Fine-grained urban flow inference (FUFI) is crucial for traffic management, as it infers high-resolution urban flow maps from coarse-grained observations. Existing FUFI methods typically focus on a single city and rely on comprehensive training with large-scale datasets to achieve precise inferences. However, data availability in developing cities may be limited, posing challenges to the development of well-performing models. To address this issue, we propose cross-city fine-grained urban flow inference, which aims to transfer spatio-temporal knowledge from data-rich cities to data-scarce areas using meta-transfer learning. This paper devises a Spatio-Temporal Deviation Alignment (STDA) framework to mitigate spatio-temporal distribution deviations and urban structural deviations between multiple source cities and the target city. Furthermore, STDA presents a cross-city normalization method that adaptively combines batch and instance normalization to maintain consistency between city-variant and city-invariant features. Besides, we design an urban structure alignment module to align spatial topological differences across cities. STDA effectively reduces distribution and structural deviations among different datasets while avoiding negative transfer. Extensive experiments conducted on three real-world datasets demonstrate that STDA consistently outperforms state-of-the-art baselines.

Zero-shot hashing targets to learn the hash codes of images in unseen classes based on the limited training data provided by seen classes. In zero-shot hashing, transferring the supervised knowledge, such as attributes and semantic relations, from seen classes to unseen ones is a widely employed method, where the performance is always subject to the ability to capture these supervised knowledge (which is always difficult to obtain). Therefore, in this study, we propose a new methodology for zero-shot hashing via an asymmetric ratio similarity matrix (ASZH), which only needs to calculate the semantic similarity among seen classes for hash learning. Specifically, we use an asymmetric ratio matrix in the similarity calculation to further explore the influence of similarity, where the values of positive weights for similar samples are not equivalent to those of negative ones for dissimilar samples. Additionally, a theoretical analysis regarding the utilization of an asymmetric ratio matrix is provided in this study. The experiments on three large benchmark datasets indicate that the proposed method achieves excellent performance than several state-of-the-art hashing methods.

We propose an efficient bus crowdedness classification system that can be used in daily life. In particular, we analyze and study the data collected from real bus, aiming to deal with the difficulty of bus congestion classification. Besides, we combine deep learning and computer vision technology to extract images or videos from the internal surveillance cameras of the bus. The information of crowd will finally be integrated with algorithms into a complete classification system. As a consequence, when the user enters the system and submits the image or video to be detected, the system will display the classification results in turn. The classification results include passenger density distribution, number of passengers, date, and algorithm running time. In addition, the user can use the mouse to delineate an area in the passenger density distribution map and count any image area.

Recent studies have verified that learning compact hash codes can facilitate big data retrieval processing. In particular, learning the deep hash function can greatly improve the retrieval performance. However, the existing deep supervised hashing algorithm treats all the samples in the same way, which leads to insufficient learning of difficult samples. Therefore, we cannot obtain the accurate learning of the similarity relation, making it difficult to achieve satisfactory performance. In light of this, this work proposes a deep supervised hashing model, called deep adaptive attention triple hashing (DAATH), which weights the similarity prediction scores of positive and negative samples in the form of triples, thus giving different degrees of attention to different samples. Compared with the traditional triple loss, it places a greater emphasis on the difficult triple, dramatically reducing the redundant calculation. Extensive experiments have been conducted to show that DAAH consistently outperforms the state-of-the-arts, confirmed its the effectiveness.

Hashing can compress heterogeneous high-dimensional data into compact binary codes. For most existing hash methods, they first predetermine a fixed length for the hash code and then train the model based on this fixed length. However, when the task requirements change, these methods need to retrain the model for a new length of hash codes, which increases time cost. To address this issue, we propose a deep supervised hashing method, called deep multiple length hashing(DMLH), which can learn multiple length hash codes simultaneously based on a multi-task learning network. This proposed DMLH can well utilize the relationships with a hard parameter sharing-based multi-task network. Specifically, in DMLH, the multiple hash codes with different lengths are regarded as different views of the same sample. Furthermore, we introduce a type of mutual information loss to mine the association among hash codes of different lengths. Extensive experiments have indicated that DMLH outperforms most existing models, verifying its effectiveness.

