作者: 注射器 時(shí)間: 2025-3-21 20:34
https://doi.org/10.1007/978-3-319-30376-5ted, or the Manhattan assumption is not satisfied, homogeneous texture detected by the proposed approach provides alternative cues to obtain an indoor scene geometric layout. Second, low-level feature descriptors extracted upon affine rectification of detected texture are found to be not only class-作者: Accomplish 時(shí)間: 2025-3-22 01:12
https://doi.org/10.1007/978-3-662-65136-0yzing existing ZSL approaches applied to the generalized setting. Extensive empirical studies reveal strengths and weaknesses of those approaches on three well-studied benchmark datasets, including the large-scale ImageNet with more than 20,000 unseen categories. We complement our comparative studie作者: Chivalrous 時(shí)間: 2025-3-22 08:29
https://doi.org/10.1007/978-3-662-60847-0world computer game. Experiments on semantic segmentation datasets show that using the acquired data to supplement real-world images significantly increases accuracy and that the acquired data enables reducing the amount of hand-labeled real-world data: models trained with game data and just . of th作者: 尊重 時(shí)間: 2025-3-22 12:25
The Driving Force of the Collectivereedy local neighborhood search algorithm to restrict the search space of hypotheses. We evaluated the proposed approach on several thousand frames of PRID and Grand Central datasets, and obtained significantly better results compared to existing methods.作者: NOT 時(shí)間: 2025-3-22 14:07
The Driving Force of the Collectiverectly into state-of-the-art lasso optimizers. This results in a conceptually simple, easily implemented method that outperforms the previous state of the art in solving sparse vector quantization. Our implementation is publicly available (.).作者: NOT 時(shí)間: 2025-3-22 17:13
https://doi.org/10.1007/978-3-319-07506-8 clothing bounding boxes and human joints in two applications, fashion attribute prediction and clothes retrieval, showing that fashion landmark is a more discriminative representation to understand fashion images.作者: amygdala 時(shí)間: 2025-3-22 21:53 作者: 圖畫(huà)文字 時(shí)間: 2025-3-23 01:31
Facilitating and Exploring Planar Homogeneous Texture for Indoor Scene Understandingted, or the Manhattan assumption is not satisfied, homogeneous texture detected by the proposed approach provides alternative cues to obtain an indoor scene geometric layout. Second, low-level feature descriptors extracted upon affine rectification of detected texture are found to be not only class-作者: 方舟 時(shí)間: 2025-3-23 08:56 作者: 連鎖 時(shí)間: 2025-3-23 11:08
Playing for Data: Ground Truth from Computer Gamesworld computer game. Experiments on semantic segmentation datasets show that using the acquired data to supplement real-world images significantly increases accuracy and that the acquired data enables reducing the amount of hand-labeled real-world data: models trained with game data and just . of th作者: circumvent 時(shí)間: 2025-3-23 15:49 作者: Occipital-Lobe 時(shí)間: 2025-3-23 18:37
Revisiting Additive Quantizationrectly into state-of-the-art lasso optimizers. This results in a conceptually simple, easily implemented method that outperforms the previous state of the art in solving sparse vector quantization. Our implementation is publicly available (.).作者: FORGO 時(shí)間: 2025-3-23 22:22 作者: LIMN 時(shí)間: 2025-3-24 05:10 作者: ICLE 時(shí)間: 2025-3-24 07:51 作者: 祖先 時(shí)間: 2025-3-24 11:56 作者: opinionated 時(shí)間: 2025-3-24 15:30 作者: floaters 時(shí)間: 2025-3-24 22:31
P53 and Apoptosis in the Drosophila Model,chieve efficient vehicle detection and annotation for real-world traffic surveillance data. We evaluate DAVE on a new self-collected UTS dataset and the public PASCAL VOC2007 car and LISA 2010 datasets, with consistent improvements over existing algorithms.作者: 混合,攙雜 時(shí)間: 2025-3-25 01:51
