作者: 柳樹；枯黃 時(shí)間: 2025-3-21 23:22 作者: FRONT 時(shí)間: 2025-3-22 00:23 作者: 美色花錢 時(shí)間: 2025-3-22 08:31 作者: TAP 時(shí)間: 2025-3-22 10:46 作者: 富饒 時(shí)間: 2025-3-22 13:29
David T. Kresge,J. Royce Ginn,John T. Grayctor calculation plays an essential role in reducing the performance gap to their real-valued counterparts. However, existing BNNs neglect the intrinsic bilinear relationship of real-valued weights and scale factors, resulting in a sub-optimal model caused by an insufficient training process. To add作者: 富饒 時(shí)間: 2025-3-22 20:34 作者: heterogeneous 時(shí)間: 2025-3-22 21:24
David T. Kresge,J. Royce Ginn,John T. Gray common characteristics, while finer-grained hierarchy classification relies on local and discriminative features. Therefore, humans should also subconsciously focus on different object regions when classifying different hierarchies. This granularity-wise attention is confirmed by our collected huma作者: 下船 時(shí)間: 2025-3-23 01:31 作者: Mammal 時(shí)間: 2025-3-23 07:08 作者: Afflict 時(shí)間: 2025-3-23 12:56
Thomas R. Gulledge Jr.,Norman K. Womerissue, this paper proposes the locality guidance for improving the performance of VTs on tiny datasets. We first analyze that the local information, which is of great importance for understanding images, is hard to be learned with limited data due to the high flexibility and intrinsic globality of t作者: Uncultured 時(shí)間: 2025-3-23 14:42
Thomas R. Gulledge Jr.,Norman K. Womeritting to noisy labels. The key success of LNL lies in identifying as many clean samples as possible from massive noisy data, while rectifying the wrongly assigned noisy labels. Recent advances employ the predicted label distributions of individual samples to perform noise verification and noisy lab作者: incision 時(shí)間: 2025-3-23 20:35
The Economics of Managing Biotechnologiesdata. Recently, a pioneer claims that the commonly used replay-based method in class-incremental learning (CIL) is ineffective and thus not preferred for FSCIL. This has, if truth, a significant influence on the fields of FSCIL. In this paper, we show through empirical results that adopting the data作者: 令人發(fā)膩 時(shí)間: 2025-3-24 01:10
Regulatory Harmony — Who’s Calling the Tune?ning new tasks. Cognitive science points out that the competition of similar knowledge is an important cause of forgetting. In this paper, we design a paradigm for lifelong learning based on meta-learning and associative mechanism of the brain. It tackles the problem from two aspects: extracting kno作者: Cabinet 時(shí)間: 2025-3-24 04:07
The Circulation Process of Capital scenarios. However, little attention has been given to how to quantify the dominance severity of head classes in the representation space. Motivated by this, we generalize the cosine-based classifiers to a von Mises-Fisher (vMF) mixture model, denoted as vMF classifier, which enables to quantitativ作者: LINE 時(shí)間: 2025-3-24 08:24 作者: 疲憊的老馬 時(shí)間: 2025-3-24 13:11
Simple Exchange v. Developed Exchangemages. Each event stream is generally split into multiple sliding windows for subsequent processing. However, most existing event-based methods ignore the motion continuity between adjacent spatiotemporal windows, which will result in the loss of dynamic information and additional computational cost作者: Spongy-Bone 時(shí)間: 2025-3-24 17:47
Simple Exchange v. Developed Exchangenario. Typical neural classifiers are based on the closed world assumption, where the training data and the test data are drawn . from the same distribution, and as a result, give over-confident predictions even faced with . inputs. For tackling this problem, previous studies either use real outlier作者: lacrimal-gland 時(shí)間: 2025-3-24 20:56
The Circulation Process of Capitalchy aware features in order to improve the classifier to make semantically meaningful mistakes while maintaining or reducing the overall error. In this paper, we propose a novel approach for learning . that leverages classifiers at each level of the hierarchy that are constrained to generate predict作者: 散步 時(shí)間: 2025-3-25 02:13 作者: 沙發(fā) 時(shí)間: 2025-3-25 03:52
https://doi.org/10.1007/978-3-642-72785-6cently, many vision transformer architectures have been proposed and they show promising performance. A key component in vision transformers is the fully-connected self-attention which is more powerful than CNNs in modelling long range dependencies. However, since the current dense self-attention us作者: 態(tài)學(xué) 時(shí)間: 2025-3-25 11:13 作者: glans-penis 時(shí)間: 2025-3-25 15:08 作者: 天真 時(shí)間: 2025-3-25 19:46 作者: Osmosis 時(shí)間: 2025-3-25 20:30
