作者: 相一致 時間: 2025-3-21 22:26 作者: ovation 時間: 2025-3-22 02:04 作者: 枕墊 時間: 2025-3-22 07:53 作者: heterogeneous 時間: 2025-3-22 09:59
,The Basis of the ‘Golden Age’,n visual comparison, our method produces higher quality saliency maps which stress out the total object meanwhile suppress background clutters. Both qualitative and quantitative experiments show our approach outperforms 8 state-of-the-art methods, achieving the highest precision rate 96% (3% improve作者: 不足的東西 時間: 2025-3-22 16:34 作者: 不足的東西 時間: 2025-3-22 18:45
The Forging of Gas Turbine Discs,es the advantages of accurately detecting objects or parts via chamfer matching and the robustness of a max-margin learning. Our results on standard benchmark datasets show that our method significantly outperforms current directional chamfer matching, thus redefining the state-of-the-art in this fi作者: 多節(jié) 時間: 2025-3-22 22:10 作者: macabre 時間: 2025-3-23 01:51 作者: 中世紀(jì) 時間: 2025-3-23 09:01 作者: N斯巴達人 時間: 2025-3-23 11:55
Arbitrary-Shape Object Localization Using Adaptive Image Gridspartition method which takes image content into account and can be efficiently implemented by dynamic programming. The use of adaptive partition further improves the localization accuracy of our approach. Experiments on PASCAL VOC 2007 and VOC 2008 datasets demonstrate the effectiveness of our appro作者: 罵人有污點 時間: 2025-3-23 16:20
Salient Object Detection via Color Contrast and Color Distributionn visual comparison, our method produces higher quality saliency maps which stress out the total object meanwhile suppress background clutters. Both qualitative and quantitative experiments show our approach outperforms 8 state-of-the-art methods, achieving the highest precision rate 96% (3% improve作者: 冰雹 時間: 2025-3-23 18:55
Data Decomposition and Spatial Mixture Modeling for Part Based Modelproposed data decomposition framework. We evaluate our system on the challenging PASCAL VOC2007 and PASCAL VOC2010 datasets, demonstrating the state-of-the-art performance compared with other related methods in terms of accuracy and efficiency.作者: objection 時間: 2025-3-24 00:59
Max-Margin Regularization for Reducing Accidentalness in Chamfer Matchinges the advantages of accurately detecting objects or parts via chamfer matching and the robustness of a max-margin learning. Our results on standard benchmark datasets show that our method significantly outperforms current directional chamfer matching, thus redefining the state-of-the-art in this fi作者: 惡臭 時間: 2025-3-24 04:06
Coupling-and-Decoupling: A Hierarchical Model for Occlusion-Free Car Detectionearance templates for the X pairs, single X’s and latent parts of the single X’s, respectively. The part appearance templates can also be shared among different single X’s. In detection, a dynamic programming (DP) algorithm is used and as a natural consequence we decouple the two single X’s from the作者: 指令 時間: 2025-3-24 08:09
Conference proceedings 2013CCV 2012, held in Daejeon, Korea, in November 2012. The total of 226 contributions presented in these volumes was carefully reviewed and selected from 869submissions. The papers are organized in topical sections on object detection, learning and matching; object recognition; feature, representation,作者: 褲子 時間: 2025-3-24 13:56 作者: 儀式 時間: 2025-3-24 15:32
Takashi Inoguchi,Lien Thi Quynh Le our method on common object discovery and model learning, which needs no fine/coarse alignment in the input data; in addition, it achieves comparable results with standard two-class MIL learning algorithms but our method is learning from one-class data only.作者: initiate 時間: 2025-3-24 20:01
Local Context Priors for Object Proposal Generationg the Caltech pedestrian and PASCAL VOC dataset show that our method achieves the detection performance of an exhaustive search approach with much less computational load. Since we model the prior distribution over the proposals locally, it generalizes well and can be successfully applied across datasets.作者: Insubordinate 時間: 2025-3-24 23:50
One-Class Multiple Instance Learning via Robust PCA for Common Object Discovery our method on common object discovery and model learning, which needs no fine/coarse alignment in the input data; in addition, it achieves comparable results with standard two-class MIL learning algorithms but our method is learning from one-class data only.作者: 簡潔 時間: 2025-3-25 06:01 作者: 雄辯 時間: 2025-3-25 08:42 作者: Focus-Words 時間: 2025-3-25 15:34 作者: sclera 時間: 2025-3-25 17:54 作者: Charade 時間: 2025-3-25 20:23 作者: CANON 時間: 2025-3-26 01:36
The Development of Gas Turbine Materialsrate that combining features with multiple levels of spatial locality performs better than using just a single level. Our model performs better than all previous single-feature methods when tested on the Caltech 101 and 256 object recognition datasets.作者: 敲詐 時間: 2025-3-26 07:38 作者: 拔出 時間: 2025-3-26 08:43
