1.4k citations · 4.1k across the 14 of their papers we have counts for
21 papers
Detecting Defects that Matter: An Application-Driven Benchmark for Anomaly Detection in Manufacturing and Retail Logistics (VAND 4.0 Challenge)
Lars Heckler-Kram, Dorian Henning, Ashwin Vaidya +6
Existing Anomaly Detection benchmarks are saturated and often unrealistic. As part of the VAND 4.0 Challenge, we introduce a hidden-test, application-driven benchmark across two de…
Kaputt: A Large-Scale Dataset for Visual Defect Detection
Sebastian Höfer, Dorian Henning, Artemij Amiranashvili +6
We present a novel large-scale dataset for defect detection in a logistics setting. Recent work on industrial anomaly detection has primarily focused on manufacturing scenarios wit…
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
Patrick Dendorfer, Aljoša Ošep, Anton Milan +5
Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not b…
MOT20: A benchmark for multi object tracking in crowded scenes
Patrick Dendorfer, Hamid Rezatofighi, Anton Milan +6
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide…
Learn to Predict Sets Using Feed-Forward Neural Networks
Hamid Rezatofighi, Tianyu Zhu, Roman Kaskman +6
This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a se…
CVPR19 Tracking and Detection Challenge: How crowded can it get?
Patrick Dendorfer, Hamid Rezatofighi, Anton Milan +6
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide…
RGB-D Object Detection and Semantic Segmentation for Autonomous Manipulation in Clutter
Max Schwarz, Anton Milan, Arul Selvam Periyasamy +1
Autonomous robotic manipulation in clutter is challenging. A large variety of objects must be perceived in complex scenes, where they are partially occluded and embedded among many…
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
Mykhaylo Andriluka, Umar Iqbal, Eldar Insafutdinov +4
Human poses and motions are important cues for analysis of videos with people and there is strong evidence that representations based on body pose are highly effective for a variet…
Design of a Multi-Modal End-Effector and Grasping System: How Integrated Design helped win the Amazon Robotics Challenge
S. Wade-McCue, N. Kelly-Boxall, M. McTaggart +19
We present the grasping system and design approach behind Cartman, the winning entrant in the 2017 Amazon Robotics Challenge. We investigate the design processes leading up to the…
Mechanical Design of a Cartesian Manipulator for Warehouse Pick and Place
M. McTaggart, D. Morrison, A. W. Tow +8
Robotic manipulation and grasping in cluttered and unstructured environments is a current challenge for robotics. Enabling robots to operate in these challenging environments have…
Semantic Segmentation from Limited Training Data
A. Milan, T. Pham, K. Vijay +22
We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transpa…
Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge
D. Morrison, A. W. Tow, M. McTaggart +19
The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place robot for autonomous warehousing, addressing development in robotic vision and manipulation. Th…
Joint Learning of Set Cardinality and State Distribution
S. Hamid Rezatofighi, Anton Milan, Qinfeng Shi +2
We present a novel approach for learning to predict sets using deep learning. In recent years, deep neural networks have shown remarkable results in computer vision, natural langua…
Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking
Laura Leal-Taixé, Anton Milan, Konrad Schindler +3
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide…
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
Guosheng Lin, Anton Milan, Chunhua Shen +1
Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification probl…
DeepSetNet: Predicting Sets with Deep Neural Networks
S. Hamid Rezatofighi, Vijay Kumar B G, Anton Milan +3
This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detectio…
PoseTrack: Joint Multi-Person Pose Estimation and Tracking
Umar Iqbal, Anton Milan, Juergen Gall
In this work, we introduce the challenging problem of joint multi-person pose estimation and tracking of an unknown number of persons in unconstrained videos. Existing methods for…
Online Multi-Target Tracking Using Recurrent Neural Networks
Anton Milan, Seyed Hamid Rezatofighi, Anthony Dick +2
We present a novel approach to online multi-target tracking based on recurrent neural networks (RNNs). Tracking multiple objects in real-world scenes involves many challenges, incl…
MOT16: A Benchmark for Multi-Object Tracking
Anton Milan, Laura Leal-Taixe, Ian Reid +2
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide…
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
Laura Leal-Taixé, Anton Milan, Ian Reid +2
In the recent past, the computer vision community has developed centralized benchmarks for the performance evaluation of a variety of tasks, including generic object and pedestrian…
Multiple Object Tracking: A Literature Review
Wenhan Luo, Junliang Xing, Anton Milan +3
Multiple Object Tracking (MOT) has gained increasing attention due to its academic and commercial potential. Although different approaches have been proposed to tackle this problem…