作者: 有權 時間: 2025-3-21 21:17
TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapesrefreshing the performance records on various standard benchmarks. However, limited by the representations (axis-aligned rectangles, rotated rectangles or quadrangles) adopted to describe text, existing methods may fall short when dealing with much more free-form text instances, such as curved text,作者: maintenance 時間: 2025-3-22 00:35 作者: 集合 時間: 2025-3-22 06:22
Robust Image Stitching with Multiple Registrationst is also used by millions of consumers in smartphones and other cameras. Traditionally, the problem is decomposed into three phases: registration, which picks a single transformation of each source image to align it to the other inputs, seam?finding, which selects a source image for each pixel in t作者: 項目 時間: 2025-3-22 11:16
CTAP: Complementary Temporal Action Proposal Generationral intervals in videos that are likely to contain an action. Previous methods can be divided to two groups: sliding window ranking and actionness score grouping. Sliding windows uniformly cover all segments in videos, but the temporal boundaries are imprecise; grouping based method may have more pr作者: ligature 時間: 2025-3-22 13:34
Effective Use of Synthetic Data for Urban Scene Semantic Segmentationges, researchers have investigated the use of synthetic data, which can be labeled automatically. Unfortunately, a network trained on synthetic data performs relatively poorly on real images. While this can be addressed by domain adaptation, existing methods all require having access to real images 作者: ligature 時間: 2025-3-22 18:59 作者: flaggy 時間: 2025-3-22 21:43 作者: antidote 時間: 2025-3-23 03:06
Linear Span Network for Object Skeleton Detectionrst re-visit the implementation of HED, the essential principle of which can be ideally described with a linear reconstruction model. Hinted by this, we formalize a Linear Span framework, and propose Linear Span Network (LSN) which introduces Linear Span Units (LSUs) to minimizes the reconstruction 作者: 躲債 時間: 2025-3-23 06:58
SaaS: Speed as a Supervisor for Semi-supervised Learning measure the quality of an iterative estimate of the posterior probability of unknown labels. Training speed in supervised learning correlates strongly with the percentage of correct labels, so we use it as an inference criterion for the unknown labels, without attempting to infer the model paramete作者: bronchodilator 時間: 2025-3-23 09:57 作者: Flagging 時間: 2025-3-23 14:27
Egocentric Activity Prediction via Event Modulated Attentionunderstanding techniques are mostly NOT capable of predictive tasks, as their synchronous processing architecture performs poorly in either modeling event dependency or pruning temporal redundant features. This work explicitly addresses these issues by proposing an asynchronous gaze-event driven att作者: annexation 時間: 2025-3-23 18:58
How Good Is My GAN?y visual inspection, a number of quantitative criteria have emerged only recently. We argue here that the existing ones are insufficient and need to be in adequation with the task at hand. In this paper we introduce two measures based on image classification—GAN-train and GAN-test, which approximate作者: elucidate 時間: 2025-3-24 02:01 作者: 木訥 時間: 2025-3-24 05:56
Audio-Visual Event Localization in Unconstrained Videosnd audible in a video segment. We collect an . (AVE) dataset to systemically investigate three temporal localization tasks: supervised and weakly-supervised audio-visual event localization, and cross-modality localization. We develop an audio-guided visual attention mechanism to explore audio-visual作者: BLAZE 時間: 2025-3-24 09:31
Grounding Visual Explanationsa strong class prior, although the evidence may not actually be in the image. This is particularly concerning as ultimately such agents fail in building trust with human users. To overcome this limitation, we propose a phrase-critic model to refine generated candidate explanations augmented with fli作者: choleretic 時間: 2025-3-24 10:46 作者: 指數(shù) 時間: 2025-3-24 15:44
