Current few-shot learning techniques predominantly leverage amortization techniques based on meta-learning frameworks, which effectively adapt to unknown tasks with limited examples. However, these approaches face significant challenges in cross-domain scenarios, where the data distributions between the source domain (training data) and the target domain (testing data) differ substantially. This domain shift can lead to models that overfit the global discriminative model while underfitting their local amortization on the adaptable few-shot structure. To mitigate this problem, our proposal makes an upgrade on Conditional Neural Adaptive Processes, reformulating its conditioning mechanism to better handle cross-domain adaptation. This results in calibrated amortization of task-specific feature extractors and the construction of a robust non-parametric classifier. In our implementation, we first employ generative modeling or deterministic self-attention to all labeled context features, establishing a strong task-level alignment that adapts the extractor across domains. Additionally, we introduce a novel channel-wise normalization to further enhance the adaptation process. Our experiments on the Meta-dataset benchmark demonstrate an average$6.9\sim 9$% improvement in out-of-distribution tasks, underscoring the effectiveness of exploiting calibrated adaptation in few-shot cross-domain classification.

Cross-domain few-shot learning aims to achieve swift generalization between a source domain and a target domain using a limited number of images. Current research predominantly relies on generalized feature embeddings, employing metric classifiers in Euclidean space for classification. However, due to existing disparities among different data domains, attaining generalized features in the embedding becomes challenging. Additionally, the rise in data domains leads to high-dimensional Euclidean spaces. To address the above problems, we introduce a cross-domain few-shot learning method named Hyperbolic Insights with Knowledge Distillation (HIKD). By integrating knowledge distillation, it enhances the model's generalization performance, thereby significantly improving task performance. Hyperbolic space, in comparison to Euclidean space, offers a larger capacity and supports the learning of hierarchical structures among images, which can aid generalized learning across different data domains. So we map the Euclidean space features to the hyperbolic space via hyperbolic embedding and utilize hyperbolic fitting distillation method in the meta-training phase to obtain multi-domain unified generalization representation. In the meta-testing phase, accounting for biases between the source and target domains, we present a hyperbolic adaptive module to adjust embedded features and eliminate inter-domain gap. Experiments on the Meta-Dataset demonstrate that HIKD outperforms state-of-the-arts methods with the average accuracy of 80.6%.

Prompt learning has made significant progress in vision-language models (VLMs), enabling pre-trained models like CLIP to perform cross-domain tasks with few-shot or even zero-shot learning. However, existing methods tend to overfit the training data after fine-tuning on the target domain, leading to a decline in generalization ability and limiting their performance on unseen categories.To address these challenges, we propose a multi-regularization guided knowledge distillation towards generalizable prompt learning. This approach enhances the model's adaptability and generalization through different stages of regularization while mitigating performance degradation caused by target domain training. Specifically, within the image encoder of CLIP, we introduce Residual Regularization, which binds additional residual connections to certain transformer blocks. This design provides greater flexibility, allowing the model to adjust to new data distributions when adapting to the target domain.Furthermore, during training, we impose Self-distillation Regularization to ensure that while adapting to the target domain, the model preserves its prior generalization knowledge. Specifically, we regularize the intermediate layer outputs of Transformer Blocks to prevent the model from excessively favoring target domain data. Additionally, we employ an unsupervised knowledge distillation strategy to enforce multi-level alignment between the teacher and student models by Direction Distillation Regularization. This ensures that both models maintain consistent visual feature orientations under the same textual features, thereby enhancing overall model stability and cross-domain adaptability.Experimental results demonstrate that our method achieves more stable classification performance in both cross-domain few-shot classification and domain adaptation settings.

Visual question answering requires a system to provide an accurate natural language answer given an image and a natural language question. However, it is widely recognized that previous generic VQA methods often tend to memorize biases present in the training data rather than learning proper behaviors, such as grounding images before predicting answers. Therefore, these methods usually achieve high in-distribution but poor out-of-distribution performance. In recent years, various datasets and debiasing methods have been proposed to evaluate and enhance the VQA robustness, respectively. This paper provides the first comprehensive survey focused on this emerging fashion. Specifically, we first provide an overview of the development process of datasets from in-distribution and out-of-distribution perspectives. Then, we examine the evaluation metrics employed by these datasets. Third, we propose a typology that presents the development process, similarities and differences, robustness comparison, and technical features of existing debiasing methods. Furthermore, we analyze and discuss the robustness of representative vision-and-language pre-training models on VQA. Finally, through a thorough review of the available literature and experimental analysis, we discuss the key areas for future research from various viewpoints.

As the exploration and utilization of outer space persist, the proliferation of space targets has significantly increased, underscoring the growing importance of space situational awareness. However, space target images encounter numerous challenges, including overexposure, excessive shadowing, star noise, and motion blur, distinct from natural images. While existing models can address specific issues in space target recognition images, their ability for generalizing to unseen data remains relatively weak. Furthermore, the uniform background and minimal interclass differences in space target images impose significant constraints on recognition accuracy. To tackle these challenges, we propose a domain-aware generalized meta-learning for space target recognition. In the meta-training phase, we introduce a distillation module to generalize the prior knowledge of auxiliary domains. This module distills features and predictions from auxiliary domains, providing prior information to develop a model capable of generalization across diverse domains. In the meta-testing phase, the frozen generalized embedding function is connected with a feature bias module to mitigate domain bias issues. Building on the advanced awareness of the space target domain, which is marked by substantial intraclass variations and minimal interclass variations, we introduce a feature refinement module. This module resolves fine-grained issues by reconstructing features and augmenting the proto loss to narrow the intraclass data distance. In practice, our method is evaluated under out-of-distribution settings on the BUAA-SID-share1.0 dataset, achieving an impressive accuracy of 96.0%, surpassing existing space target recognition algorithms.

Most few-shot learning methods employ either adaptive approaches or parameter amortization techniques. However, their reliance on pre-trained models presents a significant vulnerability. When an attacker's trigger activates a hidden backdoor, it may result in the misclassification of images, profoundly affecting the model's performance. In our research, we explore adaptive defenses against backdoor attacks for few-shot learning. We introduce a specialized stochastic process tailored to task characteristics that safeguards the classification model against attack-induced incorrect feature extraction. This process functions during forward propagation and is thus termed an "in-process defense." Our method employs an adaptive strategy, effectively generating task-level representations, enabling rapid adaptation to pre-trained models, and proving effective in few-shot classification scenarios for countering backdoor attacks. We apply latent stochastic processes to approximate task distributions and derive task-level representations from the support set. This task-level representation guides feature extraction, leading to backdoor trigger mismatching and forming the foundation of our parameter defense strategy. Benchmark tests on Meta-Dataset reveal that our approach not only withstands backdoor attacks but also shows an improved adaptation in addressing few-shot classification tasks.