9.8k citations · 13.3k across the 16 of their papers we have counts for
15 papers · 1 filter
Binary Neural Networks as a general-propose compute paradigm for on-device computer vision
Guhong Nie, Lirui Xiao, Menglong Zhu +6
For binary neural networks (BNNs) to become the mainstream on-device computer vision algorithm, they must achieve a superior speed-vs-accuracy tradeoff than 8-bit quantization and…
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
Deng-Ping Fan, Zheng Lin, Jia-Xing Zhao +5
The use of RGB-D information for salient object detection has been extensively explored in recent years. However, relatively few efforts have been put towards modeling salient obje…
Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
Mason Liu, Menglong Zhu, Marie White +2
With a single eye fixation lasting a fraction of a second, the human visual system is capable of forming a rich representation of a complex environment, reaching a holistic underst…
Detect-to-Retrieve: Efficient Regional Aggregation for Image Search
Marvin Teichmann, Andre Araujo, Menglong Zhu +1
Retrieving object instances among cluttered scenes efficiently requires compact yet comprehensive regional image representations. Intuitively, object semantics can help build the i…
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu +2
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across…
Mobile Video Object Detection with Temporally-Aware Feature Maps
Mason Liu, Menglong Zhu
This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices. Our approach combines fast single-imag…
Automatic Spatially-aware Fashion Concept Discovery
Xintong Han, Zuxuan Wu, Phoenix X. Huang +5
This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites. We first fine-tune GoogleNet by jointly mod…
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard, Menglong Zhu, Bo Chen +5
We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separa…
MonoCap: Monocular Human Motion Capture using a CNN Coupled with a Geometric Prior
Xiaowei Zhou, Menglong Zhu, Georgios Pavlakos +3
Recovering 3D full-body human pose is a challenging problem with many applications. It has been successfully addressed by motion capture systems with body worn markers and multiple…
Speed/accuracy trade-offs for modern convolutional object detectors
Jonathan Huang, Vivek Rathod, Chen Sun +8
The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To…
Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video
Xiaowei Zhou, Menglong Zhu, Spyridon Leonardos +2
This paper addresses the challenge of 3D full-body human pose estimation from a monocular image sequence. Here, two cases are considered: (i) the image locations of the human joint…
Sparse Representation for 3D Shape Estimation: A Convex Relaxation Approach
Xiaowei Zhou, Menglong Zhu, Spyridon Leonardos +1
We investigate the problem of estimating the 3D shape of an object defined by a set of 3D landmarks, given their 2D correspondences in a single image. A successful approach to alle…
Multi-Image Matching via Fast Alternating Minimization
Xiaowei Zhou, Menglong Zhu, Kostas Daniilidis
In this paper we propose a global optimization-based approach to jointly matching a set of images. The estimated correspondences simultaneously maximize pairwise feature affinities…
Pose and Shape Estimation with Discriminatively Learned Parts
Menglong Zhu, Xiaowei Zhou, Kostas Daniilidis
We introduce a new approach for estimating the 3D pose and the 3D shape of an object from a single image. Given a training set of view exemplars, we learn and select appearance-bas…
Active Deformable Part Models
Menglong Zhu, Nikolay Atanasov, George J. Pappas +1
This paper presents an active approach for part-based object detection, which optimizes the order of part filter evaluations and the time at which to stop and make a prediction. St…