作者: 不愛防注射 時間: 2025-3-21 21:44 作者: 牽索 時間: 2025-3-22 02:15
On the Ring-LWE and Polynomial-LWE Problemsesult, the security of DNN system has attracted great attention from the community. In typical scenes, the input images of DNN are collected through the camera. In this paper, we propose a new type of security threat, which attacks a DNN classifier by perturbing the optical path of the camera input 作者: 瘙癢 時間: 2025-3-22 06:29
Overdrive: Making SPDZ Great Againposed, FL remains vulnerable to security attacks (such as poisoning attacks and evasion attacks) because of its distributed nature. Additionally, real-world training data used in FL are usually Non-Independent and Identically Distributed (Non-IID), which further weakens the robustness of the existin作者: 痛打 時間: 2025-3-22 11:07
Advances in Cryptology – EUROCRYPT 2018sarial defenses have been carried out in academia. However, there is few research on adversarial attacks and adversarial defenses of point cloud semantic segmentation models, especially in the field of autonomous driving. The stability and robustness of point cloud semantic segmentation models are o作者: ARM 時間: 2025-3-22 14:33 作者: ARM 時間: 2025-3-22 18:27
Overdrive: Making SPDZ Great Again with Chronic Lymphocytic Leukemia (CLL) is about 65%. Neoplastic lymphomas accelerated Chronic Lymphocytic Leukemia (aCLL) and Richter Transformation - Diffuse Large B-cell Lymphoma (RT-DLBL) are the aggressive and rare variant of this cancer that are subjected to less survival rate in patients and作者: 向外才掩飾 時間: 2025-3-23 00:33
Marcel Keller,Valerio Pastro,Dragos Rotaruble across a variety of datasets coming from different scanners, hospitals, and acquisition protocols. In practice, this remains a challenge due to the complexities of the different types of domain shifts. In this paper, we address the domain-shift by proposing a novel domain adaptation framework fo作者: 粘 時間: 2025-3-23 05:05
Pavel Hubá?ek,Alon Rosen,Margarita Vald numerous and can be found in several contexts such as cosmetics or digital media retouching, to name a few. Recently, advancements in conditional generative modeling have shown astonishing results at modifying facial attributes in a realistic manner. However, current methods are still prone to arti作者: V切開 時間: 2025-3-23 06:28 作者: Morphine 時間: 2025-3-23 10:31
Vincent Grosso,Fran?ois-Xavier Standaertficiency of operations. However, this research area is still underrepresented compared to other automotive domains, especially regarding available image data, which is essential for training and benchmarking AI-based approaches. To mitigate this gap, we introduce a novel dataset specialized on stati作者: 粗魯?shù)娜?nbsp; 時間: 2025-3-23 14:09
Vincent Grosso,Fran?ois-Xavier Standaertpatial and/or temporal resolution issue. Most existing methods extensively exploit various hand-crafted priors to regularize the ill-posed hyperspectral reconstruction problem, and are incapable of handling wide spectral variety, often resulting in poor reconstruction quality. In recent year, deep c作者: aplomb 時間: 2025-3-23 21:49 作者: 致敬 時間: 2025-3-23 22:18
Jesper Buus Nielsen,Vincent Rijmen surpassed the human performance benchmarks on existing digit datasets, given that these datasets contain digits that have limited variability. In this paper, we introduce Caltech Football Numbers (CaltechFN), an image dataset of highly variable American football digits that aims to serve as a more 作者: 蓋他為秘密 時間: 2025-3-24 03:45 作者: 兇兆 時間: 2025-3-24 06:56
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234141.jpg作者: ablate 時間: 2025-3-24 14:42
Computer Vision – ACCV 2022 Workshops978-3-031-27066-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 出血 時間: 2025-3-24 15:29
https://doi.org/10.1007/978-3-031-27066-6computer networks; computer security; computer systems; computer vision; correlation analysis; data secur作者: phlegm 時間: 2025-3-24 19:36
978-3-031-27065-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: CODE 時間: 2025-3-25 00:01
Miruna Rosca,Damien Stehlé,Alexandre Walleto-expression recognition across three (positive, negative, and surprise) and five (happiness, other, anger, contempt, and surprise) classes. When compared with the state-of-the-art, the results report a significant improvement in accuracy and the F1 score. The proposal is also robust against the unbalanced class sizes of the SAMM dataset.作者: 實施生效 時間: 2025-3-25 05:29
