Decoupled Iterative Refinement Framework for Interacting Hands Reconstruction from a Single RGB Image

Reconstructing interacting hands from a single RGB image is a very challenging task. On the one hand, severe mutual occlusion and similar local appearance between two hands confuse the extraction of visual features, resulting in the misalignment of estimated hand meshes and the image. On the other hand, there are complex spatial relationship between interacting hands, which significantly increases the solution space of hand poses and increases the difficulty of network learning. In this paper, we propose a decoupled iterative refinement framework to achieve pixel-alignment hand reconstruction while efficiently modeling the spatial relationship between hands. Specifically, we define two feature spaces with different characteristics, namely 2D visual feature space and 3D joint feature space. First, we obtain joint-wise features from the visual feature map and utilize a graph convolution network and a transformer to perform intra- and inter-hand information interaction in the 3D joint feature space, respectively. Then, we project the joint features with global information back into the 2D visual feature space in an obfuscation-free manner and utilize the 2D convolution for pixel-wise enhancement. By performing multiple alternate enhancements in the two feature spaces, our method can achieve an accurate and robust reconstruction of interacting hands. Our method outperforms all existing two-hand reconstruction methods by a large margin on the InterHand2.6M dataset.Comment: Accepted to ICCV 2023 (Oral

AFFECT-PRESERVING VISUAL PRIVACY PROTECTION

The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this dissertation, we propose to balance the privacy protection and the utility of the data by preserving the privacy-insensitive information, such as pose and expression, which is useful in many applications involving visual understanding. The Intellectual Merits of the dissertation include a novel framework for visual privacy protection by manipulating facial image and body shape of individuals, which: (1) is able to conceal the identity of individuals; (2) provide a way to preserve the utility of the data, such as expression and pose information; (3) balance the utility of the data and capacity of the privacy protection. The Broader Impacts of the dissertation focus on the significance of privacy protection on visual data, and the inadequacy of current privacy enhancing technologies in preserving affect and behavioral attributes of the visual content, which are highly useful for behavior observation in educational and medical settings. This work in this dissertation represents one of the first attempts in achieving both goals simultaneously

DEEP INFERENCE ON MULTI-SENSOR DATA

Computer vision-based intelligent autonomous systems engage various types of sensors to perceive the world they navigate in. Vision systems perceive their environments through inferences on entities (structures, humans) and their attributes (pose, shape, materials) that are sensed using RGB and Near-InfraRed (NIR) cameras, LAser Detection And Ranging (LADAR), radar and so on. This leads to challenging and interesting problems in efficient data-capture, feature extraction, and attribute estimation, not only for RGB but various other sensors. In some cases, we encounter very limited amounts of labeled training data. In certain other scenarios we have sufficient data, but annotations are unavailable for supervised learning. This dissertation explores two approaches to learning under conditions of minimal to no ground truth. The first approach applies projections on training data that make learning efficient by improving training dynamics. The first and second topics in this dissertation belong to this category. The second approach makes learning without ground-truth possible via knowledge transfer from a labeled source domain to an unlabeled target domain through projections to domain-invariant shared latent spaces. The third and fourth topics in this dissertation belong to this category. For the first topic we study the feasibility and efficacy of identifying shapes in LADAR data in several measurement modes. We present results on efficient parameter learning with less data (for both traditional machine learning as well as deep models) on LADAR images. We use a LADAR apparatus to obtain range information from a 3-D scene by emitting laser beams and collecting the reflected rays from target objects in the region of interest. The Agile Beam LADAR concept makes the measurement and interpretation process more efficient using a software-defined architecture that leverages computational imaging principles. Using these techniques, we show that object identification and scene understanding can be accurately performed in the LADARmeasurement domain thereby rendering the efforts of pixel-based scene reconstruction superfluous. Next, we explore the effectiveness of deep features extracted by Convolutional Neural Networks (CNNs) in the Discrete Cosine Transform (DCT) domain for various image classification tasks such as pedestrian and face detection, material identification and object recognition. We perform the DCT operation on the feature maps generated by convolutional layers in CNNs. We compare the performance of the same network with the same hyper-parameters with or without the DCT step. Our results indicate that a DCT operation incorporated into the network after the first convolution layer can have certain advantages such as convergence over fewer training epochs and sparser weight matrices that are more conducive to pruning and hashing techniques. Next, we present an adversarial deep domain adaptation (ADA)-based approach for training deep neural networks that fit 3Dmeshes on humans in monocular RGB input images. Estimating a 3D mesh from a 2D image is helpful in harvesting complete 3Dinformation about body pose and shape. However, learning such an estimation task in a supervised way is challenging owing to the fact that ground truth 3D mesh parameters for real humans do not exist. We propose a domain adaptation based single-shot (no re-projection, no iterative refinement), end-to-end training approach with joint optimization on real and synthetic images on a shared common task. Through joint inference on real and synthetic data, the network extracts domain invariant features that are further used to estimate the 3D mesh parameters in a single shot with no supervision on real samples. While we compute regression loss on synthetic samples with ground truth mesh parameters, knowledge is transferred from synthetic to real data through ADA without direct ground truth for supervision. Finally, we propose a partially supervised method for satellite image super-resolution by learning a unified representation of samples from different domains (captured by different sensors) in a shared latent space. The training samples are drawn from two datasets which we refer to as source and target domains. The source domain consists of fewer samples which are of higher resolution and contain very detailed and accurate annotations. In contrast, samples from the target domain are low-resolution and available ground truth is sparse. The pipeline consists of a feature extractor and a super-resolving module which are trained end-to-end. Using a deep feature extractor, we jointly learn (on two datasets) a common embedding space for all samples. Partial supervision is available for the samples in the source domain which have high-resolution ground truth. Adversarial supervision is used to successfully super-resolve low-resolution RGB satellite imagery from target domain without direct paired supervision from high resolution counterparts

A Survey of Deep Learning-Based Object Detection

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline, thoroughly and deeply, in this survey, we first analyze the methods of existing typical detection models and describe the benchmark datasets. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.Comment: 30 pages,12 figure

Deep Learning-Based Action Recognition

The classification of human action or behavior patterns is very important for analyzing situations in the field and maintaining social safety. This book focuses on recent research findings on recognizing human action patterns. Technology for the recognition of human action pattern includes the processing technology of human behavior data for learning, technology of expressing feature values ​​of images, technology of extracting spatiotemporal information of images, technology of recognizing human posture, and technology of gesture recognition. Research on these technologies has recently been conducted using general deep learning network modeling of artificial intelligence technology, and excellent research results have been included in this edition