Browse > Computer Vision > Object Detection

Object Detection

669 papers with code ยท Computer Vision

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 )

Leaderboards

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Latest papers without code

Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding

27 May 2020

We present Center3D, a one-stage anchor-free approach, to efficiently estimate 3D location and depth using only monocular RGB images.

3D OBJECT DETECTION DEPTH ESTIMATION

Accelerating Neural Network Inference by Overflow Aware Quantization

27 May 2020

The inherent heavy computation of deep neural networks prevents their widespread applications.

IMAGE CLASSIFICATION OBJECT DETECTION QUANTIZATION SEMANTIC SEGMENTATION

Concurrent Segmentation and Object Detection CNNs for Aircraft Detection and Identification in Satellite Images

27 May 2020

Detecting and identifying objects in satellite images is a very challenging task: objects of interest are often very small and features can be difficult to recognize even using very high resolution imagery.

OBJECT DETECTION

Perceptual Extreme Super Resolution Network with Receptive Field Block

26 May 2020

Third, we alternately use different upsampling methods in the upsampling stage to reduce the high computation complexity and still remain satisfactory performance.

OBJECT DETECTION SUPER-RESOLUTION

Robust Object Detection under Occlusion with \\Context-Aware CompositionalNets

24 May 2020

In this work, we propose to overcome two limitations of CompositionalNets which will enable them to detect partially occluded objects: 1) CompositionalNets, as well as other DCNN architectures, do not explicitly separate the representation of the context from the object itself.

ROBUST OBJECT DETECTION

One-Shot Unsupervised Cross-Domain Detection

23 May 2020

Despite impressive progress in object detection over the last years, it is still an open challenge to reliably detect objects across visual domains.

OBJECT DETECTION

Self-supervised Robust Object Detectors from Partially Labelled datasets

23 May 2020

With the goal of training \emph{one integrated robust object detector with high generalization performance}, we propose a training framework to overcome missing-label challenge of the merged datasets.

OBJECT DETECTION

Delving into the Imbalance of Positive Proposals in Two-stage Object Detection

23 May 2020

The first imbalance lies in the large number of low-quality RPN proposals, which makes the R-CNN module (i. e., post-classification layers) become highly biased towards the negative proposals in the early training stage.

OBJECT DETECTION

KL-Divergence-Based Region Proposal Network for Object Detection

22 May 2020

The learning of the region proposal in object detection using the deep neural networks (DNN) is divided into two tasks: binary classification and bounding box regression task.

OBJECT DETECTION REGION PROPOSAL