作者: minaret 時(shí)間: 2025-3-21 23:00
The Economics of American Higher Educationocuses on maintaining the continuity of generated images (frames), story visualization emphasizes preserving the global consistency of characters and scenes across different story pictures, which is very challenging since story sentences only provide sparse signals for generating images. Therefore, 作者: aristocracy 時(shí)間: 2025-3-22 04:26
Jill S. Harris,Claudia Ferranteincorperate the linguistic knowledge to promote context reasoning over image regions by proposing a Graph Interaction unit (GI unit) and a Semantic Context Loss (SC-loss). The GI unit is capable of enhancing feature representations of convolution networks over high-level semantics and learning the s作者: Feckless 時(shí)間: 2025-3-22 05:50 作者: Foment 時(shí)間: 2025-3-22 12:26 作者: 外貌 時(shí)間: 2025-3-22 14:28 作者: 外貌 時(shí)間: 2025-3-22 20:21
https://doi.org/10.1007/978-1-349-25485-9s function. Only this final state of the weights is typically kept for testing, while the wealth of information on the geometry of the weight space, accumulated over the descent towards the minimum is discarded. In this work we propose to make use of this knowledge and leverage it for computing the 作者: NAIVE 時(shí)間: 2025-3-22 22:05
Insurance and the Selection Problemon adversarial attacks have mainly focused on static scenes; however, it remains unclear whether such attacks are effective against embodied agents, which could navigate and interact with a dynamic environment. In this work, we take the first step to study adversarial attacks for embodied agents. In作者: 按等級(jí) 時(shí)間: 2025-3-23 03:28
Investment Finance and the Selection Problems built on top of millions of annotated samples. However, as we endeavor to take the performance to the next level, the reliance on annotated data becomes a major obstacle. We desire to explore an alternative approach, namely using captioned images for training, as an attempt to mitigate this diffic作者: CURL 時(shí)間: 2025-3-23 06:31 作者: 完成 時(shí)間: 2025-3-23 11:25 作者: 箴言 時(shí)間: 2025-3-23 14:41 作者: regale 時(shí)間: 2025-3-23 18:10
https://doi.org/10.1007/978-3-030-58520-4artificial intelligence; computer vision; data security; face recognition; image coding; image compressio作者: chlorosis 時(shí)間: 2025-3-24 00:57
978-3-030-58519-8Springer Nature Switzerland AG 2020作者: Gratulate 時(shí)間: 2025-3-24 03:05
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: 機(jī)制 時(shí)間: 2025-3-24 09:13 作者: 不利 時(shí)間: 2025-3-24 11:20
Conference proceedings 2020n, 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 deal with top作者: cravat 時(shí)間: 2025-3-24 17:32
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: 激怒某人 時(shí)間: 2025-3-24 19:04 作者: 使激動(dòng) 時(shí)間: 2025-3-24 23:20 作者: 反復(fù)無(wú)常 時(shí)間: 2025-3-25 03:27 作者: 媽媽不開(kāi)心 時(shí)間: 2025-3-25 08:43
Sports Economics, Management and Policydic decomposition theory (., a high-rank tensor can be expressed as a combination of rank-1 tensors.), we design a low-rank-to-high-rank context reconstruction framework (., RecoNet). Specifically, we first introduce the tensor generation module (TGM), which generates a number of rank-1 tensors to c作者: acquisition 時(shí)間: 2025-3-25 15:26 作者: 極大的痛苦 時(shí)間: 2025-3-25 16:58 作者: 他姓手中拿著 時(shí)間: 2025-3-25 23:49
https://doi.org/10.1007/978-1-349-25485-9on and regression benchmarks, and on out-of-distribution detection for classification and semantic segmentation. We achieve competitive results, while preserving computational efficiency in comparison to ensemble approaches.作者: Colonoscopy 時(shí)間: 2025-3-26 03:59
Insurance and the Selection Probleme temporal dimension, along the spatial dimension, we adversarially perturb the physical properties (., texture and 3D shape) of the contextual objects that appeared in the most important scene views. Extensive experiments on the EQA-v1 dataset for several embodied tasks in both the white-box and bl作者: 細(xì)微的差異 時(shí)間: 2025-3-26 04:41
Investment Finance and the Selection Problems. In this work, we propose a simple yet effective method, which trains a face recognition model by progressively expanding the labeled set via both selective propagation and caption-driven expansion. We build a large-scale dataset of captioned images, which contain 6.3. faces from 305. subjects. Ou作者: Preserve 時(shí)間: 2025-3-26 12:16
Insurance and the Hidden Action Problemk easier and enables the generation of multiple neutral-pose results among which users can choose the best one they like. Qualitative and quantitative evaluations show the superiority of our pipeline over alternatives.作者: 說(shuō)明 時(shí)間: 2025-3-26 13:25
