Multi-modal (vision-language) models, such as CLIP, are replacing traditional supervised pre-training models (e.g., ImageNet-based pre-training) as the new generation of visual foundation models. These models with robust and aligned semantic representations learned from billions of internet image-text pairs and can be applied to various downstream tasks in a zero-shot manner. However, in some fine-grained domains like medical imaging and remote sensing, the performance of multi-modal foundation models often leaves much to be desired. Consequently, many researchers have begun to explore few-shot adaptation methods for these models, gradually deriving three main technical approaches: (1) prompt-based methods, (2) adapter-based methods, and (3) external knowledge-based methods. Nevertheless, this rapidly developing field has produced numerous results without a comprehensive survey to systematically organize the research progress. Therefore, in this survey, we introduce and analyze the research advancements in few-shot adaptation methods for multi-modal models, summarizing commonly used datasets and experimental setups, and comparing the results of different methods. In addition, due to the lack of reliable theoretical support for existing methods, we derive the few-shot adaptation generalization error bound for multi-modal models. The theorem reveals that the generalization error of multi-modal foundation models is constrained by three factors: domain gap, model capacity, and sample size. Based on this, we propose three possible solutions from the following aspects: (1) adaptive domain generalization, (2) adaptive model selection, and (3) adaptive knowledge utilization.Kindly check and confirm the edit made in the title.The title is correct.

Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images for fine-grained recognition has attracted broad attention. However, the presence of label noise and hard examples in web images are two obstacles for training robust fine-grained recognition models. Therefore, in this paper, we propose a novel approach for removing irrelevant samples from real-world web images during training, while employing useful hard examples to update the network. Thus, our approach can alleviate the harmful effects of irrelevant noisy web images and hard examples to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods. The data and source code of this work have been made publicly available at: https://github.com/NUST-Machine-Intelligence-Laboratory/Advanced-Softly-Update-Drop .

Detecting objects from Unmanned Aerial Vehicles (UAV) is often hindered by a large number of small objects, resulting in low detection accuracy. To address this issue, mainstream approaches typically utilize multi-stage inferences. Despite their remarkable detecting accuracies, real-time efficiency is sacrificed, making them less practical to handle real applications. To this end, we propose to improve the single-stage inference accuracy through learning scale-invariant features. Specifically, a Scale-Invariant Feature Disentangling module is designed to disentangle scale-related and scale-invariant features. Then an Adversarial Feature Learning scheme is employed to enhance disentanglement. Finally, scale-invariant features are leveraged for robust UAV-based object detection. Furthermore, we construct a multi-modal UAV object detection dataset, State-Air, which incorporates annotated UAV state parameters. We apply our approach to three lightweight detection frameworks on two benchmark datasets. Extensive experiments demonstrate that our approach can effectively improve model accuracy and achieve state-of-the-art (SoTA) performance on three datasets. Our code and dataset are publicly available at https://github.com/1e12Leon/SIFDAL.

The lack of sufficient training data has been one obstacle to fine-grained visual classification research because labeling subcategories generally requires specialist knowledge. As one optional approach to alleviating the data-hunger problem, leveraging web images as training data is drawing increasing attention. Nevertheless, web images potentially have false labels, which can misguide the training process. Although several works have been proposed to deal with label noise, it still can be difficult for the network to tackle complex real-world noisy labels without any prior knowledge. In the literature, we propose to leverage a small and clean meta-set to provide reliable prior knowledge for tackling noisy web images. Specifically, our method trains a network with two peer predicting heads, which learn from noisy web images (web head) and meta ones (meta head), respectively. The meta head produces pseudo soft labels for web images to revise their training loss, which can overcome the high noise ratio problem. Furthermore, a selection net is trained in a meta-learning strategy to identify in- and out-of-distribution noisy images. Then in-distribution ones are reused for training with pseudo soft labels produced by the meta head as supervision, while out-of-distribution ones are discarded. In this manner, the misguidance caused by label noise is remarkably alleviated and in-distribution noisy samples are properly exploited to boost model performance. The superiority of our proposed approach is demonstrated by mathematical theory with great interpretability as well as extensive experimental results on the real-world dataset WebFG-496. Our source code is available at:.

Manually labeling fine-grained datasetsis laborious and typically requires domain-specific expert knowledge. Conversely, a vast amount of web data is relatively easy to obtain with nearly no human effort. Therefore, learning from noisy web data for fine-grained tasks is attracting increasing attention in recent years. However, the presence of noise in web images is a huge obstacle for training robust fine-grained recognition models. To this end, we propose a novel approach to identify noisy images as well as specifically distinguish in- and out-of-distribution samples. It can purify the noisy web training set by discarding out-of-distribution noise and relabeling in-distribution noisy samples. Then we can train the model on the purified dataset to alleviate the harmful effects of noise and make the most of web images to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods. The data and source code of this work have been made publicly available at: https://github.com/NUST-Machine-Intelligence-Laboratory/Dataset-Purification .