作者: Herbivorous 時間: 2025-3-21 20:51
Günther Schuh,Patrick Wegehauptnce, EdgeCRF based on patches extracted from colour edges works effectively only when the presence of noise is insignificant, which is not the case for many real images; and, CRFNet, a recent method based on fully supervised deep learning works only for the CRFs that are in the training data, and he作者: 開頭 時間: 2025-3-22 01:07 作者: uncertain 時間: 2025-3-22 08:13
https://doi.org/10.1007/978-3-642-17032-4 work, we explore learning from abundant, randomly generated synthetic data, together with unlabeled or partially labeled target domain data, instead. Randomly generated synthetic data has the advantage of controlled variability in the lane geometry and lighting, but it is limited in terms of photo-作者: Cultivate 時間: 2025-3-22 09:02 作者: 不遵守 時間: 2025-3-22 13:49
https://doi.org/10.1007/978-3-658-45553-8ims. Despite the effort of many companies requiring their own mobile applications to capture images for online transactions, it is difficult to restrict users from taking a picture of other’s images displayed on a screen. To detect such cases, we propose a novel approach using paired images with dif作者: 不遵守 時間: 2025-3-22 19:31
https://doi.org/10.1007/978-3-658-45553-8t via e.g. blurring, adding noise, or graying out, which often produce unrealistic, out-of-samples. Instead, we propose to integrate a generative inpainter into three representative attribution methods to remove an input feature. Our proposed change improved all three methods in (1) generating more 作者: 詩集 時間: 2025-3-23 00:34
FinTech and Financial Inclusion,r sound modalities contribute to the result, i.e. do we need both image and sound for sound source localization? To address this question, we develop an unsupervised learning system that solves sound source localization by decomposing this task into two steps: (i) “potential sound source localizatio作者: 鬧劇 時間: 2025-3-23 04:38 作者: Bother 時間: 2025-3-23 07:23
https://doi.org/10.1007/978-3-031-24563-3nd 3D model-based methods proposed recently have their benefits and limitations. Whereas 3D model-based methods provide realistic deformations of the clothing, it needs a difficult 3D model construction process and cannot handle the non-clothing areas well. Image-based deep neural network methods ar作者: Pde5-Inhibitors 時間: 2025-3-23 11:39
The Digital Future of Hospitalitytly packed luggages, such images typically suffer from penetration-induced occlusions, severe object overlapping and violent changes in appearance. For this particular application, few research efforts have been made. To deal with the overlapping in X-ray images classification, we propose a novel Se作者: goodwill 時間: 2025-3-23 14:49
,The Second Division—Space Colonization,In this paper, we specify a new Interactive Action Translation (IAT) task which aims to learn end-to-end action interaction from unlabeled interactive pairs, removing explicit action recognition. To enable learning on small-scale data, we propose a Paired-Embedding (PE) method for effective and reli作者: aquatic 時間: 2025-3-23 20:03
,The First Division—Security Wing, image of a specific style, the model can synthesize meaningful details with colors and textures. Based on the GAN framework, the model consists of three novel modules designed explicitly for better artistic style capturing and generation. To enforce the content faithfulness, we introduce the dual-m作者: Chagrin 時間: 2025-3-23 23:05 作者: 健談的人 時間: 2025-3-24 06:21
The New Wave of Non-Scripted Entertainmently trained to solve one single specific task, and comes with a completely independent set of parameters. While this guarantees high performance, it is also highly inefficient, as each model has to be separately downloaded and stored. In this paper we address the question: can task-specific detectors作者: visual-cortex 時間: 2025-3-24 06:54 作者: 因無茶而冷淡 時間: 2025-3-24 13:57
