作者: indignant 時(shí)間: 2025-3-21 20:50 作者: 缺陷 時(shí)間: 2025-3-22 01:13
Super-Identity Convolutional Neural Network for Face Hallucinationy metric for faces from these two domains. Extensive experimental evaluations demonstrate that the proposed SICNN achieves superior visual quality over the state-of-the-art methods on a challenging task to super-resolve 12?.?14 faces with an 8. upscaling factor. In addition, SICNN significantly impr作者: 縫紉 時(shí)間: 2025-3-22 06:40 作者: CLOT 時(shí)間: 2025-3-22 10:05
Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3input images while adding photorealism and retaining identity information. We combine face images generated by the proposed method with a real data set to train face recognition algorithms and evaluate the model quantitatively on two challenging data sets: LFW and IJB-A. The generated images by our 作者: faculty 時(shí)間: 2025-3-22 16:16
HairNet: Single-View Hair Reconstruction Using Convolutional Neural Networks continuous representation for hairstyles, which allows us to interpolate naturally between hairstyles. We use a large set of rendered synthetic hair models to train our network. Our method scales to real images because an intermediate 2D orientation field, automatically calculated from the real ima作者: faculty 時(shí)間: 2025-3-22 17:25 作者: 形容詞 時(shí)間: 2025-3-22 21:47 作者: BALK 時(shí)間: 2025-3-23 04:27
Populations of Small Solar System Bodies,viors are heavily influenced by known areas in the images (., upcoming turns). CAR-Net successfully attends to these salient regions. Additionally, CAR-Net reaches state-of-the-art accuracy on the standard trajectory forecasting benchmark, Stanford Drone Dataset (SDD). Finally, we show CAR-Net’s abi作者: 入會(huì) 時(shí)間: 2025-3-23 08:35
https://doi.org/10.1057/9780333982921y metric for faces from these two domains. Extensive experimental evaluations demonstrate that the proposed SICNN achieves superior visual quality over the state-of-the-art methods on a challenging task to super-resolve 12?.?14 faces with an 8. upscaling factor. In addition, SICNN significantly impr作者: HEAVY 時(shí)間: 2025-3-23 12:04 作者: 終止 時(shí)間: 2025-3-23 15:37 作者: 高談闊論 時(shí)間: 2025-3-23 20:48
Induction, Conceptual Spaces and AI, continuous representation for hairstyles, which allows us to interpolate naturally between hairstyles. We use a large set of rendered synthetic hair models to train our network. Our method scales to real images because an intermediate 2D orientation field, automatically calculated from the real ima作者: TEM 時(shí)間: 2025-3-23 23:07
https://doi.org/10.1007/1-4020-3399-0n. Motivated by the routing to make higher capsule have agreement with lower capsule, we extend the mechanism as a compensation for the rapid loss of information in nearby layers. We devise a feedback agreement unit to send back higher capsules as feedback. It could be regarded as an additional regu作者: IRS 時(shí)間: 2025-3-24 05:27
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: 啟發(fā) 時(shí)間: 2025-3-24 07:27 作者: MORPH 時(shí)間: 2025-3-24 14:35
0302-9743 missions. The 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..978-3-030-01251-9978-3-030-01252-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Arthropathy 時(shí)間: 2025-3-24 16:45 作者: 訓(xùn)誡 時(shí)間: 2025-3-24 23:01 作者: Thrombolysis 時(shí)間: 2025-3-25 02:49
The Solar System Beyond Neptuneains state-of-the-art performance on several image forensics benchmarks, despite never seeing any manipulated images at training. That said, it is merely a step in the long quest for a truly general purpose visual forensics tool.作者: inculpate 時(shí)間: 2025-3-25 04:56 作者: 中和 時(shí)間: 2025-3-25 08:14 作者: 籠子 時(shí)間: 2025-3-25 14:32
Deep Boosting for Image Denoisingion to derive a lightweight yet efficient convolutional network as the boosting unit, named Dilated Dense Fusion Network (DDFN). Comprehensive experiments demonstrate that our DBF outperforms existing methods on widely used benchmarks, in terms of different denoising tasks.作者: GOAT 時(shí)間: 2025-3-25 19:44
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagesling and physically accurate 3D geometry. Extensive experiments show that our method not only qualitatively produces mesh model with better details, but also achieves higher 3D shape estimation accuracy compared to the state-of-the-art.作者: 貞潔 時(shí)間: 2025-3-25 20:15
Fighting Fake News: Image Splice Detection via Learned Self-Consistencyains state-of-the-art performance on several image forensics benchmarks, despite never seeing any manipulated images at training. That said, it is merely a step in the long quest for a truly general purpose visual forensics tool.作者: Ebct207 時(shí)間: 2025-3-26 03:05
Depth-Aware CNN for RGB-D Segmentationany additional parameters, both operators can be easily integrated into existing CNNs. Extensive experiments and ablation studies on challenging RGB-D semantic segmentation benchmarks validate the effectiveness and flexibility of our approach.作者: 和平 時(shí)間: 2025-3-26 08:23
Integrating Egocentric Videos in Top-View Surveillance Videos: Joint Identification and Temporal Aliendent on the other two. We propose a unified framework to jointly solve all three problems. We evaluate the efficacy of the proposed approach on a publicly available dataset containing a variety of videos recorded in different scenarios.作者: landmark 時(shí)間: 2025-3-26 11:08 作者: Pastry 時(shí)間: 2025-3-26 16:25 作者: 不再流行 時(shí)間: 2025-3-26 18:29
