作者: 空中 時(shí)間: 2025-3-21 23:19 作者: 有權(quán) 時(shí)間: 2025-3-22 02:29 作者: 并排上下 時(shí)間: 2025-3-22 05:50 作者: SSRIS 時(shí)間: 2025-3-22 08:46
https://doi.org/10.1007/978-3-319-92885-2 trainable. Using this method in the basic SSD system, our models achieve consistent and significant boosts compared with the original model and its other variations, without losing real-time processing speed.作者: 把手 時(shí)間: 2025-3-22 14:59
https://doi.org/10.1007/978-3-031-21952-8ibution, avoiding to manually impose any threshold on the proportion of outliers in the training set. Extensive experimental evaluations on four different tasks (facial and fashion landmark detection, age and head pose estimation) lead us to conclude that our novel robust technique provides reliabil作者: 把手 時(shí)間: 2025-3-22 20:19 作者: incision 時(shí)間: 2025-3-23 00:37
Unsupervised Holistic Image Generation from Key Local Patchest images are realistic. The proposed network is trained without supervisory signals since no labels of key parts are required. Experimental results on seven datasets demonstrate that the proposed algorithm performs favorably on challenging objects and scenes.作者: Oscillate 時(shí)間: 2025-3-23 02:18 作者: 館長(zhǎng) 時(shí)間: 2025-3-23 09:15 作者: abduction 時(shí)間: 2025-3-23 12:16 作者: tooth-decay 時(shí)間: 2025-3-23 17:25
Deep Feature Pyramid Reconfiguration for Object Detection trainable. Using this method in the basic SSD system, our models achieve consistent and significant boosts compared with the original model and its other variations, without losing real-time processing speed.作者: ANA 時(shí)間: 2025-3-23 18:41 作者: 爆米花 時(shí)間: 2025-3-23 22:40
Parallel Feature Pyramid Network for Object Detectionize the elements of the feature pool to a uniform size and aggregate their contextual information to generate each level of the final FP. The experimental results confirmed that PFPNet increases the performance of the latest version of the single-shot multi-box detector (SSD) by mAP of 6.4% AP and e作者: 軟膏 時(shí)間: 2025-3-24 03:06 作者: Coronary-Spasm 時(shí)間: 2025-3-24 06:51 作者: 不透氣 時(shí)間: 2025-3-24 13:43 作者: 同來(lái)核對(duì) 時(shí)間: 2025-3-24 18:07 作者: LIMN 時(shí)間: 2025-3-24 20:52
Irina Evseeva,Lydia Foeken,Juliana Villan mode. Our model is able to generate multiple diverse and plausible motion sequences in the future from the same input. We apply our approach to both facial and full body motion, and demonstrate applications like analogy-based motion transfer and video synthesis.作者: 闖入 時(shí)間: 2025-3-25 02:29 作者: GEST 時(shí)間: 2025-3-25 03:57
The New Comparative Civil Procedurelabelled data functioning as labelled data. We inspect its effectiveness with elaborate ablation study on seven public face/person classification benchmarks. Without any bells and whistles, TCP can achieve significant performance gains over most state-of-the-art methods in both fully-supervised and semi-supervised manners.作者: 冥界三河 時(shí)間: 2025-3-25 08:28
The New Comparative Civil Procedures back to their data manifold, and a manifold margin is defined as the distance between the pullback representations to distinguish between real and fake samples and learn the optimal generators. We justify the effectiveness of the proposed model both theoretically and empirically.作者: inferno 時(shí)間: 2025-3-25 13:20 作者: TAIN 時(shí)間: 2025-3-25 17:54 作者: Debark 時(shí)間: 2025-3-25 21:04 作者: 種子 時(shí)間: 2025-3-26 02:55
Transductive Centroid Projection for Semi-supervised Large-Scale Recognitionlabelled data functioning as labelled data. We inspect its effectiveness with elaborate ablation study on seven public face/person classification benchmarks. Without any bells and whistles, TCP can achieve significant performance gains over most state-of-the-art methods in both fully-supervised and semi-supervised manners.作者: GRIPE 時(shí)間: 2025-3-26 07:53
