作者: nominal 時間: 2025-3-21 20:38 作者: Admire 時間: 2025-3-22 00:55
Windows Presentation Foundation UI, it in an efficient and cost-effective way. Our method takes cross-domain and cross-scale images as input, and consequently synthesizes HR colorization results to facilitate the trade-off between spatial-temporal resolution and color depth in the single-camera imaging system. In contrast to the prev作者: 不整齊 時間: 2025-3-22 05:35 作者: Oversee 時間: 2025-3-22 09:20 作者: gnarled 時間: 2025-3-22 13:56 作者: 讓步 時間: 2025-3-22 19:43 作者: forager 時間: 2025-3-22 23:13 作者: Hyperlipidemia 時間: 2025-3-23 01:42
Authentication and Authorization,l-trained model may not effectively generalize to a new scenario captured by another camera. Therefore, it is desirable to adapt the model trained on an annotated source domain to the target domain. To achieve domain adaptation for trajectory prediction, we propose a Cross-domain Trajectory Predicti作者: LEVY 時間: 2025-3-23 08:40
Automatic Property Declaration,generative models. However, without or with only globally-pooled appearance representation from a reference, the low-quality generated images restrict the recognition accuracy. The intuition of our paper is the spatially-distributed appearance contains details beneficial to higher-quality image synt作者: ASSAY 時間: 2025-3-23 13:44
Automatic Property Declaration,ers and float-point operations (FLOPs) of DLIC severely limit their application on mobile devices. To reduce the parameters and FLOPs while maintaining the superior compression gain, this paper proposes lightweight algorithms especially for the feature analysis, synthesis, and fusion modules in DLIC作者: Osteoporosis 時間: 2025-3-23 17:42
Business Framework Implementation,ntly learns multiple dense prediction tasks in a unified multi-task learning architecture that is trained end-to-end. Specifically, the DGMLP consists of (i) a spatial deformable MLP to capture the valuable spatial information for different tasks and (ii) a spatial gating MLP to learn the shared fea作者: 整潔漂亮 時間: 2025-3-23 21:22 作者: 直覺好 時間: 2025-3-24 01:47 作者: 拔出 時間: 2025-3-24 06:00 作者: 不易燃 時間: 2025-3-24 09:32 作者: contrast-medium 時間: 2025-3-24 12:06
Managed Types, Instances, and Memory,s Microsoft Kinect, the captured RGB-D images provide users with a higher viewing experience, but also pose a higher challenge to the current saliency detection technology. In this paper, we propose a shape-aware saliency object detection approach SASD, manifesting in two aspects: 1) obtaining high 作者: 小官 時間: 2025-3-24 16:18 作者: OREX 時間: 2025-3-24 20:02
Managed Types, Instances, and Memory,iew RGB image. Novel view synthesis by neural radiance fields has achieved great improvement with the development of deep learning. However, how to make the method generic across scenes has always been a challenging task. A good idea is to introduce 2D image features as prior knowledge for adaptive 作者: IVORY 時間: 2025-3-25 01:00 作者: 遭遇 時間: 2025-3-25 03:49 作者: 厭惡 時間: 2025-3-25 09:28 作者: fetter 時間: 2025-3-25 15:10 作者: 認(rèn)識 時間: 2025-3-25 16:15 作者: micturition 時間: 2025-3-25 19:58 作者: infringe 時間: 2025-3-26 03:41
Unsupervised Domain Adaptation for?Semantic Segmentation with?Global and?Local Consistencyfirst constrain global style consistency through a generative adversarial network to acquire real-like latent domain images. Then we enhance local content consistency based on pixel-wise entropy minimization. Experimental results show that our method has superiority over other competitive methods on GTA5 . Cityscapes.作者: 小故事 時間: 2025-3-26 05:33 作者: MAG 時間: 2025-3-26 10:31 作者: 燦爛 時間: 2025-3-26 14:53
Windows Presentation Foundation UI,bidirectional fused images for training. BSAM ensures the correct scene layout, facilitating the model to adapt to the different scenario characteristics. Extensive experiments on two benchmarks (GTA5 to Cityscapes and SYNTHIA to Cityscapes) demonstrate that BSAM achieves state-of-the-art performance.作者: 遭受 時間: 2025-3-26 18:21
Authentication and Authorization,scriminator is utilized to adversarially regularize the future trajectory predictions to be in line with the observed trajectories. Extensive experiments demonstrate the effectiveness of our method on domain adaptation for pedestrian trajectory prediction.作者: CHOIR 時間: 2025-3-27 00:51 作者: Meager 時間: 2025-3-27 01:40
