作者: FOLLY 時間: 2025-3-21 22:29 作者: 凹室 時間: 2025-3-22 02:16 作者: 哺乳動物 時間: 2025-3-22 04:42 作者: Defense 時間: 2025-3-22 11:54
Efficient and?Accurate Quantized Image Super-Resolution on?Mobile NPUs, Mobile AI & AIM 2022 Challen. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized 作者: Mumble 時間: 2025-3-22 15:11 作者: Mumble 時間: 2025-3-22 20:14 作者: CONE 時間: 2025-3-23 00:21
AIM 2022 Challenge on?Super-Resolution of?Compressed Image and?Video: Dataset, Methods and?Resultse super-resolution of compressed image, and Track?2 targets the super-resolution of compressed video. In Track 1, we use the popular dataset DIV2K as the training, validation and test sets. In Track 2, we propose the LDV 3.0 dataset, which contains 365 videos, including the LDV 2.0 dataset (335 vide作者: 無禮回復(fù) 時間: 2025-3-23 03:10
Swin-Unet: Unet-Like Pure Transformer for?Medical Image Segmentationsed on U-shaped architecture and skip-connections have been widely applied in various medical image tasks. However, although CNN has achieved excellent performance, it cannot learn global semantic information interaction well due to the locality of convolution operation. In this paper, we propose Sw作者: 蚊子 時間: 2025-3-23 06:29
Self-attention Capsule Network for?Tissue Classification in?Case of?Challenging Medical Image Statisclassification. These challenges are - significant data heterogeneity with statistics variability across imaging domains, insufficient spatial context and local fine-grained details, and limited training data. Moreover, our proposed method solves limitations of the baseline Capsule Networks (CapsNet作者: 亞當(dāng)心理陰影 時間: 2025-3-23 11:01
ReLaX: Retinal Layer Attribution for?Guided Explanations of?Automated Optical Coherence Tomography Cans requires trained eye care professionals who are still prone to making errors. With better systems for diagnosis, many cases of vision loss caused by retinal disease could be entirely avoided. In this work, we present ReLaX, a novel deep learning framework for explainable, accurate classification作者: 肉身 時間: 2025-3-23 15:27
Neural Registration and?Segmentation of?White Matter Tracts in?Multi-modal Brain MRI tractography workflows rely on classical registration tools that prospectively align the multiple brain MRI modalities required for the task. Brain lesions and patient motion may challenge the robustness and accuracy of these tool, eventually requiring additional manual intervention. We present a n作者: PANIC 時間: 2025-3-23 21:17
Complementary Phase Encoding for?Pair-Wise Neural Deblurring of?Accelerated Brain MRIver, due to long scanning times and capital costs, access to MRI lags behind CT. Typical brain protocols lasting over 30?min set a clear limitation to patient experience, scanner throughput, operation profitability, and lead to long waiting times for an appointment..As image quality, in terms of spa作者: 演講 時間: 2025-3-23 22:37 作者: invert 時間: 2025-3-24 04:27 作者: 襲擊 時間: 2025-3-24 07:20
Simultaneous Detection and?Classification of?Partially and?Weakly Supervised Cellsng and grading at the slide level is routinely performed by pathologists, but analysis at the cell level, often desired in personalized cancer treatment, is both impractical and non-comprehensive. With its remarkable success in natural images, deep learning is already the gold standard in computatio作者: Transfusion 時間: 2025-3-24 14:15 作者: 結(jié)合 時間: 2025-3-24 14:50
Unacceptable Weapons: Gas and Bacteria,ods and benchmark establish the state-of-the-art for this low-level vision inverse problem, and generating realistic raw sensor readings can potentially benefit other tasks such as denoising and super-resolution.作者: 蘑菇 時間: 2025-3-24 21:04 作者: 吵鬧 時間: 2025-3-24 23:13 作者: 脖子 時間: 2025-3-25 06:46
https://doi.org/10.1007/978-3-030-02616-5edicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.作者: 修剪過的樹籬 時間: 2025-3-25 08:50
Research in Management Accounting & Control real pre-surgical dataset. The proposed method outperforms the state-of-the-art TractSeg and AGYnet algorithms on both datasets, quantitatively and qualitatively, suggesting its applicability to automatic WM tract mapping in neuro-surgical MRI.作者: Supplement 時間: 2025-3-25 14:23 作者: decipher 時間: 2025-3-25 16:33 作者: 同位素 時間: 2025-3-25 20:06
