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Object Detection

303 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 without code

Learning Loss for Active Learning

CVPR 2019 Donggeun Yoo et al

In this paper, we propose a novel active learning method that is simple but task-agnostic, and works efficiently with the deep networks.

ACTIVE LEARNING IMAGE CLASSIFICATION OBJECT DETECTION POSE ESTIMATION

01 Jun 2019

Gaussian Temporal Awareness Networks for Action Localization

CVPR 2019 Fuchen Long et al

Temporally localizing actions in a video is a fundamental challenge in video understanding.

ACTION LOCALIZATION OBJECT DETECTION VIDEO UNDERSTANDING

01 Jun 2019

Learning to Generate Synthetic Data via Compositing

CVPR 2019 Shashank Tripathi et al

The synthesizer and target networks are trained in an adversarial manner wherein each network is updated with a goal to outdo the other.

HUMAN DETECTION OBJECT DETECTION

01 Jun 2019

Bag of Tricks for Image Classification with Convolutional Neural Networks

CVPR 2019 Tong He et al

Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods.

IMAGE CLASSIFICATION OBJECT DETECTION SEMANTIC SEGMENTATION TRANSFER LEARNING

01 Jun 2019

Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression

CVPR 2019 Hamid Rezatofighi et al

By incorporating this generalized IoU ( GIoU) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, IoU based, and new, GIoU based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO.

OBJECT DETECTION

01 Jun 2019

Adapting Object Detectors via Selective Cross-Domain Alignment

CVPR 2019 Xinge Zhu et al

State-of-the-art object detectors are usually trained on public datasets.

DOMAIN ADAPTATION IMAGE CLASSIFICATION OBJECT DETECTION

01 Jun 2019

Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation

CVPR 2019 Yunhang Shen et al

In this paper, we join weakly supervised object detection and segmentation tasks with a multi-task learning scheme for the first time, which uses their respective failure patterns to complement each other's learning.

MULTI-TASK LEARNING SEMANTIC SEGMENTATION WEAKLY SUPERVISED OBJECT DETECTION

01 Jun 2019

PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud

CVPR 2019 Shaoshuai Shi et al

In this paper, we propose PointRCNN for 3D object detection from raw point cloud.

3D OBJECT DETECTION OBJECT PROPOSAL GENERATION

01 Jun 2019

Libra R-CNN: Towards Balanced Learning for Object Detection

CVPR 2019 Jiangmiao Pang et al

In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level.

OBJECT DETECTION

01 Jun 2019

Feature Selective Anchor-Free Module for Single-Shot Object Detection

CVPR 2019 Chenchen Zhu et al

The general concept of the FSAF module is online feature selection applied to the training of multi-level anchor-free branches.

OBJECT DETECTION

01 Jun 2019