Search Results for author:
Found 23 papers, 11 papers with code
PANDA: Pose Aligned Networks for Deep Attribute Modeling
We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, appearance, articulation and occlusion.
Ranked #7 on
Facial Attribute Classification
on LFWA
Training Convolutional Networks with Noisy Labels
The availability of large labeled datasets has allowed Convolutional Network models to achieve impressive recognition results.
Learning Spatiotemporal Features with 3D Convolutional Networks
We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset.
Ranked #8 on
Action Recognition
on Sports-1M
Action Recognition In Videos
Dynamic Facial Expression Recognition
Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues
We explore the task of recognizing peoples' identities in photo albums in an unconstrained setting.
Improving Image Classification with Location Context
With the widespread availability of cellphones and cameras that have GPS capabilities, it is common for images being uploaded to the Internet today to have GPS coordinates associated with them.
ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks
This paper aims to classify and locate objects accurately and efficiently, without using bounding box annotations.
Ranked #5 on
Weakly Supervised Object Detection
on MS COCO
Unsupervised Learning of Edges
In this work we present a simple yet effective approach for training edge detectors without human supervision.
Metric Learning with Adaptive Density Discrimination
Beyond classification, we further validate the saliency of the learnt representations via their attribute concentration and hierarchy recovery properties, achieving 10-25% relative gains on the softmax classifier and 25-50% on triplet loss in these tasks.
Deep End2End Voxel2Voxel Prediction
Over the last few years deep learning methods have emerged as one of the most prominent approaches for video analysis.
VideoMCC: a New Benchmark for Video Comprehension
Language has been exploited to sidestep the problem of defining video categories, by formulating video understanding as the task of captioning or description.
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks and achieving better optimization performance.
End-to-end Planning of Fixed Millimeter-Wave Networks
This article discusses a framework to support the design and end-to-end planning of fixed millimeter-wave networks.
Pose-Aware Person Recognition
Person recognition methods that use multiple body regions have shown significant improvements over traditional face-based recognition.
ConvNet Architecture Search for Spatiotemporal Feature Learning
Learning image representations with ConvNets by pre-training on ImageNet has proven useful across many visual understanding tasks including object detection, semantic segmentation, and image captioning.
Ranked #71 on
Action Recognition
on HMDB-51
A Closer Look at Spatiotemporal Convolutions for Action Recognition
In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition.
Ranked #3 on
Action Recognition
on Sports-1M
Detect-and-Track: Efficient Pose Estimation in Videos
This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video.
Ranked #8 on
Pose Tracking
on PoseTrack2017
(using extra training data)
Large-Scale Visual Relationship Understanding
Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of <subject, relation, object> triples.
Exploring the Limits of Weakly Supervised Pretraining
ImageNet classification is the de facto pretraining task for these models.
Ranked #218 on
Image Classification
on ImageNet
(using extra training data)
What Makes a Video a Video: Analyzing Temporal Information in Video Understanding Models and Datasets
The ability to capture temporal information has been critical to the development of video understanding models.
Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset
The videos retrieved by the search engines are then veried for correctness by human annotators.
Exploring the Challenges towards Lifelong Fact Learning
So far life-long learning (LLL) has been studied in relatively small-scale and relatively artificial setups.
Large Scale Holistic Video Understanding
HVU is organized hierarchically in a semantic taxonomy that focuses on multi-label and multi-task video understanding as a comprehensive problem that encompasses the recognition of multiple semantic aspects in the dynamic scene.
Ranked #11 on
Action Recognition
on UCF101
Billion-scale semi-supervised learning for image classification
This paper presents a study of semi-supervised learning with large convolutional networks.
Ranked #6 on
Image Classification
on OmniBenchmark
(using extra training data)