https://doi.org/10.1007/978-1-349-18031-8inst corrupted images as well as accuracy on clean data. Being complementary to popular data augmentation methods, EWS consistently improves robustness when combined with these approaches. To highlight the flexibility of our approach, we combine EWS also with popular adversarial training methods resulting in improved adversarial robustness.作者: 繁榮中國 時(shí)間: 2025-3-26 01:33
,Improving Robustness by?Enhancing Weak Subnets,inst corrupted images as well as accuracy on clean data. Being complementary to popular data augmentation methods, EWS consistently improves robustness when combined with these approaches. To highlight the flexibility of our approach, we combine EWS also with popular adversarial training methods resulting in improved adversarial robustness.作者: modest 時(shí)間: 2025-3-26 07:42
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: 盲信者 時(shí)間: 2025-3-26 08:31 作者: 薄荷醇 時(shí)間: 2025-3-26 13:06 作者: 言行自由 時(shí)間: 2025-3-26 16:57 作者: palliate 時(shí)間: 2025-3-26 22:42 作者: Arthr- 時(shí)間: 2025-3-27 04:33 作者: 易發(fā)怒 時(shí)間: 2025-3-27 09:10 作者: Insulin 時(shí)間: 2025-3-27 13:00
DaViT: Dual Attention Vision Transformers,ge, the channel attention naturally captures global interactions and representations by taking all spatial positions into account when computing attention scores between channels; (ii) the spatial attention refines the local representations by performing fine-grained interactions across spatial loca作者: 放牧 時(shí)間: 2025-3-27 14:50 作者: Etching 時(shí)間: 2025-3-27 18:50 作者: 非秘密 時(shí)間: 2025-3-28 00:39 作者: 冒號(hào) 時(shí)間: 2025-3-28 02:54
,Few-Shot Class-Incremental Learning via?Entropy-Regularized Data-Free Replay,l the generated data with one-hot-like labels. This modification allows the network to learn by solely minimizing the cross-entropy loss, which mitigates the problem of balancing different objectives in the conventional knowledge distillation approach. Finally, we show extensive experimental results作者: 瘋狂 時(shí)間: 2025-3-28 09:18
,Anti-retroactive Interference for?Lifelong Learning,proposed learning paradigm can make the models of different tasks converge to the same optimum. The proposed method is validated on the MNIST, CIFAR100, CUB200 and ImageNet100 datasets. The code is available at ..作者: fulcrum 時(shí)間: 2025-3-28 12:32
,Towards Calibrated Hyper-Sphere Representation via?Distribution Overlap Coefficient for?Long-Tailedth classifier weights. Furthermore, a novel post-training calibration algorithm is devised to zero-costly boost the performance via inter-class overlap coefficients. Our method outperforms previous work with a large margin and achieves state-of-the-art performance on long-tailed image classification作者: 卡死偷電 時(shí)間: 2025-3-28 17:26
,Dynamic Metric Learning with?Cross-Level Concept Distillation,: we only pull closer positive pairs. To facilitate the cross-level semantic structure of the image representations, we propose a hierarchical concept refiner to construct multiple levels of concept embeddings of an image and then pull closer the distance of the corresponding concepts. Extensive exp作者: 盤旋 時(shí)間: 2025-3-28 20:44 作者: 下垂 時(shí)間: 2025-3-28 23:24 作者: 總 時(shí)間: 2025-3-29 06:29 作者: 發(fā)現(xiàn) 時(shí)間: 2025-3-29 10:57
,Learning to?Detect Every Thing in?an?Open World,eads to significant improvements on many datasets in the open-world instance segmentation task, outperforming baselines on cross-category generalization on COCO, as well as cross-dataset evaluation on UVO, Objects365, and Cityscapes. ..作者: circuit 時(shí)間: 2025-3-29 12:26
,KVT: ,-NN Attention for?Boosting Vision Transformers,ar tokens from the keys for each query to compute the attention map. The proposed .-NN attention naturally inherits the local bias of CNNs without introducing convolutional operations, as nearby tokens tend to be more similar than others. In addition, the .-NN attention allows for the exploration of作者: 完成 時(shí)間: 2025-3-29 17:30
Registration Based Few-Shot Anomaly Detection,-training or parameter fine-tuning for new categories. Experimental results have shown that the proposed method outperforms the state-of-the-art FSAD methods by 3%–8% in AUC on the MVTec and MPDD benchmarks. Source code is available at: ..作者: 乞丐 時(shí)間: 2025-3-29 23:16