Conference proceedings 2018The papers are organized in topical?sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization;?matching and recognition; video attention; and poster sessions..作者: moribund 時間: 2025-3-24 22:47 作者: 大氣層 時間: 2025-3-25 00:47
Conference proceedings 2018, ECCV 2018, held in Munich, Germany, in September 2018..The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical?sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstructi作者: 補充 時間: 2025-3-25 05:56
Structure and Power Redistributione show that, by using such techniques, inpainting reduces to the problem of learning two image-feature translation functions in much smaller space and hence easier to train. We evaluate our method on several public datasets and show that we generate results of better visual quality than previous state-of-the-art methods.作者: FLIP 時間: 2025-3-25 10:29 作者: 演繹 時間: 2025-3-25 14:32
Thermodynamics and Radiative Transferasures and demonstrate a clear difference in performance. Furthermore, we observe that the increasing difficulty of the dataset, from CIFAR10 over CIFAR100 to ImageNet, shows an inverse correlation with the quality of the GANs, as clearly evident from our measures.作者: 宣誓書 時間: 2025-3-25 15:56 作者: 可卡 時間: 2025-3-25 21:00
Linear Span Network for Object Skeleton Detectionency of feature integration, which enhances the capability of fitting complex ground-truth. As a result, LSN can effectively suppress the cluttered backgrounds and reconstruct object skeletons. Experimental results validate the state-of-the-art performance of the proposed LSN.作者: Nomadic 時間: 2025-3-26 03:53
How Good Is My GAN?asures and demonstrate a clear difference in performance. Furthermore, we observe that the increasing difficulty of the dataset, from CIFAR10 over CIFAR100 to ImageNet, shows an inverse correlation with the quality of the GANs, as clearly evident from our measures.作者: Lineage 時間: 2025-3-26 06:38 作者: 花束 時間: 2025-3-26 08:53
Green Innovation in the B2B Context image classification and object detection tasks, and report the highest ImageNet-1k single-crop, top-1 accuracy to date: 85.4% (97.6% top-5). We also perform extensive experiments that provide novel empirical data on the relationship between large-scale pretraining and transfer learning performance.作者: exclamation 時間: 2025-3-26 16:09 作者: 持續(xù) 時間: 2025-3-26 19:03 作者: 固執(zhí)點好 時間: 2025-3-27 00:04 作者: VAN 時間: 2025-3-27 03:01
Exploring the Limits of Weakly Supervised Pretraining image classification and object detection tasks, and report the highest ImageNet-1k single-crop, top-1 accuracy to date: 85.4% (97.6% top-5). We also perform extensive experiments that provide novel empirical data on the relationship between large-scale pretraining and transfer learning performance.作者: Deject 時間: 2025-3-27 08:39
3D-CODED: 3D Correspondences by Deep Deformationn the difficult FAUST-inter challenge, with an average correspondence error of 2.88?cm. We show, on the TOSCA dataset, that our method is robust to many types of perturbations, and generalizes to non-human shapes. This robustness allows it to perform well on real unclean, meshes from the SCAPE dataset.作者: 土坯 時間: 2025-3-27 12:36 作者: 羅盤 時間: 2025-3-27 16:09
0302-9743 ter Vision, ECCV 2018, held in Munich, Germany, in September 2018..The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical?sections on learning for vision; computational photography; human analysis; human sensing; stereo and re作者: 震驚 時間: 2025-3-27 18:18
Research Design and Methodologyy with the percentage of correct labels, so we use it as an inference criterion for the unknown labels, without attempting to infer the model parameters at first. Despite its simplicity, SaaS achieves competitive results in semi-supervised learning benchmarks.作者: dyspareunia 時間: 2025-3-28 00:06
SaaS: Speed as a Supervisor for Semi-supervised Learningy with the percentage of correct labels, so we use it as an inference criterion for the unknown labels, without attempting to infer the model parameters at first. Despite its simplicity, SaaS achieves competitive results in semi-supervised learning benchmarks.作者: 描述 時間: 2025-3-28 02:04
