Search Results for author:
Found 19 papers, 12 papers with code
Unsupervised 4D Cardiac Motion Tracking with Spatiotemporal Optical Flow Networks
Thus it is difficult to perform supervised learning for motion tracking.
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 #32 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 #245 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 #224 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 #133 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.
