arXiv subjects

Jingtao Li

Publications and source records attributed to Jingtao Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagery

Advanced interpretation of hyperspectral remote sensing images benefits many precise Earth observation tasks. Recently, visual foundation models have promoted the remote sensing interpretation but concentrating on RGB and multispectral images. Due to the varied hyperspectral channels,existing foundation models would face image-by-image tuning situation, imposing great pressure on hardware and time resources. In this paper, we propose a tuning-free hyperspectral foundation model called HyperFree, by adapting the existing visual prompt engineering. To process varied channel numbers, we design a learned weight dictionary covering full-spectrum from $0.4 \sim 2.5 \, μ\text{m}$, supporting to build the embedding layer dynamically. To make the prompt design more tractable, HyperFree can generate multiple semantic-aware masks for one prompt by treating feature distance as semantic-similarity. After pre-training HyperFree on constructed large-scale high-resolution hyperspectral images, HyperFree (1 prompt) has shown comparable results with specialized models (5 shots) on 5 tasks and 11 datasets.Code and dataset are accessible at https://rsidea.whu.edu.cn/hyperfree.htm.

cs.CV↗

arXiv2025

HOpenCls: Training Hyperspectral Image Open-Set Classifiers in Their Living Environments

Hyperspectral image (HSI) open-set classification is critical for HSI classification models deployed in real-world environments, where classifiers must simultaneously classify known classes and reject unknown classes. Recent methods utilize auxiliary unknown classes data to improve classification performance. However, the auxiliary unknown classes data is strongly assumed to be completely separable from known classes and requires labor-intensive annotation. To address this limitation, this paper proposes a novel framework, HOpenCls, to leverage the unlabeled wild data-that is the mixture of known and unknown classes. Such wild data is abundant and can be collected freely during deploying classifiers in their living environments. The key insight is reformulating the open-set HSI classification with unlabeled wild data as a positive-unlabeled (PU) learning problem. Specifically, the multi-label strategy is introduced to bridge the PU learning and open-set HSI classification, and then the proposed gradient contraction and gradient expansion module to make this PU learning problem tractable from the observation of abnormal gradient weights associated with wild data. Extensive experiment results demonstrate that incorporating wild data has the potential to significantly enhance open-set HSI classification in complex real-world scenarios.

cs.CV↗

arXiv2024

Learning a Cross-modality Anomaly Detector for Remote Sensing Imagery

Remote sensing anomaly detector can find the objects deviating from the background as potential targets for Earth monitoring. Given the diversity in earth anomaly types, designing a transferring model with cross-modality detection ability should be cost-effective and flexible to new earth observation sources and anomaly types. However, the current anomaly detectors aim to learn the certain background distribution, the trained model cannot be transferred to unseen images. Inspired by the fact that the deviation metric for score ranking is consistent and independent from the image distribution, this study exploits the learning target conversion from the varying background distribution to the consistent deviation metric. We theoretically prove that the large-margin condition in labeled samples ensures the transferring ability of learned deviation metric. To satisfy this condition, two large margin losses for pixel-level and feature-level deviation ranking are proposed respectively. Since the real anomalies are difficult to acquire, anomaly simulation strategies are designed to compute the model loss. With the large-margin learning for deviation metric, the trained model achieves cross-modality detection ability in five modalities including hyperspectral, visible light, synthetic aperture radar (SAR), infrared and low-light in zero-shot manner.

cs.CV↗

arXiv2024

AnomalyCD: A benchmark for Earth anomaly change detection with high-resolution and time-series observations