A Cluster Sampling Method for Image Matting via Sparse Codingng-based methods fail to select the best pairs. In this paper, we derive a new objective function for directly obtaining the estimation of the alpha matte from a bunch of samples. Comparison on a standard benchmark dataset demonstrates that the proposed approach generates more robust and accurate alpha matte than state-of-the-art methods.作者: fastness 時(shí)間: 2025-3-25 06:46 作者: iodides 時(shí)間: 2025-3-25 08:25
DAVE: A Unified Framework for Fast Vehicle Detection and Annotationchieve efficient vehicle detection and annotation for real-world traffic surveillance data. We evaluate DAVE on a new self-collected UTS dataset and the public PASCAL VOC2007 car and LISA 2010 datasets, with consistent improvements over existing algorithms.作者: Licentious 時(shí)間: 2025-3-25 15:12 作者: fallible 時(shí)間: 2025-3-25 16:59
Modeling Context in Referring Expressionshe language generation process together, so that we generate expressions for all objects of a particular category jointly. Evaluation on three recent datasets - RefCOCO, RefCOCO+, and RefCOCOg (Datasets and toolbox can be downloaded from .), shows the advantages of our methods for both referring expression generation and comprehension.作者: carotid-bruit 時(shí)間: 2025-3-25 22:09 作者: 品嘗你的人 時(shí)間: 2025-3-26 01:33 作者: LUDE 時(shí)間: 2025-3-26 07:08 作者: Seminar 時(shí)間: 2025-3-26 11:39 作者: 兇殘 時(shí)間: 2025-3-26 14:01 作者: 新陳代謝 時(shí)間: 2025-3-26 20:44
https://doi.org/10.1057/978-1-137-46839-0sed of hazy images and corresponding transmission maps based on the NYU Depth dataset. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of quality and speed.作者: plasma 時(shí)間: 2025-3-27 00:43 作者: Horizon 時(shí)間: 2025-3-27 01:32
The Ethics of Competition and Cooperationg, and is solved efficiently using a weighted covariance matrix. Experimental results suggest the effectiveness of our method over several state-of-the-art methods in terms of both accuracy and efficiency of visual motif discovery.作者: 退出可食用 時(shí)間: 2025-3-27 08:20 作者: 扔掉掐死你 時(shí)間: 2025-3-27 13:26
P53 and Apoptosis in the Drosophila Model,ities are learned from relative keypoint locations and are independent of the image. We finally combine the keypoints votes and joint probabilities in order to identify the optimal pose configuration. We show our competitive performance on the MPII Human Pose and Leeds Sports Pose datasets.作者: Neutral-Spine 時(shí)間: 2025-3-27 14:06
Image Co-localization by Mimicking a Good Detector’s Confidence Score Distribution proposals, and low scores to most of them. Thus, we devise an entropy-based objective function to enforce the above property when learning the common object detector. Once the detector is learnt, we resort to a segmentation approach to refine the localization. We show that despite its simplicity, our approach outperforms state-of-the-arts.作者: Corral 時(shí)間: 2025-3-27 19:32
Single Image Dehazing via Multi-scale Convolutional Neural Networkssed of hazy images and corresponding transmission maps based on the NYU Depth dataset. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of quality and speed.作者: 吸氣 時(shí)間: 2025-3-28 01:57
Photometric Stereo Under Non-uniform Light Intensities and Exposuresctors. In addition, we show that our method is advantageous for general photometric stereo settings, where auto-exposure control is desirable. We compare our method with conventional least-squares and robust photometric stereo methods, and the experimental result shows superior accuracy of our method in this practical circumstance.作者: 欺騙世家 時(shí)間: 2025-3-28 03:54
Visual Motif Discovery via First-Person Visiong, and is solved efficiently using a weighted covariance matrix. Experimental results suggest the effectiveness of our method over several state-of-the-art methods in terms of both accuracy and efficiency of visual motif discovery.作者: interrupt 時(shí)間: 2025-3-28 08:00
Fundamental Matrices from Moving Objects Using Line Motion Barcodess, and candidate pairs of corresponding epipoar lines are found by the similarity of their motion barcodes. As in previous methods we assume that cameras are relatively stationary and that moving objects have already been extracted using background subtraction.作者: Headstrong 時(shí)間: 2025-3-28 13:21
