Object detection is the task of detecting instances of objects of a certain class within an image. The state-of-the-art methods can be categorized into two main types: one-stage methods and two stage-methods. One-stage methods prioritize inference speed, and example models include YOLO, SSD and RetinaNet. Two-stage methods prioritize detection accuracy, and example models include Faster R-CNN, Mask R-CNN and Cascade R-CNN.
The most popular benchmark is the MSCOCO dataset. Models are typically evaluated according to a Mean Average Precision metric.
( Image credit: Detectron )
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For object detection, how to address the contradictory requirement between feature map resolution and receptive field on high-resolution inputs still remains an open question.
In addition, we propose a novel sample-weighted loss function which can model sample weights for SWIPENet, which uses a novel sample re-weighting algorithm, namely Invert Multi-Class Adaboost (IMA), to reduce the influence of noise on the proposed SWIPENet.
Contrastive learning between multiple views of the data has recently achieved state of the art performance in the field of self-supervised representation learning.
#3 best model for Self-Supervised Image Classification on ImageNet
However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of the same shape, whereas objects are usually of diverse shapes and align along various directions; (2) detection models are typically trained with generic knowledge and may not generalize well to handle specific objects at test time; (3) the limited dataset hinders the development on this task.
In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD).
Object detection in camera images, using deep learning has been proven successfully in recent years.
Deep learning-based detectors usually produce a redundant set of object bounding boxes including many duplicate detections of the same object.