Various Earth anomalies have destroyed the stable, balanced state, resulting in fatalities and serious destruction of property. With the advantages of large-scale and precise observation, high-resolution remote sensing images have been widely used for anomaly monitoring and localization. Powered by the deep representation, the existing methods have achieved remarkable advances, primarily in classification and change detection techniques. However, labeled samples are difficult to acquire due to the low probability of anomaly occurrence, and the trained models are limited to fixed anomaly categories, which hinders the application for anomalies with few samples or unknown anomalies. In this paper, to tackle this problem, we propose the anomaly change detection (AnomalyCD) technique, which accepts time-series observations and learns to identify anomalous changes by learning from the historical normal change pattern. Compared to the existing techniques, AnomalyCD processes an unfixed number of time steps and can localize the various anomalies in a unified manner, without human supervision. To benchmark AnomalyCD, we constructed a high-resolution dataset with time-series images dedicated to various Earth anomalies (the AnomalyCDD dataset). AnomalyCDD contains high-resolution (from 0.15 to 2.39 m/pixel), time-series (from 3 to 7 time steps), and large-scale images (1927.93 km2 in total) collected globally Furthermore, we developed a zero-shot baseline model (AnomalyCDM), which implements the AnomalyCD technique by extracting a general representation from the segment anything model (SAM) and conducting temporal comparison to distinguish the anomalous changes from normal changes. AnomalyCDM is designed as a two-stage workflow to enhance the efficiency, and has the ability to process the unseen images directly, without retraining for each scene.

cs.CV↗

arXiv2024

COALA: A Practical and Vision-Centric Federated Learning Platform

We present COALA, a vision-centric Federated Learning (FL) platform, and a suite of benchmarks for practical FL scenarios, which we categorize into three levels: task, data, and model. At the task level, COALA extends support from simple classification to 15 computer vision tasks, including object detection, segmentation, pose estimation, and more. It also facilitates federated multiple-task learning, allowing clients to tackle multiple tasks simultaneously. At the data level, COALA goes beyond supervised FL to benchmark both semi-supervised FL and unsupervised FL. It also benchmarks feature distribution shifts other than commonly considered label distribution shifts. In addition to dealing with static data, it supports federated continual learning for continuously changing data in real-world scenarios. At the model level, COALA benchmarks FL with split models and different models in different clients. COALA platform offers three degrees of customization for these practical FL scenarios, including configuration customization, components customization, and workflow customization. We conduct systematic benchmarking experiments for the practical FL scenarios and highlight potential opportunities for further advancements in FL. Codes are open sourced at https://github.com/SonyResearch/COALA.

cs.CV↗

arXiv2024

Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget

As scaling laws in generative AI push performance, they also simultaneously concentrate the development of these models among actors with large computational resources. With a focus on text-to-image (T2I) generative models, we aim to address this bottleneck by demonstrating very low-cost training of large-scale T2I diffusion transformer models. As the computational cost of transformers increases with the number of patches in each image, we propose to randomly mask up to 75% of the image patches during training. We propose a deferred masking strategy that preprocesses all patches using a patch-mixer before masking, thus significantly reducing the performance degradation with masking, making it superior to model downscaling in reducing computational cost. We also incorporate the latest improvements in transformer architecture, such as the use of mixture-of-experts layers, to improve performance and further identify the critical benefit of using synthetic images in micro-budget training. Finally, using only 37M publicly available real and synthetic images, we train a 1.16 billion parameter sparse transformer with only \$1,890 economical cost and achieve a 12.7 FID in zero-shot generation on the COCO dataset. Notably, our model achieves competitive FID and high-quality generations while incurring 118$\times$ lower cost than stable diffusion models and 14$\times$ lower cost than the current state-of-the-art approach that costs \$28,400. We aim to release our end-to-end training pipeline to further democratize the training of large-scale diffusion models on micro-budgets.

cs.CV↗

arXiv2024

Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization

Synthetic image data generation represents a promising avenue for training deep learning models, particularly in the realm of transfer learning, where obtaining real images within a specific domain can be prohibitively expensive due to privacy and intellectual property considerations. This work delves into the generation and utilization of synthetic images derived from text-to-image generative models in facilitating transfer learning paradigms. Despite the high visual fidelity of the generated images, we observe that their naive incorporation into existing real-image datasets does not consistently enhance model performance due to the inherent distribution gap between synthetic and real images. To address this issue, we introduce a novel two-stage framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model to improve its transferability and subsequently uses real data for rapid adaptation. Alongside, We propose dataset style inversion strategy to improve the stylistic alignment between synthetic and real images. Our proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements, with up to 30% accuracy increase on classification tasks. Intriguingly, we note that the enhancements were not yet saturated, indicating that the benefits may further increase with an expanded volume of synthetic data.

