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Unsupervised person re-identification (re-ID) remains a challenging task. While extensive research has focused on the framework design and loss function, this paper shows that sampling strategy plays an equally important role. We analyze the reasons for the performance differences between various sampling strategies under the same framework and loss function. We suggest that deteriorated over-fitting is an important factor causing poor performance, and enhancing statistical stability can rectify this problem. Inspired by that, a simple yet effective approach is proposed, termed group sampling, which gathers samples from the same class into groups. The model is thereby trained using normalized group samples, which helps alleviate the negative impact of individual samples. Group sampling updates the pipeline of pseudo-label generation by guaranteeing that samples are more efficiently classified into the correct classes. It regulates the representation learning process, enhancing statistical stability for feature representation in a progressive fashion. Extensive experiments on Market-1501, DukeMTMC-reID and MSMT17 show that group sampling achieves performance comparable to state-of-the-art methods and outperforms the current techniques under purely camera-agnostic settings. Code has been available at https://github.com/ucas-vg/GroupSampling. [ABSTRACT FROM AUTHOR]

Detecting oriented and densely packed objects is a challenging problem considering that the receptive field intersection between objects causes spatial feature aliasing. In this paper, we propose a convex-hull feature adaptation (CFA) approach, with the aim to configure convolutional features in accordance with irregular object layouts. CFA roots in the convex-hull feature representation, which defines a set of dynamically sampled feature points guided by the convex intersection over union (CIoU) to bound object extent. CFA pursues optimal feature assignment by constructing convex-hull sets and iteratively splitting positive or negative convex-hulls. By simultaneously considering overlapping convex-hulls and objects and penalizing convex-hulls shared by multiple objects, CFA defines a systematic way to adapt convolutional features on regular grids to objects of irregular shapes. Experiments on DOTA and SKU110K-R datasets show that CFA achieved new state-of-the-art performance for detecting oriented and densely packed objects. CFA also sets a solid baseline for convex polygon prediction on the MS COCO dataset defined for general object detection. Code is available at https://github.com/SDL-GuoZonghao/BeyondBoundingBox. [ABSTRACT FROM AUTHOR]

Modern CNN-based object detectors assign anchors for ground-truth objects under the restriction of object-anchor Intersection-over-Union (IoU). In this study, we propose a learning-to-match (LTM) method to break IoU restriction, allowing objects to match anchors in a flexible manner. LTM updates hand-crafted anchor assignment to “free” anchor matching by formulating detector training in the Maximum Likelihood Estimation (MLE) framework. During the training phase, LTM is implemented by converting the detection likelihood to anchor matching loss functions which are plug-and-play. Minimizing the matching loss functions drives learning and selecting features which best explain a class of objects with respect to both classification and localization. LTM is extended from anchor-based detectors to anchor-free detectors, validating the general applicability of learnable object-feature matching mechanism for visual object detection. Experiments on MS COCO dataset demonstrate that LTM detectors consistently outperform counterpart detectors with significant margins. The last but not the least, LTM requires negligible computational cost in both training and inference phases as it does not involve any additional architecture or parameter. Code has been made publicly available. [ABSTRACT FROM AUTHOR]

Pedestrian detection in the wild remains a challenging problem especially for scenes containing serious occlusion. In this paper, we propose a novel feature learning method in the deep learning framework, referred to as Feature Calibration Network (FC-Net), to adaptively detect pedestrians under various occlusions. FC-Net is based on the observation that the visible parts of pedestrians are selective and decisive for detection, and is implemented as a self-paced feature learning framework with a self-activation (SA) module and a feature calibration (FC) module. In a new self-activated manner, FC-Net learns features which highlight the visible parts and suppress the occluded parts of pedestrians. The SA module estimates pedestrian activation maps by reusing classifier weights, without any additional parameter involved, therefore resulting in an extremely parsimony model to reinforce the semantics of features, while the FC module calibrates the convolutional features for adaptive pedestrian representation in both pixel-wise and region-based ways. Experiments on CityPersons and Caltech datasets demonstrate that FC-Net improves detection performance on occluded pedestrians up to 10% while maintaining excellent performance on non-occluded instances. [ABSTRACT FROM AUTHOR]

Neural architecture search (NAS) has achieved unprecedented performance in various computer vision tasks. However, most existing NAS methods are defected in search efficiency and model generalizability. In this paper, we propose a novel NAS framework, termed MIGO-NAS, with the aim to guarantee the efficiency and generalizability in arbitrary search spaces. On the one hand, we formulate the search space as a multivariate probabilistic distribution, which is then optimized by a novel multivariate information-geometric optimization (MIGO). By approximating the distribution with a sampling, training, and testing pipeline, MIGO guarantees the memory efficiency, training efficiency, and search flexibility. Besides, MIGO is the first time to decrease the estimation error of natural gradient in multivariate distribution. On the other hand, for a set of specific constraints, the neural architectures are generated by a novel dynamic programming network generation (DPNG), which significantly reduces the training cost under various hardware environments. Experiments validate the advantages of our approach over existing methods by establishing a superior accuracy and efficiency i.e., 2.39 test error on CIFAR-10 benchmark and 21.7 on ImageNet benchmark, with only 1.5 GPU hours and 96 GPU hours for searching, respectively. Besides, the searched architectures can be well generalize to computer vision tasks including object detection and semantic segmentation, i.e., 25 × FLOPs compression, with 6.4 mAP gain over Pascal VOC dataset, and 29. 9 × FLOPs compression, with only 1.41 percent performance drop over Cityscapes dataset. The code is publicly available. [ABSTRACT FROM AUTHOR]

Correlation filter trackers building on deep convolution neural networks (CNNs) contribute efficient visual object trackers but remain challenged with severe target appearance variations. The reason for this is that CNNs trained for image classification tasks are less discriminative to the dynamic variations of targets and backgrounds. In this paper, we propose an adaptive discriminative deep correlation filter (adaDDCF), which, by incorporating discriminative feature fine-tuning with adaptive appearance modeling, pursues stable object tracking in complex backgrounds. In adaDDCF, a convolutional Fisher discriminative analysis (FDA) layer is implemented for positive and negative instance mining and scene-specific feature learning. A correlation layer is then embedded to learn the correlation response of consecutive frames for target appearance modeling. With an online learning procedure using forward–backward propagation, the FDA layer and the correlation layer are effectively coupled, leading to effective and discriminative fine-tuning for the proposed tracker, which consequently alleviates the target drifting problem. Extensive experiments on the challenging benchmarks OTB2013, OTB2015, and OTB50 demonstrate that the proposed adaDDCF tracker outperforms many state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

Pedestrian detection in images and video frames is challenged by the view and posture problem. In this paper, we propose a new pedestrian detection approach by error correcting output code (ECOC) classification of manifold subclasses. The motivation is that pedestrians across views and postures form a manifold and that the ECOC method constructs a nonlinear classification boundary that can discriminate the manifold from negative samples. The pedestrian manifold is first constructed with a local linear embedding algorithm and then divided into subclasses with a K-means clustering algorithm. The neighboring relationships of these subclasses are used to make the encoding rule for ECOCs, which we use to train multiple base classifiers with histogram of oriented gradient features and linear support vector machines. In the detection procedure, image windows are tested with all base classifiers, and their output codes are fed into an ECOC decoding procedure to decide whether it is a pedestrian or not. Experiments on three data sets show that the results of our approach improve the state of the art. [ABSTRACT FROM PUBLISHER]