During the long history of computer vision, one of the grand challenges has been semantic segmentation which is the ability to segment an unknown image into different parts and objects (e.g., beach, ocean, sun, dog, swimmer). Furthermore, segmentation is even deeper than object recognition because recognition is not necessary for segmentation. Specifically, humans can perform image segmentation without even knowing what the objects are (for example, in satellite imagery or medical X-ray scans, there may be several objects which are unknown, but they can still be segmented within the image typically for further investigation). Performing segmentation without knowing the exact identity of all objects in the scene is an important part of our visual understanding process which can give us a powerful model to understand the world and also be used to improve or augment existing computer vision techniques. Herein this work, we review the field of semantic segmentation as pertaining to deep convolutional neural networks. We provide comprehensive coverage of the top approaches and summarize the strengths, weaknesses and major challenges.

Objects are often organized in a semantic hierarchy of categories, where fine-level categories are grouped into coarse-level categories according to their semantic relations. While previous works usually only classify objects into the leaf categories, we argue that generating hierarchical labels can actually describe how the leaf categories evolved from higher level coarse-grained categories, thus can provide a better understanding of the objects. In this paper, we propose to utilize the CNN-RNN framework to address the hierarchical image classification task. CNN allows us to obtain discriminative features for the input images, and RNN enables us to jointly optimize the classification of coarse and fine labels. This framework can not only generate hierarchical labels for images, but also improve the traditional leaf-level classification performance due to incorporating the hierarchical information. Moreover, this framework can be built on top of any CNN architecture which is primarily designed for leaf-level classification. Accordingly, we build a high performance network based on the CNN-RNN paradigm which outperforms the original CNN (wider-ResNet) and also the current state-of-the-art. In addition, we investigate how to utilize the CNN-RNN framework to improve the fine category classification when a fraction of the training data is only annotated with coarse labels. Experimental results demonstrate that CNN-RNN can use the coarse-labeled training data to improve the classification of fine categories, and in some cases it even surpasses the performance achieved by fully annotated training data. This reveals that, CNN-RNN can alleviate the challenge of specialized and expensive annotation of fine labels.

Federated learning is a distributed machine learning paradigm that emerged as a solution to the need for privacy protection in artificial intelligence. Like traditional machine learning, federated learning is threatened by multiple attacks, such as backdoor attacks, Byzantine attacks, and adversarial attacks. The weaknesses are exacerbated by the inaccessibility of data in federated learning, which makes it more difficult to defend against these threats. This points to the need for further research into defensive approaches to make federated learning a real solution for distributed machine learning paradigm with securing data privacy. Our survey provides a taxonomy of these threats and defense methods, describing the general situation of this vulnerability in federated learning. We also sort out the relationship between these methods, their advantages and disadvantages, and discuss future research directions regarding the security issues of federated learning from multiple perspectives.

Zero-shot anomaly detection (ZSAD) enables the inspection of unseen objects by bridging textual prompts and visual features, showing great potential in flexible manufacturing. While existing ZSAD methods rely on predefined prompts and struggle with unseen defects, Multimodal Large Language Models (MLLMs) offer promising solutions through their generative and interpretative capabilities. However, adapting MLLMs to Industrial Anomaly Detection (IAD) remains challenging due to fine-grained anomaly patterns and subtle visual distinctions. We propose VMAD (Visual-enhanced MLLM Anomaly Detection), a framework that enriches MLLM with visual IAD knowledge through two key components: a Defect-Sensitive Structure Learning scheme that transfers patch-similarities for improved discrimination, and a Locality-enhanced Token Compression that leverages multi-level local features for fine-grained detection. We also introduce RIAD, a comprehensive IAD dataset with detailed anomaly annotations. Extensive experiments on MVTec-AD, Visa, WFDD, and RIAD demonstrate VMAD’s superior performance. The dataset and code will be publicly available at https://github.com/denghuilin-cyber/VMAD.

Convolutional Neural Networks (CNNs) have attracted significant attention in visual recognition. Several recent studies have shown that, in addition to the fully-connected layers, the features derived from the convolutional layers of CNNs can also achieve promising performance in image classification tasks. In this paper, we propose a new feature from the convolutional layers, called Bag of Surrogate Parts (BoSP), and its spatial variant, Spatial-BoSP (S-BoSP). The main idea is, we assume the feature maps in the convolutional layers as surrogate parts, and densely sample and assign image regions to these surrogate parts by observing the activation values. Together with BoSP/SBoSP, we further propose another two schemes to enhance the performance: scale pooling and bilinear prediction. Scale pooling aims to handle the objects with different scales and deformations, and bilinear prediction combines the predictions of global and part features. By conducting extensive experiments on generic object, fine-grained object and scene datasets, we find the proposed feature can not only achieve superior performance to the fully-connected feature, but also produce competitive, or in some cases remarkably better performance than the state-of-the-art.