With the development of convolutional neural networks, hundreds of deep learning–based dehazing methods have been proposed. In this article, we provide a comprehensive survey on supervised, semi-supervised, and unsupervised single image dehazing. We first discuss the physical model, datasets, network modules, loss functions, and evaluation metrics that are commonly used. Then, the main contributions of various dehazing algorithms are categorized and summarized. Further, quantitative and qualitative experiments of various baseline methods are carried out. Finally, the unsolved issues and challenges that can inspire the future research are pointed out. A collection of useful dehazing materials is available at https://github.com/Xiaofeng-life/AwesomeDehazing .

Nearest neighbor search is a fundamental problem in various domains such as computer vision, data mining and machine learning. With the explosive growth of data on the Internet, many new data structures using spatial partitions and recursive hyperplane decomposition (eg. k-d trees) are proposed to speed up the nearest neighbor search. However, these data structures are facing big data challenges. To meet these challenges, binary hashing based approximate nearest neighbor search methods attract substantial attention due to their fast query speed and drastically reduced storage. Since the most notably Locality Sensitive Hashing (LSH) was proposed, a large number of binary hashing methods have emerged. In this paper, we first illustrate the development of binary hashing research work by proposing an overall and clear classification of them. Then we conduct extensive experiments to compare the performance of these methods on five famous and public datasets. Finally, we present our view on this topic.

Adversarial training has achieved remarkable advancements in defending against adversarial attacks. Among them, fast adversarial training (FAT) is gaining attention for its ability to achieve competitive robustness with fewer computing resources. Existing FAT methods typically employ a uniform strategy that optimizes all training data equally without considering the influence of different examples, which leads to an imbalanced optimization. However, this imbalance remains unexplored in the field of FAT. In this paper, we conduct a comprehensive study of the imbalance issue in FAT and observe an obvious class disparity regarding their performances. This disparity could be embodied from a perspective of alignment between clean and robust accuracy. Based on the analysis, we mainly attribute the observed misalignment and disparity to the imbalanced optimization in FAT, which motivates us to optimize different training data adaptively to enhance robustness. Specifically, we take disparity and misalignment into consideration. First, we introduce self-knowledge guided regularization, which assigns differentiated regularization weights to each class based on its training state, alleviating class disparity. Additionally, we propose self-knowledge guided label relaxation, which adjusts label relaxation according to the training accuracy, alleviating the misalignment and improving robustness. By combining these methods, we formulate the Self-Knowledge Guided FAT (SKG-FAT), leveraging naturally generated knowledge during training to enhance the adversarial robustness without compromising training efficiency. Extensive experiments on four standard datasets demonstrate that the SKG-FAT improves the robustness and preserves competitive clean accuracy, outperforming the state-of-the-art methods. Code and checkpoints are available at SFG-FAT Code Implementation.

Cross-modal retrieval, as an emerging field within multimedia research, has gained significant attention in recent years. Unsupervised cross-modal hashing methods are attractive due to their ability to capture latent relationships within the data without label supervision and to produce compact hash codes for high search efficiency. However, the text modality exhibits worse representation ability compared with the image modality, leading to weak guidance to construct the joint similarity matrix. Moreover, most unsupervised cross-modal hashing methods are based on pairwise similarities for training, resulting in non-aggregating data distribution in the hash space. In this paper, we propose a novel Vision-guided Text Mining for Unsupervised Cross-modal Hashing via Community Similarity Quantization, termed VTM-UCH. Specifically, we first find the one-to-one correspondence between each word and each vision (image or object) based on the Contrastive Language-Image Pre-training (CLIP) model and compute the text similarities according to the clustering of their corresponding visions. Then, we define the fine-grained object-level image similarities and design the joint similarity matrix based on the text and image similarities. Accordingly, we construct an undirected graph to compute the communities as the pseudo-centers and adjust the pairwise similarities to improve the hash codes distribution. The experimental results on two common datasets verify the accuracy improvements in comparison with state-of-the-art baselines.

Adversarial training (AT) is an effective defense method against gradient-based attacks to enhance the robustness of neural networks. Among them, single-step AT has emerged as a hotspot topic due to its simplicity and efficiency, requiring only one gradient propagation in generating adversarial examples. Nonetheless, the problem of catastrophic overfitting (CO) that causes training collapse remains poorly understood, and there exists a gap between the robust accuracy achieved through single- and multi-step AT. In this paper, we present a surprising finding that the taxonomy of adversarial examples reveals the truth of CO. Based on this conclusion, we propose taxonomy driven fast adversarial training (TDAT) which jointly optimizes learning objective, loss function, and initialization method, thereby can be regarded as a new paradigm of single-step AT. Compared with other fast AT methods, TDAT can boost the robustness of neural networks, alleviate the influence of misclassified examples, and prevent CO during the training process while requiring almost no additional computational and memory resources. Our method achieves robust accuracy improvement of 1.59%, 1.62%, 0.71%, and 1.26% on CIFAR-10, CIFAR-100, Tiny ImageNet, and ImageNet-100 datasets, when against projected gradient descent PGD10 attack with perturbation budget 8/255. Furthermore, our proposed method also achieves state-of-the-art robust accuracy against other attacks. Code is available at https://github.com/bookman233/TDAT.

Hashing has been widely researched to solve the large-scale approximate nearest neighbor search problem owing to its time and storage superiority. In recent years, a number of online hashing methods have emerged, which can update the hash functions to adapt to the new stream data and realize dynamic retrieval. However, existing online hashing methods are required to update the whole database with the latest hash functions when a query arrives, which leads to low retrieval efficiency with the continuous increase of the stream data. On the other hand, these methods ignore the supervision relationship among the examples, especially in the multi-label case. In this paper, we propose a novel Fast Online Hashing (FOH) method which only updates the binary codes of a small part of the database. To be specific, we first build a query pool in which the nearest neighbors of each central point are recorded. When a new query arrives, only the binary codes of the corresponding potential neighbors are updated. In addition, we create a similarity matrix which takes the multi-label supervision information into account and bring in the multi-label projection loss to further preserve the similarity among the multi-label data. The experimental results on two common benchmarks show that the proposed FOH can achieve dramatic superiority on query time up to 6.28 seconds less than state-of-the-art baselines with competitive retrieval accuracy.