Cross-Camera Deep Colorizationfor cross-domain image alignment. Through extensive experiments on various datasets and multiple settings, we validate the flexibility and effectiveness of our approach. Remarkably, our method consistently achieves substantial improvements, ., around 10dB PSNR gain, upon the state-of-the-art methods. Code is at: ..作者: 假 時間: 2025-3-27 05:51
BSAM: Bidirectional Scene-Aware Mixup for?Unsupervised Domain Adaptation in?Semantic Segmentationbidirectional fused images for training. BSAM ensures the correct scene layout, facilitating the model to adapt to the different scenario characteristics. Extensive experiments on two benchmarks (GTA5 to Cityscapes and SYNTHIA to Cityscapes) demonstrate that BSAM achieves state-of-the-art performance.作者: Obligatory 時間: 2025-3-27 12:06 作者: 羽飾 時間: 2025-3-27 16:48 作者: Contort 時間: 2025-3-27 19:12 作者: construct 時間: 2025-3-28 01:53 作者: 搖曳的微光 時間: 2025-3-28 02:35 作者: finale 時間: 2025-3-28 06:35 作者: 灌輸 時間: 2025-3-28 12:49
Attentive Cascaded Pyramid Network for?Online Video Stabilizationrform offline stabilization and result in long latency, or dismiss the nonuniform motion field in each frame and lead to large distortion. The non-uniform motion includes dynamic foreground motion and non-planar background motion. To better describe the shaky motion field online, we propose a novel 作者: BYRE 時間: 2025-3-28 15:29 作者: airborne 時間: 2025-3-28 21:16 作者: 積習(xí)已深 時間: 2025-3-28 23:48 作者: electrolyte 時間: 2025-3-29 06:02 作者: 大方一點 時間: 2025-3-29 08:35
Cross-domain Trajectory Prediction with?CTP-Netl-trained model may not effectively generalize to a new scenario captured by another camera. Therefore, it is desirable to adapt the model trained on an annotated source domain to the target domain. To achieve domain adaptation for trajectory prediction, we propose a Cross-domain Trajectory Predicti作者: 碎石頭 時間: 2025-3-29 12:14 作者: CODA 時間: 2025-3-29 15:55
Lightweight Image Compression Based on?Deep Learningers and float-point operations (FLOPs) of DLIC severely limit their application on mobile devices. To reduce the parameters and FLOPs while maintaining the superior compression gain, this paper proposes lightweight algorithms especially for the feature analysis, synthesis, and fusion modules in DLIC作者: keloid 時間: 2025-3-29 22:02 作者: 斜谷 時間: 2025-3-30 03:58 作者: BACLE 時間: 2025-3-30 06:03 作者: HUMP 時間: 2025-3-30 08:44 作者: Polydipsia 時間: 2025-3-30 12:47 作者: Cubicle 時間: 2025-3-30 16:35
SASD: A Shape-Aware Saliency Object Detection Approach for?RGB-D Imagess Microsoft Kinect, the captured RGB-D images provide users with a higher viewing experience, but also pose a higher challenge to the current saliency detection technology. In this paper, we propose a shape-aware saliency object detection approach SASD, manifesting in two aspects: 1) obtaining high 作者: FIG 時間: 2025-3-30 21:21 作者: Jejune 時間: 2025-3-31 03:43
CDNeRF: A Multi-modal Feature Guided Neural Radiance Fieldsiew RGB image. Novel view synthesis by neural radiance fields has achieved great improvement with the development of deep learning. However, how to make the method generic across scenes has always been a challenging task. A good idea is to introduce 2D image features as prior knowledge for adaptive 作者: HUMP 時間: 2025-3-31 07:43 作者: 去世 時間: 2025-3-31 09:34
Attentive Cascaded Pyramid Network for?Online Video Stabilizationlti-scale residual pyramid structure to do coarse to fine stabilization. Experimental results on public benchmarks show that our proposed method can achieve state-of-the-art performance both qualitatively and quantitatively, comparing to both online and offline methods.作者: indifferent 時間: 2025-3-31 14:12
Amodal Layout Completion in?Complex Outdoor Scenes propose four challenging IoU variants to measure completion performances for different completion conditions. Experiment results show the ALCN achieves state-of-the-art layout completion performances in most cases and improves the layout-to-image generation performance.