https://doi.org/10.1007/978-1-4614-0908-3ress this task, we propose a deep learning framework of cross-modality co-attention for video event localization. Our proposed audiovisual transformer (AV-transformer) is able to exploit intra and inter-frame visual information, with audio features jointly observed to perform co-attention over the a作者: Enthralling 時間: 2025-3-24 15:23
Hollywood’s Global Economic Leadership language video. To achieve this sign spotting task, we train a model using multiple types of available supervision by: (1) . existing sparsely labelled footage; (2) . associated subtitles (readily available translations of the signed content) which provide additional .; (3) . words (for which no co作者: Odyssey 時間: 2025-3-24 21:35 作者: 憤慨點吧 時間: 2025-3-25 00:06 作者: 污穢 時間: 2025-3-25 06:03
https://doi.org/10.1007/978-3-030-69544-6artificial intelligence; biomedical image analysis; computer networks; computer vision; databases; image 作者: 下級 時間: 2025-3-25 09:00 作者: aggressor 時間: 2025-3-25 13:18 作者: Serenity 時間: 2025-3-25 19:33
https://doi.org/10.1007/978-3-658-45553-8develop a new framework to concentrate on the difference of DoF in paired images, while avoiding learning individual display artifacts. Since DoF lies on the optical fundamentals, the framework can be widely utilized with any camera, and its performance shows at least . improvement compared to the conventional classification models.作者: 躺下殘殺 時間: 2025-3-25 23:24 作者: 斷斷續(xù)續(xù) 時間: 2025-3-26 00:48
The New Wave of Non-Scripted Entertainment propose a technique to learn a model patch with a size that is dependent on the difficulty of the task to be learned, and validate our approach on 10 different object detection tasks. Our approach achieves similar accuracy as previously proposed approaches, while being significantly more compact.作者: 精密 時間: 2025-3-26 08:17 作者: Engulf 時間: 2025-3-26 09:59 作者: 肌肉 時間: 2025-3-26 15:16
Hollywood’s Global Economic Leadershipur approach on low-shot sign spotting benchmarks. In addition, we contribute a machine-readable British Sign Language (BSL) dictionary dataset of isolated signs, ., to facilitate study of this task. The dataset, models and code are available at our project page (.).作者: Radiation 時間: 2025-3-26 17:52 作者: Abrupt 時間: 2025-3-26 23:13 作者: acrophobia 時間: 2025-3-27 03:30
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection propose a technique to learn a model patch with a size that is dependent on the difficulty of the task to be learned, and validate our approach on 10 different object detection tasks. Our approach achieves similar accuracy as previously proposed approaches, while being significantly more compact.作者: 一致性 時間: 2025-3-27 07:22 作者: Bronchial-Tubes 時間: 2025-3-27 12:13
Audiovisual Transformer with Instance Attention for Audio-Visual Event Localizationwhich would identify image regions at the instance level which are associated with the sound/event of interest. Experiments on a benchmark dataset confirm the effectiveness of our proposed framework, with ablation studies performed to verify the design of our propose network model.作者: 紡織品 時間: 2025-3-27 16:30
Watch, Read and Lookup: Learning to Spot Signs from Multiple Supervisorsur approach on low-shot sign spotting benchmarks. In addition, we contribute a machine-readable British Sign Language (BSL) dictionary dataset of isolated signs, ., to facilitate study of this task. The dataset, models and code are available at our project page (.).作者: 浮夸 時間: 2025-3-27 21:00 作者: 率直 時間: 2025-3-27 22:21
Explaining Image Classifiers by Removing Input Features Using Generative Modelsand saliency metrics; and (3) being more robust to hyperparameter changes. Our findings were consistent across both ImageNet and Places365 datasets and two different pairs of classifiers and inpainters.作者: 流動才波動 時間: 2025-3-28 02:54
0302-9743 , Japan, in November/ December 2020.*.The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; segmentation and grouping..Part II: low-level vision, i作者: dyspareunia 時間: 2025-3-28 06:44 作者: OGLE 時間: 2025-3-28 11:36
0302-9743 iomedical image analysis..Part VI: applications of computer vision; vision for X; datasets and performance analysis..*The conference was held virtually..978-3-030-69543-9978-3-030-69544-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: urethritis 時間: 2025-3-28 17:45
