作者: Glaci冰 時間: 2025-3-21 21:03 作者: 欲望 時間: 2025-3-22 01:15
Adaptive Text Recognition Through Visual Matching,itive nature of characters in languages, and decouples the visual decoding and linguistic modelling stages through intermediate representations in the form of .. By doing this, we turn text recognition into a visual matching problem, thereby achieving generalization in appearance and flexibility in 作者: gastritis 時間: 2025-3-22 07:12 作者: Hyaluronic-Acid 時間: 2025-3-22 09:22
Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wilous methods, we make two main contributions: First, instead of comparing real-world images and synthetic renderings in the RGB or mask space, we compare them in a feature space optimized for 3D pose refinement. Second, we introduce a novel differentiable renderer that learns to approximate the raste作者: 嬉耍 時間: 2025-3-22 14:46 作者: 嬉耍 時間: 2025-3-22 21:01 作者: 貿易 時間: 2025-3-22 21:40 作者: 漂白 時間: 2025-3-23 01:44
General 3D Room Layout from a Single View by Render-and-Compare,contrast with previous single-view methods restricted to cuboid-shaped layouts. This input view can consist of a color image only, but considering a depth map results in a more accurate reconstruction. Our approach is formalized as solving a constrained discrete optimization problem to find the set 作者: 偽造 時間: 2025-3-23 07:06
Neural Dense Non-Rigid Structure from Motion with Latent Space Constraints, 2D point tracks. Compared to the competing methods, our combination of loss functions is fully-differentiable and can be readily integrated into deep-learning systems. We formulate the deformation model by an auto-decoder and impose subspace constraints on the recovered latent space function in a f作者: 食料 時間: 2025-3-23 12:26
Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorability, goal, we develop a predictive model of human visual event memory and how those memories decay over time. We introduce ., a new, dynamic video memorability dataset containing human annotations at different viewing delays. Based on our findings we propose a new mathematical formulation of memorabilit作者: Collision 時間: 2025-3-23 15:07 作者: 親屬 時間: 2025-3-23 21:35 作者: Resection 時間: 2025-3-24 00:20 作者: 拍翅 時間: 2025-3-24 05:29
PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations,ailed shapes of objects with arbitrary topology. Since a continuous function is learned, the reconstructions can also be extracted at any arbitrary resolution. However, large datasets such as ShapeNet are required to train such models..In this paper, we present a new mid-level patch-based surface re作者: craven 時間: 2025-3-24 06:42
How Does Lipschitz Regularization Influence GAN Training?,ct effect of .-Lipschitz regularization is to restrict the .2-norm of the neural network gradient to be smaller than a threshold . (e.g., .) such that .. In this work,?we uncover an even more important effect of Lipschitz regularization by examining its impact on the loss function: .. Our analysis s作者: Obligatory 時間: 2025-3-24 11:55
0302-9743 uter Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers dea作者: 緊張過度 時間: 2025-3-24 18:26
Conference proceedings 2020g; 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: medieval 時間: 2025-3-24 21:59 作者: cataract 時間: 2025-3-25 00:21
https://doi.org/10.1007/978-3-031-04162-4antitatively, we created one together with several appropriate metrics. Our dataset consists of 293 images from ScanNet, which we annotated with precise 3D layouts. It offers three times more samples than the popular NYUv2 303 benchmark, and a much larger variety of layouts.作者: Aggregate 時間: 2025-3-25 05:03 作者: 流逝 時間: 2025-3-25 09:03
General 3D Room Layout from a Single View by Render-and-Compare,antitatively, we created one together with several appropriate metrics. Our dataset consists of 293 images from ScanNet, which we annotated with precise 3D layouts. It offers three times more samples than the popular NYUv2 303 benchmark, and a much larger variety of layouts.作者: Bridle 時間: 2025-3-25 12:10
Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorability,d semantic information (in the form of textual captions) to fully represent the meaning of events. Our experiments on two video memorability benchmarks, including Memento10k, show that our model significantly improves upon the best prior approach (by 12% on average).作者: Anthropoid 時間: 2025-3-25 15:56
Temporal Aggregate Representations for Long-Range Video Understanding,pation capabilities of our model, we conduct experiments on Breakfast, 50Salads, and EPIC-Kitchens datasets, where we achieve state-of-the-art results. With minimal modifications, our model can also be extended for video segmentation and action recognition.作者: 擦試不掉 時間: 2025-3-25 22:05 作者: Anhydrous 時間: 2025-3-26 03:34 作者: Synthesize 時間: 2025-3-26 05:38
Islamic Banking and Finance in Africaaining the transformer model with CTC loss. These two methods also help reduce the training computation, both in terms of time and space, significantly. We evaluated our model on popular CSLR datasets, and show its effectiveness compared to the state-of-the-art methods.作者: Conscientious 時間: 2025-3-26 10:29
Harry de Gorter,Dusan Drabik,David R. Justg a novel structure of Non-manifold Volumetric Grid to the re-design of both TSDF and EDG, which allows connectivity updates by cell splitting and replication. Experiments show convincing reconstruction results for dynamic scenes of topology changes, as compared to the state-of-the-art methods.作者: Infect 時間: 2025-3-26 16:26 作者: Overthrow 時間: 2025-3-26 19:17 作者: macular-edema 時間: 2025-3-26 23:22 作者: HAVOC 時間: 2025-3-27 04:32
How Does Lipschitz Regularization Influence GAN Training?,e not degenerated and that a wide range of functions can be used as loss function as long as they are sufficiently degenerated by regularization. Basically, Lipschitz regularization ensures that all loss functions . Empirically, we verify our proposition on the MNIST, CIFAR10 and CelebA datasets.作者: 無情 時間: 2025-3-27 05:23 作者: Largess 時間: 2025-3-27 13:20 作者: Carminative 時間: 2025-3-27 16:35
https://doi.org/10.1007/978-3-030-58517-4computer networks; computer vision; education; engineering; Human-Computer Interaction (HCI); image codin作者: 使虛弱 時間: 2025-3-27 18:21 作者: 憤憤不平 時間: 2025-3-27 23:54
Auditor Independence as an Economic Decisione, and motion blur. Previous approaches exploit to propagate and aggregate features across video frames by using optical flow-warping. However, directly applying image-level optical flow onto the high-level features might not establish accurate spatial correspondences. Therefore, a novel module call作者: 谷類 時間: 2025-3-28 04:14 作者: 饑荒 時間: 2025-3-28 09:55
Auditor Independence as an Economic Decisionitive nature of characters in languages, and decouples the visual decoding and linguistic modelling stages through intermediate representations in the form of .. By doing this, we turn text recognition into a visual matching problem, thereby achieving generalization in appearance and flexibility in 作者: incredulity 時間: 2025-3-28 11:51
The Economics of Bank Bankruptcy Lawsity cameras. An obstacle to using these sensors with current powerful deep neural networks is the lack of large labeled training datasets. This paper proposes a Network Grafting Algorithm (NGA), where a new front end network driven by unconventional visual inputs replaces the front end network of a作者: Plaque 時間: 2025-3-28 17:11 作者: 吹牛需要藝術 時間: 2025-3-28 19:15 作者: bromide 時間: 2025-3-28 23:08
Explaining Banking Failures in Africaral extent, scaling, and level of semantic abstraction with a flexible multi-granular temporal aggregation framework. We show that it is possible to achieve state of the art in both next action and dense anticipation with simple techniques such as max-pooling and attention. To demonstrate the antici作者: orthopedist 時間: 2025-3-29 05:54 作者: Relinquish 時間: 2025-3-29 07:55 作者: Conduit 時間: 2025-3-29 12:25
