Brain cancer is acknowledged as one of the most aggressive tumors, with a significant impact on patient survival rates. Unfortunately, approximately 70% patients diagnosed this malignant do not survive. This paper introduces method designed to detect and localize brain by proposing an automated approach for detection localization cancer. The utilizes magnetic resonance imaging analysis. By leve...

Many recent studies have shown that deep neural models are vulnerable to adversarial samples: images with imperceptible perturbations, for example, can fool image classifiers. In this paper, we present the first type-specific approach generating examples object detection, which entails detecting bounding boxes around multiple objects in and classifying them at same time, making it a harder task...

Video object detection has been an important yet challenging topic in computer vision. Traditional methods mainly focus on designing the image-level or box-level feature propagation strategies to exploit temporal information. This paper argues that with a more effective and efficient framework, video detectors can gain improvement terms of both accuracy speed. For this purpose, studies object-l...

Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using model trained only on image-level annotations. Current state-of-the-art models benefit from self-supervised instance-level supervision, but since weak supervision does not include count or location information, the most common “argmax” labeling method often ignores many instances of objects. To alleviate ...

Object detectors that solely rely on image contrast are struggling to detect camouflaged objects in images because of the high similarity between and their surroundings. To address this issue, paper, we investigate role part-object relationship for object detection. Specifically, propose a Part-Object Contrast Integrated Network (POCINet) covering both search identification stages, where each s...