Unsupervised Learning of Long-Term Motion Dynamics for Videos

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, Depth, and RGB-D videos.Comment: CVPR 201

Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance

One in twenty-five patients admitted to a hospital will suffer from a hospital acquired infection. If we can intelligently track healthcare staff, patients, and visitors, we can better understand the sources of such infections. We envision a smart hospital capable of increasing operational efficiency and improving patient care with less spending. In this paper, we propose a non-intrusive vision-based system for tracking people's activity in hospitals. We evaluate our method for the problem of measuring hand hygiene compliance. Empirically, our method outperforms existing solutions such as proximity-based techniques and covert in-person observational studies. We present intuitive, qualitative results that analyze human movement patterns and conduct spatial analytics which convey our method's interpretability. This work is a step towards a computer-vision based smart hospital and demonstrates promising results for reducing hospital acquired infections.Comment: Machine Learning for Healthcare Conference (MLHC

Differentially Private Video Activity Recognition

In recent years, differential privacy has seen significant advancements in image classification; however, its application to video activity recognition remains under-explored. This paper addresses the challenges of applying differential privacy to video activity recognition, which primarily stem from: (1) a discrepancy between the desired privacy level for entire videos and the nature of input data processed by contemporary video architectures, which are typically short, segmented clips; and (2) the complexity and sheer size of video datasets relative to those in image classification, which render traditional differential privacy methods inadequate. To tackle these issues, we propose Multi-Clip DP-SGD, a novel framework for enforcing video-level differential privacy through clip-based classification models. This method samples multiple clips from each video, averages their gradients, and applies gradient clipping in DP-SGD without incurring additional privacy loss. Moreover, we incorporate a parameter-efficient transfer learning strategy to make the model scalable for large-scale video datasets. Through extensive evaluations on the UCF-101 and HMDB-51 datasets, our approach exhibits impressive performance, achieving 81% accuracy with a privacy budget of epsilon=5 on UCF-101, marking a 76% improvement compared to a direct application of DP-SGD. Furthermore, we demonstrate that our transfer learning strategy is versatile and can enhance differentially private image classification across an array of datasets including CheXpert, ImageNet, CIFAR-10, and CIFAR-100

Alleviating Human-level Shift : A Robust Domain Adaptation Method for Multi-person Pose Estimation

Human pose estimation has been widely studied with much focus on supervised learning requiring sufficient annotations. However, in real applications, a pretrained pose estimation model usually need be adapted to a novel domain with no labels or sparse labels. Such domain adaptation for 2D pose estimation hasn't been explored. The main reason is that a pose, by nature, has typical topological structure and needs fine-grained features in local keypoints. While existing adaptation methods do not consider topological structure of object-of-interest and they align the whole images coarsely. Therefore, we propose a novel domain adaptation method for multi-person pose estimation to conduct the human-level topological structure alignment and fine-grained feature alignment. Our method consists of three modules: Cross-Attentive Feature Alignment (CAFA), Intra-domain Structure Adaptation (ISA) and Inter-domain Human-Topology Alignment (IHTA) module. The CAFA adopts a bidirectional spatial attention module (BSAM)that focuses on fine-grained local feature correlation between two humans to adaptively aggregate consistent features for adaptation. We adopt ISA only in semi-supervised domain adaptation (SSDA) to exploit the corresponding keypoint semantic relationship for reducing the intra-domain bias. Most importantly, we propose an IHTA to learn more domain-invariant human topological representation for reducing the inter-domain discrepancy. We model the human topological structure via the graph convolution network (GCN), by passing messages on which, high-order relations can be considered. This structure preserving alignment based on GCN is beneficial to the occluded or extreme pose inference. Extensive experiments are conducted on two popular benchmarks and results demonstrate the competency of our method compared with existing supervised approaches.Comment: Accepted By ACM MM'202