Person re-identification (Re-ID) requires discriminative features focusing on the full person to cope with inaccurate person bounding box detection, background clutter, and occlusion. Many recent person Re-ID methods attempt to learn such features describing full person details via part-based feature representation. However, the spatial context between these parts is ignored for the independent extractor on each separate part. In this paper, we propose to apply Long Short-Term Memory (LSTM) in an end-to-end way to model the pedestrian, seen as a sequence of body parts from head to foot. Integrating the * Corresponding author

In this paper we tackle the problem of capturing the dense, detailed 3D geometry of generic, complex non-rigid meshes using a single RGB-only commodity video camera and a direct approach. While robust and even real-time solutions exist to this problem if the observed scene is static, for non-rigid dense shape capture current systems are typically restricted to the use of complex multi-camera rigs, take advantage of the additional depth channel available in RGB-D cameras, or deal with specific shapes such as faces or planar surfaces. In contrast, our method makes use of a single RGB video as input; it can capture the deformations of generic shapes; and the depth estimation is dense, per-pixel and direct. We first compute a dense 3D template of the shape of the object, using a short rigid sequence, and subsequently perform online reconstruction of the non-rigid mesh as it evolves over time. Our energy optimization approach minimizes a robust photometric cost that simultaneously estimates the temporal correspondences and 3D deformations with respect to the template mesh. In our experimental evaluation we show a range of qualitative results on novel datasets; we compare against an existing method that requires multi-frame optical flow; and perform a quantitative evaluation against other template-based approaches on a ground truth dataset.

Abstract. Consider a video sequence captured by a single camera observing a complex dynamic scene containing an unknown mixture of multiple moving and possibly deforming objects. In this paper we propose an unsupervised approach to the challenging problem of simultaneously segmenting the scene into its constituent objects and reconstructing a 3D model of the scene. The strength of our approach comes from the ability to deal with real-world dynamic scenes and to handle seamlessly different types of motion: rigid, articulated and non-rigid. We formulate the problem as hierarchical graph-cut based segmentation where we decompose the whole scene into background and foreground objects and model the complex motion of non-rigid or articulated objects as a set of overlapping rigid parts. We evaluate the motion segmentation functionality of our approach on the Berkeley Motion Segmentation Dataset. In addition, to validate the capability of our approach to deal with realworld scenes we provide 3D reconstructions of some challenging videos from the YouTube-Objects dataset.

Person re-identification (re-ID) is a highly challenging task due to large variations of pose, viewpoint, illumination, and occlusion. Deep metric learning provides a satisfactory solution to person re-ID by training a deep network under supervision of metric loss, e.g., triplet loss. However, the performance of deep metric learning is greatly limited by traditional sampling methods. To solve this problem, we propose a Hard-Aware Point-to-Set (HAP2S) loss with a soft hard-mining scheme. Based on the point-to-set triplet loss framework, the HAP2S loss adaptively assigns greater weights to harder samples. Several advantageous properties are observed when compared with other state-of-the-art loss functions: 1) Accuracy: HAP2S loss consistently achieves higher re-ID accuracies than other alternatives on three large-scale benchmark datasets; 2) Robustness: HAP2S loss is more robust to outliers than other losses; 3) Flexibility: HAP2S loss does not rely on a specific weight function, i.e., different instantiations of HAP2S loss are equally effective. 4) Generality: In addition to person re-ID, we apply the proposed method to generic deep metric learning benchmarks including CUB-200-2011 and Cars196, and also achieve state-of-the-art results.

The stable operation of lithium-based batteries at low temperatures is critical for applications in cold climates. However, low-temperature operations are plagued by insufficient dynamics in the bulk of the electrolyte and at electrode|electrolyte interfaces. Here, we report a quasi-solid-state polymer electrolyte with an ionic conductivity of 2.2 × 10−4 S cm−1 at −20 °C. The electrolyte is prepared via in situ polymerization using a 1,3,5-trioxane-based precursor. The polymer-based electrolyte enables a dual-layered solid electrolyte interphase formation on the Li metal electrode and stabilizes the LiNi0.8Co0.1Mn0.1O2-based positive electrode, thus improving interfacial charge-transfer at low temperatures. Consequently, the growth of dendrites at the lithium metal electrode is hindered, thus enabling stable Li||LiNi0.8Co0.1Mn0.1O2 coin and pouch cell operation even at −30 °C. In particular, we report a Li||LiNi0.8Co0.1Mn0.1O2 coin cell cycled at −20 °C and 20 mA g−1 capable of retaining more than 75% (i.e., around 151 mAh g−1) of its first discharge capacity cycle at 30 °C and same specific current.