A Study of Chaos in the Asteroid Beltf attention for image captioning. In particular, we highlight the complementary nature of the two types of attention and develop a model (Boosted Attention) to integrate them for image captioning. We validate the proposed approach with state-of-the-art performance across various evaluation metrics.作者: 傀儡 時(shí)間: 2025-3-26 23:56 作者: Demulcent 時(shí)間: 2025-3-27 02:57
https://doi.org/10.1007/978-94-015-9221-5actor, which can be estimated additionally if a prior of the hand size is given. We implicitly learn depth maps and heatmap distributions with a novel CNN architecture. Our system achieves state-of-the-art accuracy for 2D and 3D hand pose estimation on several challenging datasets in presence of severe occlusions.作者: ellagic-acid 時(shí)間: 2025-3-27 08:34 作者: Offset 時(shí)間: 2025-3-27 12:43
Rights, Games and Social Choice,of their limitations providing new insight into KT as well as novel KT applications, ranging from KT from handcrafted feature extractors to cross-modal KT from the textual modality into the representation extracted from the visual modality of the data.作者: 本土 時(shí)間: 2025-3-27 17:28 作者: 難管 時(shí)間: 2025-3-27 18:51
Boosted Attention: Leveraging Human Attention for Image Captioningf attention for image captioning. In particular, we highlight the complementary nature of the two types of attention and develop a model (Boosted Attention) to integrate them for image captioning. We validate the proposed approach with state-of-the-art performance across various evaluation metrics.作者: Nonconformist 時(shí)間: 2025-3-28 01:54
Image Inpainting for Irregular Holes Using Partial Convolutionsomatically generate an updated mask for the next layer as part of the forward pass. Our model outperforms other methods for irregular masks. We show qualitative and quantitative comparisons with other methods to validate our approach.作者: Nmda-Receptor 時(shí)間: 2025-3-28 04:43 作者: 低能兒 時(shí)間: 2025-3-28 06:22 作者: Sleep-Paralysis 時(shí)間: 2025-3-28 13:11 作者: Intruder 時(shí)間: 2025-3-28 15:27
Deep Boosting for Image Denoising existing boosting algorithms are surpassed by the emerging learning-based models. In this paper, we propose a novel deep boosting framework (DBF) for denoising, which integrates several convolutional networks in a feed-forward fashion. Along with the integrated networks, however, the depth of the b作者: biosphere 時(shí)間: 2025-3-28 21:27 作者: insomnia 時(shí)間: 2025-3-29 02:24
K-convexity Shape Priors for Segmentationsubsets. Since an arbitrary shape can always be divided into convex parts, our regularization model restricts the number of such parts. Previous .-part shape priors are limited to disjoint parts. For example, one approach segments an object via optimizing its . coverage by disjoint convex parts, whi作者: 誰(shuí)在削木頭 時(shí)間: 2025-3-29 04:46 作者: 啜泣 時(shí)間: 2025-3-29 08:52 作者: Legion 時(shí)間: 2025-3-29 13:06 作者: olfction 時(shí)間: 2025-3-29 15:49
Fighting Fake News: Image Splice Detection via Learned Self-Consistencyver, remains a challenging problem due to the lack of sufficient amounts of manipulated training data. In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs. The algorithm uses the automatically recorded作者: EVEN 時(shí)間: 2025-3-29 20:30 作者: 無(wú)情 時(shí)間: 2025-3-30 03:46 作者: CBC471 時(shí)間: 2025-3-30 06:42
CAR-Net: Clairvoyant Attentive Recurrent Networknt. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction ta作者: deforestation 時(shí)間: 2025-3-30 11:48 作者: staging 時(shí)間: 2025-3-30 15:39
Super-Identity Convolutional Neural Network for Face Hallucinationy information. However, previous face hallucination approaches largely ignore facial identity recovery. This paper proposes Super-Identity Convolutional Neural Network (SICNN) to recover identity information for generating faces closed to the real identity. Specifically, we define a super-identity l作者: 好開玩笑 時(shí)間: 2025-3-30 17:00
What Do I Annotate Next? An Empirical Study of Active Learning for Action Localizationdata is scarce. In this paper, we introduce a novel active learning framework for temporal localization that aims to mitigate this data dependency issue. We equip our framework with active selection functions that can . from previously annotated datasets. We study the performance of two state-of-the作者: Mucosa 時(shí)間: 2025-3-30 21:40
Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3essions, poses, and illuminations conditioned by synthetic images sampled from a 3D morphable model. Previous adversarial style-transfer methods either supervise their networks with a large volume of paired data or train highly under-constrained two-way generative networks in an unsupervised fashion作者: constellation 時(shí)間: 2025-3-31 01:37 作者: Terrace 時(shí)間: 2025-3-31 08:27
Neural Network Encapsulation which resemble lower counterparts in the higher layer should be activated. However, the computational complexity becomes a bottleneck for scaling up to larger networks, as lower capsules need to correspond to each and every higher capsule. To resolve this limitation, we approximate the routing proc作者: 慢慢流出 時(shí)間: 2025-3-31 11:38 作者: Crohns-disease 時(shí)間: 2025-3-31 15:00
Integrating Egocentric Videos in Top-View Surveillance Videos: Joint Identification and Temporal Aliy with videos captured by top-view surveillance cameras. In this paper, we aim to relate these two sources of information from a surveillance standpoint, namely in terms of identification and temporal alignment. Given an egocentric video and a top-view video, our goals are to: (a) identify the egoce作者: 清真寺 時(shí)間: 2025-3-31 21:07
