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Found 34 papers, 8 papers with code
A Roadmap for Greater Public Use of Privacy-Sensitive Government Data: Workshop Report
Government agencies collect and manage a wide range of ever-growing datasets.
Physically Disentangled Representations
In this paper, we show the utility of inverse rendering in learning representations that yield improved accuracy on downstream clustering, linear classification, and segmentation tasks with the help of our novel Leave-One-Out, Cycle Contrastive loss (LOOCC), which improves disentanglement of scene parameters and robustness to out-of-distribution lighting and viewpoints.
Learning to Censor by Noisy Sampling
The goal of this work is to protect sensitive information when learning from point clouds; by censoring the sensitive information before the point cloud is released for downstream tasks.
Decouple-and-Sample: Protecting sensitive information in task agnostic data release
While releasing datasets continues to make a big impact in various applications of computer vision, its impact is mostly realized when data sharing is not inhibited by privacy concerns.
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning
In this work, we introduce AdaSplit which enables efficiently scaling SL to low resource scenarios by reducing bandwidth consumption and improving performance across heterogeneous clients.
Sanitizer: Sanitizing data for anonymizing sensitive information
This is done in a two-step process: first, we develop a method that encodes unstructured image-like modality into a structured representation bifurcated by sensitive and non-sensitive representation.
Vaccination Worldwide: Strategies, Distribution and Challenges
It is a bane of the \"uber connected world that we live in that this virus has affected almost all countries and caused mortality and economic upheaval at a scale whose effects are going to be felt for generations to come.
Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation
Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks.
Ranked #1 on
Action Segmentation
on Breakfast
(mIoU metric)
COVID-19 Tests Gone Rogue: Privacy, Efficacy, Mismanagement and Misunderstandings
In this work, we discuss challenges complicating the existing covid-19 testing ecosystem and highlight the need to improve the testing experience for the user and reduce privacy invasions.
Computers and Society
Vi2CLR: Video and Image for Visual Contrastive Learning of Representation
In this paper, we introduce a novel self-supervised visual representation learning method which understands both images and videos in a joint learning fashion.
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks
Recent deep learning models have shown remarkable performance in image classification.
Digital Landscape of COVID-19 Testing: Challenges and Opportunities
We summarize the challenges experienced using these tools in terms of quality of information, privacy, and user-centric issues.
Computers and Society
Challenges of Equitable Vaccine Distribution in the COVID-19 Pandemic
The COVID-19 pandemic has led to a need for widespread and rapid vaccine development.
SplitNN-driven Vertical Partitioning
In this work, we introduce SplitNN-driven Vertical Partitioning, a configuration of a distributed deep learning method called SplitNN to facilitate learning from vertically distributed features.
Clustering based Contrastive Learning for Improving Face Representations
We demonstrate our method on the challenging task of learning representations for video face clustering.
Deep Multimodal Feature Encoding for Video Ordering
True understanding of videos comes from a joint analysis of all its modalities: the video frames, the audio track, and any accompanying text such as closed captions.
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations
Recently, there has been the development of Split Learning, a framework for distributed computation where model components are split between the client and server (Vepakomma et al., 2018b).
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries
In this work we introduce ExpertMatcher, a method for automating deep learning model selection using autoencoders.
DynamoNet: Dynamic Action and Motion Network
With these overall objectives, to this end, we introduce a novel unified spatio-temporal 3D-CNN architecture (DynamoNet) that jointly optimizes the video classification and learning motion representation by predicting future frames as a multi-task learning problem.
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 #7 on
Action Recognition
on UCF101
Self-Supervised Learning of Face Representations for Video Face Clustering
In this paper, we address video face clustering using unsupervised methods.
Efficient Parameter-free Clustering Using First Neighbor Relations
We present a new clustering method in the form of a single clustering equation that is able to directly discover groupings in the data.
Can Image Enhancement be Beneficial to Find Smoke Images in Laparoscopic Surgery?
For feature extraction, we use statistical features based on bivariate histogram distribution of gradient magnitude~(GM) and Laplacian of Gaussian~(LoG).
Compression of Deep Neural Networks by combining pruning and low rank decomposition
Large number of weights in deep neural networks make the models difficult to be deployed in low memory environments such as, mobile phones, IOT edge devices as well as "inferencing as a service" environments on the cloud.
Spatio-Temporal Channel Correlation Networks for Action Classification
Our experiments show that adding STC blocks to current state-of-the-art architectures outperforms the state-of-the-art methods on the HMDB51, UCF101 and Kinetics datasets.
Classification-Driven Dynamic Image Enhancement
In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily human perception.
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
Thus, by finetuning this network, we beat the performance of generic and recent methods in 3D CNNs, which were trained on large video datasets, e. g. Sports-1M, and finetuned on the target datasets, e. g. HMDB51/UCF101.
Weakly Supervised Object Discovery by Generative Adversarial & Ranking Networks
We approach GANs with a novel training method and learning objective, to discover multiple object instances for three cases: 1) synthesizing a picture of a specific object within a cluttered scene; 2) localizing different categories in images for weakly supervised object detection; and 3) improving object discov- ery in object detection pipelines.
Ranked #2 on
Weakly Supervised Object Detection
on COCO test-dev
Classification Driven Dynamic Image Enhancement
In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily human perception.
Weakly Supervised Cascaded Convolutional Networks
The final stage of both architectures is a part of a convolutional neural network that performs multiple instance learning on proposals extracted in the previous stage(s).
Ranked #2 on
Weakly Supervised Object Detection
on ImageNet
Deep Temporal Linear Encoding Networks
Advantages of TLEs are: (a) they encode the entire video into a compact feature representation, learning the semantics and a discriminative feature space; (b) they are applicable to all kinds of networks like 2D and 3D CNNs for video classification; and (c) they model feature interactions in a more expressive way and without loss of information.
Does V-NIR based Image Enhancement Come with Better Features?
Image enhancement using the visible (V) and near-infrared (NIR) usually enhances useful image details.
Low-Cost Scene Modeling using a Density Function Improves Segmentation Performance
We propose a low cost and effective way to combine a free simulation software and free CAD models for modeling human-object interaction in order to improve human & object segmentation.
Image-level Classification in Hyperspectral Images using Feature Descriptors, with Application to Face Recognition
In this paper, we proposed a novel pipeline for image-level classification in the hyperspectral images.
