作者: Outshine 時(shí)間: 2025-3-21 22:41
,Unsupervised Multi-modal Medical Image Registration via?Invertible Translation,d local attention mechanism. Additionally, we design a novel barrier loss function based on Normalized Mutual Information to impose constraints on the registration network, which enhances the registration accuracy. The superior performance of INNReg is demonstrated through experiments on two public 作者: Hangar 時(shí)間: 2025-3-22 03:38
,Functional Transform-Based Low-Rank Tensor Factorization for?Multi-dimensional Data Recovery,a general FLRTF-based multi-dimensional data recovery model. Experimental results, including video frame interpolation/extrapolation, MSI band interpolation, and MSI spectral super-resolution tasks, substantiate that FLRTF has superior performance as compared with representative data recovery method作者: explicit 時(shí)間: 2025-3-22 05:20 作者: 尖酸一點(diǎn) 時(shí)間: 2025-3-22 09:08 作者: faddish 時(shí)間: 2025-3-22 13:41 作者: faddish 時(shí)間: 2025-3-22 18:57 作者: Efflorescent 時(shí)間: 2025-3-23 01:16
,High-Precision Self-supervised Monocular Depth Estimation with?Rich-Resource Prior, the depth. Experimental results demonstrate that our model outperform other single-image model and can achieve comparable or even better performance than models with rich-resource inputs, only using low-resolution single-image input.作者: Biomarker 時(shí)間: 2025-3-23 03:18 作者: SUE 時(shí)間: 2025-3-23 08:13 作者: Condescending 時(shí)間: 2025-3-23 10:13
,UDiffText: A Unified Framework for?High-Quality Text Synthesis in?Arbitrary Images via?Character-Awl attention control under the supervision of character-level segmentation maps. Finally, by employing an inference stage refinement process, we achieve a notably high sequence accuracy when synthesizing text in arbitrarily given images. Both qualitative and quantitative results demonstrate the super作者: 基因組 時(shí)間: 2025-3-23 17:26
,Confidence Self-calibration for?Multi-label Class-Incremental Learning,tion of over-confident output distributions. Our approach attains new state-of-the-art results in MLCIL tasks on both MS-COCO and PASCAL VOC datasets, with the calibration of label confidences confirmed through our methodology. Our code is available at ..作者: 溺愛 時(shí)間: 2025-3-23 18:47
,OMG: Occlusion-Friendly Personalized Multi-concept Generation in?Diffusion Models, be combined with various single-concept models, such as LoRA and InstantID without additional tuning. Especially, LoRA models on . can be exploited directly. Extensive experiments demonstrate that OMG exhibits superior performance in multi-concept personalization.作者: 臭了生氣 時(shí)間: 2025-3-23 22:27
,Versatile Incremental Learning: Towards Class and?Domain-Agnostic Incremental Learning,avoid confusion with the previously learned knowledge and thereby accumulate the new knowledge more effectively. Moreover, we introduce an Incremental Classifier (IC) which expands its output nodes to address the overwriting issue from different domains corresponding to a single class while maintain作者: 分離 時(shí)間: 2025-3-24 04:58 作者: flavonoids 時(shí)間: 2025-3-24 10:21
,An Incremental Unified Framework for?Small Defect Inspection,ork adaptability for new objects. Additionally, we prioritize retaining the features of established objects during weight updates. Demonstrating prowess in both image and pixel-level defect inspection, our approach achieves state-of-the-art performance, supporting dynamic and scalable industrial ins作者: Aprope 時(shí)間: 2025-3-24 14:17
,Enhancing Optimization Robustness in?1-Bit Neural Networks Through Stochastic Sign Descent,ImageNet ILSVRC2012 by 0.96% with eightfold fewer training iterations. In the case of ReActNet, Diode not only matches but slightly exceeds previous benchmarks without resorting to complex multi-stage optimization strategies, effectively halving the training duration. Additionally, Diode proves its 作者: 小步走路 時(shí)間: 2025-3-24 15:06 作者: harbinger 時(shí)間: 2025-3-24 20:00
M. Takedal,G. Van Tendeloo,S. Amelinckxd local attention mechanism. Additionally, we design a novel barrier loss function based on Normalized Mutual Information to impose constraints on the registration network, which enhances the registration accuracy. The superior performance of INNReg is demonstrated through experiments on two public 作者: collagenase 時(shí)間: 2025-3-25 02:19
Electron Microscopy of Ordering in Alloysa general FLRTF-based multi-dimensional data recovery model. Experimental results, including video frame interpolation/extrapolation, MSI band interpolation, and MSI spectral super-resolution tasks, substantiate that FLRTF has superior performance as compared with representative data recovery method作者: harmony 時(shí)間: 2025-3-25 05:59
1.5.1.7.3 Cold working, plastic deformation,vel alignment capabilities and significant bandwidth to create a high-resolution triplane. CRM further employs Flexicubes as geometric representation, facilitating direct end-to-end optimization on textured meshes. Overall, our model delivers a high-fidelity textured mesh from an image in just 10?s,作者: HARP 時(shí)間: 2025-3-25 10:23
