Overview of DEXTR - Object Segmentation Using Extreme Points
DEXTR, or Deep Extreme Cut, is a computer vision technique that allows the precise segmentation of an object in an image. This is accomplished by using the extreme points of an object, or the left-most, right-most, top, and bottom pixels, as guiding signals for the input to the network. The extreme points are annotated and used to create a heatmap with activations in those regions.
The heatmap is created by centering a 2D Gaussian around each of the extreme points to create a single heatmap. This heatmap is then concatenated with the RGB channels of the input image to form a 4-channel input for the CNN or Convolutional Neural Network. The input image is then cropped by the bounding box that is formed from the extreme point annotations. The bounding box is relaxed by several pixels to include context on the resulting crop.
ResNet-101 is used as the backbone of the architecture. The fully connected layers and the max pooling layers in the last two stages are removed to preserve acceptable output resolution for dense prediction. Atrous convolutions are introduced in the last two stages to maintain the same receptive field. After the last ResNet-101 stage, a pyramid scene parsing module is introduced to aggregate global context to the final feature map.
The output of the CNN is a probability map that represents whether a pixel belongs to the object that we want to segment or not. The CNN is trained to minimize the standard cross entropy loss which takes into account that different classes occur with different frequency in a dataset.
How DEXTR Works
DEXTR works by first identifying the extreme points of an object in an image. These extreme points are annotated and used to create a heatmap with activations in those regions. The heatmap is then concatenated with the RGB channels of the input image to form a 4-channel input for the CNN. The input image is cropped by the bounding box that is formed from the extreme point annotations. To include context on the resulting crop, the bounding box is relaxed by several pixels.
The ResNet-101 is used as the backbone architecture. The fully connected layers and the max pooling layers in the last two stages are removed to preserve acceptable output resolution for dense prediction. Atrous convolutions are introduced in the last two stages to maintain the same receptive field. After the last ResNet-101 stage, a pyramid scene parsing module is introduced to aggregate global context to the final feature map.
The output of the CNN is a probability map that represents whether a pixel belongs to the object that we want to segment or not. The CNN is trained to minimize the standard cross entropy loss which takes into account that different classes occur with different frequency in a dataset.
Applications of DEXTR
DEXTR has numerous applications. One example is semantic segmentation, which involves labelling each pixel of an image with its semantic category. Another example is instance segmentation, which involves identifying each individual object instance in an image and labelling each pixel with its instance label. DEXTR can also be used for image segmentation in medical imaging to identify and segment out relevant parts of an image for a more accurate diagnosis.
DEXTR can also be used for object detection, which involves localizing objects with bounding boxes in an image. By using the extreme points of an object, DEXTR can provide more accurate and precise object segmentation, which can in turn be used for object detection.
Advantages of DEXTR
DEXTR has several advantages over traditional object segmentation techniques. By using the extreme points of an object, DEXTR can provide more precise object segmentation, which can lead to more accurate and reliable results. Additionally, DEXTR allows for faster and more efficient object segmentation, as it requires less labeling and annotation than other techniques. DEXTR is also highly adaptable and can be used in a variety of settings and applications, making it a versatile tool for computer vision research.
DEXTR, or Deep Extreme Cut, is a computer vision technique that allows for precise object segmentation using the extreme points of an object in an image. By creating a heatmap with activations in the regions of the extreme points, the input to the CNN is used to focus on the object of interest. ResNet-101 is used as the backbone of the architecture, and a pyramid scene parsing module is introduced to aggregate global context to the final feature map. DEXTR has numerous applications in semantic and instance segmentation, object detection, and medical imaging. DEXTR is highly adaptable and offers advantages over traditional object segmentation techniques in terms of accuracy, efficiency, and versatility.