Class-Wise Dynamic Graph Convolution for Semantic Segmentation, learn the feature aggregation and weight allocation. Then the refined feature and the original feature are fused to get the final prediction. We conduct extensive experiments on three popular semantic segmentation benchmarks including Cityscapes, PASCAL VOC 2012 and COCO Stuff, and achieve state-of作者: 過(guò)分自信 時(shí)間: 2025-3-26 18:10
Character-Preserving Coherent Story Visualization,rporating figure-ground information). Moreover, we propose a metric named Fréchet Story Distance (FSD) to evaluate the performance of story visualization. Extensive experiments demonstrate that CP-CSV maintains the details of character information and achieves high consistency among different frames作者: foppish 時(shí)間: 2025-3-26 21:20 作者: magnate 時(shí)間: 2025-3-27 01:39
Tensor Low-Rank Reconstruction for Semantic Segmentation,dic decomposition theory (., a high-rank tensor can be expressed as a combination of rank-1 tensors.), we design a low-rank-to-high-rank context reconstruction framework (., RecoNet). Specifically, we first introduce the tensor generation module (TGM), which generates a number of rank-1 tensors to c作者: 終止 時(shí)間: 2025-3-27 07:45 作者: aerial 時(shí)間: 2025-3-27 10:20 作者: entail 時(shí)間: 2025-3-27 14:45 作者: 無(wú)法取消 時(shí)間: 2025-3-27 18:30
Spatiotemporal Attacks for Embodied Agents,e temporal dimension, along the spatial dimension, we adversarially perturb the physical properties (., texture and 3D shape) of the contextual objects that appeared in the most important scene views. Extensive experiments on the EQA-v1 dataset for several embodied tasks in both the white-box and bl作者: CLEFT 時(shí)間: 2025-3-28 01:33 作者: Peculate 時(shí)間: 2025-3-28 05:25 作者: 現(xiàn)實(shí) 時(shí)間: 2025-3-28 08:39
Class-Wise Dynamic Graph Convolution for Semantic Segmentation,ons, pyramid pooling or self-attention mechanism. In order to avoid potential misleading contextual information aggregation in previous works, we propose a class-wise dynamic graph convolution (CDGC) module to adaptively propagate information. The graph reasoning is performed among pixels in the sam作者: Anonymous 時(shí)間: 2025-3-28 13:22
Character-Preserving Coherent Story Visualization,ocuses on maintaining the continuity of generated images (frames), story visualization emphasizes preserving the global consistency of characters and scenes across different story pictures, which is very challenging since story sentences only provide sparse signals for generating images. Therefore, 作者: limber 時(shí)間: 2025-3-28 16:25 作者: helper-T-cells 時(shí)間: 2025-3-28 21:50
Tensor Low-Rank Reconstruction for Semantic Segmentation,o be effective for context information collection. Since the desired context consists of spatial-wise and channel-wise attentions, 3D representation is an appropriate formulation. However, these non-local methods describe 3D context information based on a 2D similarity matrix, where space compressio作者: 國(guó)家明智 時(shí)間: 2025-3-28 23:20
Attentive Normalization,e modules, however. In this paper, we propose a light-weight integration between the two schema and present Attentive Normalization (AN). Instead of learning a single affine transformation, AN learns a mixture of affine transformations and utilizes their weighted-sum as the final affine transformati作者: backdrop 時(shí)間: 2025-3-29 04:43 作者: 煤渣 時(shí)間: 2025-3-29 09:39 作者: 啤酒 時(shí)間: 2025-3-29 15:28 作者: 天文臺(tái) 時(shí)間: 2025-3-29 17:06
Caption-Supervised Face Recognition: Training a State-of-the-Art Face Model Without Manual Annotatis built on top of millions of annotated samples. However, as we endeavor to take the performance to the next level, the reliance on annotated data becomes a major obstacle. We desire to explore an alternative approach, namely using captioned images for training, as an attempt to mitigate this diffic作者: 傻瓜 時(shí)間: 2025-3-29 22:19
Unselfie: Translating Selfies to Neutral-Pose Portraits in the Wild,require specialized equipment or a third-party photographer. However, in selfies, constraints such as human arm length often make the body pose look unnatural. To address this issue, we introduce ., a novel photographic transformation that automatically translates a selfie into a neutral-pose portra作者: CAMEO 時(shí)間: 2025-3-30 00:59
https://doi.org/10.1007/978-3-319-93411-2Network security; Privacy; Anonymity; Cryptography; Security and privacy for big data; Security and priva