https://doi.org/10.1007/978-94-011-0505-7% for ViT-B, +0.5% for Swin-B), and especially enhance the advanced model VOLO-D5 to 87.3% that only uses ImageNet-1K data, and the superiority can also be maintained on out-of-distribution data and transferred to downstream tasks. The code is available at: ..作者: 脖子 時(shí)間: 2025-3-30 03:10
David T. Kresge,J. Royce Ginn,John T. Grayllable learning process. We obtain robust RBONNs, which show impressive performance over state-of-the-art BNNs on various models and datasets. Particularly, on the task of object detection, RBONNs have great generalization performance. Our code is open-sourced on ..作者: aggressor 時(shí)間: 2025-3-30 06:18
International Economic Association Seriesconnections (...., temporal feedback connections) between layers. Interestingly, SNASNet found by our search algorithm achieves higher performance with backward connections, demonstrating the importance of designing SNN architecture for suitably using temporal information. We conduct extensive exper作者: Nutrient 時(shí)間: 2025-3-30 09:41
David T. Kresge,J. Royce Ginn,John T. Grayorthogonal fusion module to enhance the region feature representation by blending the original feature and an orthogonal component extracted from adjacent hierarchies. Experiments on five hierarchical fine-grained datasets demonstrate the effectiveness of CHRF compared with the state-of-the-art meth作者: Pde5-Inhibitors 時(shí)間: 2025-3-30 12:54
Efficient Long-distance Fuel Transportationge, the channel attention naturally captures global interactions and representations by taking all spatial positions into account when computing attention scores between channels; (ii) the spatial attention refines the local representations by performing fine-grained interactions across spatial loca作者: Banquet 時(shí)間: 2025-3-30 20:15
Modal Split, Efficiency and Public Policyd label-wise prior, we achieve a desirable assignment plan that allows us to find matched visible and infrared samples, and thereby facilitates cross-modality learning. Besides, a prediction alignment loss is designed to eliminate the negative effects brought by the incorrect pseudo labels. Extensiv作者: AIL 時(shí)間: 2025-3-31 00:04
Thomas R. Gulledge Jr.,Norman K. WomerVTs to a large extent. Therefore, our locality guidance approach is very simple and efficient, and can serve as a basic performance enhancement method for VTs on tiny datasets. Extensive experiments demonstrate that our method can significantly improve VTs when training from scratch on tiny datasets作者: 全國性 時(shí)間: 2025-3-31 01:57 作者: 沙發(fā) 時(shí)間: 2025-3-31 06:48
The Economics of Managing Biotechnologiesl the generated data with one-hot-like labels. This modification allows the network to learn by solely minimizing the cross-entropy loss, which mitigates the problem of balancing different objectives in the conventional knowledge distillation approach. Finally, we show extensive experimental results作者: 反抗者 時(shí)間: 2025-3-31 11:10
Regulatory Harmony — Who’s Calling the Tune?proposed learning paradigm can make the models of different tasks converge to the same optimum. The proposed method is validated on the MNIST, CIFAR100, CUB200 and ImageNet100 datasets. The code is available at ..作者: BLA 時(shí)間: 2025-3-31 16:22 作者: Expediency 時(shí)間: 2025-3-31 20:09
The Economics of Marx’s Grundrisse: we only pull closer positive pairs. To facilitate the cross-level semantic structure of the image representations, we propose a hierarchical concept refiner to construct multiple levels of concept embeddings of an image and then pull closer the distance of the corresponding concepts. Extensive exp作者: 充氣球 時(shí)間: 2025-3-31 23:50 作者: 大量殺死 時(shí)間: 2025-4-1 04:25
Simple Exchange v. Developed Exchangeibution (.) features. Benefiting from the adversarial training scheme, the discriminator can well separate . and . features, allowing more robust . detection. The proposed BAL achieves . performance on classification benchmarks, reducing up to 13.9% FPR95 compared with previous methods.作者: 小故事 時(shí)間: 2025-4-1 06:58
The Circulation Process of Capitaly loss that treats all mistakes as equal. We evaluate HAF?on three hierarchical datasets and achieve state-of-the-art results on the iNaturalist-19 and CIFAR-100 datasets. The source code is available at ..作者: 炸壞 時(shí)間: 2025-4-1 10:17
The Marketplace for Medical Technology,eads to significant improvements on many datasets in the open-world instance segmentation task, outperforming baselines on cross-category generalization on COCO, as well as cross-dataset evaluation on UVO, Objects365, and Cityscapes. ..