cs.CV↗

arXiv2023

Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery

Positive-unlabeled learning (PU learning) in hyperspectral remote sensing imagery (HSI) is aimed at learning a binary classifier from positive and unlabeled data, which has broad prospects in various earth vision applications. However, when PU learning meets limited labeled HSI, the unlabeled data may dominate the optimization process, which makes the neural networks overfit the unlabeled data. In this paper, a Taylor variational loss is proposed for HSI PU learning, which reduces the weight of the gradient of the unlabeled data by Taylor series expansion to enable the network to find a balance between overfitting and underfitting. In addition, the self-calibrated optimization strategy is designed to stabilize the training process. Experiments on 7 benchmark datasets (21 tasks in total) validate the effectiveness of the proposed method. Code is at: https://github.com/Hengwei-Zhao96/T-HOneCls.

cs.CV↗

arXiv2023

Aparecium: Revealing Secrets from Physical Photographs

Watermarking is a crucial tool for safeguarding copyrights and can serve as a more aesthetically pleasing alternative to QR codes. In recent years, watermarking methods based on deep learning have proved superior robustness against complex physical distortions than traditional watermarking methods. However, they have certain limitations that render them less effective in practice. For instance, current solutions necessitate physical photographs to be rectangular for accurate localization, cannot handle physical bending or folding, and require the hidden area to be completely captured at a close distance and small angle. To overcome these challenges, we propose a novel deep watermarking framework dubbed \textit{Aparecium}. Specifically, we preprocess secrets (i.e., watermarks) into a pattern and then embed it into the cover image, which is symmetrical to the final decoding-then-extracting process. To capture the watermarked region from complex physical scenarios, a locator is also introduced. Besides, we adopt a three-stage training strategy for training convergence. Extensive experiments demonstrate that \textit{Aparecium} is not only robust against different digital distortions, but also can resist various physical distortions, such as screen-shooting and printing-shooting, even in severe cases including different shapes, curvature, folding, incompleteness, long distances, and big angles while maintaining high visual quality. Furthermore, some ablation studies are also conducted to verify our design.

cs.MM↗

arXiv2023

Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors

Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision applications. However, it is a challenging task due to the complex distribution and the irregular shapes of objects, and the lack of abnormal samples. To tackle these problems, an anomaly segmentation model based on pixel descriptors (ASD) is proposed for anomaly segmentation in HSR imagery. Specifically, deep one-class classification is introduced for anomaly segmentation in the feature space with discriminative pixel descriptors. The ASD model incorporates the data argument for generating virtual ab-normal samples, which can force the pixel descriptors to be compact for normal data and meanwhile to be diverse to avoid the model collapse problems when only positive samples participated in the training. In addition, the ASD introduced a multi-level and multi-scale feature extraction strategy for learning the low-level and semantic information to make the pixel descriptors feature-rich. The proposed ASD model was validated using four HSR datasets and compared with the recent state-of-the-art models, showing its potential value in Earth vision applications.

cs.CV↗

arXiv2023

One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning

Hyperspectral anomaly detection (HAD) involves identifying the targets that deviate spectrally from their surroundings, without prior knowledge. Recently, deep learning based methods have become the mainstream HAD methods, due to their powerful spatial-spectral feature extraction ability. However, the current deep detection models are optimized to complete a proxy task (two-step paradigm), such as background reconstruction or generation, rather than achieving anomaly detection directly. This leads to suboptimal results and poor transferability, which means that the deep model is trained and tested on the same image. In this paper, an unsupervised transferred direct detection (TDD) model is proposed, which is optimized directly for the anomaly detection task (one-step paradigm) and has transferability. Specially, the TDD model is optimized to identify the spectral deviation relationship according to the anomaly definition. Compared to learning the specific background distribution as most models do, the spectral deviation relationship is universal for different images and guarantees the model transferability. To train the TDD model in an unsupervised manner, an anomaly sample simulation strategy is proposed to generate numerous pairs of anomaly samples. Furthermore, a global self-attention module and a local self-attention module are designed to help the model focus on the "spectrally deviating" relationship. The TDD model was validated on four public HAD datasets. The results show that the proposed TDD model can successfully overcome the limitation of traditional model training and testing on a single image, and the model has a powerful detection ability and excellent transferability.