Figs. 158 - 181, Tables 15 - 19, domain in a coarse-to-fine manner, leading to substantial improvement in efficiency. We demonstrate the effectiveness of our method in various NLOS scenarios with sparse scanning patterns. Experiments conducted on both synthetic and real-world data support the efficacy in general NLOS scenarios, an作者: Synchronism 時(shí)間: 2025-3-25 12:05
1.5.1.7.3 Cold working, plastic deformation,her, we propose a pose-guided heatmap alignment module to eliminate the influence of gait-irrelevant covariates. Furthermore, a global-local network incorporating an efficient fusion branch is designed to improve the extraction of semantic information. Compared to skeleton-based methods, GaitHeat ex作者: jovial 時(shí)間: 2025-3-25 16:19 作者: amplitude 時(shí)間: 2025-3-25 19:58
1.5.1.9 3d elements in Cu, Ag or Au, the depth. Experimental results demonstrate that our model outperform other single-image model and can achieve comparable or even better performance than models with rich-resource inputs, only using low-resolution single-image input.作者: absolve 時(shí)間: 2025-3-26 00:18 作者: Flawless 時(shí)間: 2025-3-26 04:53
1.5.4.4 Mn alloys and compounds,e initial high-resolution results. At each denoising iteration, we further correct and update the initial results using the proposed Octadecaplex Tangent Information Interaction (OTII) and Gradient Decomposition (GD) technique to ensure better consistency. Finally, the TP images are transformed back作者: refraction 時(shí)間: 2025-3-26 10:28
1.5.4.2 Ti and V alloys and compounds,l attention control under the supervision of character-level segmentation maps. Finally, by employing an inference stage refinement process, we achieve a notably high sequence accuracy when synthesizing text in arbitrarily given images. Both qualitative and quantitative results demonstrate the super作者: 樹膠 時(shí)間: 2025-3-26 12:49 作者: 精密 時(shí)間: 2025-3-26 18:53 作者: Eosinophils 時(shí)間: 2025-3-27 00:09 作者: QUAIL 時(shí)間: 2025-3-27 04:15 作者: Free-Radical 時(shí)間: 2025-3-27 08:37
Medien ? Kultur ? Kommunikationork adaptability for new objects. Additionally, we prioritize retaining the features of established objects during weight updates. Demonstrating prowess in both image and pixel-level defect inspection, our approach achieves state-of-the-art performance, supporting dynamic and scalable industrial ins作者: 雪上輕舟飛過 時(shí)間: 2025-3-27 13:03
Alltag in den Medien - Medien im AlltagImageNet ILSVRC2012 by 0.96% with eightfold fewer training iterations. In the case of ReActNet, Diode not only matches but slightly exceeds previous benchmarks without resorting to complex multi-stage optimization strategies, effectively halving the training duration. Additionally, Diode proves its 作者: 制度 時(shí)間: 2025-3-27 16:24 作者: Gobble 時(shí)間: 2025-3-27 19:48
0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..978-3-031-72750-4978-3-031-72751-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Corporeal 時(shí)間: 2025-3-28 00:06
1.5.1.7.3 Cold working, plastic deformation,asing complexity. Extensive experiments conducted on two MM-Fi and WiPose datasets underscore the superiority of our method over state-of-the-art approaches, while ensuring minimal computational overhead, rendering it highly suitable for large-scale scenarios.作者: labile 時(shí)間: 2025-3-28 03:18 作者: modest 時(shí)間: 2025-3-28 06:15 作者: semble 時(shí)間: 2025-3-28 11:01
1.5.1.9 3d elements in Cu, Ag or Au, errors. Extensive experiments demonstrate that SGS-SLAM delivers state-of-the-art performance in camera pose estimation, map reconstruction, precise semantic segmentation, and object-level geometric accuracy, while ensuring real-time rendering capabilities.作者: 變化 時(shí)間: 2025-3-28 16:03 作者: 外科醫(yī)生 時(shí)間: 2025-3-28 21:04 作者: 拖網(wǎng) 時(shí)間: 2025-3-28 23:44
,YOLOv9: Learning What You Want to?Learn Using Programmable Gradient Information,appropriate neural network architecture has to be designed. Existing methods ignore a fact that when input data undergoes layer-by-layer feature transformation, large amount of information will be lost. This paper delve into the important issues of information bottleneck and reversible functions. We作者: 灌輸 時(shí)間: 2025-3-29 05:56 作者: 惰性氣體 時(shí)間: 2025-3-29 10:56
,Functional Transform-Based Low-Rank Tensor Factorization for?Multi-dimensional Data Recovery, discrete transforms along the third (., temporal/spectral) dimension are dominating in existing t-LRTF methods, which hinders their performance in addressing temporal/spectral degeneration scenarios, ., video frame interpolation and multispectral image (MSI) spectral super-resolution. To overcome t作者: Nonporous 時(shí)間: 2025-3-29 13:02 作者: MOAT 時(shí)間: 2025-3-29 17:40
,Domain Reduction Strategy for?Non-Line-of-Sight Imaging, with significantly reduced reconstruction time. In NLOS imaging, the visible surfaces of the target objects are notably sparse. To mitigate unnecessary computations arising from empty regions, we design our method to render the transients through partial propagations from a continuously sampled set作者: POINT 時(shí)間: 2025-3-29 22:24
