作者: 獨(dú)特性 時(shí)間: 2025-3-21 23:45 作者: 外來 時(shí)間: 2025-3-22 01:35
Introspective Learning by Distilling Knowledge from Online Self-explanatione created explanations to improve the learning process has been less explored. The explanations extracted from a model can be used to guide the learning process of the model itself. Another type of information used to guide the training of a model is the knowledge provided by a powerful teacher mode作者: 考博 時(shí)間: 2025-3-22 06:00
Hyperparameter-Free Out-of-Distribution Detection Using Cosine Similarityring the nature of OOD samples, detection methods should not have hyperparameters that need to be tuned depending on incoming OOD samples. However, most recently proposed methods do not meet this requirement, leading to a compromised performance in real-world applications. In this paper, we propose 作者: inculpate 時(shí)間: 2025-3-22 09:05
Meta-Learning with Context-Agnostic Initialisationsl properties within training data (which we refer to as context), not relevant to the target task, which act as a distractor to meta-learning, particularly when the target task contains examples from a novel context not seen during training..We address this oversight by incorporating a context-adver作者: 不能仁慈 時(shí)間: 2025-3-22 14:17
Second Order Enhanced Multi-glimpse Attention in Visual Question Answeringion from both visual and textual modalities. Previous endeavours of VQA are made on the good attention mechanism, and multi-modal fusion strategies. For example, most models, till date, are proposed to fuse the multi-modal features based on implicit neural network through cross-modal interactions. T作者: 不能仁慈 時(shí)間: 2025-3-22 19:54
Localize to Classify and Classify to Localize: Mutual Guidance in Object Detectionand ground truth boxes to evaluate the matching quality between anchors and objects. In this paper, we question this use of IoU and propose a new anchor matching criterion guided, during the training phase, by the optimization of both the localization and the classification tasks: the predictions re作者: 美麗的寫 時(shí)間: 2025-3-22 21:46 作者: 輕率的你 時(shí)間: 2025-3-23 01:51 作者: 合同 時(shí)間: 2025-3-23 09:17 作者: 教育學(xué) 時(shí)間: 2025-3-23 12:46
Feature Variance Ratio-Guided Channel Pruning for Deep Convolutional Network Accelerationsome limitations in modern networks, where the magnitude of parameters can vary independently of the importance of corresponding channels. To recognize redundancies more accurately and therefore, accelerate networks better, we propose a novel channel pruning criterion based on the Pearson correlatio作者: Obverse 時(shí)間: 2025-3-23 17:50 作者: 哺乳動物 時(shí)間: 2025-3-23 20:10 作者: Modify 時(shí)間: 2025-3-23 23:08
Regularizing Meta-learning via Gradient Dropoutew-shot classification, reinforcement learning, and domain generalization. However, meta-learning models are prone to overfitting when there are no sufficient training tasks for the meta-learners to generalize. Although existing approaches such as Dropout are widely used to address the overfitting p作者: LARK 時(shí)間: 2025-3-24 06:16 作者: 吵鬧 時(shí)間: 2025-3-24 10:04 作者: Culpable 時(shí)間: 2025-3-24 11:02 作者: Corporeal 時(shí)間: 2025-3-24 16:13
Double Targeted Universal Adversarial Perturbations for them to be deployed in security-sensitive applications, such as autonomous driving. Image-dependent perturbations can fool a network for one specific image, while universal adversarial perturbations are capable of fooling a network for samples from all classes without selection. We introduce a 作者: 老人病學(xué) 時(shí)間: 2025-3-24 19:45 作者: 保留 時(shí)間: 2025-3-25 02:55
Conference proceedings 2021for computer vision, generative models for computer vision..Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis..Part VI: applications of computer vision; vision for X; datasets and performance analysis..*The conference was held virtually..作者: NIL 時(shí)間: 2025-3-25 05:28
Where does Management Knowledge come from?elationship between image regions. Our design widens the original transformer layer’s inner architecture to adapt to the structure of images. With only regions feature as inputs, our model achieves new state-of-the-art performance on both MSCOCO offline and online testing benchmarks. The code is available at ..作者: 省略 時(shí)間: 2025-3-25 09:11 作者: maintenance 時(shí)間: 2025-3-25 14:19 作者: anaphylaxis 時(shí)間: 2025-3-25 17:24
Mary L. Fennell,Richard B. Warneckeatterns. We also propose four gate functions that control the gradient and can deliver diverse combinations of knowledge transfer. Searching the graph structure enables us to discover more effective knowledge transfer methods than a manually designed one. Experimental results show that the proposed method achieved performance improvements.作者: Fibrinogen 時(shí)間: 2025-3-25 21:40 作者: 種族被根除 時(shí)間: 2025-3-26 03:03
Feature Variance Ratio-Guided Channel Pruning for Deep Convolutional Network Accelerationprunes channels globally with little human intervention. Moreover, it can automatically find important layers in the network. Extensive numerical experiments on CIFAR-10 and ImageNet with widely varying architectures present state-of-the-art performance of our method.作者: FLOUR 時(shí)間: 2025-3-26 07:11 作者: Guaff豪情痛飲 時(shí)間: 2025-3-26 08:40
Knowledge Transfer Graph for Deep Collaborative Learningatterns. We also propose four gate functions that control the gradient and can deliver diverse combinations of knowledge transfer. Searching the graph structure enables us to discover more effective knowledge transfer methods than a manually designed one. Experimental results show that the proposed method achieved performance improvements.作者: 放逐 時(shí)間: 2025-3-26 16:05 作者: incision 時(shí)間: 2025-3-26 17:48 作者: 客觀 時(shí)間: 2025-3-26 21:29
