision to attend subtle key regions Source: [Look at here : Utilizing supervision to attend subtle key regions]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:46:07.580Z Authors: Changhwan Lee, Yeesuk Kim, Bong Gun Lee, Doosup Kim, Jongseong Jang Published: 2021-11-25T19:40:42.000Z ## Abstract Despite the success of deep learning in computer vision, algorithms to recognize subtle and small objects (or regions) is still challenging. For example, recognizing a baseball or a frisbee on a ground scene or a bone fracture in an X-ray image can easily result in overfitting, unless a huge amount of training data is available. To mitigate this problem, we need a way to force a model should identify subtle regions in limited training data. In this paper, we propose a simple but efficient supervised augmentation method called Cut\\&Remain. It achieved better performance on various medical image domain (internally sourced- and public dataset) and a natural image domain (MS-COCO$\_s$) than other supervised augmentation and the explicit guidance methods. In addition, using the class activation map, we identified that the Cut\\&Remain methods drive a model to focus on relevant subtle and small regions efficiently. We also show that the performance monotonically increased along the Cut\\&Remain ratio, indicating that a model can be improved even though only limited amount of Cut\\&Remain is applied for, so that it allows low supervising (annotation) cost for improvement. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()