作者: 聯(lián)想 時間: 2025-3-21 20:42 作者: BALK 時間: 2025-3-22 03:36 作者: Priapism 時間: 2025-3-22 05:33 作者: 逃避現(xiàn)實(shí) 時間: 2025-3-22 10:16 作者: Intersect 時間: 2025-3-22 13:08 作者: Intersect 時間: 2025-3-22 19:32 作者: 責(zé)任 時間: 2025-3-22 23:04 作者: degradation 時間: 2025-3-23 03:56
,Camera Pose Auto-encoders for?Improving Pose Regression,s from which the camera position and orientation are regressed. APRs provide a different tradeoff between localization accuracy, runtime, and memory, compared to structure-based localization schemes that provide state-of-the-art accuracy. In this work, we introduce Camera Pose Auto-Encoders (PAEs), 作者: sinoatrial-node 時間: 2025-3-23 08:27 作者: 石墨 時間: 2025-3-23 10:23
,Bagging Regional Classification Activation Maps for?Weakly Supervised Object Localization,y supervised object localization (WSOL). However, CAM directly uses the classifier trained on image-level features to locate objects, making it prefers to discern global discriminative factors rather than regional object cues. Thus only the discriminative locations are activated when feeding pixel-l作者: Nomadic 時間: 2025-3-23 14:04 作者: CRUE 時間: 2025-3-23 19:49 作者: Firefly 時間: 2025-3-24 01:20
,GTCaR: Graph Transformer for?Camera Re-localization, (SfM) and SLAM. In this paper we propose a neural network approach with a graph Transformer backbone, namely . (.raph .ransformer for .mera .e-localization), to address the multi-view camera re-localization problem. In contrast with prior work where the pose regression is mainly guided by photometr作者: 過濾 時間: 2025-3-24 02:31
,3D Object Detection with?a?Self-supervised Lidar Scene Flow Backbone,ource-consuming, and depending only on supervised learning limits the applicability of trained models. Self-supervised training strategies can alleviate these issues by learning a general point cloud backbone model for downstream 3D vision tasks. Against this backdrop, we show the relationship betwe作者: 公式 時間: 2025-3-24 09:49 作者: heterogeneous 時間: 2025-3-24 12:04 作者: 誘拐 時間: 2025-3-24 17:27 作者: 倔強(qiáng)不能 時間: 2025-3-24 22:01 作者: 領(lǐng)導(dǎo)權(quán) 時間: 2025-3-24 23:55 作者: agonist 時間: 2025-3-25 03:47 作者: cathartic 時間: 2025-3-25 11:22
0302-9743 puter Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022..?The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforce作者: 鐵塔等 時間: 2025-3-25 12:00
0302-9743 uction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-20079-3978-3-031-20080-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 鎮(zhèn)痛劑 時間: 2025-3-25 16:14 作者: intangibility 時間: 2025-3-25 23:47
Michael A. Crew,Paul R. Kleindorferrmance. Until submission, CMKD ranks . among the monocular 3D detectors with publications on both KITTI . set and Waymo . set with significant performance gains compared to previous state-of-the-art methods. Our code will be released at ..作者: left-ventricle 時間: 2025-3-26 01:01
J. Harvey B.Sc. (Econ.), Dip. Ed. (Oxford) modules are constructed to distinguish multiple unknown classes. Moreover, two novel evaluation protocols are designed to evaluate unknown-class detection. Abundant experiments and visualizations prove the effectiveness of the proposed method. Code is available at ..作者: 減弱不好 時間: 2025-3-26 06:17
,Cornerformer: Purifying Instances for?Corner-Based Detectors,ach other. Second, we design an Attenuation-Auto-Adjusted NMS (A.-NMS) to maximally leverage the semantic outputs and avoid true objects from being removed. Experiments on object detection and human pose estimation show the superior performance of Cornerformer in terms of accuracy and inference speed.作者: 表臉 時間: 2025-3-26 11:01 作者: 美學(xué) 時間: 2025-3-26 16:00 作者: 不可接觸 時間: 2025-3-26 17:17 作者: nurture 時間: 2025-3-26 23:52
Michael A. Crew,Paul R. Kleindorfertive novel view synthesis, our method successfully addresses photometric distortions in outdoor environments that existing photometric-based methods fail to handle. With domain-invariant feature matching, our solution improves pose regression accuracy using semi-supervised learning on unlabeled data作者: Melanocytes 時間: 2025-3-27 03:30
Deterministic Models of Peak-load Pricingstance construction. Specifically, there are three factors, namely, 1) the corner keypoints are prone to false-positives; 2) incorrect matches emerge upon corner keypoint pull-push embeddings; and 3) the heuristic NMS cannot adjust the corners pull-push mechanism. Accordingly, this paper presents an作者: 無節(jié)奏 時間: 2025-3-27 06:00
Michael A. Crew,Paul R. Kleindorferrely on point-based or 3D voxel-based convolutions, which are both computationally inefficient for onboard deployment. In contrast, pillar-based methods use solely 2D convolutions, which consume less computation resources, but they lag far behind their voxel-based counterparts in detection accuracy.作者: 紋章 時間: 2025-3-27 13:13 作者: CURB 時間: 2025-3-27 15:52 作者: 失望昨天 時間: 2025-3-27 18:50
