Search Results for author: Dragomir Anguelov

Found 28 papers, 9 papers with code

Offboard 3D Object Detection from Point Cloud Sequences

no code implementations CVPR 2021 Charles R. Qi, Yin Zhou, Mahyar Najibi, Pei Sun, Khoa Vo, Boyang Deng, Dragomir Anguelov

While current 3D object recognition research mostly focuses on the real-time, onboard scenario, there are many offboard use cases of perception that are largely under-explored, such as using machines to automatically generate high-quality 3D labels.

3D Object Detection 3D Object Recognition

Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout

1 code implementation NeurIPS 2020 Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, Dragomir Anguelov

The vast majority of deep models use multiple gradient signals, typically corresponding to a sum of multiple loss terms, to update a shared set of trainable weights.

Transfer Learning

Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection

no code implementations20 May 2020 Alex Bewley, Pei Sun, Thomas Mensink, Dragomir Anguelov, Cristian Sminchisescu

This paper presents a novel 3D object detection framework that processes LiDAR data directly on its native representation: range images.

3D Object Detection Autonomous Driving

STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction

no code implementations CVPR 2020 Zhishuai Zhang, Jiyang Gao, Junhua Mao, Yukai Liu, Dragomir Anguelov, Cong-Cong Li

For the Waymo Open Dataset, we achieve a bird-eyes-view (BEV) detection AP of 80. 73 and trajectory prediction average displacement error (ADE) of 33. 67cm for pedestrians, which establish the state-of-the-art for both tasks.

Autonomous Driving Object Detection +2

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation

2 code implementations CVPR 2020 Jiyang Gao, Chen Sun, Hang Zhao, Yi Shen, Dragomir Anguelov, Cong-Cong Li, Cordelia Schmid

Behavior prediction in dynamic, multi-agent systems is an important problem in the context of self-driving cars, due to the complex representations and interactions of road components, including moving agents (e. g. pedestrians and vehicles) and road context information (e. g. lanes, traffic lights).

Self-Driving Cars

End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds

no code implementations15 Oct 2019 Yin Zhou, Pei Sun, Yu Zhang, Dragomir Anguelov, Jiyang Gao, Tom Ouyang, James Guo, Jiquan Ngiam, Vijay Vasudevan

In this paper, we aim to synergize the birds-eye view and the perspective view and propose a novel end-to-end multi-view fusion (MVF) algorithm, which can effectively learn to utilize the complementary information from both.

3D Object Detection

3D Bounding Box Estimation Using Deep Learning and Geometry

8 code implementations CVPR 2017 Arsalan Mousavian, Dragomir Anguelov, John Flynn, Jana Kosecka

In contrast to current techniques that only regress the 3D orientation of an object, our method first regresses relatively stable 3D object properties using a deep convolutional neural network and then combines these estimates with geometric constraints provided by a 2D object bounding box to produce a complete 3D bounding box.

3D Object Detection Semantic Segmentation +2

SSD: Single Shot MultiBox Detector

207 code implementations8 Dec 2015 Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg

Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference.

Object Detection

Self-informed neural network structure learning

no code implementations20 Dec 2014 David Warde-Farley, Andrew Rabinovich, Dragomir Anguelov

We study the problem of large scale, multi-label visual recognition with a large number of possible classes.

Object Recognition

Scalable, High-Quality Object Detection

no code implementations3 Dec 2014 Christian Szegedy, Scott Reed, Dumitru Erhan, Dragomir Anguelov, Sergey Ioffe

Using the multi-scale convolutional MultiBox (MSC-MultiBox) approach, we substantially advance the state-of-the-art on the ILSVRC 2014 detection challenge data set, with $0. 5$ mAP for a single model and $0. 52$ mAP for an ensemble of two models.

Object Detection

Going Deeper with Convolutions

65 code implementations CVPR 2015 Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).

Classification General Classification +3

Self-taught Object Localization with Deep Networks

no code implementations13 Sep 2014 Loris Bazzani, Alessandro Bergamo, Dragomir Anguelov, Lorenzo Torresani

This paper introduces self-taught object localization, a novel approach that leverages deep convolutional networks trained for whole-image recognition to localize objects in images without additional human supervision, i. e., without using any ground-truth bounding boxes for training.

Object Localization

Capturing Long-tail Distributions of Object Subcategories

no code implementations CVPR 2014 Xiangxin Zhu, Dragomir Anguelov, Deva Ramanan

We argue that object subcategories follow a long-tail distribution: a few subcategories are common, while many are rare.

Scalable Object Detection using Deep Neural Networks

6 code implementations CVPR 2014 Dumitru Erhan, Christian Szegedy, Alexander Toshev, Dragomir Anguelov

Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012).

Object Detection Object Recognition

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