Fine-grained image retrieval (FGIR) is a crucial task in computer vision, with broad applications in areas such as biodiversity monitoring, e-commerce, and medical diagnostics. However, capturing discriminative feature information to generate binary codes is difficult because of high intraclass variance and low interclass variance. To address this challenge, we (i) build a novel and highly reliable fine-grained deep hash learning framework for more accurate retrieval of fine-grained images. (ii) We propose a part significant region erasure method that forces the network to generate compact binary codes. (iii) We introduce a CNN-guided Transformer structure for use in fine-grained retrieval tasks to capture fine-grained images effectively in contextual feature relationships to mine more discriminative regional features. (iv) A multistage mixture loss is designed to optimize network training and enhance feature representation. Experiments were conducted on three publicly available fine-grained datasets. The results show that our method effectively improves the performance of fine-grained image retrieval.

The field of Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is undergoing a paradigm shift, transitioning from specialized models designed for individual tasks to more general retrieval models capable of managing various specialized scenarios. Inspired by the impressive generalization ability of the Contrastive Language-Image Pretraining (CLIP) model, we propose a CLIP-driven universal framework (Dr. CLIP), which leverages prompt learning to guide the synergy between CLIP and ZS-SBIR. Dr. CLIP can perfectly cover four variants of ZS-SBIR tasks (inter-category, intra-category, cross-datasets, and generalization). Moreover, we decompose the synergy into classification learning, metric learning, and ranking learning, as well as introduce three key components to enhance learning effectiveness. i) a forgetting suppression idea is applied to prevent catastrophic forgetting and constrains the feature distribution of the new categories in classification learning. ii) a domain balanced loss is proposed to address sample imbalance and establish effective cross-domain correlations in metric learning. iii) a pair-relation strategy is introduced to capture relevance and ranking relationships between instances in ranking learning. Eventually, we reorganize and redivide three coarse-grained datasets and two fine-grained datasets to accommodate the training settings for four ZS-SBIR tasks. The comparison experiments confirmed our method surpassed the state-of-the-art (SOTA) methods by a significant margin (1.95% ~ 19.14,% mAP), highlighting its generality and superiority. The code is available at https://github.com/x-28/CDUF.git.

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.

Accurate bus ridership forecasts can help city managers develop more effective transportation plans, such as bus schedules. With the continuous development of intelligent transportation systems, many methods have been proposed to predict bus ridership. However, there are two limitations to these existing efforts. First, the existing methods mainly consider temporal properties such as closeness, period, trends, holidays, and weekends, respectively. However, the temporal characteristics can be correlated and affect each other among different time scales. Besides, the spatial interactions are dynamic and complex. Thus, how to extract and represent the high-level temporal features and the dynamic spatial dependency among multiple time scales is significant and profound for bus ridership prediction. In this paper, we propose a novel dynamic spatio-temporal multi-scale representation method DSTMR to predict bus ridership. Specifically, DSTMR consists of several dynamic spatio-temporal representation blocks (DSTs). In DST, the graph generator is used to learn the adjacency of bus stations, the graph convolution module is employed to represent the dynamic spatial relationship, and the temporal convolution module is designed to extract the high-level temporal features. To demonstrate the effectiveness of our proposed method, we first construct a dataset (ASBus) based on real-world data, which is available for further study. Extensive experiments on two real-world datasets validate the effectiveness of our method.