Achieving fairness in machine learning remains a critical challenge, especially due to the opaque effects of data augmentation on input spaces within nonlinear neural networks. Nevertheless, current approaches that emphasize augmenting latent features, rather than input spaces, offer limited insights into their ability to detect and mitigate bias. In response, we introduce the concept of the "unfair region" in the latent space, a subspace that highlights areas where misclassification rates for certain demographic groups are disproportionately high, leading to unfair prediction results. To address this, we propose Adversarial Latent Feature Augmentation (ALFA), a method that leverages adversarial fairness attacks to perturb latent space features, which are then used as data augmentation for fine-tuning. ALFA intentionally shifts latent features into unfair regions, and the last layer of the network is fine-tuned with these perturbed features, leading to a corrected decision boundary that enhances fairness in classification in a cost-effective manner. We present a theoretical framework demonstrating that our adversarial fairness objective reliably generates biased feature perturbations, and that fine-tuning on samples from these unfair regions ensures fairness improvements. Extensive experiments across diverse datasets, modalities, and backbone networks validate that training with these adversarial features significantly enhances fairness while maintaining predictive accuracy in classification tasks.

As the diversity of real-world object categories increases, the need for sophisticated classification methods also grows. However, Novel Category Discovery (NCD), which aims to predict unseen categories, often falls short in scenarios where new categories are constantly updated and data distributions are potentially biased. In addition, existing dynamic NCD approaches assume that incremental stages introduce a fixed number of new classes and often overlook distributional biases in real-world classes. To address these limitations, we propose a novel framework, Novel Category Discovery for Dynamic Long-Tailed distribution (NCD-DLT), which deals with the more realistic and challenging scenario where imbalanced, unlabeled data are introduced incrementally and sporadically over time. Unlike conventional methods requiring k-means clustering on all test samples, our approach identifies novel categories on-the-fly, predicting categories for individual data points as they arrive. We propose an advanced hash-based clustering technique, leveraging a double-hashing strategy to mitigate collisions and incorporating a greedy hash regularization loss for sparse representations to enhance clustering capabilities. Furthermore, we implement distillation losses during training to preserve the model's discriminative power across stages without forgetting prior knowledge. Finally, we introduce a novel graph merging algorithm based on the Hash Hamming Graph, revealing the dataset's clustering structure. It serves as a mechanism for pseudo-labeling in training and acts as a post-processing tool, reallocating less confident samples to more appropriate clusters. Our comprehensive approach addresses the limitations of existing NCD methods in the dynamic scenario of novel category discovery in long-tailed distributions, demonstrating improved accuracy for both uniform and long-tailed scenarios.

Recent advancements in Vision-Language Models (VLMs) have enabled complex multimodal tasks by processing text and image data simultaneously, significantly enhancing the field of artificial intelligence. However, these models often exhibit biases that can skew outputs towards societal stereotypes, thus necessitating debiasing strategies. Existing debiasing methods focus narrowly on specific modalities or tasks, and require extensive retraining. To address these limitations, this paper introduces Selective Feature Imputation for Debiasing (SFID), a novel methodology that integrates feature pruning and low confidence imputation (LCI) to effectively reduce biases in VLMs. SFID is versatile, maintaining the semantic integrity of outputs and costly effective by eliminating the need for retraining. Our experimental results demonstrate SFID's effectiveness across various VLMs tasks including zero-shot classification, text-to-image retrieval, image captioning, and text-to-image generation, by significantly reducing gender biases without compromising performance. This approach not only enhances the fairness of VLMs applications but also preserves their efficiency and utility across diverse scenarios.

Image processing via convolutional neural network (CNN) has been developed rapidly for remote sensing technology. Moreover, techniques for accurately extracting building footprints from remote sensed images have attracted considerable interest owing to their wide variety of common applications, including monitoring natural disasters and urban development. Extraction of building footprints can be performed easily by semantic segmentation using U-Net-like CNN architectures. However, obtaining precise boundaries of segmentation masks remains challenging due to various impediments surrounding target objects. In this study, we propose a method to elaborate edges of buildings detected in remote sensed images to enhance the boundaries of segmentation masks. The proposed method adoptsholistically nested edge detection (HED), which extracts edge features at an encoder of a given architecture. In the proposedboundary enhancement (BE) module, an extracted edge and segmentation mask are combined, sharing mutual information. To enable the proposed method efficiently to adapt to a wide variety of conditions, we design a distinctive approach adopting a HED unit and BE module, which is applicable to various semantic segmentation networks containing encoder-decoder structures. Experiments were conducted on five different datasets (DeepGlobe, Urban3D, WHU [high-resolution (HR), low-resolution (LR)], and Massachusetts). The results demonstrate that our proposed approaches improved on the performance of prior methods for extracting building footprints. Comparative experiments were conducted on various backbone architectures including U-Net, ResUNet++, TernausNet, and U-shape spatial pyramid pooling (USPP) to ensure the effectiveness of the proposed method. Based on various evaluation metrics and qualitative analysis, our results show that the proposed method achieved improved performance compared with prior methods for all datasets and backbone networks.