作者: mediocrity 時(shí)間: 2025-3-21 20:15
Introduction: Mainstreaming the Marginal,r a predicted value might provide great information beyond the prediction itself. To address this goal, using a probabilistic loss was proven efficient for aleatoric uncertainty, which aims at capturing noise originating from the observations. For multidimensional predictions, this estimated noise i作者: Cuisine 時(shí)間: 2025-3-22 02:47 作者: 相符 時(shí)間: 2025-3-22 05:45 作者: 使出神 時(shí)間: 2025-3-22 11:54
Sony Jalarajan Raj,Adith K. Suresheven an appropriate evaluation metric is still missing. Indeed, existing metrics only focus on the writing orders but overlook the fidelity of glyphs. Taking both facets into account, we come up with two new metrics, the adaptive intersection on union (AIoU) which eliminates the influence of various作者: 擁擠前 時(shí)間: 2025-3-22 13:02
Deepali Yadav,Vipin K. Kadavatherally deliver incomplete CD regions and irregular CD boundaries due to the limited representation ability of the extracted visual features. To relieve these issues, in this work we propose a novel learning framework named Fully Transformer Network (FTN) for remote sensing image CD, which improves t作者: 擁擠前 時(shí)間: 2025-3-22 20:41
Emergence of the Second Digital Wave,d at image-level or pixel-level. Considering that pixel-level anomaly classification achieves better representation learning in a finer-grained manner, we regard data augmentation transforms as a self-supervised segmentation task from which to extract the critical and representative information from作者: 獎(jiǎng)牌 時(shí)間: 2025-3-23 01:01 作者: Felicitous 時(shí)間: 2025-3-23 03:10
The Digital Synaptic Neural Substrateogy of lane lines in complex scenarios; moreover, different types and instances of lane lines need to be distinguished. Most existing studies are based only on a single-level feature map extracted by deep neural networks. However, both high-level and low-level features are important for lane detecti作者: Rheumatologist 時(shí)間: 2025-3-23 08:40
The Impetus for Digital Televisionintensities. Due to the similar visual appearance of TCs in adjacent intensities, the discriminative image representation plays an important role in TC intensity estimation. Existing works mainly revolve around the continuity of intensity which may result in a crowded feature distribution and perfor作者: 清洗 時(shí)間: 2025-3-23 12:32 作者: –LOUS 時(shí)間: 2025-3-23 14:20 作者: 使虛弱 時(shí)間: 2025-3-23 21:28
The Digital Transformation of Georgiadequate labeled training data are not always accessible when it comes to a new scene. Semi-supervised learning is promising for the case where a small amount of manually annotated images and a large amount of unannotated images are handy. In the semi-supervised setting, data generation is a powerful作者: 有法律效應(yīng) 時(shí)間: 2025-3-24 00:39
The Digital Transformation of Georgiadied to address this task: global and local image features. Those features can be extracted separately or jointly in a single model. State-of-the-art methods usually learn them with Convolutional Neural Networks (CNNs) and perform retrieval with multi-scale image representation. This paper’s main co作者: 燦爛 時(shí)間: 2025-3-24 03:39 作者: 幸福愉悅感 時(shí)間: 2025-3-24 07:02 作者: 驚惶 時(shí)間: 2025-3-24 11:40
Vision Digitised Automotive Industry 2030,a new tailored benchmark dataset and model for it. Our new dataset, KITTI-Materials, based on the well-established KITTI dataset, consists of 1000 frames covering 24 different road scenes of urban/suburban landscapes, annotated with one of 20 material categories for every pixel in high quality. It i作者: Genome 時(shí)間: 2025-3-24 14:51 作者: 不愿 時(shí)間: 2025-3-24 19:07
https://doi.org/10.1007/978-3-030-83826-3 available from the code snippet by treating each snippet as a two-dimensional image, which naturally encodes the context and retains the underlying structural information through an explicit spatial representation. To codify snippets as images, we propose an ASCII codepoint-based image representati作者: 防水 時(shí)間: 2025-3-25 01:33