https://doi.org/10.1007/978-3-030-17659-4gful information transfer by dynamically learning the reliability of local features from multiple frames. Comprehensive experiments on two video person re-identification benchmark datasets have demonstrated the effectiveness and state-of-the-art performance of the proposed method.作者: Palliation 時間: 2025-3-25 11:16
Micro-expression Recognition Using a?Shallow ConvLSTM-Based Networko-expression recognition across three (positive, negative, and surprise) and five (happiness, other, anger, contempt, and surprise) classes. When compared with the state-of-the-art, the results report a significant improvement in accuracy and the F1 score. The proposal is also robust against the unbalanced class sizes of the SAMM dataset.作者: 縫紉 時間: 2025-3-25 11:43
Temporal Extension Topology Learning for?Video-Based Person Re-identificationgful information transfer by dynamically learning the reliability of local features from multiple frames. Comprehensive experiments on two video person re-identification benchmark datasets have demonstrated the effectiveness and state-of-the-art performance of the proposed method.作者: antidote 時間: 2025-3-25 17:34 作者: 殘廢的火焰 時間: 2025-3-25 21:25
Nils Fleischhacker,Vipul Goyal,Abhishek Jainacy versus 87.4%, 0.921 AUC versus 0.837), and still has an acceptable performance on severely noised images photographed by smartphone, providing doctors in clinical facilities with outdated Ultrasound instruments a simple and feasible solution to diagnose BA with our online tool作者: 羽毛長成 時間: 2025-3-26 01:39 作者: 不可接觸 時間: 2025-3-26 06:45
Conference proceedings 2023stems; computer vision for medical computing; machine learning and computing for visual semantic analysis; vision transformers theory and applications; and deep learning-based small object detection from images and videos..作者: 獨輪車 時間: 2025-3-26 10:33 作者: 名詞 時間: 2025-3-26 13:32
Conference proceedings 2023 took place in Macao, China, in December 2022.?.The 25 papers included in this book were carefully reviewed and selected from 40 submissions. They have been organized in topical sections as follows: Learning with limited data for face analysis; adversarial machine learning towards advanced vision sy作者: 排斥 時間: 2025-3-26 19:44
https://doi.org/10.1007/978-3-319-78381-9idirectional propagation (UP) structure for propagation. Meanwhile, in the UP structure, the facial prior information is filtered and accumulated in the face super-resolution cell (FSRC), and the high-dimensional hidden state is introduced to propagate effective temporal information between frames a作者: Grating 時間: 2025-3-26 22:16
On the Ring-LWE and Polynomial-LWE Problemsclass. This adversarial pattern is universal for the class, which means that it can mislead the DNN model on all input images of the class with high probability. We demonstrate our idea on MNIST dataset, and the results show that ADVFilter can achieve up to 90. success rate with only 16 correspondin作者: 愛了嗎 時間: 2025-3-27 03:48
Overdrive: Making SPDZ Great Againni-FL first performs unsupervised learning for the gradients received to define the grouping policy. Then, the server divides the gradients received into different groups according to the grouping policy defined and performs byzantine-robust aggregation. Finally, the server calculates the weighted m作者: indubitable 時間: 2025-3-27 08:24 作者: 系列 時間: 2025-3-27 12:07 作者: DEBT 時間: 2025-3-27 16:29
Marcel Keller,Valerio Pastro,Dragos Rotarurom the target domain along with labeled source samples are used to adapt the detector using an over-fitting aware and periodic gradient update based joint few-shot fine-tuning technique. Further, we utilize a self-supervision scheme to obtain pseudo-labels having high-confidence on the unlabeled ta作者: 遷移 時間: 2025-3-27 20:14
Pavel Hubá?ek,Alon Rosen,Margarita Valdh a texture that is statistically consistent with the surrounding skin. To achieve this, we introduce a novel loss term that reuses the wrinkle segmentation network to penalize those regions that still contain wrinkles after the inpainting. We evaluate our method qualitatively and quantitatively, sh作者: Ejaculate 時間: 2025-3-27 22:11 作者: Scleroderma 時間: 2025-3-28 03:41 作者: accomplishment 時間: 2025-3-28 09:25
