Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users' historical check-in data. Therefore, it is crucial to model both users' general taste and recent sequential behavior. Moreover, different users show different dependencies on the two parts. However, most existing methods learn the same dependencies for different users. Besides, the locations and categories of POIs contain different information about users' preference. However, current researchers always treat them as the same factors or believe that categories determined where to go. To this end, we propose a novel method named Personalized Long- and Short-term Preference Learning (PLSPL) to learn the specific preference for each user. Specially, we combine the long- and short-term preference via user-based attention mechanism to learn the personalized weights on different parts for different users. Besides, the context information such as the category and check-in time is also important to capture user preference. Therefore, in long-term module, we consider the contextual features of POIs in users' history records and leverage attention mechanism to capture users' preference. In the short-term module, we train two LSTM models for location- and category-based sequence, respectively.

This paper introduces a convolutional neural network (CNN) semantic re-ranking system to enhance the performance of sketch-based image retrieval (SBIR). Distinguished from the existing approaches, the proposed system can leverage category information brought by CNNs to support effective similarity measurement between the images. To achieve effective classification of query sketches and high-quality initial retrieval results, one CNN model is trained for classification of sketches, another for that of natural images. Through training dual CNN models, the semantic information of both the sketches and natural images is captured by deep learning. In order to measure the category similarity between images, a category similarity measurement method is proposed. Category information is then used for re-ranking. Re-ranking operation first infers the retrieval category of the query sketch and then uses the category similarity measurement to measure the category similarity between the query sketch and each initial retrieval result. Finally, the initial retrieval results are re-ranked. The experiments on different types of SBIR datasets demonstrate the effectiveness of the proposed re-ranking method. Comparisons with other re-ranking algorithms are also given to show the proposed method's superiority. Further, compared to the baseline systems, the proposed re-ranking approach achieves significantly higher precision in the top ten different SBIR methods and datasets.

Occluded person re-identification is one of the most challenging tasks in security surveillance. Most existing methods focus on extracting human body features from occluded pedestrian images. This paper prioritizes a difference between occluded and non-occluded person re-ID: When computing the similarity between a holistic pedestrian image and an occluded pedestrian image, a certain part of the human body in this holistic image can be distractive for pedestrian retrieval. To solve this problem, we propose an occluded person re-ID framework named attribute-based shift attention network (ASAN). First, unlike other methods that use off-the-shelf tools to locate pedestrian body parts in the occluded images, we design an attribute-guided occlusion-sensitive pedestrian segmentation (AOPS) module. AOPS is a weakly supervised method that leverages the semantic-level attribute annotations in person re-ID datasets. Second, guided by the pedestrian masks provided by AOPS, a shift feature adaption (SFA) module extracts the visible part of the human body feature in a part-based manner. After that, a visible region matching (VRM) algorithm is proposed to filter out the interfer-ence information in the holistic person images during the retrieval phase and further purify the representation of pedestrian features. Extensive experiments with ablation analysis demonstrate our method’s effectiveness. And the state-of-the-art results are achieved on four occluded datasets Partial-REID, Partial-iLIDS, Occluded-DukeMTMC, and Occluded REID. Moreover, the experiments on two holistic person re-ID datasets Market-1501 and DukeMTMC-reID, and a vehicle re-ID dataset VeRi-776 show that ASAN also has a good generality.

The paper presents a sketch-based image retrieval algorithm. One of the main challenges in sketch-based image retrieval (SBIR) is to measure the similarity between a sketch and an image. To tackle this problem, we propose an SBIR-based approach by salient contour reinforcement. In our approach, we divide the image contour into two types. The first is the global contour map. The second, called the salient contour map, is helpful to find out the object in images similar to the query. In addition, based on the two contour maps, we propose a new descriptor, namely an angular radial orientation partitioning (AROP) feature. It fully utilizes the edge pixels' orientation information in contour maps to identify the spatial relationships. Our AROP feature based on the two candidate contour maps is both efficient and effective to discover false matches of local features between sketches and images, and can greatly improve the retrieval performance. The application of the retrieval system based on this algorithm is established. The experiments on the image dataset with 0.3 million images show the effectiveness of the proposed method and comparisons with other algorithms are also given. Compared to baseline performance, the proposed method achieves 10% higher precision in top 5.

Deep supervised hashing has emerged as an influential solution to large-scale semantic image retrieval problems in computer vision. In the light of recent progress, image label is the common way to define whether two images belong to the same category, but it contains little supervised information. The one-hot label can’t accurately define the similarity of two images, which is important for image retrieval. In this paper, we propose an effective method, Deep Transfer Hashing(DTH) which uses the knowledge from teacher model as the supervised information. Inspired by knowledge distillation for model compression and deep hashing for fast image retrieval, we transfer the knowledge from a complex convolutional neural network(teacher) to a small neural network which is used for fast image retrieval. The distance of the knowledge from teacher model can indicate the similarity of images. By minimizing the hashing codes distribution between the hashing layers of teacher model and student model, we can improve the retrieval performance. And we also evaluate the performance of the compressed model at inference stage. We test our method on widely used datasets CIFAR-10 and NUS-WIDE and we compare our method with other state-of-the-art methods in image retrieval domain. The experimental results show that our method can improve the image retrieval baseline by a large margin and better than other methods.

Fine-grained food recognition is the detailed classification that provides more specialized and professional attribute information of food. It is the basic work to realize healthy diet recommendations and cooking instructions, nutrition intake management, and cafeteria self-checkout system. Chinese food lacks structured information, and ingredients composition is an important consideration. The current approaches mostly focus on global dish appearance without any analysis of ingredient composition and fully considering the attention of regional features. In this paper, we propose an Attention Fusion Network (AFN) and Food-Ingredient Joint Learning module for fine-grained food and ingredients recognition. The AFN first focuses on the food discrimination region against unstructured defeat and generates the feature embeddings jointly aware of the ingredients and food. The Food-Ingredient Joint Learning module aims at alleviating the issue of ingredients imbalance. Therefore, we propose a balance focal loss to optimize the feature expression ability of the network for ingredients. In experiments, the results of ingredients recognition show the state-of-the-art performances on fine-grained Chinese food dataset VIREO Food-172.