Human Pose Estimation Using Deep Consensus Votingities are learned from relative keypoint locations and are independent of the image. We finally combine the keypoints votes and joint probabilities in order to identify the optimal pose configuration. We show our competitive performance on the MPII Human Pose and Leeds Sports Pose datasets.作者: achlorhydria 時(shí)間: 2025-3-28 17:43 作者: 大范圍流行 時(shí)間: 2025-3-28 19:54 作者: MIRTH 時(shí)間: 2025-3-29 02:00
Facilitating and Exploring Planar Homogeneous Texture for Indoor Scene Understandingropose to exploit such structure to facilitate high-level scene understanding. By robustly fitting a texture projection model to optimal dominant frequency estimates in image patches, we arrive at a projective-invariant method to localize such semantically meaningful regions in multi-planar scenes. 作者: 翅膀拍動(dòng) 時(shí)間: 2025-3-29 05:58 作者: paradigm 時(shí)間: 2025-3-29 07:15
Modeling Context in Referring Expressionslanguage referring expressions for objects in images. In particular, we focus on incorporating better measures of visual context into referring expression models and find that visual comparison to other objects within an image helps improve performance significantly. We also develop methods to tie t作者: Blazon 時(shí)間: 2025-3-29 11:52
Taxonomy-Regularized Semantic Deep Convolutional Neural Networksouped and discriminative information provided by the taxonomy, by focusing on the general and specific components that comprise each category, through the min- and difference-pooling operations. Without using any additional parameters or substantial increase in time complexity, our model is able to 作者: 干涉 時(shí)間: 2025-3-29 17:21 作者: 單調(diào)性 時(shí)間: 2025-3-29 20:22
Human Re-identification in Crowd Videos Using Personal, Social and Environmental Constraintse to large number of people and frequent occlusions, coupled with changes in their appearance due to different properties and exposure of cameras. To solve this problem, we model multiple Personal, Social and Environmental (PSE) constraints on human motion across cameras in crowded scenes. The perso作者: 鋼筆記下懲罰 時(shí)間: 2025-3-30 00:47 作者: minimal 時(shí)間: 2025-3-30 04:37 作者: 允許 時(shí)間: 2025-3-30 11:32 作者: calumniate 時(shí)間: 2025-3-30 14:47
Visual Motif Discovery via First-Person Visionnd that of a familiar social situation such as when interacting with a clerk at a store. The goal of this study is to discover visual motifs from a collection of first-person videos recorded by a wearable camera. To achieve this goal, we develop a commonality clustering method that leverages three i作者: AMBI 時(shí)間: 2025-3-30 17:51 作者: assail 時(shí)間: 2025-3-30 23:22
Fundamental Matrices from Moving Objects Using Line Motion Barcodesd it is difficult to find corresponding feature points. Prior methods searched for corresponding epipolar lines using points on the convex hull of the silhouette of a single moving object. These methods fail when the scene includes multiple moving objects. This paper extends previous work to scenes 作者: AMOR 時(shí)間: 2025-3-31 04:26 作者: 整理 時(shí)間: 2025-3-31 08:05 作者: FORGO 時(shí)間: 2025-3-31 12:23
Leveraging Visual Question Answering for Image-Caption Rankingcurate answer. In this work we view VQA as a “feature extraction” module to extract image and caption representations. We employ these representations for the task of image-caption ranking. Each feature dimension captures (imagines) whether a fact (question-answer pair) could plausibly be true for t作者: 外面 時(shí)間: 2025-3-31 16:19
DAVE: A Unified Framework for Fast Vehicle Detection and Annotationn this paper, we present a fast framework of Detection and Annotation for Vehicles (DAVE), which effectively combines vehicle detection and attributes annotation. DAVE consists of two convolutional neural networks (CNNs): a fast vehicle proposal network (FVPN) for vehicle-like objects extraction and作者: 絕緣 時(shí)間: 2025-3-31 20:19 作者: Lament 時(shí)間: 2025-3-31 21:52