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234135.jpg作者: 護(hù)身符 時(shí)間: 2025-3-25 06:05 作者: ADOPT 時(shí)間: 2025-3-25 11:31
978-3-031-26283-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 言外之意 時(shí)間: 2025-3-25 13:33 作者: maroon 時(shí)間: 2025-3-25 18:31
Conference proceedings 2023cember 2022...The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; optimization methods;.Part II: applications of 作者: 共同給與 時(shí)間: 2025-3-25 22:14 作者: Decline 時(shí)間: 2025-3-26 00:52 作者: nettle 時(shí)間: 2025-3-26 07:28
Conference proceedings 2023ing, and shape representation; datasets and performance analysis;.Part VI: biomedical image analysis; deep learning for computer vision; ..Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods. .作者: metropolitan 時(shí)間: 2025-3-26 10:02 作者: SPALL 時(shí)間: 2025-3-26 16:27
Introduction: Mainstreaming the Marginal,mental comparison to the existing approaches, our model offers the best trade-off between uncertainty orientation likeliness, model accuracy and computation costs. Our industrial application is skin color estimation based on a selfie picture, which is at the core of an online make-up assistant but i作者: Arroyo 時(shí)間: 2025-3-26 20:23
Introduction: Mainstreaming the Marginal,gation. In our experiments, the proposed block consistently shows significant performance improvement across various backbone networks, resulting in state-of-the-art results in RGB-T and RGB-D crowd counting.作者: BUDGE 時(shí)間: 2025-3-26 23:39
Sony Jalarajan Raj,Adith K. Sureshyph structure, and a global tracing decoder overcomes the memory difficulty of long trajectory prediction. Our experiments demonstrate that the two new metrics AIoU and LDTW together can truly assess the quality of handwriting trajectory recovery and the proposed PEN-Net exhibits satisfactory perfor作者: 羅盤(pán) 時(shí)間: 2025-3-27 02:35 作者: 為現(xiàn)場(chǎng) 時(shí)間: 2025-3-27 06:50
Emergence of the Second Digital Wave,ifier with a robust decision boundary. During the inference phase, the classifier is used to perform anomaly detection on the test data, while directly determining regions of unknown defects in an end-to-end manner. Our experimental results on MVTec AD dataset and BTAD dataset demonstrate the propos作者: 圓桶 時(shí)間: 2025-3-27 10:52
https://doi.org/10.1007/978-3-319-28079-0erest point detection. Experimental results demonstrate that LANet achieves state-of-the-art performance on the homography estimation benchmark. Notably, the proposed LANet is a front-end feature learning framework that can be deployed in downstream tasks that require interest points with high-quali作者: 輕率的你 時(shí)間: 2025-3-27 14:54
The Digital Synaptic Neural Substrateefined fixed-position anchors, we define learnable anchors to perform statistics of potential lane locations. Second, we propose a dynamic head aiming at leveraging low-level texture information to conditionally enhance high-level semantic features for each proposed instance. Finally, we present a s作者: surmount 時(shí)間: 2025-3-27 19:02 作者: BLANC 時(shí)間: 2025-3-27 23:15
The Impetus for Digital Televisionks, including ‘single object’ networks PointNet, PointNet++, DGCNN, and a ‘scene’ network VoteNet. Our method generates symmetric explanation maps that highlight important regions and provide insight into the decision-making process of network architectures. We perform an exhaustive evaluation of tr作者: WAIL 時(shí)間: 2025-3-28 03:28 作者: dainty 時(shí)間: 2025-3-28 07:20
The Digital Transformation of Georgiable domains to unreliable domains by incorporating a domain classifier that competes with the disentangling module to generate domain-invariant codes. An external classifier is trained on appearance-enhanced instances and sends integrity signals to the generative module, which facilitates the genera作者: 方便 時(shí)間: 2025-3-28 12:20