Efficient Single-Image Depth Estimation on?Mobile Devices, Mobile AI & AIM 2022 Challenge: Reporthe developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper.作者: 拋射物 時間: 2025-3-26 03:16
Efficient and?Accurate Quantized Image Super-Resolution on?Mobile NPUs, Mobile AI & AIM 2022 Challenedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.作者: 喚醒 時間: 2025-3-26 05:03 作者: motor-unit 時間: 2025-3-26 08:57
0302-9743 e 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online..The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop 作者: 表示向下 時間: 2025-3-26 14:43 作者: grieve 時間: 2025-3-26 17:46 作者: Dissonance 時間: 2025-3-26 21:54
AIM 2022 Challenge on?Instagram Filter Removal: Methods and?Resultsiginal images. There are two prior studies on this task as the baseline, and a total of 9 teams have competed in the final phase of the challenge. The comparison of qualitative results of the proposed solutions and the benchmark for the challenge are presented in this report.作者: fatuity 時間: 2025-3-27 01:58 作者: 沉積物 時間: 2025-3-27 08:10 作者: deforestation 時間: 2025-3-27 12:54 作者: 吃掉 時間: 2025-3-27 15:08 作者: lattice 時間: 2025-3-27 20:01 作者: 總 時間: 2025-3-27 21:56
Unacceptable Weapons: Gas and Bacteria,sor (ISP). Numerous low-level vision tasks operate in the RAW domain (.. ?image denoising, white balance) due to its linear relationship with the scene irradiance, wide-range of information at 12bits, and sensor designs. Despite this, RAW image datasets are scarce and more expensive to collect than 作者: Repatriate 時間: 2025-3-28 04:10 作者: 傻瓜 時間: 2025-3-28 08:00 作者: Lipohypertrophy 時間: 2025-3-28 12:26
https://doi.org/10.1007/978-3-030-02616-5 many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time perfo作者: Flat-Feet 時間: 2025-3-28 14:52
https://doi.org/10.1007/978-3-030-02616-5. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized 作者: 改良 時間: 2025-3-28 20:27
https://doi.org/10.1007/978-3-8350-5402-8ution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-作者: 剛毅 時間: 2025-3-29 02:19
Research in Management Accounting & Controlr this task. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale EBB! bokeh dataset consisting of 5K shallow/wide depth-作者: 鴿子 時間: 2025-3-29 07:00
https://doi.org/10.1007/978-3-8350-5402-8e super-resolution of compressed image, and Track?2 targets the super-resolution of compressed video. In Track 1, we use the popular dataset DIV2K as the training, validation and test sets. In Track 2, we propose the LDV 3.0 dataset, which contains 365 videos, including the LDV 2.0 dataset (335 vide作者: 后退 時間: 2025-3-29 08:44
Research in Management Accounting & Controlsed on U-shaped architecture and skip-connections have been widely applied in various medical image tasks. However, although CNN has achieved excellent performance, it cannot learn global semantic information interaction well due to the locality of convolution operation. In this paper, we propose Sw作者: 靈敏 時間: 2025-3-29 14:31
https://doi.org/10.1007/978-3-8350-5402-8classification. These challenges are - significant data heterogeneity with statistics variability across imaging domains, insufficient spatial context and local fine-grained details, and limited training data. Moreover, our proposed method solves limitations of the baseline Capsule Networks (CapsNet作者: BARK 時間: 2025-3-29 18:12 作者: 愛了嗎 時間: 2025-3-29 22:53
Research in Management Accounting & Control tractography workflows rely on classical registration tools that prospectively align the multiple brain MRI modalities required for the task. Brain lesions and patient motion may challenge the robustness and accuracy of these tool, eventually requiring additional manual intervention. We present a n作者: defendant 時間: 2025-3-30 00:04 作者: Alopecia-Areata 時間: 2025-3-30 07:43
Conclusions and Recommendations,rious correlations from the training signal. So called ‘shortcuts’ can occur during learning, for example, when there are specific frequencies present in the image data that correlate with the output predictions. Both high and low frequencies can be characteristic of the underlying noise distributio作者: 脆弱帶來 時間: 2025-3-30 08:51 作者: Living-Will 時間: 2025-3-30 12:23 作者: 諄諄教誨 時間: 2025-3-30 16:58 作者: syncope 時間: 2025-3-31 00:40
https://doi.org/10.1007/978-3-031-25066-8artificial intelligence; computer networks; computer vision; deep learning; education; Human-Computer Int