Generalized Loss-Sensitive Adversarial Learning with Manifold Marginss back to their data manifold, and a manifold margin is defined as the distance between the pullback representations to distinguish between real and fake samples and learn the optimal generators. We justify the effectiveness of the proposed model both theoretically and empirically.作者: 小步走路 時(shí)間: 2025-3-26 12:20 作者: Antarctic 時(shí)間: 2025-3-26 14:00 作者: 較早 時(shí)間: 2025-3-26 16:48
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: 青少年 時(shí)間: 2025-3-27 00:51 作者: 全神貫注于 時(shí)間: 2025-3-27 02:41 作者: RAFF 時(shí)間: 2025-3-27 05:48
https://doi.org/10.1007/978-3-031-21952-8 long series of inane queries that add little value. We evaluate our model on the GuessWhat?! dataset and show that the resulting questions can help a standard ‘Guesser’ identify a specific object in an image at a much higher success rate.作者: Evocative 時(shí)間: 2025-3-27 09:26
The EBMT: History, Present, and Futureieving higher performance with comparable parameter sizes. Second, 2D states preserve spatial locality. Taking advantage of this, we . reveal the internal dynamics in the process of caption generation, as well as the connections between input visual domain and output linguistic domain.作者: Deference 時(shí)間: 2025-3-27 14:31 作者: Indecisive 時(shí)間: 2025-3-27 18:15
Recycle-GAN: Unsupervised Video Retargetinghen demonstrate the proposed approach for the problems where information in both space and time matters such as face-to-face translation, flower-to-flower, wind and cloud synthesis, sunrise and sunset.作者: 短程旅游 時(shí)間: 2025-3-27 22:16 作者: 澄清 時(shí)間: 2025-3-28 05:50
Rethinking the Form of Latent States in Image Captioningieving higher performance with comparable parameter sizes. Second, 2D states preserve spatial locality. Taking advantage of this, we . reveal the internal dynamics in the process of caption generation, as well as the connections between input visual domain and output linguistic domain.作者: 駕駛 時(shí)間: 2025-3-28 06:54 作者: Foam-Cells 時(shí)間: 2025-3-28 13:35
MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamicsn mode. Our model is able to generate multiple diverse and plausible motion sequences in the future from the same input. We apply our approach to both facial and full body motion, and demonstrate applications like analogy-based motion transfer and video synthesis.作者: outskirts 時(shí)間: 2025-3-28 15:56
Snap Angle Prediction for 360, Panoramasage may enable content-aware projection with fewer perceptible distortions. Whereas existing approaches assume the viewpoint is fixed, intuitively some viewing angles within the sphere preserve high-level objects better than others. To discover the relationship between these optimal . and the spheri作者: crescendo 時(shí)間: 2025-3-28 20:08 作者: commune 時(shí)間: 2025-3-28 23:38
DF-Net: Unsupervised Joint Learning of Depth and Flow Using Cross-Task Consistencyled video sequences. Existing unsupervised methods often exploit brightness constancy and spatial smoothness priors to train depth or flow models. In this paper, we propose to leverage geometric consistency as additional supervisory signals. Our core idea is that for rigid regions we can use the pre作者: 懶惰民族 時(shí)間: 2025-3-29 04:33 作者: Rejuvenate 時(shí)間: 2025-3-29 09:59
Transductive Centroid Projection for Semi-supervised Large-Scale Recognitiononal complexity when collaborating with Convolutional Neural Networks. To this end, we design a simple but effective learning mechanism that merely substitutes the last fully-connected layer with the proposed Transductive Centroid Projection (TCP) module. It is inspired by the observation of the wei作者: 怪物 時(shí)間: 2025-3-29 14:49 作者: FECT 時(shí)間: 2025-3-29 17:45
Into the Twilight Zone: Depth Estimation Using Joint Structure-Stereo Optimizationenoising approach – which we show to be ineffective for stereo due to its artefacts and the questionable use of the PSNR metric, we propose to instead rely on structures comprising of piecewise constant regions and principal edges in the given image, as these are the important regions for extracting作者: 灌輸 時(shí)間: 2025-3-29 21:26
Recycle-GAN: Unsupervised Video Retargetingative to a domain, i.e., if contents of John Oliver’s speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert’s style. Our approach combines both spatial and temporal information along with adversarial losses for content translation and style 作者: Crepitus 時(shí)間: 2025-3-30 02:46 作者: 香料 時(shí)間: 2025-3-30 06:23
Open Set Domain Adaptation by Backpropagatione proposed for closed-set scenario, where the source and the target domain completely share the class of their samples. However, in practice, a target domain can contain samples of classes that are not shared by the source domain. We call such classes the “unknown class” and algorithms that work wel作者: Verify 時(shí)間: 2025-3-30 11:39
Deep Feature Pyramid Reconfiguration for Object Detectiondesigns for feature pyramids are still inefficient to integrate the semantic information over different scales. In this paper, we begin by investigating current feature pyramids solutions, and then reformulate the feature pyramid construction as the feature reconfiguration process. Finally, we propo作者: inscribe 時(shí)間: 2025-3-30 14:36
Goal-Oriented Visual Question Generation via Intermediate Rewardsabout images is proven to be an inscrutable challenge. Towards this end, we propose a Deep Reinforcement Learning framework based on three new intermediate rewards, namely ., . and . that encourage the generation of succinct questions, which in turn uncover valuable information towards the overall g作者: 監(jiān)禁 時(shí)間: 2025-3-30 17:53 作者: Commemorate 時(shí)間: 2025-3-30 23:23 作者: 幸福愉悅感 時(shí)間: 2025-3-31 04:49 作者: 符合規(guī)定 時(shí)間: 2025-3-31 08:22
Joint Map and Symmetry Synchronizationair is unique. This assumption, however, easily breaks when visual objects possess self-symmetries. In this paper, we study the problem of jointly optimizing symmetry groups and pair-wise maps among a collection of symmetric objects. We introduce a lifting map representation for encoding both symmet作者: 頑固 時(shí)間: 2025-3-31 09:44
MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamicse leverage this structure and present a novel . for learning motion sequence generation. Our model jointly learns a feature embedding for motion modes (that the motion sequence can be reconstructed from) and a feature transformation that represents the transition of one motion mode to the next motio作者: 預(yù)防注射 時(shí)間: 2025-3-31 15:50
Rethinking the Form of Latent States in Image CaptioningExisting captioning models usually represent latent states as vectors, taking this practice for granted. We rethink this choice and study an alternative formulation, namely using two-dimensional maps to encode latent states. This is motivated by the curiosity about a question: . Our study on MSCOCO 作者: crucial 時(shí)間: 2025-3-31 20:10
https://doi.org/10.1007/978-3-030-01228-13D; artificial intelligence; computer vision; data security; image coding; image processing; image reconst作者: Infelicity 時(shí)間: 2025-3-31 22:03 作者: indecipherable 時(shí)間: 2025-4-1 04:22
Computer Vision – ECCV 2018978-3-030-01228-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 翅膀拍動(dòng) 時(shí)間: 2025-4-1 09:56
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234194.jpg作者: 身心疲憊 時(shí)間: 2025-4-1 10:40 作者: paragon 時(shí)間: 2025-4-1 16:15 作者: 偉大 時(shí)間: 2025-4-1 19:26
The New Comparative Civil Procedureled video sequences. Existing unsupervised methods often exploit brightness constancy and spatial smoothness priors to train depth or flow models. In this paper, we propose to leverage geometric consistency as additional supervisory signals. Our core idea is that for rigid regions we can use the pre作者: 慟哭 時(shí)間: 2025-4-1 23:09
https://doi.org/10.1007/978-3-319-21981-3n applying convolutional neural networks (CNNs) to style transfer for monocular images or videos. However, style transfer for stereoscopic images is still a missing piece. Different from processing a monocular image, the two views of a stylized stereoscopic pair are required to be consistent to prov