Kooperationsprozesse und Echtzeitmanagementding in only one stage by adopting feature sharing and multi-task learning strategy. Experiments on several benchmarks demonstrate that the proposed method surpasses the previous state-of-the-art segmentation-free methods. (The code is available at ..)作者: 不持續(xù)就爆 時間: 2025-3-28 22:21 作者: stress-response 時間: 2025-3-29 00:53 作者: 旅行路線 時間: 2025-3-29 03:05 作者: FRAUD 時間: 2025-3-29 08:44
https://doi.org/10.1007/978-3-642-17032-4ing strategy for labeling in-the-wild facial expressions. Then, RAF-AU was finely annotated by experienced coders, on which we also conducted a preliminary investigation of which key AUs contribute most to a perceived emotion, and the relationship between AUs and facial expressions. Finally, we prov作者: HERE 時間: 2025-3-29 12:46 作者: escalate 時間: 2025-3-29 17:14 作者: 鉤針織物 時間: 2025-3-29 21:46
https://doi.org/10.1007/978-3-031-24563-3man body model, and transfer to the shape and pose of the target person. Our cloth reconstruction method can be easily applied to diverse cloth categories. Our method produces final try-on output with naturally deformed clothing and preserving details in high resolution.作者: burnish 時間: 2025-3-30 02:01
The Digital Future of Hospitalitynt variations. Motivated by these, our SXMNet is boosted by bottom-up attention and neural-guided Meta Fusion. Raw input image is exploited to generate high-quality attention masks in a bottom-up way for pyramid feature refinement. Subsequently, the per-stage predictions according to the refined fea作者: 小平面 時間: 2025-3-30 05:05
,The First Division—Security Wing,ed on previous state-of-the-art techniques, modified for the proposed task (17% better Frechet Inception distance and 18% better style classification score). Moreover, the lightweight design of the proposed modules enables the high-quality synthesis at . resolution.作者: 粗俗人 時間: 2025-3-30 11:03 作者: STEER 時間: 2025-3-30 15:39 作者: Keratin 時間: 2025-3-30 20:09 作者: Saline 時間: 2025-3-30 22:08
FootNet: An Efficient Convolutional Network for Multiview 3D Foot Reconstructionng from completely synthetic data and (3) a dataset of multiview feet images for evaluation. We fully ablate our system and show our design choices to improve performance at every stage. Our final design has a vertex error of only 1 mm (for 25 cm long synthetic feet) and 4 . error in foot length on 作者: 食草 時間: 2025-3-31 04:10
Synthetic-to-Real Domain Adaptation for?Lane Detectione unsupervised domain adaptation setting in which no target domain labels are available and in the semi-supervised setting in which a small portion of the target images are labeled. In extensive experiments using three different datasets, we demonstrate the possibility to save costly target domain l作者: 鬧劇 時間: 2025-3-31 06:05
RAF-AU Database: In-the-Wild Facial Expressions with Subjective Emotion Judgement and Objective AU Aing strategy for labeling in-the-wild facial expressions. Then, RAF-AU was finely annotated by experienced coders, on which we also conducted a preliminary investigation of which key AUs contribute most to a perceived emotion, and the relationship between AUs and facial expressions. Finally, we prov作者: 辯論的終結(jié) 時間: 2025-3-31 12:55
Do We Need Sound for Sound Source Localization?ts, we show that visual information is dominant in “sound” source localization when evaluated with the currently adopted benchmark dataset. Moreover, we show that the majority of sound-producing objects within the samples in this dataset can be inherently identified using only visual information, an作者: bourgeois 時間: 2025-3-31 13:34
Modular Graph Attention Network for Complex Visual Relational Reasoningsolute location, visual relationship and relative locations, which mimics the human language understanding mechanism. Moreover, to capture the complex logic in a query, we construct a relational graph to represent the visual objects and their relationships, and propose a multi-step reasoning method
