Deep image hashing aims to map an input image to compact binary codes by deep neural network, to enable efficient image retrieval across large-scale dataset. Due to the explosive growth of modern data, deep hashing has gained growing attention from research community. Recently, convolutional neural networks like ResNet have dominated in deep hashing. Nevertheless, motivated by the recent advancements of vision transformers, we propose a pure transformer-based framework, called as HashFormer, to tackle the deep hashing task. Specifically, we utilize vision transformer (ViT) as our backbone, and treat binary codes as the intermediate representations for our surrogate task, i.e., image classification. In addition, we observe that the binary codes suitable for classification are sub-optimal for retrieval. To mitigate this problem, we present a novel average precision loss, which enables us to directly optimize the retrieval accuracy. To the best of our knowledge, our work is one of the pioneer works to address deep hashing learning problems without convolutional neural networks (CNNs). We perform comprehensive experiments on three widely-studied datasets: CIFAR-10, NUSWIDE and ImageNet. The proposed method demonstrates promising results against existing state-of-the-art works, validating the advantages and merits of our HashFormer.

HashFormer: Vision Transformer Based Deep Hashing for Image Retrieval

Deep learning has shown a tremendous growth in hashing techniques for image retrieval. Recently, Transformer has emerged as a new architecture by utilizing self-attention without convolution. Transformer is also extended to Vision Transformer (ViT) for the visual recognition with a promising performance on ImageNet. In this paper, we propose a Vision Transformer based Hashing (VTS) for image retrieval. We utilize the pre-trained ViT on ImageNet as the backbone network and add the hashing head. The proposed VTS model is fine tuned for hashing under six different image retrieval frameworks, including Deep Supervised Hashing (DSH), HashNet, GreedyHash, Improved Deep Hashing Network (IDHN), Deep Polarized Network (DPN) and Central Similarity Quantization (CSQ) with their objective functions. We perform the extensive experiments on CIFAR10, ImageNet, NUS-Wide, and COCO datasets. The proposed VTS based image retrieval outperforms the recent state-of-the-art hashing techniques with a great margin. We also find the proposed VTS model as the backbone network is better than the existing networks, such as AlexNet and ResNet.

Vision Transformer Hashing for Image Retrieval.

The recently developed vision transformer (ViT) has achieved promising results on image retrieval compared to convolutional neural networks. However, most of these vision transformer-based image retrieval methods use the original ViT model to extract global features, ignoring the importance of local features for image retrieval. In this work, we propose a vision transformer-based multiscale feature fusion image retrieval method (MSViT) to achieve the fusion of global features with local features. The challenge of this research work is how to learn the feature representation ability of transformer model, so as to improve the performance of image retrieval model. First, a transformer-based two-branch network structure is proposed to obtain different scale features by processing image patches with different granularities. Second, we present a multiscale feature fusion strategy, which can efficiently and effectively fuse the feature information of different sizes on two branches. Finally, to more fully utilize the label information to supervise the network training process, we optimize the construction rules for the triplet data. The comparison of experimental results with ten CNN-based and six transformer-based image retrieval methods on four publicly available image datasets shows that our method outperforms the state-of-the-art methods. And ablation experiments show that the designed multiscale feature fusion strategy and improved triplet loss function have an implicit improvement on the performance of MSViT.

MSViT: Training Multiscale Vision Transformers for Image Retrieval

Transformer based on self-attention mechanism has made remarkable achievements in natural language processing, which inspired the application research of Transformer in computer vision. The current deep hashing algorithms extract image features through the convolutional neural network (CNN). CNN concentrates on local information, and features lack global dependency information, which has an impact on image retrieval accuracy. To remedy the above defects, this paper proposes deep internally connected Transformer hashing for image retrieval (DICTH). DICTH has designed an improved Transformer block: internally connected Transformer block (ICT). ICT performs an embedded transformation on the feature maps, splices the generated Keys and Queries, to explore the rich context information between Query–Key pairs, and then dynamically encodes through multi-layer convolution to learn the context multi-head self-attention matrix. By combining ICT and ResNet18 to achieve self-attention injection, a long-distance dependency is established in the feature space to make up for the shortcomings of pure CNN in the feature extraction process and guide the algorithm to learn more accurate hash codes. At the same time, in the face of complex label information in big data sets, this paper uses an improved cross-entropy loss function: T-cross-entropy loss, to promote network learning of hash codes with more ability to distinguish between classes. In this paper, a lot of experiments have been conducted on CIFAR10, NUS-WIDE and MS-COCO datasets to verify the performance of DICTH. 

Deep internally connected transformer hashing for image retrieval

With the explosive increase of remote sensing data, how to search for remote sensing data quickly and accurately in a vast dataset is an incredibly critical matter for research subjects. The deep hashing method has become the dominant method for remote sensing image retrieval because of its low-cost storage and high-speed retrieval. However, for the reason of the limitation of fixed convolutional kernels, deep hashing frameworks based on Convolutional Neural Networks (CNNs) fail to obtain the global semantic information well, which leads to the generation of suboptimal solutions. Furthermore, existing hashing methods commonly employ the random sampling strategy or hardest sample mining to build training batches, resulting in bad local minima. To remedy these problems, a novel Deep Global Semantic Structure-preserving Hashing framework via corrective triplet loss (DGSSH) is proposed for remote sensing image retrieval to learn a discriminative and stable embedding space, achieving intra-class confusion and inter-class diversity. Specifically speaking, the feature extraction module based on Swim Transformer architecture is developed to capture global semantic information and multiscale features from remote sensing images. Based on a distribution matching constraint, the corrective triplet loss for deep hashing schemes is designed to reduce the distribution shift caused by the random selection or hardest sample mining. Meanwhile, to reduce the time overhead of the model, the asymmetric learning strategy is employed to perform effective compact representation learning. Numerous experiments have been carried out on three publicly available benchmarks, which indicates that the proposed DGSSH framework could achieve optimal performance for remote sensing image retrieval applications. The source code of our DGSSH framework is hosted at https://github.com/QinLab-WFU/DGSSH.git. 

Deep global semantic structure-preserving hashing via corrective triplet loss for remote sensing image retrieval

Deep hamming hashing has gained growing popularity in approximate nearest neighbour search for large-scale image retrieval. Until now, the deep hashing for the image retrieval community has been dominated by convolutional neural network architectures, e.g. \texttt{Resnet}\cite{he2016deep}. In this paper, inspired by the recent advancements of vision transformers, we present \textbf{Transhash}, a pure transformer-based framework for deep hashing learning. Concretely, our framework is composed of two major modules: (1) Based on \textit{Vision Transformer} (ViT), we design a siamese vision transformer backbone for image feature extraction. To learn fine-grained features, we innovate a dual-stream feature learning on top of the transformer to learn discriminative global and local features. (2) Besides, we adopt a Bayesian learning scheme with a dynamically constructed similarity matrix to learn compact binary hash codes. The entire framework is jointly trained in an end-to-end manner.~To the best of our knowledge, this is the first work to tackle deep hashing learning problems without convolutional neural networks (\textit{CNNs}). We perform comprehensive experiments on three widely-studied datasets: \textbf{CIFAR-10}, \textbf{NUSWIDE} and \textbf{IMAGENET}. The experiments have evidenced our superiority against the existing state-of-the-art deep hashing methods. Specifically, we achieve 8.2\%, 2.6\%, 12.7\% performance gains in terms of average \textit{mAP} for different hash bit lengths on three public datasets, respectively.

TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval

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TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval