作者: Dri727 時間: 2025-3-21 22:46 作者: Presbyopia 時間: 2025-3-22 02:54
Trans6D: Transformer-Based 6D Object Pose Estimation and?Refinementdows, cross-attention, and token pooling operations, which is used to predict dense 2D-3D correspondence maps; (ii) a pure Transformer-based pose refinement module (Trans6D+) which refines the estimated poses iteratively. Extensive experiments show that the proposed approach achieves state-of-the-ar作者: overrule 時間: 2025-3-22 07:14
Learning to?Estimate Multi-view Pose from?Object Silhouettes cues for multi-view relationships in a data-driven way. We show that our network generalizes to unseen synthetic and real object instances under reasonable assumptions about the input pose distribution of the images, and that the estimates are suitable to initialize state-of-the-art 3D reconstructi作者: Obsessed 時間: 2025-3-22 08:44 作者: nautical 時間: 2025-3-22 16:57
Fuse and?Attend: Generalized Embedding Learning for?Art and?Sketchesmains. During training, given a query image from a domain, we employ gated fusion and attention to generate a positive example, which carries a broad notion of the semantics of the query object category (from across multiple domains). By virtue of Contrastive Learning, we pull the embeddings of the 作者: nautical 時間: 2025-3-22 17:03 作者: 連系 時間: 2025-3-22 22:45 作者: endure 時間: 2025-3-23 02:07 作者: GLIB 時間: 2025-3-23 05:53
Lothar Lammersen,Robert Schwagers. To tackle these limitations, we propose a new localization uncertainty estimation method called UAD for anchor-free object detection. Our method captures the uncertainty in four directions of box offsets?(left, right, top, bottom) that are homogeneous, so that it can tell which direction is uncer作者: 配偶 時間: 2025-3-23 13:34 作者: 強化 時間: 2025-3-23 16:51
The Importance of Social Security,dows, cross-attention, and token pooling operations, which is used to predict dense 2D-3D correspondence maps; (ii) a pure Transformer-based pose refinement module (Trans6D+) which refines the estimated poses iteratively. Extensive experiments show that the proposed approach achieves state-of-the-ar作者: 省略 時間: 2025-3-23 18:52 作者: Throttle 時間: 2025-3-24 00:14
https://doi.org/10.1007/b138877ce-level pose estimation. We propose ., a two-stage pipeline that learns to estimate category-level transparent object pose using localized depth completion and surface normal estimation. TransNet is evaluated in terms of pose estimation accuracy on a recent, large-scale transparent object dataset a作者: Incisor 時間: 2025-3-24 06:01
Christina Elschner,Robert Schwagermains. During training, given a query image from a domain, we employ gated fusion and attention to generate a positive example, which carries a broad notion of the semantics of the query object category (from across multiple domains). By virtue of Contrastive Learning, we pull the embeddings of the 作者: FLAIL 時間: 2025-3-24 07:45
Christina Elschner,Robert Schwagerividualized sketching styles. We thus propose data generation and standardization mechanisms. Instead of distortion-free line drawings, synthesized sketches are adopted as input training data. Additionally, we propose a sketch standardization module to handle different sketch distortions and styles.作者: 思想 時間: 2025-3-24 11:18 作者: 文藝 時間: 2025-3-24 15:17
Immanent and Transeunt Causation, model to exploit features at different layers of the network. We evaluate HS-I3D on the ChaLearn 2022 Sign Spotting Challenge - MSSL track and achieve a state-of-the-art 0.607 F1 score, which was the top-1 winning solution of the competition.作者: BAN 時間: 2025-3-24 22:22
Conference proceedings 2023ng for Next-Generation Industry-LevelAutonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for 作者: 偽書 時間: 2025-3-25 00:39
0302-9743 xt in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for 978-3-031-25084-2978-3-031-25085-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 有害 時間: 2025-3-25 04:00 作者: grounded 時間: 2025-3-25 09:13 作者: entice 時間: 2025-3-25 14:44 作者: Mosaic 時間: 2025-3-25 19:32 作者: 性滿足 時間: 2025-3-25 21:08
YORO - Lightweight End to?End Visual Groundingith ablations on architecture design choices. YORO is shown to support real-time inference and outperform all approaches in this class (single-stage methods) by large margins. It is also the fastest VG model and achieves the best speed/accuracy trade-off in the literature. Code released (Code available at .).作者: 誘惑 時間: 2025-3-26 04:07 作者: 變白 時間: 2025-3-26 06:16 作者: BROTH 時間: 2025-3-26 09:00 作者: braggadocio 時間: 2025-3-26 14:44 作者: 過分 時間: 2025-3-26 18:42 作者: 墊子 時間: 2025-3-26 23:15
CenDerNet: Center and?Curvature Representations for?Render-and-Compare 6D Pose Estimationers; Third, 6D object poses are estimated using 3D centers and curvature heatmaps. By jointly optimizing poses across views using a render-and-compare approach, our method naturally handles occlusions and object symmetries. We show that CenDerNet outperforms previous methods on two industry-relevant datasets: DIMO and T-LESS.作者: 商議 時間: 2025-3-27 02:54 作者: 丑惡 時間: 2025-3-27 06:45