Computer Vision – ECCV 2018978-3-030-01216-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: ONYM 時間: 2025-3-28 08:46
Structure and Power Redistributiona learning-based approach to generate visually coherent completion given a high-resolution image with missing components. In order to overcome the difficulty to directly learn the distribution of high-dimensional image data, we divide the task into inference and translation as two separate steps and作者: PET-scan 時間: 2025-3-28 13:23 作者: 積極詞匯 時間: 2025-3-28 16:47 作者: GULF 時間: 2025-3-28 20:52 作者: macrophage 時間: 2025-3-28 23:40
Leslie Willcocks,Will Venters,Edgar Whitleyral intervals in videos that are likely to contain an action. Previous methods can be divided to two groups: sliding window ranking and actionness score grouping. Sliding windows uniformly cover all segments in videos, but the temporal boundaries are imprecise; grouping based method may have more pr作者: BLAZE 時間: 2025-3-29 07:09 作者: demote 時間: 2025-3-29 07:25 作者: Onerous 時間: 2025-3-29 14:42
Research Design and MethodologyR imaging, input images are first aligned using optical flows before merging, which are still error-prone due to occlusion and large motions. In stark contrast to flow-based methods, we formulate HDR imaging as an image translation problem .. Moreover, our simple translation network can automaticall作者: 虛構的東西 時間: 2025-3-29 16:50
Research Design and Methodologyrst re-visit the implementation of HED, the essential principle of which can be ideally described with a linear reconstruction model. Hinted by this, we formalize a Linear Span framework, and propose Linear Span Network (LSN) which introduces Linear Span Units (LSUs) to minimizes the reconstruction 作者: 集聚成團 時間: 2025-3-29 20:45 作者: engrave 時間: 2025-3-30 02:08
Green Innovation in the B2B Contextg task for these models. Yet, ImageNet is now nearly ten years old and is by modern standards “small”. Even so, relatively little is known about the behavior of pretraining with datasets that are multiple orders of magnitude larger. The reasons are obvious: such datasets are difficult to collect and作者: Obsequious 時間: 2025-3-30 05:57 作者: patriarch 時間: 2025-3-30 09:23 作者: 禁令 時間: 2025-3-30 15:13 作者: Adherent 時間: 2025-3-30 19:18
Transport and Balance of Momentumnd audible in a video segment. We collect an . (AVE) dataset to systemically investigate three temporal localization tasks: supervised and weakly-supervised audio-visual event localization, and cross-modality localization. We develop an audio-guided visual attention mechanism to explore audio-visual作者: 不幸的人 時間: 2025-3-30 21:30 作者: Moderate 時間: 2025-3-31 01:47 作者: 發(fā)現(xiàn) 時間: 2025-3-31 07:20 作者: reception 時間: 2025-3-31 10:15
https://doi.org/10.1007/978-3-030-01216-83D; artificial intelligence; estimation; face recognition; image processing; image reconstruction; image s作者: Conducive 時間: 2025-3-31 16:57
978-3-030-01215-1Springer Nature Switzerland AG 2018作者: MENT 時間: 2025-3-31 21:18
Attention-GAN for Object Transfiguration in Wild Images作者: 深淵 時間: 2025-4-1 00:36
TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapesetry attributes are estimated via a Fully Convolutional Network (FCN) model. In experiments, the text detector based on TextSnake achieves state-of-the-art or comparable performance on Total-Text and SCUT-CTW1500, the two newly published benchmarks with special emphasis on curved text in natural ima作者: 干旱 時間: 2025-4-1 03:14 作者: 正論 時間: 2025-4-1 09:27
Robust Image Stitching with Multiple Registrationsd we show here that their energy functions can be readily modified with new terms that discourage duplication and tearing, common problems that are exacerbated by the use of multiple registrations. Our techniques are closely related to layer-based stereo [., .], and move image stitching closer to ex作者: conscience 時間: 2025-4-1 10:20 作者: Capture 時間: 2025-4-1 14:34
Effective Use of Synthetic Data for Urban Scene Semantic Segmentationle their texture in synthetic images is not photo-realistic, their shape looks natural. Our experiments evidence the effectiveness of our approach on Cityscapes and CamVid with models trained on synthetic data only.