The Digital Transformation of Georgiaion to aggregate the token embeddings output from the multi-atrous layer to get both global and local features. The entire network can be learned end-to-end, requiring only image-level labels. Extensive experiments show the proposed method outperforms the state-of-the-art methods on the Revisited Ox作者: 初次登臺(tái) 時(shí)間: 2025-3-28 16:23 作者: 橢圓 時(shí)間: 2025-3-28 19:09 作者: 碳水化合物 時(shí)間: 2025-3-28 23:49 作者: Offstage 時(shí)間: 2025-3-29 04:34 作者: 永久 時(shí)間: 2025-3-29 08:21
https://doi.org/10.1007/978-3-030-83826-3tically incorrect code which is not possible from methods that depend on the Abstract Syntax Tree (AST). We demonstrate the effectiveness of CV4Code by learning Convolutional and Transformer networks to predict the functional task, .. ? the problem it solves, of the source code directly from its two作者: 消息靈通 時(shí)間: 2025-3-29 15:14 作者: AXIS 時(shí)間: 2025-3-29 16:03
Spatio-Channel Attention Blocks for?Cross-modal Crowd Countinggation. In our experiments, the proposed block consistently shows significant performance improvement across various backbone networks, resulting in state-of-the-art results in RGB-T and RGB-D crowd counting.作者: Restenosis 時(shí)間: 2025-3-29 23:17
Complex Handwriting Trajectory Recovery: Evaluation Metrics and?Algorithmyph structure, and a global tracing decoder overcomes the memory difficulty of long trajectory prediction. Our experiments demonstrate that the two new metrics AIoU and LDTW together can truly assess the quality of handwriting trajectory recovery and the proposed PEN-Net exhibits satisfactory perfor作者: Inertia 時(shí)間: 2025-3-30 01:37 作者: follicular-unit 時(shí)間: 2025-3-30 04:28 作者: Genistein 時(shí)間: 2025-3-30 09:05 作者: 過(guò)份艷麗 時(shí)間: 2025-3-30 15:30
DILane: Dynamic Instance-Aware Network for?Lane Detectionefined fixed-position anchors, we define learnable anchors to perform statistics of potential lane locations. Second, we propose a dynamic head aiming at leveraging low-level texture information to conditionally enhance high-level semantic features for each proposed instance. Finally, we present a s作者: 難取悅 時(shí)間: 2025-3-30 18:51
CIRL: A Category-Instance Representation Learning Framework for?Tropical Cyclone Intensity Estimatioloss is applied on top of the framework which can lead to a more uniform feature distribution. In addition, a non-parameter smoothing algorithm is proposed to aggregate temporal information from the image sequence. Extensive experiments demonstrate that our method, with the result of 7.35 knots at R作者: Spinous-Process 時(shí)間: 2025-3-30 21:55
Explaining Deep Neural Networks for?Point Clouds Using Gradient-Based Visualisationsks, including ‘single object’ networks PointNet, PointNet++, DGCNN, and a ‘scene’ network VoteNet. Our method generates symmetric explanation maps that highlight important regions and provide insight into the decision-making process of network architectures. We perform an exhaustive evaluation of tr作者: 護(hù)航艦 時(shí)間: 2025-3-31 01:31 作者: acrophobia 時(shí)間: 2025-3-31 07:18 作者: Institution 時(shí)間: 2025-3-31 10:02 作者: DEAWL 時(shí)間: 2025-3-31 15:30
Improving Surveillance Object Detection with?Adaptive Omni-Attention over?Both?Inter-frame and?Intran reverse in both single-frame and multi-frame feature maps. The experimental results on the UA-DETRAC and the UAVDT datasets have demonstrated the promising performance of our proposed detector in both accuracy and speed. (Code is available at ..)作者: Hot-Flash 時(shí)間: 2025-3-31 19:22 作者: Redundant 時(shí)間: 2025-3-31 22:20
RGB Road Scene Material Segmentationenging task. RMSNet encodes multi-scale hierarchical features with self-attention. We construct the decoder of RMSNet based on a novel lightweight self-attention model, which we refer to as .. SAMixer achieves adaptive fusion of informative texture and context cues across multiple feature levels. It作者: Allure 時(shí)間: 2025-4-1 05:14
