Trademark sticker detection stands as a crucial visual task within industrial quality inspection scenarios, de-manding precise determination of the accurate positioning of adhered trademarks on products. Despite the remarkable suc-cess achieved by current deep learning networks, their training requirements often involve extensive datasets, posing significant challenges for direct application in trademark sticker detection systems. A major obstacle lies in the vast variety of trademarks encountered in real-world industrial settings, coupled with the rapid speed of trademark updates and iterations. This renders it impractical to gather samples of every trademark type for training purposes. Furthermore, the process of collecting and annotating industrial data samples can be both time-consuming and costly. To overcome these limitations, this paper introduces a novel approach that utilizes image subtraction to analyze the differences between the image with adhered trademarks and the image without adhered trademarks. This approach circumvents the need for vast sample sizes and extensive data annotations. By integrating minimum pooling techniques into the analysis of these difference images, this paper introduces an efficient approach that effectively filters out noise and alleviates the generalization challenges arising from frequent trademark replacements. Experimental findings underscore the robust flexibility of our method in precisely and adaptively detecting and recognizing packaging box labels. Consequently, this approach significantly enhances the quality of product packaging and trademark sticker detection, offering a viable solution for addressing the challenges posed by the diverse and rapidly evolving nature of industrial trademarks.

Inspired by the remarkable zero-shot generalization capacity of vision-language pre-trained model, we seek to leverage the supervision from CLIP model to alleviate the burden of data labeling. However, such supervision inevitably contains the label noise, which significantly degrades the discriminative power of the classification model. In this work, we propose Transductive CLIP, a novel framework for learning a classification network with noisy labels from scratch. Firstly, a class-conditional contrastive learning mechanism is proposed to mitigate the reliance on pseudo labels and boost the tolerance to noisy labels. Secondly, ensemble labels is adopted as a pseudo label updating strategy to stabilize the training of deep neural networks with noisy labels. This framework can reduce the impact of noisy labels from CLIP model effectively by combining both techniques. Experiments on multiple benchmark datasets demonstrate the substantial improvements over other state-of-the-art methods.

Abstract The weakly supervised methods for salient object detection are attractive, since they greatly release the burden of annotating time‐consuming pixel‐wise masks. However, the image‐level annotations utilized by current weakly supervised salient object detection models are too weak to provide sufficient supervision for this dense prediction task. To this end, a weakly supervised salient object detection method is proposed via double object proposals guidance, which is generated under the supervision of double bounding boxes annotations. With the double object proposals, the authors' method is capable of capturing both accurate but incomplete salient foreground and background information, which contributes to generating saliency maps with uniformly highlighted saliency regions and effectively suppressed background. In addition, an unsupervised salient object segmentation method is proposed, taking advantage of the non‐parametric statistical active contour model (NSACM), for segmenting salient objects with complete and compact boundaries. Experiments on five benchmark datasets show that the authors' weakly supervised salient object detection approach consistently outperforms other weakly supervised and unsupervised methods by a considerable margin, and even has comparable performance to the fully supervised ones.

Pedestrians in videos are usually in a moving state, resulting in serious spatial misalignment like scale variations and pose changes, which makes the video-based person re-identification problem more challenging. To address the above issue, in this paper, we propose a Frame-Guided Region-Aligned model (FGRA) for discriminative representation learning in two steps in an end-to-end manner. Firstly, based on a frame-guided feature learning strategy and a non-parametric alignment module, a novel alignment mechanism is proposed to extract well-aligned region features. Secondly, in order to form a sequence representation, an effective feature aggregation strategy that utilizes temporal alignment score and spatial attention is adopted to fuse region features in the temporal and spatial dimensions, respectively. Experiments are conducted on benchmark datasets to demonstrate the effectiveness of the proposed method to solve the misalignment problem and the superiority of the proposed method to the existing video-based person re-identification methods.

Multi‐source domain adaptation (MDA) aims to leverage knowledge from multiple source domains to improve the classification performance on target domains. Different degrees of distribution discrepancies between every two domains pose a huge challenge to MDA tasks. Most works focus on extracting features shared by all domains, which is critical but not enough to reduce distribution discrepancies. In this paper, we propose a method named as common‐specific feature learning (CSFL). Constituting a framework of feature learning, CSFL explores a subspace where the combination of common and specific features makes learned representations comprehensive. Based on this framework, we conduct a metric learning method for learning a discriminative feature representation. Considering redundant information caused by source domains is likely to hurt the performance, we impose an effective low‐rank constraint to remove the redundant information. Further, we adopt structure consistent constraint to preserve the local structure in each domain. CSFL has obtained about 1–5% improvement of mean accuracy, compared to the state‐of‐the‐art shallow methods. Further, compared with 90.2% and 89.4% of the best baseline deep method, CSFL achieves mean accuracy of 90.8% and 89.7% on the Office‐31 and ImageCLEF‐DA datasets respectively. The encouraging results validate the effectiveness of our method.

Domain adaptation is still a challenging task due to the fact that the distribution discrepancy between source domain and target domain weakens the transfer ability. Intuitively, it is crucial to discover a more discriminative feature representation across domains. However, previous methods do not take the target discriminative information into account since (most) target data are unlabelled. Here, the authors propose a transfer metric learning method which decreases intra‐class distance and increases inter‐class distance simultaneously even in the case of target data are unlabelled. The shared features are more discriminative, hence the model could be more robust for target data. Specially, the global optimal solution can be obtained by solving a generalised eigen‐decomposition problem. Extensive experiments on image datasets demonstrate that compared to several state‐of‐the‐art methods, authors’ method achieves significant improvement of 9.0% in average classification accuracy.

Stochastic algorithms have become more and more popular in the minimization of finite sums due to their efficiency and effectiveness. Recent advances include the stochastic average gradient algorithm, the stochastic variance reduced gradient algorithm, and the SAGA algorithm, a set of incremental gradient algorithm. However, both the stochastic average gradient algorithm and the SAGA algorithm require to store gradients for each sample, which is expensive and impractical especially in the case of large scale problems. To the best of our knowledge, existing memory-free algorithm like the stochastic variance reduced gradient algorithm might not be efficient (fast) enough in this case. Taking these into account, we propose a new optimisation algorithm in this class with low memory requirement but still achieves faster convergence rate than the state-of-the-art, called Practical SAGA. Remarkly, as a variant of the SAGA algorithm, the Practical SAGA algorithm enjoys the advantages of the SAGA algorithm, for example, supports non-strongly convex problems directly. Extensive experiments on four benchmarks show the efficiency and effectiveness of the Practical SAGA.