Sustainable Finance and Banking in Africa 2D point tracks. Compared to the competing methods, our combination of loss functions is fully-differentiable and can be readily integrated into deep-learning systems. We formulate the deformation model by an auto-decoder and impose subspace constraints on the recovered latent space function in a f作者: emission 時間: 2025-3-29 16:40 作者: Conclave 時間: 2025-3-29 19:53 作者: Schlemms-Canal 時間: 2025-3-30 02:10
Harry de Gorter,Dusan Drabik,David R. Justextracted from the TSDF volume as the canonical surface representation to help estimating deformation field. However, the surface and Embedded Deformation Graph (EDG) representations bring conflicts under topology changes since the surface mesh has fixed-connectivity but the deformation field can be作者: Debility 時間: 2025-3-30 04:43
Harry de Gorter,Dusan Drabik,David R. Justapproaches have been proposed to reduce image compression artifacts at the decoder side; however, they require a series of architecture-identical models to process images with different quality, which are inefficient and resource-consuming. Besides, it is common in practice that compressed images ar作者: 大氣層 時間: 2025-3-30 10:10
Cryptography Versus Incentives,ailed shapes of objects with arbitrary topology. Since a continuous function is learned, the reconstructions can also be extracted at any arbitrary resolution. However, large datasets such as ShapeNet are required to train such models..In this paper, we present a new mid-level patch-based surface re作者: Cholagogue 時間: 2025-3-30 13:47 作者: 闡釋 時間: 2025-3-30 19:22 作者: Homocystinuria 時間: 2025-3-30 21:48 作者: Guileless 時間: 2025-3-31 04:05 作者: 血統(tǒng) 時間: 2025-3-31 08:57
Learning Object Permanence from Video,bject. We then present a unified deep architecture that learns to predict object location under these four scenarios. We evaluate the architecture and system on a new dataset based on CATER, with per-frame labels, and find that it outperforms previous localization methods and various baselines.作者: 改正 時間: 2025-3-31 09:13 作者: Inertia 時間: 2025-3-31 17:15
Learning to Exploit Multiple Vision Modalities by Using Grafted Networks, in inference costs. Particularly, the grafted network driven by thermal frames showed a relative improvement of 49.11% over the use of intensity frames. The grafted front end has only 5–8% of the total parameters and can be trained in a few hours on a single GPU equivalent to 5% of the time that wo作者: fluffy 時間: 2025-3-31 21:20 作者: Hay-Fever 時間: 2025-4-1 01:11
Contextual Diversity for Active Learning,robability vector predicted by a CNN for a region of interest typically contains information from a larger receptive field. Exploiting this observation, we use the proposed CD measure within two AL frameworks: (1) a core-set based strategy and (2) a reinforcement learning based policy, for active fr作者: 符合國情 時間: 2025-4-1 05:34 作者: 小樣他閑聊 時間: 2025-4-1 05:59
Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images,omatically decide to terminate or continue enhancement according to the assessed quality of enhanced images. Consequently, slight artifacts can be removed in a simpler and faster process, while the severe artifacts can be further removed in a more elaborate process. Extensive experiments demonstrate作者: 下船 時間: 2025-4-1 12:52
PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations,trained using much fewer shapes, compared to existing approaches. We show several applications of our new representation, including shape interpolation and partial point cloud completion. Due to explicit control over positions, orientations and scales of patches, our representation is also more cont作者: Repatriate 時間: 2025-4-1 17:43
Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift,作者: 乳汁 時間: 2025-4-1 20:04
3D Fluid Flow Reconstruction Using Compact Light Field PIV,作者: FOVEA 時間: 2025-4-1 23:56 作者: cloture 時間: 2025-4-2 06:30
Auditor Independence as an Economic Decisionbject. We then present a unified deep architecture that learns to predict object location under these four scenarios. We evaluate the architecture and system on a new dataset based on CATER, with per-frame labels, and find that it outperforms previous localization methods and various baselines.
