We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.

/pdf/you-only-look-once-unified-real-time-object-detection-3gan6roe1o.pdf

You Only Look Once: Unified, Real-Time Object Detection

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.

/pdf/robust-face-recognition-via-sparse-representation-4bfqv6y74o.pdf

Robust Face Recognition via Sparse Representation

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

/pdf/integrating-single-cell-transcriptomic-data-across-different-3dtsi5w7ob.pdf

Integrating single-cell transcriptomic data across different conditions, technologies, and species.

3D Morph able Model (3DMM) has been widely used in face analysis for many years. The most challenging part of 3DMM is to find the correspondences between 3D points and 2D pixels. Existing methods only use key points, edges, specular highlights and image pixels to complete the task, which are not accurate or robust. This paper proposes a new algorithm called Sparse SIFT Flow (SSF) to improve the reconstruction accuracy. We mark a set of salient points to control the shape of facial components and use SSF to find their corresponding pixels on the input image. We also incorporate SSF into Multi-Features Framework to construct a robust 3DMM fitting algorithm. Compared with the state-of-the art, our approach significantly improves the fitting results in facial component area.

Robust 3D Morphable Model Fitting by Sparse SIFT Flow

Although face recognition has made dramatic improvements in recent years, there are still many challenges in real-world applications such as face recognition for the elderly and children, for the surveillance scenes and for Near infrared vs. Visible light (NIR-VIS) heterogeneous scene, etc. Due to the existence of these challenges, there are usually domain gaps between training (source domain) and test (target domain). A common way to improve the performance on the target domain is fine-tuning the base model trained on source domain using target data. However, it will severely degrade performance on the source domain. Another way which jointly trains models using both source and target data, suffers from the heavy computations and large data storage, especially when we continue to encounter new domains. In response to these problems, we introduce a new challenging task: Single Exemplar Domain Incremental Learning (SE-DIL), which utilizes the target domain data and just one exemplar per identity from source domain data to quickly improve the performance on the target domain while keeping the performance on the source domain. To deal with SE-DIL, we propose our Fast Adapting without Forgetting (FAwF) method with three components: margin-based exemplar selection, prototype-based class extension and hard&soft knowledge distillation. Through FAwF, we can well maintain the source domain performance with only one sample per source domain class, greatly reducing the fine-tuning time-cost and data storage. Besides, we collected a large-scale children face dataset KidsFace with $12 {K}$ identities for studying the SE-DIL in face recognition. Extensive analysis and experiments on our KidsFace-Test protocol and other challenging face test sets show that our method performs better than the state-of-the-art methods on both target and source domain.

Fast Adapting Without Forgetting for Face Recognition

http://www.nlpr.ia.ac.cn/2013papers/gjkw/gk72.pdf

Dynamics of a mean-shift-like algorithm and its applications on clustering

We investigate into a nonlinear mapping by which multi-view face patterns in the input space are mapped into invariant points in a low dimensional feature space. The invariance to both illumination and view is achieved in two-stages. First, a nonlinear mapping from the input space to a low dimensional feature space is learned from multi-view face examples to achieve illumination invariance. The illumination invariant feature points of face patterns across views are on a curve parameterized by the view parameter, and the view parameter of a face pattern can be estimated from the location of the feature point on the curve by using least squares fit. Then the second nonlinear mapping, which is from the illumination invariant feature space to another feature space of the same dimension, is performed to achieve invariance to both illumination and view. This amounts to do a normalization based on the view estimate. By the two stage nonlinear mapping, multi-view face patterns are mapped to a zero mean Gaussian distribution in the latter feature space. Properties of the nonlinear mappings and the Gaussian face distribution are explored and supported by experiments.

Nonlinear mapping from multi-view face patterns to a Gaussian distribution in a low dimensional space

Head and human detection have been rapidly improved with the development of deep convolutional neural networks. However, these two tasks are often studied separately without considering their inherent correlation, leading to that 1) head detection is often trapped in more false positives, and 2) the performance of human detector frequently drops dramatically in crowd scenes. To handle these two issues, we present a novel joint head and human detection network, namely JointDet, which effectively detects head and human body simultaneously. Moreover, we design a head-body relationship discriminating module to perform relational learning between heads and human bodies, and leverage this learned relationship to regain the suppressed human detections and reduce head false positives. To verify the effectiveness of the proposed method, we annotate head bounding boxes of the CityPersons and Caltech-USA datasets, and conduct extensive experiments on the CrowdHuman, CityPersons and Caltech-USA datasets. As a consequence, the proposed JointDet detector achieves state-of-the-art performance on these three benchmarks. To facilitate further studies on the head and human detection problem, all new annotations, source codes and trained models are available at https://github.com/ChiCheng123/JointDet.

/pdf/relational-learning-for-joint-head-and-human-detection-2h78f2w032.pdf

Stan Z. Li

Papers

Robust 3D Morphable Model Fitting by Sparse SIFT Flow

Fast Adapting Without Forgetting for Face Recognition

Dynamics of a mean-shift-like algorithm and its applications on clustering

Nonlinear mapping from multi-view face patterns to a Gaussian distribution in a low dimensional space

Relational Learning for Joint Head and Human Detection