cs.CV↗

arXiv2023

Model Extraction Attacks on Split Federated Learning

Federated Learning (FL) is a popular collaborative learning scheme involving multiple clients and a server. FL focuses on protecting clients' data but turns out to be highly vulnerable to Intellectual Property (IP) threats. Since FL periodically collects and distributes the model parameters, a free-rider can download the latest model and thus steal model IP. Split Federated Learning (SFL), a recent variant of FL that supports training with resource-constrained clients, splits the model into two, giving one part of the model to clients (client-side model), and the remaining part to the server (server-side model). Thus SFL prevents model leakage by design. Moreover, by blocking prediction queries, it can be made resistant to advanced IP threats such as traditional Model Extraction (ME) attacks. While SFL is better than FL in terms of providing IP protection, it is still vulnerable. In this paper, we expose the vulnerability of SFL and show how malicious clients can launch ME attacks by querying the gradient information from the server side. We propose five variants of ME attack which differs in the gradient usage as well as in the data assumptions. We show that under practical cases, the proposed ME attacks work exceptionally well for SFL. For instance, when the server-side model has five layers, our proposed ME attack can achieve over 90% accuracy with less than 2% accuracy degradation with VGG-11 on CIFAR-10.

cs.LG↗

arXiv2023

Accelerating Graph Analytics on a Reconfigurable Architecture with a Data-Indirect Prefetcher

The irregular nature of memory accesses of graph workloads makes their performance poor on modern computing platforms. On manycore reconfigurable architectures (MRAs), in particular, even state-of-the-art graph prefetchers do not work well (only 3% speedup), since they are designed for traditional CPUs. This is because caches in MRAs are typically not large enough to host a large quantity of prefetched data, and many employs shared caches that such prefetchers simply do not support. This paper studies the design of a data prefetcher for an MRA called Transmuter. The prefetcher is built on top of Prodigy, the current best-performing data prefetcher for CPUs. The key design elements that adapt the prefetcher to the MRA include fused prefetcher status handling registers and a prefetch handshake protocol to support run-time reconfiguration, in addition, a redesign of the cache structure in Transmuter. An evaluation of popular graph workloads shows that synergistic integration of these architectures outperforms a baseline without prefetcher by 1.27x on average and by as much as 2.72x on some workloads.

cs.AR↗

arXiv2022

Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase

Nowadays, AI companies improve service quality by aggressively collecting users' data generated by edge devices, which jeopardizes data privacy. To prevent this, Federated Learning is proposed as a private learning scheme, using which users can locally train the model without collecting users' raw data to servers. However, for machine-learning applications on edge devices that have hard memory constraints, implementing a large model using FL is infeasible. To meet the memory requirement, a recent collaborative learning scheme named split federal learning is a potential solution since it keeps a small model on the device and keeps the rest of the model on the server. In this work, we implement a simply SFL framework on the Arduino board and verify its correctness on the Chinese digits audio dataset for keyword spotting application with over 90% accuracy. Furthermore, on the English digits audio dataset, our SFL implementation achieves 13.89% higher accuracy compared to a state-of-the-art FL implementation.

