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object-detection

Object Proposal Generation

3260 papers • 126 benchmarks • 313 datasets

Object proposal generation is a preprocessing technique that has been widely used in current object detection pipelines to guide the search of objects and avoid exhaustive sliding window search across images. ( Image credit: Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in object-detection

Trend

Dataset

Best Model

Actions

PASCAL VOC 2012, 60 proposals per image

PASCAL VOC 2012, 60 proposals per image

MS COCO

MS COCO

Libraries

i

Use these libraries to find object-detection models and implementations

Datasets

No datasets available.

Subtasks

No subtasks available.

Most implemented papers

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

Hongsheng Li, Xiaogang Wang, Shaoshuai Shi•Mon Dec 10 2018

Extensive experiments on the 3D detection benchmark of KITTI dataset show that the proposed architecture outperforms state-of-the-art methods with remarkable margins by using only point cloud as input.

2764

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

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CASENet: Deep Category-Aware Semantic Edge Detection

Srikumar Ramalingam, Ming-Yu Liu, Chen Feng, Zhiding Yu•Fri May 26 2017

This work proposes a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features.

289 0

Multi-view 3D Object Detection Network for Autonomous Driving

Ji Wan, Xiaozhi Chen, Huimin Ma, Bo Li, Tian Xia•Tue Nov 22 2016

This paper proposes Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes and designs a deep fusion scheme to combine region-wise features from multiple views and enable interactions between intermediate layers of different paths.

3066 0

Recurrent Pixel Embedding for Instance Grouping

Charless C. Fowlkes, Shu Kong•Thu Dec 21 2017

A differentiable, end-to-end trainable framework for solving pixel-level grouping problems such as instance segmentation consisting of two novel components, implementing a variant of mean-shift clustering as a recurrent neural network parameterized by kernel bandwidth.

184 0

Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation

Pablo Arbeláez, J. Pont-Tuset, J. Malik, F. Marqués, Jonathan T. Barron•Sun Mar 01 2015

This paper proposes a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG), and develops a fast normalized cuts algorithm and proposes a high-performance hierarchical segmenter that makes effective use of multiscale information.

593 0

Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval

Zhi-Hua Zhou, Jianxin Wu, Xiu-Shen Wei, Jian-Hao Luo•Sun Apr 17 2016

The selective convolutional descriptor aggregation (SCDA) method is proposed, which is unsupervised, using no image label or bounding box annotation, and achieves comparable retrieval results with the state-of-the-art general image retrieval approaches.

434 0

Semantic Instance Segmentation via Deep Metric Learning

K. Murphy, Z. Wojna, Hyun Oh Song, S. Guadarrama, Peng Wang, A. Fathi, V. Rathod•Wed Mar 29 2017

A new method for semantic instance segmentation is proposed, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together, based on a deep, fully convolutional embedding model.

203 0

Object Proposal Generation Applying the Distance Dependent Chinese Restaurant Process

S. Frintrop, M. Lauri•Tue Apr 11 2017

This paper proposes object proposal generation based on non-parametric Bayesian inference that allows quantification of the likelihood of the proposals, and applies Markov chain Monte Carlo to draw samples of image segmentations via the distance dependent Chinese restaurant process.

1 0

Seq-NMS for Video Object Detection

T. Paine, Shuicheng Yan, Thomas S. Huang, Prajit Ramachandran, Humphrey Shi, M. Babaeizadeh, Wei Han, Pooya Khorrami, Jianan Li•Wed Feb 17 2016

It is shown that the proposed modification of the post-processing phase that uses high-scoring object detections from nearby frames to boost scores of weaker detections within the same clip obtains superior results to state-of-the-art single image object detection techniques.

312 0

Convolutional Channel Features

Binh Yang, Zhen Lei, S. Li, Junjie Yan•Mon Apr 27 2015

Convolutional Channel Features (CCF) serves as a good way of tailoring pre-trained CNN models to diverse tasks without fine-tuning the whole network to each task by achieving state-of-the-art performances in pedestrian detection, face detection, edge detection and object proposal generation.

309 0

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Object Proposal Generation | State-of-the-Art