Michael A. Crew,Paul R. Kleindorferods. However, the existing methods usually apply non-end-to-end training strategies and insufficiently leverage the LiDAR information, where the rich potential of the LiDAR data has not been well exploited. In this paper, we propose the .ross-.odality .nowledge .istillation (CMKD) network for monocu作者: 擋泥板 時間: 2025-3-28 01:55 作者: blackout 時間: 2025-3-28 02:45 作者: enhance 時間: 2025-3-28 07:47 作者: Commemorate 時間: 2025-3-28 12:54
Economic Theory of Bilateral Accidents,ns for certain tasks and datasets, where the overall performance is mostly driven by common examples. However, even the best performing models suffer from the most naive mistakes when it comes to rare examples that do not appear frequently in the training data, such as vehicles with irregular geomet作者: HAWK 時間: 2025-3-28 18:12 作者: 社團(tuán) 時間: 2025-3-28 22:37
J. Harvey B.Sc. (Econ.), Dip. Ed. (Oxford)ied unknown classes. However, it cannot distinguish unknown instances as multiple unknown classes. In this work, we propose a novel OWOD problem called Unknown-Classified Open World Object Detection (UC-OWOD). UC-OWOD aims to detect unknown instances and classify them into different unknown classes.作者: 變色龍 時間: 2025-3-29 01:41
Alessandro Cigno,Furio C. Rosatipresent its knowledge: as a global 3D grid of features and an array of view-specific 2D grids. We progressively exchange information between the two with a dedicated bidirectional attention mechanism. We exploit knowledge about the image formation process to significantly sparsify the attention weig作者: 一罵死割除 時間: 2025-3-29 03:07 作者: 斷斷續(xù)續(xù) 時間: 2025-3-29 08:47
Alain de Crombrugghe,Louis Geversource-consuming, and depending only on supervised learning limits the applicability of trained models. Self-supervised training strategies can alleviate these issues by learning a general point cloud backbone model for downstream 3D vision tasks. Against this backdrop, we show the relationship betwe作者: botany 時間: 2025-3-29 14:30 作者: 廢除 時間: 2025-3-29 19:31 作者: outskirts 時間: 2025-3-29 21:49 作者: carbohydrate 時間: 2025-3-30 00:16
L. V. Kantorovich,V. L. Makarov, existing efforts mainly focus on improving matching accuracy while ignoring its efficiency, which is crucial for real-world applications. In this paper, we propose an efficient structure named Efficient Correspondence Transformer (.) by finding correspondences in a coarse-to-fine manner, which sig作者: reaching 時間: 2025-3-30 07:04
https://doi.org/10.1007/978-3-031-20080-9Computer Science; Informatics; Conference Proceedings; Research; Applications作者: 嬰兒 時間: 2025-3-30 11:24
978-3-031-20079-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Diverticulitis 時間: 2025-3-30 13:53 作者: Herpetologist 時間: 2025-3-30 18:21
,DFNet: Enhance Absolute Pose Regression with?Direct Feature Matching,door and outdoor scenes. Hence, our method achieves a state-of-the-art accuracy by outperforming existing single-image APR methods by as much as 56%, comparable to 3D structure-based methods. (The code is available in ..)作者: Infraction 時間: 2025-3-30 22:44
,PillarNet: Real-Time and?High-Performance Pillar-Based 3D Object Detection,nd compatible with classical 2D CNN backbones, such as VGGNet and ResNet. Additionally, PillarNet benefits from our designed orientation-decoupled IoU regression loss along with the IoU-aware prediction branch. Extensive experimental results on the large-scale nuScenes Dataset and Waymo Open Dataset作者: chronicle 時間: 2025-3-31 02:01
,Robust Object Detection with?Inaccurate Bounding Boxes,bject-aware instance extension. The former aims to select accurate instances for training, instead of directly using inaccurate box annotations. The latter focuses on generating high-quality instances for selection. Extensive experiments on synthetic noisy datasets (., noisy PASCAL VOC and MS-COCO) 作者: 木質(zhì) 時間: 2025-3-31 08:56 作者: 熄滅 時間: 2025-3-31 10:34 作者: PLAYS 時間: 2025-3-31 14:35
Towards Accurate Active Camera Localization,challenging localization scenarios from both synthetic and scanned real-world indoor scenes. Experimental results demonstrate that our algorithm outperforms both the state-of-the-art Markov Localization based approach and other compared approaches on the fine-scale camera pose accuracy. Code and dat作者: 休息 時間: 2025-3-31 20:18 作者: Apraxia 時間: 2025-3-31 23:59
,Improving the?Intra-class Long-Tail in?3D Detection via?Rare Example Mining, active learning based on the criteria of uncertainty, difficulty, or diversity. In this study, we identify a new conceptual dimension - rareness - to mine new data for improving the long-tail performance of models. We show that rareness, as opposed to difficulty, is the key to data-centric improvem