Vincent Grosso,Fran?ois-Xavier Standaertfeature hallucination, and aims to construct a practical model with small size and high efficiency for real imaging systems. Specifically, we exploit a deep feature hallucination module (DFHM) for duplicating more features with cheap operations as the main component, and stack multiple of them to co作者: 特別容易碎 時間: 2025-3-28 10:25 作者: 寬敞 時間: 2025-3-28 17:12 作者: 嚴厲批評 時間: 2025-3-28 19:55
FAPN: Face Alignment Propagation Network for?Face Video Super-Resolutionidirectional propagation (UP) structure for propagation. Meanwhile, in the UP structure, the facial prior information is filtered and accumulated in the face super-resolution cell (FSRC), and the high-dimensional hidden state is introduced to propagate effective temporal information between frames a作者: 溝通 時間: 2025-3-28 23:33 作者: 改良 時間: 2025-3-29 06:11
Enhancing Federated Learning Robustness Through Clustering Non-IID Featuresni-FL first performs unsupervised learning for the gradients received to define the grouping policy. Then, the server divides the gradients received into different groups according to the grouping policy defined and performs byzantine-robust aggregation. Finally, the server calculates the weighted m作者: Gullible 時間: 2025-3-29 07:40
Towards Improving the Anti-attack Capability of the RangeNet++he real world based on the range image, then add it into the training set for training. The experimental results show that the proposed approaches can effectively improve the RangeNet?+??+’s defense ability against adversarial attacks, and meanwhile enhance the RangeNet++ model’s robustness.作者: 稀釋前 時間: 2025-3-29 13:10
Understanding Tumor Micro Environment Using Graph Theoryl level graph-based algorithms, including the global cell graph, cluster cell graph, hierarchical graph modeling and FLocK. The proposed method achieves better performance than the existing algorithms with mean diagnosis accuracy of 0.70833.作者: fixed-joint 時間: 2025-3-29 18:59
Handling Domain Shift for?Lesion Detection via?Semi-supervised Domain Adaptationrom the target domain along with labeled source samples are used to adapt the detector using an over-fitting aware and periodic gradient update based joint few-shot fine-tuning technique. Further, we utilize a self-supervision scheme to obtain pseudo-labels having high-confidence on the unlabeled ta作者: CRUC 時間: 2025-3-29 22:21 作者: Creditee 時間: 2025-3-30 01:53 作者: 令人苦惱 時間: 2025-3-30 07:01
Towards Scene Understanding for?Autonomous Operations on?Airport Apronsons. The results are quite promising for future applications and provide essential insights regarding the selection of aggregation strategies as well as current potentials and limitations of similar approaches in this research domain.作者: 尖叫 時間: 2025-3-30 12:10 作者: 比目魚 時間: 2025-3-30 16:25
A Transformer-Based Model for?Preoperative Early Recurrence Prediction of?Hepatocellular Carcinoma wlearning transformer-based model on multi-modality MRI to tackle the preoperative early recurrence prediction task of HCC. Enlightened by the vigorous capacity of context modeling of the transformer architecture, our proposed model exploits it to dig out the inter-modality correlations, and the perf作者: quiet-sleep 時間: 2025-3-30 17:39 作者: Gingivitis 時間: 2025-3-30 21:32
FAPN: Face Alignment Propagation Network for?Face Video Super-Resolutionemise of ensuring authenticity. The existing video super-resolution (VSR) technology usually uses inter-frame information to achieve better super-resolution (SR) performance. However, due to the complex temporal dependence between frames, as the number of input frames increases, the information cann作者: Nomadic 時間: 2025-3-31 02:55
Micro-expression Recognition Using a?Shallow ConvLSTM-Based Networkds focus on the use of spatial features to perform micro-expression recognition. Thus, they fail to capture the spatiotemporal information available in a video sequence..This paper proposes a shallow convolutional long short-term memory (ConvLSTM) based network to perform micro-expression recognitio作者: 懸崖 時間: 2025-3-31 05:04
ADVFilter: Adversarial Example Generated by?Perturbing Optical Pathesult, the security of DNN system has attracted great attention from the community. In typical scenes, the input images of DNN are collected through the camera. In this paper, we propose a new type of security threat, which attacks a DNN classifier by perturbing the optical path of the camera input