Beyond Dataset Bias: Multi-task Unaligned Shared Knowledge Transferng, we also make it possible to use different features for different databases. We call the algorithm MUST, Multitask Unaligned Shared knowledge Transfer. Through extensive experiments on five public datasets, we show that MUST consistently improves the cross-datasets generalization performance.作者: configuration 時間: 2025-3-26 15:32
Cross-Database Transfer Learning via Learnable and Discriminant Error-Correcting Output Codese lack of training data in the target domain. Our approach is evaluated on several benchmark datasets, and leads to about 40% relative improvement in accuracy when only one training sample is available.作者: 屈尊 時間: 2025-3-26 17:08
The Pooled NBNN Kernel: Beyond Image-to-Class and Image-to-Imageo combine them in a multi kernel framework. We refer to our method as the .. This new scheme leads to significant improvement over the standard image-to-image and image-to-class baselines, with only a small increase in computational cost.作者: 同位素 時間: 2025-3-26 23:08
Spatially Local Coding for Object Recognitionrate that combining features with multiple levels of spatial locality performs better than using just a single level. Our model performs better than all previous single-feature methods when tested on the Caltech 101 and 256 object recognition datasets.作者: Pepsin 時間: 2025-3-27 03:31 作者: grandiose 時間: 2025-3-27 08:07
Mid-Victorian Investment in Land, use a modified similarity measure to combine the representative DOTs with weight templates. In experiments, the proposed method achieved object detection that was better or at least comparable to that of existing methods while being very fast for both training and testing.作者: Fierce 時間: 2025-3-27 09:42 作者: 闡明 時間: 2025-3-27 15:13 作者: 使出神 時間: 2025-3-27 18:23 作者: Finasteride 時間: 2025-3-27 21:57 作者: 極為憤怒 時間: 2025-3-28 04:13 作者: COUCH 時間: 2025-3-28 10:11 作者: 門閂 時間: 2025-3-28 14:00 作者: Yag-Capsulotomy 時間: 2025-3-28 17:52 作者: 要素 時間: 2025-3-28 22:30 作者: GLOOM 時間: 2025-3-29 02:46 作者: 解決 時間: 2025-3-29 06:55
Tell Me What You Like and I’ll Tell You What You Are: Discriminating Visual Preferences on Flickr Dait is a process of interpretation which have also roots in one’s life experiences. This aspect represents nowadays a major problem for inferring automatically the quality of a picture. In this paper, instead of trying to solve this age-old problem, we consider an intriguing, orthogonal direction, ai作者: DEAWL 時間: 2025-3-29 09:22 作者: Evacuate 時間: 2025-3-29 11:35 作者: A簡潔的 時間: 2025-3-29 19:38 作者: Bereavement 時間: 2025-3-29 20:33 作者: 四海為家的人 時間: 2025-3-30 03:48 作者: disrupt 時間: 2025-3-30 04:44
Data Decomposition and Spatial Mixture Modeling for Part Based Model . multiple features, more components or parts) have been proposed. Nevertheless, those enhanced models bring high computation cost together with the risk of over-fitting. To tackle this problem, we propose a data decomposition method for part based models which not only accelerates training and tes作者: 教義 時間: 2025-3-30 09:45 作者: Dungeon 時間: 2025-3-30 12:35 作者: 胖人手藝好 時間: 2025-3-30 19:26 作者: Acetaldehyde 時間: 2025-3-31 00:21
The Pooled NBNN Kernel: Beyond Image-to-Class and Image-to-Imaged by performing image-to-class comparisons. Here, we show that these are just two special cases of a more general formulation, where the feature space is partitioned into subsets of different granularity. This way, a series of representations can be derived that trade-off generalization against spec作者: PHON 時間: 2025-3-31 01:08 作者: 紅潤 時間: 2025-3-31 08:11
Spatially Local Coding for Object Recognitionures are coded across elements of a visual vocabulary, and then these codes are pooled into histograms at several spatial granularities. We introduce spatially local coding, an alternative way to include spatial information in the image model. Instead of only coding visual appearance and leaving the作者: 矛盾 時間: 2025-3-31 11:15 作者: 要塞 時間: 2025-3-31 17:18 作者: 障礙物 時間: 2025-3-31 19:17 作者: LAIR 時間: 2025-3-31 23:15
Kyoung Mu Lee,Yasuyuki Matsushita,Zhanyi HuUp-to-date results in computer vision.Fast-track conference proceedings.State-of-the-art research作者: accordance 時間: 2025-4-1 01:52
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234107.jpg作者: Folklore 時間: 2025-4-1 10:03 作者: follicular-unit 時間: 2025-4-1 12:39
978-3-642-37330-5Springer-Verlag Berlin Heidelberg 2013作者: enterprise 時間: 2025-4-1 16:40 作者: Encoding 時間: 2025-4-1 21:08
https://doi.org/10.1007/978-3-319-61884-5 detection is carried out by using the information of object images and user’s speech in an integrated way. Originality of the method is to use logistic regression for the discrimination between unknown and known objects. The accuracy of the unknown object detection was 97% in the case when there were about fifty known objects.
