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Object Detection

444 papers with code · Computer Vision

Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.

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Latest papers with code

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

8 Oct 2019BangguWu/ECANet

To overcome the paradox of performance and complexity trade-off, this paper makes an attempt to investigate an extremely lightweight attention module for boosting the performance of deep CNNs.

DIMENSIONALITY REDUCTION IMAGE CLASSIFICATION INSTANCE SEGMENTATION OBJECT DETECTION SEMANTIC SEGMENTATION

72
08 Oct 2019

YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection

3 Oct 2019david8862/keras-YOLOv3-model-set

As such, there has been growing research interest in the design of efficient deep neural network architectures catered for edge and mobile usage.

OBJECT DETECTION

25
03 Oct 2019

Meta-learning algorithms for Few-Shot Computer Vision

30 Sep 2019ebennequin/FewShotVision

Few-Shot Learning is the challenge of training a model with only a small amount of data.

FEW-SHOT IMAGE CLASSIFICATION FEW-SHOT LEARNING FEW-SHOT OBJECT DETECTION

35
30 Sep 2019

PolarMask: Single Shot Instance Segmentation with Polar Representation

29 Sep 2019xieenze/PolarMask

In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used as a mask prediction module for instance segmentation, by easily embedding it into most off-the-shelf detection methods.

INSTANCE SEGMENTATION OBJECT DETECTION SEMANTIC SEGMENTATION

146
29 Sep 2019

On Generalizing Detection Models for Unconstrained Environments

28 Sep 2019prajjwal1/autonomous-object-detection

We address the problem of incremental learning in object detection on the India Driving Dataset (IDD).

OBJECT DETECTION TRANSFER LEARNING

1
28 Sep 2019

A*3D Dataset: Towards Autonomous Driving in Challenging Environments

17 Sep 2019I2RDL2/ASTAR-3D

With the increasing global popularity of self-driving cars, there is an immediate need for challenging real-world datasets for benchmarking and training various computer vision tasks such as 3D object detection.

3D OBJECT DETECTION AUTONOMOUS DRIVING BENCHMARKING SELF-DRIVING CARS

34
17 Sep 2019

Global Aggregation then Local Distribution in Fully Convolutional Networks

16 Sep 2019lxtGH/GALD-Net

GALD is end-to-end trainable and can be easily plugged into existing FCNs with various global aggregation modules for a wide range of vision tasks, and consistently improves the performance of state-of-the-art object detection and instance segmentation approaches.

INSTANCE SEGMENTATION OBJECT DETECTION SCENE UNDERSTANDING SEMANTIC SEGMENTATION

117
16 Sep 2019

Motion Guided Attention for Video Salient Object Detection

16 Sep 2019lhaof/Motion-Guided-Attention

In this paper, we develop a multi-task motion guided video salient object detection network, which learns to accomplish two sub-tasks using two sub-networks, one sub-network for salient object detection in still images and the other for motion saliency detection in optical flow images.

OPTICAL FLOW ESTIMATION SALIENCY DETECTION SALIENT OBJECT DETECTION

8
16 Sep 2019

Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution

15 Sep 2019thangvubk/Cascade-RPN

This paper considers an architecture referred to as Cascade Region Proposal Network (Cascade RPN) for improving the region-proposal quality and detection performance by \textit{systematically} addressing the limitation of the conventional RPN that \textit{heuristically defines} the anchors and \textit{aligns} the features to the anchors.

OBJECT DETECTION

67
15 Sep 2019