Parameter-Efficient Fine-Tuning (PEFT) is a fundamental research problem in computer vision, which aims to tune a few parameters for efficient storage and adaptation of pre-trained vision models. Recently, sensitivity-aware parameter efficient fine-tuning method (SPT) addresses this problem by identifying sensitive parameters and then leveraging its sparse characteristic to combine unstructured and structured tuning for PEFT. However, existing methods only focus on the sparse characteristic of sensitive parameters but overlook its distribution characteristic, which results in additional storage burden and limited performance improvement. In this paper, we find that the distribution of sensitive parameters is not chaotic, but concentrates on a small number of rows or columns in each parameter matrix. Inspired by this fact, we propose a Compact Dynamic-Rank Adaptation-based tuning method for Sensitivity-aware Parameter efficient fine-Tuning, called CDRA-SPT. Specifically, we first identify the sensitive parameters that require tuning for each down-stream task. Then, we reorganize the sensitive parameters by following its row and column into a compact sub-parameter matrix. Finally, a dynamic-rank adaptation is designed and applied at sub-parameter matrix level for PEFT. Its advantage is that the dynamic-rank characteristic of sub-parameter matrix can be fully exploited for PEFT. Extensive experiments show that our method achieves superior performance over previous state-of-the-art methods.

The accurate detection of mesoscale convective systems (MCSs) is crucial for meteorological monitoring due to their potential to cause significant destruction through severe weather phenomena, such as hail, thunderstorms, and heavy rainfall. However, the existing methods for MCS detection mostly targets on single-frame detection, which just considers the static characteristics and ignores the temporal evolution in the life cycle of MCS. In this article, we propose a novel encoder-decoder neural network named mesoscale convective system detection network (MCSDNet) to detect MCS regions. MCSDNet has a simple architecture and is easy to expand. Different from the previous models, MCSDNet targets on multiframes detection and leverages multiscale spatiotemporal information in remote sensing imagery (RSI). As far as we know, it is the first work to utilize multiscale spatiotemporal information to detect MCS regions. First, we design a multiscale spatiotemporal information module to extract multilevel semantic from different encoder levels, which makes our models can extract more detail spatiotemporal features. Second, spatiotemporal mix unit (STMU), a dual spatiotemporal attention, is introduced to MCSDNet to capture both intraframe features and interframe. Finally, we present MCS remote sensing image (MCSRSI) the first publicly available dataset for multiframes MCS detection based on FY-4A satellite. We also conduct several experiments on MCSRSI and find that our proposed MCSDNet achieves the best performance on MCS detection task when comparing with other baseline methods. We hope that the combination of our open-access dataset and promising results will encourage the future research for MCS detection task and provide a robust framework for related tasks in atmospheric science. Our code is available at:https://github.com/250HandsomeLiang/MCSDNet.git

Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with few examples. Recently, lots of methods have been proposed from the perspective of meta-learning and representation learning. However, few works focus on the interpretability of FSL decision process. In this paper, we take a step towards the interpretable FSL by proposing a novel meta-learning based decision tree framework, namely, MetaDT. In particular, the FSL interpretability is achieved from two aspects, i.e., a concept aspect and a visual aspect. On the concept aspect, we first introduce a tree-like concept hierarchy as FSL prior. Then, resorting to the prior, we split each few-shot task to a set of subtasks with different concept levels and then perform class prediction via a model of decision tree. The advantage of such design is that a sequence of high-level concept decisions that lead up to a final class prediction can be obtained, which clarifies the FSL decision process. On the visual aspect, a set of subtask-specific classifiers with visual attention mechanism is designed to perform decision at each node of the decision tree. As a result, a subtask-specific heatmap visualization can be obtained to achieve the decision interpretability of each tree node. At last, to alleviate the data scarcity issue of FSL, we regard the prior of concept hierarchy as an undirected graph, and then design a graph convolution-based decision tree inference network as our meta-learner to infer parameters of the decision tree. Extensive experiments on performance comparison and interpretability analysis show superiority of our MetaDT.