,HPE-Li: WiFi-Enabled Lightweight Dual Selective Kernel Convolution for?Human Pose Estimation,l cost hindering its widespread adoption. This paper introduces a novel HPE-Li approach that harnesses multi-modal sensors (. camera and WiFi) to generate accurate 3D skeletal in HPE. We then develop an efficient deep neural network to process raw WiFi signals. Our model incorporates a distinctive m作者: bacteria 時(shí)間: 2025-3-30 00:44 作者: BUDGE 時(shí)間: 2025-3-30 04:47 作者: quiet-sleep 時(shí)間: 2025-3-30 12:09
,High-Precision Self-supervised Monocular Depth Estimation with?Rich-Resource Prior,ypically achieve better performance than models that use ordinary single image input. However, these rich-resource inputs may not always be available, limiting the applicability of these methods in general scenarios. In this paper, we propose Rich-resource Prior Depth estimator (RPrDepth), which onl作者: Connotation 時(shí)間: 2025-3-30 14:26 作者: 擁擠前 時(shí)間: 2025-3-30 20:24 作者: 不愛防注射 時(shí)間: 2025-3-30 20:41
OmniSSR: Zero-Shot Omnidirectional Image Super-Resolution Using Stable Diffusion Model,sks. Most existing super-resolution methods for ODIs use end-to-end learning strategies, resulting in inferior realness of generated images and a lack of effective out-of-domain generalization capabilities in training methods. Image generation methods represented by diffusion model provide strong pr作者: 證明無罪 時(shí)間: 2025-3-31 01:26
,UDiffText: A Unified Framework for?High-Quality Text Synthesis in?Arbitrary Images via?Character-Awods produce visually appealing results, they frequently exhibit spelling errors when rendering text within the generated images. Such errors manifest as missing, incorrect or extraneous characters, thereby severely constraining the performance of text image generation based on diffusion models. To a作者: BULLY 時(shí)間: 2025-3-31 06:35
,Confidence Self-calibration for?Multi-label Class-Incremental Learning, and future labels remain unavailable. This issue leads to a proliferation of false-positive errors due to erroneously high confidence multi-label predictions, exacerbating catastrophic forgetting within the disjoint label space. In this paper, we aim to refine multi-label confidence calibration in 作者: Goblet-Cells 時(shí)間: 2025-3-31 10:14
,OMG: Occlusion-Friendly Personalized Multi-concept Generation in?Diffusion Models,hods are struggling with identity preservation, occlusion, and the harmony between foreground and background. In this work, we propose OMG, an occlusion-friendly personalized generation framework designed to seamlessly integrate multiple concepts within a single image. We propose a novel two-stage s作者: installment 時(shí)間: 2025-3-31 16:09
,Versatile Incremental Learning: Towards Class and?Domain-Agnostic Incremental Learning,ally assume that an incoming task has only increments of classes or domains, referred to as Class IL (CIL) or Domain IL (DIL), respectively. In this work, we consider a more challenging and realistic but under-explored IL scenario, named ., in which a model has no prior of which of the classes or do作者: 射手座 時(shí)間: 2025-3-31 19:45
,WeCromCL: Weakly Supervised Cross-Modality Contrastive Learning for?Transcription-Only Supervised Timinating expensive boundary annotation. The crux of this task lies in locating each transcription in scene text images without location annotations. In this work, we formulate this challenging problem as a .akly Supervised .ss-.odality .ontrastive .earning problem, and design a simple yet effective作者: PATRI 時(shí)間: 2025-3-31 23:50
,An Incremental Unified Framework for?Small Defect Inspection,ned for specific industrial products and struggle with diverse product portfolios and evolving processes. Although some previous studies attempt to address object dynamics by storing embeddings in the reserved memory bank, these methods suffer from memory capacity limitations and object distribution作者: 繁重 時(shí)間: 2025-4-1 02:40
,Enhancing Optimization Robustness in?1-Bit Neural Networks Through Stochastic Sign Descent,ligning noisy floating-point gradients with binary parameters. To address this, we introduce Diode, a groundbreaking optimizer designed explicitly for BNNs that bridges this gap by utilizing the gradient’s sign information in a unique, latent-weight-free approach. By focusing on the gradient sign’s 作者: 向外才掩飾 時(shí)間: 2025-4-1 06:25
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242327.jpg作者: mydriatic 時(shí)間: 2025-4-1 10:32 作者: 愚笨 時(shí)間: 2025-4-1 17:31 作者: 矛盾 時(shí)間: 2025-4-1 22:35 作者: 啪心兒跳動(dòng) 時(shí)間: 2025-4-2 00:23
1.5.1.7.3 Cold working, plastic deformation,er-based methods do not leverage the geometric priors of the triplane component in their architecture, often leading to sub-optimal quality given the limited size of 3D data and slow training. In this work, we present the Convolutional Reconstruction Model (CRM), a high-fidelity feed-forward single
