作者: periodontitis 時間: 2025-3-22 00:06 作者: 廚房里面 時間: 2025-3-22 00:37 作者: 明確 時間: 2025-3-22 04:49
,TinyViT: Fast Pretraining Distillation for?Small Vision Transformers, pretrained model with computation and parameter constraints. Comprehensive experiments demonstrate the efficacy of TinyViT. It achieves a top-1 accuracy of 84.8% on ImageNet-1k with only 21M parameters, being comparable to Swin-B pretrained on ImageNet-21k while using 4.2 times fewer parameters. Mo作者: 灰心喪氣 時間: 2025-3-22 09:41 作者: 咒語 時間: 2025-3-22 16:19 作者: 咒語 時間: 2025-3-22 20:40 作者: Bridle 時間: 2025-3-22 21:17
ViTAS: Vision Transformer Architecture Search, shifting to alleviate the many-to-one issue in superformer and leverage weak augmentation and regularization techniques for more steady training empirically. Based on these, our proposed method, ViTAS, has achieved significant superiority in both DeiT- and Twins-based ViTs. For example, with only 1作者: 終止 時間: 2025-3-23 04:41 作者: LATER 時間: 2025-3-23 08:21
,Uncertainty-DTW for?Time Series and?Sequences,e is the sum of base distances between features of pairs of frames of the path.) The Maximum Likelihood Estimation (MLE) applied to a path yields two terms: (i) a sum of Euclidean distances weighted by the variance inverse, and (ii) a sum of log-variance regularization terms. Thus, our uncertainty-D作者: deactivate 時間: 2025-3-23 10:48 作者: PLE 時間: 2025-3-23 16:59
,SSBNet: Improving Visual Recognition Efficiency by?Adaptive Sampling,SSB-ResNet-RS-200 achieved 82.6% accuracy on ImageNet dataset, which is 0.6% higher than the baseline ResNet-RS-152 with a similar complexity. Visualization shows the advantage of SSBNet in allowing different layers to focus on different positions, and ablation studies further validate the advantage作者: 起皺紋 時間: 2025-3-23 18:38
,Filter Pruning via?Feature Discrimination in?Deep Neural Networks, our method first selects relatively redundant layers by hard and soft changes of the network output, and then prunes only at these layers. The whole process dynamically adjusts redundant layers through iterations. Extensive experiments conducted on CIFAR-10/100 and ImageNet show that our method ach作者: 沒血色 時間: 2025-3-24 00:26 作者: 衍生 時間: 2025-3-24 05:58
,Interpretations Steered Network Pruning via?Amortized Inferred Saliency Maps,roducing a selector model that predicts real-time smooth saliency masks for pruned models. We parameterize the distribution of explanatory masks by Radial Basis Function (RBF)-like functions to incorporate geometric prior of natural images in our selector model’s inductive bias. Thus, we can obtain 作者: nuclear-tests 時間: 2025-3-24 07:38
The Reforms: Experiences and Failuresce values by regulating the contributions of individual examples in the parameter update of the network. Further, our algorithm avoids redundant labeling by promoting diversity in batch selection through propagating the confidence of each newly labeled example to the entire dataset. Experiments invo作者: arbovirus 時間: 2025-3-24 11:39 作者: 使更活躍 時間: 2025-3-24 16:26
International Economic Relationsependencies without self-attention. Extensive experiments demonstrate that our adaptive weight mixing is more efficient and effective than previous weight generation methods and our AMixer can achieve a better trade-off between accuracy and complexity than vision Transformers and MLP models on both 作者: 不遵守 時間: 2025-3-24 23:03
Reintegrating the World Economy pretrained model with computation and parameter constraints. Comprehensive experiments demonstrate the efficacy of TinyViT. It achieves a top-1 accuracy of 84.8% on ImageNet-1k with only 21M parameters, being comparable to Swin-B pretrained on ImageNet-21k while using 4.2 times fewer parameters. Mo作者: jeopardize 時間: 2025-3-25 02:22 作者: 懶惰人民 時間: 2025-3-25 04:28
The Bases of the Socialist Economic Systems training sufficiency and alleviate the disturbance. Experimental results show our scaled networks enjoy significant performance superiority on various FLOPs, but with at least . reduction on search cost. Codes are available at ..作者: unstable-angina 時間: 2025-3-25 08:34 作者: 蠟燭 時間: 2025-3-25 14:37
The Economics of UK-EU Relations shifting to alleviate the many-to-one issue in superformer and leverage weak augmentation and regularization techniques for more steady training empirically. Based on these, our proposed method, ViTAS, has achieved significant superiority in both DeiT- and Twins-based ViTs. For example, with only 1作者: Nebulous 時間: 2025-3-25 18:40
https://doi.org/10.1007/978-3-319-55495-2his framework can easily materialize into a concrete neural architecture search (NAS) space, allowing a principled NAS-for-3D exploration. In performing evolutionary NAS on the 3D object detection task on the Waymo Open Dataset, not only do we outperform the state-of-the-art models, but also report 作者: 解開 時間: 2025-3-25 23:12