Few-Shot Learning (FSL) is a challenging task, i.e., how to recognize novel classes with few examples? Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then predicting novel classes via a cosine nearest neighbor classifier with mean-based prototypes. Nevertheless, due to the data scarcity, the mean-based prototypes are usually biased. In this paper, we attempt to diminish the prototype bias by regarding it as a prototype optimization problem. To this end, we propose a novel meta-learning based prototype optimization framework to rectify prototypes, i.e., introducing a meta-optimizer to optimize prototypes. Although the existing meta-optimizers can also be adapted to our framework, they all overlook a crucial gradient bias issue, i.e., the mean-based gradient estimation is also biased on sparse data. To address the issue, we regard the gradient and its flow as meta-knowledge and then propose a novel Neural Ordinary Differential Equation (ODE)-based meta-optimizer to polish prototypes, called MetaNODE. In this meta-optimizer, we first view the mean-based prototypes as initial prototypes, and then model the process of prototype optimization as continuous-time dynamics specified by a Neural ODE. A gradient flow inference network is carefully designed to learn to estimate the continuous gradient flow for prototype dynamics. Finally, the optimal prototypes can be obtained by solving the Neural ODE. Extensive experiments on miniImagenet, tieredImagenet, and CUB-200-2011 show the effectiveness of our method.

Few-shot learning (FSL) aims to recognize a novel class with very few instances, which is a challenging task since it suffers from a data scarcity issue. One way to effectively alleviate this issue is introducing explicit knowledge summarized from human past experiences to achieve knowledge transfer for FSL. Based on this idea, in this paper, we introduce the explicit knowledge of class hierarchy (i.e., the hierarchy relations between classes) as FSL priors and propose a novel hyperbolic knowledge transfer framework for FSL, namely, HyperKT. Our insight is, in the hyperbolic space, the hierarchy relation between classes can be well preserved by resorting to the exponential growth characters of hyperbolic volume, so that better knowledge transfer can be achieved for FSL. Specifically, we first regard the class hierarchy as a tree-like structure. Then, 1) a hyperbolic representation learning module and a hyperbolic prototype inference module are employed to encode/infer each image and class prototype to the hyperbolic space, respectively; and 2) a novel hierarchical classification and relation reconstruction loss are carefully designed to learn the class hierarchy. Finally, the novel class prediction is performed in a nearest-prototype manner. Extensive experiments on three datasets show our method achieves superior performance over state-of-the-art methods, especially on 1-shot tasks.

Satellite image sequence prediction is a crucial and challenging task. Previous studies leverage optical flow methods or existing deep learning methods on spatial-temporal sequence models for the task. However, they suffer from either oversimplified model assumptions or blurry predictions and sequential error accumulation issue, for a long-term forecast requirement. In this paper, we propose a novel Multi-Scale Time Conditional Generative Adversarial Network (MSTCGAN). To address the sequential error accumulation issue, MSTCGAN adopts a parallel prediction framework to produce the future image sequences by a one-hot time condition input. In addition, a powerful multi-scale generator is designed with the multi-head axial attention, which helps to carefully preserve the fine-grained details for appearance consistency. Moreover, we develop a temporal discriminator to address the blurry issue and maintain the motion consistency in prediction. Extensive experiments have been conducted on FengYun-4A satellite data set, and the results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art approaches.

Few-Shot Remote Sensing Scene Classification (FSRSSC) is an important task, which aims to recognize novel scene classes with few examples. Recently, several studies attempt to address the FSRSSC problem by following few-shot natural image classification methods. These existing methods have made promising progress and achieved superior performance. However, they all overlook two unique characteristics of remote sensing images: (i)object co-occurrencethat multiple objects tend to appear together in a scene image and (ii)object spatial correlationthat these co-occurrence objects are distributed in the scene image following some spatial structure patterns. Such unique characteristics are very beneficial for FSRSSC, which can effectively alleviate the scarcity issue of labeled remote sensing images since they can provide more refined descriptions for each scene class. To fully exploit these characteristics, we propose a novel scene graph matching-based meta-learning framework for FSRSSC, called SGMNet. In this framework, a scene graph construction module is carefully designed to represent each test remote sensing image or each scene class as a scene graph, where the nodes reflect these co-occurrence objects meanwhile the edges capture the spatial correlations between these co-occurrence objects. Then, a scene graph matching module is further developed to evaluate the similarity score between each test remote sensing image and each scene class. Finally, based on the similarity scores, we perform the scene class prediction via a nearest neighbor classifier. We conduct extensive experiments on UCMerced LandUse, WHU19, AID, and NWPU-RESISC45 datasets. The experimental results show that our method obtains superior performance over the previous state-of-the-art methods.