cs.LG↗

arXiv2022

An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data

Sampling is an essential part of raw point cloud data processing such as in the popular PointNet++ scheme. Farthest Point Sampling (FPS), which iteratively samples the farthest point and performs distance updating, is one of the most popular sampling schemes. Unfortunately it suffers from low efficiency and can become the bottleneck of point cloud applications. We propose adjustable FPS (AFPS), parameterized by M, to aggressively reduce the complexity of FPS without compromising on the sampling performance. Specifically, it divides the original point cloud into M small point clouds and samples M points simultaneously. It exploits the dimensional locality of an approximately sorted point cloud data to minimize its performance degradation. AFPS method can achieve 22 to 30x speedup over original FPS. Furthermore, we propose the nearest-point-distance-updating (NPDU) method to limit the number of distance updates to a constant number. The combined NPDU on AFPS method can achieve a 34-280x speedup on a point cloud with 2K-32K points with algorithmic performance that is comparable to the original FPS. For instance, for the ShapeNet part segmentation task, it achieves 0.8490 instance average mIoU (mean Intersection of Union), which is only 0.0035 drop compared to the original FPS.

cs.CV↗

arXiv2022

ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning

This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activations (i.e., feature map), instead of raw data, to a central server. While such a scheme helps reduce the computational load at the client end, it opens itself to reconstruction of raw data from intermediate activation by the server. Existing works on protecting SFL only consider inference and do not handle attacks during training. So we propose ResSFL, a Split Federated Learning Framework that is designed to be MI-resistant during training. It is based on deriving a resistant feature extractor via attacker-aware training, and using this extractor to initialize the client-side model prior to standard SFL training. Such a method helps in reducing the computational complexity due to use of strong inversion model in client-side adversarial training as well as vulnerability of attacks launched in early training epochs. On CIFAR-100 dataset, our proposed framework successfully mitigates MI attack on a VGG-11 model with a high reconstruction Mean-Square-Error of 0.050 compared to 0.005 obtained by the baseline system. The framework achieves 67.5% accuracy (only 1% accuracy drop) with very low computation overhead. Code is released at: https://github.com/zlijingtao/ResSFL.

cs.LG↗

arXiv2021

NeurObfuscator: A Full-stack Obfuscation Tool to Mitigate Neural Architecture Stealing

Neural network stealing attacks have posed grave threats to neural network model deployment. Such attacks can be launched by extracting neural architecture information, such as layer sequence and dimension parameters, through leaky side-channels. To mitigate such attacks, we propose NeurObfuscator, a full-stack obfuscation tool to obfuscate the neural network architecture while preserving its functionality with very limited performance overhead. At the heart of this tool is a set of obfuscating knobs, including layer branching, layer widening, selective fusion and schedule pruning, that increase the number of operators, reduce/increase the latency, and number of cache and DRAM accesses. A genetic algorithm-based approach is adopted to orchestrate the combination of obfuscating knobs to achieve the best obfuscating effect on the layer sequence and dimension parameters so that the architecture information cannot be successfully extracted. Results on sequence obfuscation show that the proposed tool obfuscates a ResNet-18 ImageNet model to a totally different architecture (with 44 layer difference) without affecting its functionality with only 2% overall latency overhead. For dimension obfuscation, we demonstrate that an example convolution layer with 64 input and 128 output channels can be obfuscated to generate a layer with 207 input and 93 output channels with only a 2% latency overhead.

cs.CR↗

arXiv2021

Communication and Computation Reduction for Split Learning using Asynchronous Training

Split learning is a promising privacy-preserving distributed learning scheme that has low computation requirement at the edge device but has the disadvantage of high communication overhead between edge device and server. To reduce the communication overhead, this paper proposes a loss-based asynchronous training scheme that updates the client-side model less frequently and only sends/receives activations/gradients in selected epochs. To further reduce the communication overhead, the activations/gradients are quantized using 8-bit floating point prior to transmission. An added benefit of the proposed communication reduction method is that the computations at the client side are reduced due to reduction in the number of client model updates. Furthermore, the privacy of the proposed communication reduction based split learning method is almost the same as traditional split learning. Simulation results on VGG11, VGG13 and ResNet18 models on CIFAR-10 show that the communication cost is reduced by 1.64x-106.7x and the computations in the client are reduced by 2.86x-32.1x when the accuracy degradation is less than 0.5% for the single-client case. For 5 and 10-client cases, the communication cost reduction is 11.9x and 11.3x on VGG11 for 0.5% loss in accuracy.

cs.LG↗