Search Results for author:
Found 18 papers, 12 papers with code
Binary Neural Networks as a general-propose compute paradigm for on-device computer vision
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 establish a similar degree of general applicability in vision tasks.
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection
In this paper we address the question: can task-specific detectors be trained and represented as a shared set of weights, plus a very small set of additional weights for each task?
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
The use of RGB-D information for salient object detection has been extensively explored in recent years.
Ranked #4 on
RGB-D Salient Object Detection
on RGBD135
Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
Models and examples built with TensorFlow
Ranked #30 on
Video Object Detection
on ImageNet VID
(using extra training data)
Detect-to-Retrieve: Efficient Regional Aggregation for Image Search
Then, we demonstrate how a trained landmark detector, using our new dataset, can be leveraged to index image regions and improve retrieval accuracy while being much more efficient than existing regional methods.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
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 a spectrum of different model sizes.
Ranked #7 on
Retinal OCT Disease Classification
on OCT2017
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
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.
Mobile Video Object Detection with Temporally-Aware Feature Maps
This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices.
Automatic Spatially-aware Fashion Concept Discovery
This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
We present a class of efficient models called MobileNets for mobile and embedded vision applications.
Ranked #238 on
Object Detection
on COCO test-dev
MonoCap: Monocular Human Motion Capture using a CNN Coupled with a Geometric Prior
Recovering 3D full-body human pose is a challenging problem with many applications.
Speed/accuracy trade-offs for modern convolutional object detectors
On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.
Ranked #220 on
Object Detection
on COCO test-dev
(using extra training data)
Single Image Pop-Up From Discriminatively Learned Parts
We introduce a new approach for estimating a fine grained 3D shape and continuous pose of an object from a single image.
Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video
Here, two cases are considered: (i) the image locations of the human joints are provided and (ii) the image locations of joints are unknown.
Ranked #38 on
Monocular 3D Human Pose Estimation
on Human3.6M
Sparse Representation for 3D Shape Estimation: A Convex Relaxation Approach
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.
Ranked #118 on
3D Human Pose Estimation
on Human3.6M
(PA-MPJPE metric)
Multi-Image Matching via Fast Alternating Minimization
In this paper we propose a global optimization-based approach to jointly matching a set of images.
Pose and Shape Estimation with Discriminatively Learned Parts
We introduce a new approach for estimating the 3D pose and the 3D shape of an object from a single image.
Active Deformable Part Models
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.