YORO - Lightweight End to?End Visual Groundingan object referred via natural language. Unlike the recent trend in the literature of using multi-stage approaches that sacrifice speed for accuracy, YORO seeks a better trade-off between speed an accuracy by embracing a single-stage design, without CNN backbone. YORO consumes natural language queri作者: Hyperlipidemia 時間: 2025-3-27 11:32
Localization Uncertainty Estimation for?Anchor-Free Object Detectionete data, it is desirable for object detectors to take the localization uncertainty into account. However, there are several limitations of the existing uncertainty estimation methods for anchor-based object detection. 1) They model the uncertainty of the heterogeneous object properties with differe作者: 擁擠前 時間: 2025-3-27 15:32
Variational Depth Networks: Uncertainty-Aware Monocular Self-supervised Depth Estimationthey are susceptible to input ambiguities and it is therefore important to express the corresponding depth uncertainty. While there are a few truly monocular and self-supervised methods modelling uncertainty, none correlates well with errors in depth. To this end we present Variational Depth Network作者: Emmenagogue 時間: 2025-3-27 21:31
Unsupervised Joint Image Transfer and?Uncertainty Quantification Using Patch Invariant Networksundant. To ensure a structure-preserving mapping from the input to the target domain, existing methods for unpaired image transfer are commonly based on cycle-consistency, causing additional computational resources and instability due to the learning of an inverse mapping. This paper presents a nove作者: Morsel 時間: 2025-3-27 22:22 作者: 愉快么 時間: 2025-3-28 02:10 作者: 機制 時間: 2025-3-28 08:43
Trans6D: Transformer-Based 6D Object Pose Estimation and?Refinementl network (CNN)-based methods have made remarkable progress, they are not efficient in capturing global dependencies and often suffer from information loss due to downsampling operations. To extract robust feature representation, we propose a Transformer-based 6D object pose estimation approach (Tra作者: 寵愛 時間: 2025-3-28 11:14 作者: aspersion 時間: 2025-3-28 17:16
TransNet: Category-Level Transparent Object Pose Estimationparent objects harder to detect and localize than opaque objects. Even humans find certain transparent surfaces with little specular reflection or refraction, e.g. glass doors, difficult to perceive. A second challenge is that common depth sensors typically used for opaque object perception cannot o作者: sclera 時間: 2025-3-28 20:07
Fuse and?Attend: Generalized Embedding Learning for?Art and?Sketchesnting natural images need not necessarily perform well on images from other domains, such as paintings, cartoons, and sketch. This is because of the huge shift in the distribution of data from across these domains, as compared to natural images. Domains like sketch often contain sparse informative p作者: EXTOL 時間: 2025-3-29 01:00
3D Shape Reconstruction from?Free-Hand Sketchesl cues, humans can effortlessly envision a 3D object from it. This suggests that sketches encode the information necessary for reconstructing 3D shapes. Despite great progress achieved in 3D reconstruction from distortion-free line drawings, such as CAD and edge maps, little effort has been made to 作者: 影響帶來 時間: 2025-3-29 03:21
Abstract Images Have Different Levels of?Retrievability Per?Reverse Image Search Engineiagrams, and schematics. How well do general web search engines discover abstract images? Recent advancements in computer vision and machine learning have led to the rise of reverse image search engines. Where conventional search engines accept a text query and return a set of document results, incl作者: Crumple 時間: 2025-3-29 10:38 作者: 邊緣 時間: 2025-3-29 14:11
Hierarchical I3D for?Sign Spottingsingle sign class given a short video clip. Although there has been significant progress in ISLR, its real-life applications are limited. In this paper, we focus on the challenging task of Sign Spotting instead, where the goal is to simultaneously identify and localise signs in continuous co-articul作者: 不能仁慈 時間: 2025-3-29 18:59
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234284.jpg作者: vibrant 時間: 2025-3-29 22:41
https://doi.org/10.1007/978-3-031-25085-9artificial intelligence; computer vision; education; face recognition; gesture recognition; Human-Compute作者: Ccu106 時間: 2025-3-30 02:46 作者: 不足的東西 時間: 2025-3-30 04:33 作者: 拾落穗 時間: 2025-3-30 08:30
Lothar Lammersen,Robert Schwageran object referred via natural language. Unlike the recent trend in the literature of using multi-stage approaches that sacrifice speed for accuracy, YORO seeks a better trade-off between speed an accuracy by embracing a single-stage design, without CNN backbone. YORO consumes natural language queri作者: Enzyme 時間: 2025-3-30 12:30 作者: Fibrinogen 時間: 2025-3-30 19:28 作者: 蒙太奇 時間: 2025-3-30 21:44
Lothar Lammersen,Robert Schwagerundant. To ensure a structure-preserving mapping from the input to the target domain, existing methods for unpaired image transfer are commonly based on cycle-consistency, causing additional computational resources and instability due to the learning of an inverse mapping. This paper presents a nove作者: mosque 時間: 2025-3-31 03:20 作者: Aerate 時間: 2025-3-31 07:54
