For human pose estimation in still images, this paper proposes three semi- and weakly-supervised learning schemes. While recent advances of convolutional neural networks improve human pose estimation using supervised training data, our focus is to explore the semi- and weakly-supervised schemes. Our proposed schemes initially learn conventional model(s) for pose estimation from a small amount of standard training images with human pose annotations. For the first semi-supervised learning scheme, this conventional pose model detects candidate poses in training images with no human annotation. From these candidate poses, only true-positives are selected by a classifier using a pose feature representing the configuration of all body parts. The accuracies of these candidate pose estimation and true-positive pose selection are improved by action labels provided to these images in our second and third learning schemes, which are semi- and weakly-supervised learning. While the first and second learning schemes select only poses that are similar to those in the supervised training data, the third scheme selects more true-positive poses that are significantly different from any supervised poses. This pose selection is achieved by pose clustering using outlier pose detection with Dirichlet process mixtures and the Bayes factor. The proposed schemes are validated with large-scale human pose datasets.
Semi-supervised learning allows us to utilize a huge number of non-annotated images for various recognition problems (e.g., human action recognition @cite_36 , human re-identification @cite_10 , and face and gait recognition @cite_21 ). In general, semi-supervised learning annotates the images automatically by employing several cues in with the images; for example, temporal consistency in tracking @cite_43 , clustering @cite_4 , multimodal keywords @cite_59 , and domain adaptation @cite_20 .
@cite_4 @cite_36 @cite_21 @cite_43 @cite_59 @cite_10 @cite_20
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Abstract In this paper, we propose a semi-supervised facial landmark detection algorithm (SEMI) based on convolutional neural network (CNN), which can detect facial components and landmarks simultaneously. Unlike previous coarse-to-fine algorithms, our model does not need extra input such as initial landmark prediction. It also solves the occlusion problem of large area by detecting the visible facial components while existing face detectors failed to detect faces. Semi-supervised learning algorithm is also an effective data augmentation method. In our experiment, each image has two types of ground truth, one is bounding-box related (classification and coordinates) and the other is landmark coordinates inside the bounding-box. The supervised data have both two types of ground truth while the semi-supervised data only have the bounding-box. Our model was trained by the merge of two parts of data. Extensive evaluations on Helen, LFPW and 300-W show that our algorithm is able to complete the landmark task and performs better than many state-of-the-art facial landmark detecting algorithms. Learning rich visual representations often require training on datasets of millions of manually annotated examples. This substantially limits the scalability of learning effective representations as labeled data is expensive or scarce. In this paper, we address the problem of unsupervised visual representation learning from a large, unlabeled collection of images. By representing each image as a node and each nearest-neighbor matching pair as an edge, our key idea is to leverage graph-based analysis to discover positive and negative image pairs (i.e., pairs belonging to the same and different visual categories). Specifically, we propose to use a cycle consistency criterion for mining positive pairs and geodesic distance in the graph for hard negative mining. We show that the mined positive and negative image pairs can provide accurate supervisory signals for learning effective representations using Convolutional Neural Networks (CNNs). We demonstrate the effectiveness of the proposed unsupervised constraint mining method in two settings: (1) unsupervised feature learning and (2) semi-supervised learning. For unsupervised feature learning, we obtain competitive performance with several state-of-the-art approaches on the PASCAL VOC 2007 dataset. For semi-supervised learning, we show boosted performance by incorporating the mined constraints on three image classification datasets. Recent research in visual inference from monocular images has shown that discriminatively trained image-based predictors can provide fast, automatic qualitative 3D reconstructions of human body pose or scene structure in real-world environments. However, the stability of existing image representations tends to be perturbed by deformations and misalignments in the training set, which, in turn, degrade the quality of learning and generalization. In this paper we advocate the semi-supervised learning of hierarchical image descriptions in order to better tolerate variability at multiple levels of detail. We combine multilevel encodings with improved stability to geometric transformations, with metric learning and semi-supervised manifold regularization methods in order to further profile them for task-invariance -resistance to background clutter and within the same human pose class differences. We quantitatively analyze the effectiveness of both descriptors and learning methods and show that each one can contribute, sometimes substantially, to more reliable 3D human pose estimates in cluttered images. Supervised object detection and semantic segmentation require object or even pixel level annotations. When there exist image level labels only, it is challenging for weakly supervised algorithms to achieve accurate predictions. The accuracy achieved by top weakly supervised algorithms is still significantly lower than their fully supervised counterparts. In this paper, we propose a novel weakly supervised curriculum learning pipeline for multi-label object recognition, detection and semantic segmentation. In this pipeline, we first obtain intermediate object localization and pixel labeling results for the training images, and then use such results to train task-specific deep networks in a fully supervised manner. The entire process consists of four stages, including object localization in the training images, filtering and fusing object instances, pixel labeling for the training images, and task-specific network training. To obtain clean object instances in the training images, we propose a novel algorithm for filtering, fusing and classifying object instances collected from multiple solution mechanisms. In this algorithm, we incorporate both metric learning and density-based clustering to filter detected object instances. Experiments show that our weakly supervised pipeline achieves state-of-the-art results in multi-label image classification as well as weakly supervised object detection and very competitive results in weakly supervised semantic segmentation on MS-COCO, PASCAL VOC 2007 and PASCAL VOC 2012.