https://doi.org/10.1007/978-1-349-04151-0e is the sum of base distances between features of pairs of frames of the path.) The Maximum Likelihood Estimation (MLE) applied to a path yields two terms: (i) a sum of Euclidean distances weighted by the variance inverse, and (ii) a sum of log-variance regularization terms. Thus, our uncertainty-D作者: HARP 時間: 2025-3-26 01:40
The Costs of Urban Freight Transport,rom the teacher are used to train the student. We conduct extensive experiments to show that our method significantly outperforms recent SOTA few/zero-shot KD methods on image classification tasks. The code and models are available at: ..作者: Tractable 時間: 2025-3-26 05:40 作者: 打包 時間: 2025-3-26 09:38
Geo-Pollution and a Cleanup System, our method first selects relatively redundant layers by hard and soft changes of the network output, and then prunes only at these layers. The whole process dynamically adjusts redundant layers through iterations. Extensive experiments conducted on CIFAR-10/100 and ImageNet show that our method ach作者: 無能性 時間: 2025-3-26 14:28
The Current State of High-Tech Pollution, selection of effective as well as complementary augmentations, which produces significant performance boost and can be easily deployed in typical model training. Extensive experiments demonstrate that . achieves excellent performance matching or surpassing existing methods on CIFAR-10 and CIFAR-100作者: Polydipsia 時間: 2025-3-26 17:42 作者: saphenous-vein 時間: 2025-3-26 21:11
Conference proceedings 2022on, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022..?.The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement lear作者: Alienated 時間: 2025-3-27 03:37 作者: 貨物 時間: 2025-3-27 06:41
Conference proceedings 2022ning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..作者: 賞錢 時間: 2025-3-27 12:54
https://doi.org/10.1007/978-1-349-27313-3ing a hypervector mapping that inverts the translation to ensure consistency with source content. We show both qualitatively and quantitatively that our method improves over other state-of-the-art techniques.作者: 反對 時間: 2025-3-27 14:44
,Unpaired Image Translation via?Vector Symbolic Architectures,ing a hypervector mapping that inverts the translation to ensure consistency with source content. We show both qualitatively and quantitatively that our method improves over other state-of-the-art techniques.作者: 很像弓] 時間: 2025-3-27 20:23 作者: MOTTO 時間: 2025-3-27 23:31
K. Holden,D. A. Peel,J. L. Thompsonscale initialization on performance, and use rigorous statistical significance tests for evaluation. The approach can be used with existing implementations at no additional computational cost. Source code is available at ..作者: 全面 時間: 2025-3-28 03:22 作者: Watemelon 時間: 2025-3-28 09:12 作者: 夸張 時間: 2025-3-28 12:16
,BA-Net: Bridge Attention for?Deep Convolutional Neural Networks,on to enhance the performance of neural networks. BA-Net is effective, stable, and easy to use. A comprehensive evaluation of computer vision tasks demonstrates that the proposed approach achieves better performance than the existing channel attention methods regarding accuracy and computing efficiency. The source code is available at ..作者: impaction 時間: 2025-3-28 16:36
Commercial and Industrial Water Demandsunctions in various datasets and models. We call this function Smooth Activation Unit (SAU). Replacing ReLU by SAU, we get 5.63%, 2.95%, and 2.50% improvement with ShuffleNet V2 (2.0x), PreActResNet 50 and ResNet 50 models respectively on the CIFAR100 dataset and 2.31% improvement with ShuffleNet V2 (1.0x) model on ImageNet-1k dataset.作者: ovation 時間: 2025-3-28 19:05
0302-9743 puter Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022..?.The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforc作者: BUOY 時間: 2025-3-28 23:05
,Active Label Correction Using Robust Parameter Update and?Entropy Propagation,work classifiers on such noisy datasets may lead to significant performance degeneration. Active label correction (ALC) attempts to minimize the re-labeling costs by identifying examples for which providing correct labels will yield maximal performance improvements. Existing ALC approaches typically作者: Receive 時間: 2025-3-29 06:12
,Unpaired Image Translation via?Vector Symbolic Architectures, a large semantic mismatch, existing techniques often suffer from source content corruption aka semantic flipping. To address this problem, we propose a new paradigm for image-to-image translation using Vector Symbolic Architectures (VSA), a theoretical framework which defines algebraic operations i作者: 受辱 時間: 2025-3-29 10:55 作者: 慟哭 時間: 2025-3-29 13:50
,AMixer: Adaptive Weight Mixing for?Self-attention Free Vision Transformers,onvolution to mix spatial information is commonly recognized as the indispensable ingredient behind the success of vision Transformers. In this paper, we thoroughly investigate the key differences between vision Transformers and recent all-MLP models. Our empirical results show the superiority of vi作者: 幻影 時間: 2025-3-29 17:58
