Preserving Cross-Modal Consistency for CLIP-based Class-Incremental Learning

The article presents CLIPPan, an unsupervised pansharpening framework that utilizes CLIP, a visual-language model, as a supervisor. This approach addresses the challenges faced by supervised pansharpening methods, particularly the domain adaptation issues arising from the disparity between simulated low-resolution training data and real-world high-resolution scenarios. The framework is designed to improve the understanding of the pansharpening process and enhance the model's ability to recognize various image types, ultimately setting a new state of the art in unsupervised full-resolution pans…

The article discusses FedOrtho, a federated unlearning framework designed to enhance data unlearning in federated learning environments. It addresses the challenges of balancing forgetting and retention, particularly in non-IID settings. FedOrtho employs orthogonalized deep convolutional kernels and a one-shot soft pruning mechanism, achieving state-of-the-art performance on datasets like CIFAR-10 and TinyImageNet, with over 98% forgetting quality and 97% retention accuracy.

The article introduces NP-LoRA, a novel framework for Low-Rank Adaptation (LoRA) fusion that addresses the issue of interference in existing methods. Traditional weight-based merging often leads to one LoRA dominating another, resulting in degraded fidelity. NP-LoRA utilizes a projection-based approach to maintain subspace separation, thereby enhancing the quality of fusion by preventing structural interference among principal directions.

Unified Heterogeneous Knowledge Distillation (UHKD) is a proposed framework that enhances knowledge distillation (KD) by utilizing intermediate features in the frequency domain. This approach addresses the limitations of traditional KD methods, which are primarily designed for homogeneous models and struggle in heterogeneous environments. UHKD aims to improve model compression while maintaining accuracy, making it a significant advancement in the field of artificial intelligence.

The paper introduces PrivDFS, a distributed feature-sharing framework designed for input-private inference in image classification. It addresses vulnerabilities in split inference that allow Data Reconstruction Attacks (DRAs) to reconstruct inputs with high fidelity. By fragmenting the intermediate representation and processing these fragments independently across a majority-honest set of servers, PrivDFS limits the reconstruction capability while maintaining accuracy within 1% of non-private methods.

The paper discusses advancements in out-of-distribution (OOD) detection, focusing on the integration of visual and textual modalities through large language models (LLMs). It introduces a method called Positive and Negative Prompt Supervision, which aims to improve OOD detection by using class-specific prompts that capture inter-class features. This approach addresses the limitations of negative prompts that may include non-ID features, potentially leading to suboptimal outcomes.