In this paper, we address the problem of person re-identification, which refers to associating the persons captured from different cameras. We propose a simple yet effective human part-aligned representation for handling the body part misalignment problem. Our approach decomposes the human body into regions (parts) which are discriminative for person matching, accordingly computes the representations over the regions, and aggregates the similarities computed between the corresponding regions of a pair of probe and gallery images as the overall matching score. Our formulation, inspired by attention models, is a deep neural network modeling the three steps together, which is learnt through minimizing the triplet loss function without requiring body part labeling information. Unlike most existing deep learning algorithms that learn a global or spatial partition-based local representation, our approach performs human body partition, and thus is more robust to pose changes and various human spatial distributions in the person bounding box. Our approach shows state-of-the-art results over standard datasets, Market- @math , CUHK @math , CUHK @math and VIPeR.
The matching subnetwork can simply be a loss layer that penalizes the misalignment between learnt similarities and ground-truth similarities, e.g., pairwise loss @cite_25 @cite_72 @cite_66 @cite_64 @cite_74 , triplet loss and its variants @cite_4 @cite_20 @cite_61 @cite_41 . Besides using the off-the-shelf similarity function @cite_25 @cite_72 @cite_20 , e.g., cosine similarity or Euclidean distance, for comparing the feature representation, specific matching schemes are designed to eliminate the influence from body part misalignment. For instance, a matching subnetwork conducts convolution and max pooling operations, over the differences @cite_44 or the concatenation @cite_22 @cite_7 of the representations over grid cells of a pair of person images, to handle the misalignment problem. The approach with so called single-image and cross-image representations @cite_41 essentially combines the off-the-shelf distance and the matching network handling the misalignment. Instead of only matching the images over the final representation, the matching map in the intermediate features is used to guide the feature extraction in the later layers through a gated CNN @cite_19 .
@cite_61 @cite_64 @cite_4 @cite_22 @cite_7 @cite_41 @cite_66 @cite_44 @cite_19 @cite_72 @cite_74 @cite_25 @cite_20
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Matching images and sentences demands a fine understanding of both modalities. In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space. In this field, most existing works apply the ranking loss to pull the positive image text pairs close and push the negative pairs apart from each other. However, directly deploying the ranking loss is hard for network learning, since it starts from the two heterogeneous features to build inter-modal relationship. To address this problem, we propose the instance loss which explicitly considers the intra-modal data distribution. It is based on an unsupervised assumption that each image text group can be viewed as a class. So the network can learn the fine granularity from every image text group. The experiment shows that the instance loss offers better weight initialization for the ranking loss, so that more discriminative embeddings can be learned. Besides, existing works usually apply the off-the-shelf features, i.e., word2vec and fixed visual feature. So in a minor contribution, this paper constructs an end-to-end dual-path convolutional network to learn the image and text representations. End-to-end learning allows the system to directly learn from the data and fully utilize the supervision. On two generic retrieval datasets (Flickr30k and MSCOCO), experiments demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language based person retrieval, we improve the state of the art by a large margin. The code has been made publicly available. In this paper we aim to determine the location and orientation of a ground-level query image by matching to a reference database of overhead (e.g. satellite) images. For this task we collect a new dataset with one million pairs of street view and overhead images sampled from eleven U.S. cities. We explore several deep CNN architectures for cross-domain matching – Classification, Hybrid, Siamese, and Triplet networks. Classification and Hybrid architectures are accurate but slow since they allow only partial feature precomputation. We propose a new loss function which significantly improves the accuracy of Siamese and Triplet embedding networks while maintaining their applicability to large-scale retrieval tasks like image geolocalization. This image matching task is challenging not just because of the dramatic viewpoint difference between ground-level and overhead imagery but because the orientation (i.e. azimuth) of the street views is unknown making correspondence even more difficult. We examine several mechanisms to match in spite of this – training for rotation invariance, sampling possible rotations at query time, and explicitly predicting relative rotation of ground and overhead images with our deep networks. It turns out that explicit orientation supervision also improves location prediction accuracy. Our best performing architectures are roughly 2.5 times as accurate as the commonly used Siamese network baseline. This paper considers the task of matching images and sentences. The challenge consists in discriminatively embedding the two modalities onto a shared visual-textual space. Existing work in this field largely uses Recurrent Neural Networks (RNN) for text feature learning and employs off-the-shelf Convolutional Neural Networks (CNN) for image feature extraction. Our system, in comparison, differs in two key aspects. Firstly, we build a convolutional network amenable for fine-tuning the visual and textual representations, where the entire network only contains four components, i.e., convolution layer, pooling layer, rectified linear unit function (ReLU), and batch normalisation. End-to-end learning allows the system to directly learn from the data and fully utilise the supervisions. Secondly, we propose instance loss according to viewing each multimodal data pair as a class. This works with a large margin objective to learn the inter-modal correspondence between images and their textual descriptions. Experiments on two generic retrieval datasets (Flickr30k and MSCOCO) demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language person retrieval, we improve the state of the art by a large margin. Code is available at this https URL com layumi Image-Text-Embedding Person re-identification has been usually solved as either the matching of single-image representation (SIR) or the classification of cross-image representation (CIR). In this work, we exploit the connection between these two categories of methods, and propose a joint learning frame-work to unify SIR and CIR using convolutional neural network (CNN). Specifically, our deep architecture contains one shared sub-network together with two sub-networks that extract the SIRs of given images and the CIRs of given image pairs, respectively. The SIR sub-network is required to be computed once for each image (in both the probe and gallery sets), and the depth of the CIR sub-network is required to be minimal to reduce computational burden. Therefore, the two types of representation can be jointly optimized for pursuing better matching accuracy with moderate computational cost. Furthermore, the representations learned with pairwise comparison and triplet comparison objectives can be combined to improve matching performance. Experiments on the CUHK03, CUHK01 and VIPeR datasets show that the proposed method can achieve favorable accuracy while compared with state-of-the-arts.