,TinyViT: Fast Pretraining Distillation for?Small Vision Transformers,dels suffer from huge number of parameters, restricting their applicability on devices with limited resources. To alleviate this issue, we propose TinyViT, a new family of tiny and efficient small vision transformers pretrained on large-scale datasets with our proposed fast distillation framework. T作者: 天文臺 時間: 2025-3-29 23:15
Equivariant Hypergraph Neural Networks,for hypergraph learning extend graph neural networks based on message passing, which is simple yet fundamentally limited in modeling long-range dependencies and expressive power. On the other hand, tensor-based equivariant neural networks enjoy maximal expressiveness, but their application has been 作者: 暫時中止 時間: 2025-3-30 02:30
,ScaleNet: Searching for?the?Model to?Scale,t methods either simply resort to a one-shot NAS manner to construct a non-structural and non-scalable model family or rely on a manual yet fixed scaling strategy to scale an unnecessarily best base model. In this paper, we bridge both two components and propose ScaleNet to jointly search base model作者: ligature 時間: 2025-3-30 06:02
,Complementing Brightness Constancy with?Deep Networks for?Optical Flow Prediction,ances on real-world data. In this work, we introduce the COMBO deep network that explicitly exploits the brightness constancy (BC) model used in traditional methods. Since BC is an approximate physical model violated in several situations, we propose to train a physically-constrained network complem作者: 呼吸 時間: 2025-3-30 12:07 作者: obstinate 時間: 2025-3-30 14:03
,LidarNAS: Unifying and?Searching Neural Architectures for?3D Point Clouds,r, arguably due to the higher-dimensional nature of the data (as compared to images), existing neural architectures exhibit a large variety in their designs, including but not limited to the views considered, the format of the neural features, and the neural operations used. Lack of a unified framew作者: Tincture 時間: 2025-3-30 17:57
,Uncertainty-DTW for?Time Series and?Sequences,ustering time series or even matching sequence pairs in few-shot action recognition. The transportation plan of DTW contains a set of paths; each path matches frames between two sequences under a varying degree of time warping, to account for varying temporal intra-class dynamics of actions. However作者: cogitate 時間: 2025-3-31 00:09
Black-Box Few-Shot Knowledge Distillation,nal KD methods require lots of . training samples and a . teacher (parameters are accessible) to train a good student. However, these resources are not always available in real-world applications. The distillation process often happens at an external party side where we do not have access to much da作者: senile-dementia 時間: 2025-3-31 04:09
Revisiting Batch Norm Initialization,ral networks. Standard initialization of each BN in a network sets the affine transformation scale and shift to 1 and 0, respectively. However, after training we have observed that these parameters do not alter much from their initialization. Furthermore, we have noticed that the normalization proce作者: Kaleidoscope 時間: 2025-3-31 05:18
,SSBNet: Improving Visual Recognition Efficiency by?Adaptive Sampling,ng layers are not learned, and thus cannot preserve important information. As another dimension reduction method, adaptive sampling weights and processes regions that are relevant to the task, and is thus able to better preserve useful information. However, the use of adaptive sampling has been limi作者: 偶然 時間: 2025-3-31 12:21
,Filter Pruning via?Feature Discrimination in?Deep Neural Networks,g, We first propose a feature discrimination based filter importance criterion, namely Receptive Field Criterion (RFC). It turns the maximum activation responses that characterize the receptive field into probabilities, then measure the filter importance by the distribution of these probabilities fr作者: 課程 時間: 2025-3-31 15:03 作者: 翅膀拍動 時間: 2025-3-31 17:39 作者: Notorious 時間: 2025-3-31 23:49
,BA-Net: Bridge Attention for?Deep Convolutional Neural Networks,ue to heavy feature compression in the attention layer. This paper proposes a simple and general approach named Bridge Attention to address this issue. As a new idea, BA-Net straightforwardly integrates features from previous layers and effectively promotes information interchange. Only simple strat作者: GOAD 時間: 2025-4-1 03:12 作者: Armory 時間: 2025-4-1 07:12 作者: incarcerate 時間: 2025-4-1 13:22 作者: 做事過頭 時間: 2025-4-1 17:02 作者: 預示 時間: 2025-4-1 20:55
The Reforms: Experiences and Failureswork classifiers on such noisy datasets may lead to significant performance degeneration. Active label correction (ALC) attempts to minimize the re-labeling costs by identifying examples for which providing correct labels will yield maximal performance improvements. Existing ALC approaches typically作者: Processes 時間: 2025-4-2 00:19
https://doi.org/10.1007/978-1-349-27313-3 a large semantic mismatch, existing techniques often suffer from source content corruption aka semantic flipping. To address this problem, we propose a new paradigm for image-to-image translation using Vector Symbolic Architectures (VSA), a theoretical framework which defines algebraic operations i