Multi-view hashing can well support large-scale near-duplicate video retrieval, due to its desirable advantages of mutual reinforcement of multiple features, low storage cost, and fast retrieval speed. However, there are still two limitations that impede its performance. First, existing methods only consider local structures in multiple features. They ignore the global structure that is important for near-duplicate video retrieval, and cannot fully exploit the dependence and complementarity of multiple features. Second, existing works always learn hashing functions bit by bit, which unfortunately increases the time complexity of hash function learning. In this paper, we propose a supervised hashing scheme, termed as joint multi-view hashing (JMVH), to address the aforementioned problems. It jointly preserves the global and local structures of multiple features while learning hashing functions efficiently. Specially, JMVH considers features of video as items, based on which an underlying Hamming space is learned by simultaneously preserving their local and global structures. In addition, a simple but efficient multi-bit hash function learning based on generalized eigenvalue decomposition is devised to learn multiple hash functions within a single step. It can significantly reduce the time complexity of conventional hash function learning processes that sequentially learn multiple hash functions bit by bit. The proposed JMVH is evaluated on two public databases: CC_WEB_VIDEO and UQ_VIDEO. Experimental results demonstrate that the proposed JMVH achieves more than a 5 percent improvement compared to several state-of-the-art methods which indicates the superior performance of JMVH.

The linear model is commonly utilized in hashing methods owing to its efficiency. To obtain better accuracy, linear-based hashing methods focus on designing a generalized linear objective function with different constraints or penalty terms that consider neighborhood information. In this study, we propose a novel generalized framework called Model Boost (MoBoost), which can achieve the self-improvement of the linear-based hashing. The proposed MoBoost is used to improve model parameter optimization for linear-based hashing methods without adding new constraints or penalty terms. In the proposed MoBoost, given a linear-based hashing method, we first execute the method several times to get several different hash codes for training samples, and then combine these different hash codes into one set utilizing one novel fusion strategy. Based on this set of hash codes, we learn some new parameters for the linear hash function that can significantly improve accuracy. The proposed MoBoost can be generally adopted in existing linear-based hashing methods, achieving more precise and stable performance compared to the original methods while imposing negligible added expenditure in terms of time and space. Extensive experiments are performed based on three benchmark datasets, and the results demonstrate the superior performance of the proposed framework.

Automatically assessing image quality from an aesthetic perspective is of great interest to the high-level vision research community. Existing methods are typically non-personalized and quantify image aesthetics with a universal label. However, given the fact that aesthetics is a subjective perception, how to understand user aesthetic perceptions poses a formidable challenge to image aesthetics assessment. In this paper, we propose to model user aesthetic perceptions using a set of exemplar images from social media platforms, and realize personalized aesthetics assessment by transferring this knowledge to adapt the results of the trained generic model. In this way, image aesthetics is measured from both aspects of visual quality and user tastes. Extensive experiments on two benchmark datasets well verified the potential of our approach for personalized image aesthetics assessment.

Aesthetics has become increasingly prominent for image search to enhance user satisfaction. Therefore, image aesthetics assessment is emerging as a promising research topic in recent years. In this paper, distinguished from existing studies relying on a single label, we propose to quantify the image aesthetics by a distribution over quality levels. The distribution representation can effectively characterize the disagreement among the aesthetic perceptions of users regarding the same image. Our framework is developed on the foundation of label distribution learning, in which the reliability of training examples and the correlations between quality levels are fully taken into account. Extensive experiments on two benchmark datasets well verified the potential of our approach for aesthetics assessment. The role of aesthetics in image search was also rigorously investigated.