The Diffusion of Power in Global Governanceadversarial training due to the slow convergence of APA methods. We demonstrate transferability of this protection to defend against existing APAs, and show its efficacy across several contemporary CNN architectures.作者: 固執(zhí)點(diǎn)好 時(shí)間: 2025-3-27 02:56
Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection proposed method, our experiments with different state-of-the-art deep learning architectures on PASCAL VOC and MS COCO datasets demonstrate the effectiveness and generality of our Mutual Guidance strategy.作者: GORGE 時(shí)間: 2025-3-27 08:03
Vax-a-Net: Training-Time Defence Against Adversarial Patch Attacksadversarial training due to the slow convergence of APA methods. We demonstrate transferability of this protection to defend against existing APAs, and show its efficacy across several contemporary CNN architectures.作者: 手榴彈 時(shí)間: 2025-3-27 12:32 作者: lanugo 時(shí)間: 2025-3-27 15:19
978-3-030-69537-8Springer Nature Switzerland AG 2021作者: 過分自信 時(shí)間: 2025-3-27 20:55
https://doi.org/10.1007/3-540-36409-9odel to activate feature maps over the entire object by dropping the most discriminative parts. However, they are likely to induce excessive extension to the backgrounds which leads to over-estimated localization. In this paper, we consider the background as an important cue that guides the feature 作者: Constituent 時(shí)間: 2025-3-28 00:33 作者: chlorosis 時(shí)間: 2025-3-28 02:23
The Diffuse Interstellar Band Spectrume created explanations to improve the learning process has been less explored. The explanations extracted from a model can be used to guide the learning process of the model itself. Another type of information used to guide the training of a model is the knowledge provided by a powerful teacher mode作者: Systemic 時(shí)間: 2025-3-28 08:19
Adolf N. Witt,Douglas G. Furtonring the nature of OOD samples, detection methods should not have hyperparameters that need to be tuned depending on incoming OOD samples. However, most recently proposed methods do not meet this requirement, leading to a compromised performance in real-world applications. In this paper, we propose 作者: 圓木可阻礙 時(shí)間: 2025-3-28 12:33 作者: Cantankerous 時(shí)間: 2025-3-28 18:11 作者: Alienated 時(shí)間: 2025-3-28 21:28 作者: ARCH 時(shí)間: 2025-3-29 00:40
Juan C. Pastor,James Meindl,Raymond Hunt and large haze density variations. In this work, we aim to jointly solve the image dehazing and the object detection tasks in real hazy scenarios by using haze density as prior knowledge. Our proposed .nified .ehazing a.d .etection (UDnD) framework consists of three parts: a residual-aware haze den作者: Banquet 時(shí)間: 2025-3-29 05:26 作者: ligature 時(shí)間: 2025-3-29 07:37
Where does Management Knowledge come from? notion of attention: how to decide what to describe and in which order. Inspired by the successes in text analysis and translation, previous works have proposed the . architecture for image captioning. However, the structure between the . in images (usually the detected regions from object detectio作者: 燈絲 時(shí)間: 2025-3-29 14:01
https://doi.org/10.1007/978-3-642-51559-0some limitations in modern networks, where the magnitude of parameters can vary independently of the importance of corresponding channels. To recognize redundancies more accurately and therefore, accelerate networks better, we propose a novel channel pruning criterion based on the Pearson correlatio作者: opalescence 時(shí)間: 2025-3-29 17:12
The Diffusion of Electronic Data Interchangectly natural for humans, the same task has proven to be challenging for learning machines. Deep neural networks are still prone to catastrophic forgetting of previously learnt information when presented with information from a sufficiently new distribution. To address this problem, we present NeoNet作者: Vaginismus 時(shí)間: 2025-3-29 20:50 作者: 山頂可休息 時(shí)間: 2025-3-30 00:13 作者: Keratin 時(shí)間: 2025-3-30 04:05 作者: SUGAR 時(shí)間: 2025-3-30 11:53 作者: 天然熱噴泉 時(shí)間: 2025-3-30 12:36 作者: 金盤是高原 時(shí)間: 2025-3-30 18:00
Upper Gastrointestinal Bleeding (UGIB) for them to be deployed in security-sensitive applications, such as autonomous driving. Image-dependent perturbations can fool a network for one specific image, while universal adversarial perturbations are capable of fooling a network for samples from all classes without selection. We introduce a 作者: concert 時(shí)間: 2025-3-30 21:20 作者: MOT 時(shí)間: 2025-3-31 04:36
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234130.jpg作者: Interdict 時(shí)間: 2025-3-31 08:24 作者: NATTY 時(shí)間: 2025-3-31 09:12
https://doi.org/10.1007/3-540-36409-9s to activate on objects rather than locally distinctive backgrounds so that their attentions to be similar to the later layer. For better optimizing the above losses, we use the non-local attention blocks to replace channel-pooled attention leading to enhanced attention maps considering the spatial作者: 無所不知 時(shí)間: 2025-3-31 13:36 作者: 裁決 時(shí)間: 2025-3-31 19:54 作者: Meander 時(shí)間: 2025-3-31 23:32 作者: 沉積物 時(shí)間: 2025-4-1 03:25 作者: ADJ 時(shí)間: 2025-4-1 08:33
https://doi.org/10.1007/978-1-349-25899-4ere each glimpse denotes an attention map. SOMA adopts multi-glimpse attention to focus on different contents in the image. With projected the multi-glimpse outputs and question feature into a shared embedding space, an explicit second order feature is constructed to model the interaction on both th
