9.8k citations · 13.3k across the 16 of their papers we have counts for
16 papers
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…
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
Benoit Jacob, Skirmantas Kligys, Bo Chen +5
The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. W…
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…