作者: bile648 時(shí)間: 2025-3-21 23:02 作者: Flat-Feet 時(shí)間: 2025-3-22 02:53 作者: Commodious 時(shí)間: 2025-3-22 07:07
The Economics of Self-Destructive Choiceswe only have labeled images taken under very different conditions (.., indoors). In the real world, it is important that the model trained for both tasks works under various imaging conditions. However, their variation covered by existing labeled hand datasets is limited. Thus, it is necessary to ad作者: 沒有準(zhǔn)備 時(shí)間: 2025-3-22 10:41
https://doi.org/10.1007/978-3-031-69118-8rformance drops on small-size datasets, like Cityscapes. In other words, the detection transformers are generally data-hungry. To tackle this problem, we empirically analyze the factors that affect data efficiency, through a step-by-step transition from a data-efficient RCNN variant to the represent作者: 彎腰 時(shí)間: 2025-3-22 16:46 作者: 彎腰 時(shí)間: 2025-3-22 17:13
The Economics of Small Businessesoth day and night, we study multimodal object detection with RGB and thermal cameras, since the latter provides much stronger object signatures under poor illumination. We explore strategies for fusing information from different modalities. Our key contribution is a probabilistic ensembling techniqu作者: 姑姑在炫耀 時(shí)間: 2025-3-23 00:51 作者: 我不死扛 時(shí)間: 2025-3-23 02:21
Small Business in German Manufacturingobustly handle scene changes, our approach deviates from conventional feature point matching, and focuses on the spatial context provided from panorama images. Specifically, we propose efficient color histogram generation and subsequent robust localization using score maps. By utilizing the unique e作者: cyanosis 時(shí)間: 2025-3-23 05:38 作者: Sleep-Paralysis 時(shí)間: 2025-3-23 11:37
Some Empirical Aspects of Entrepreneurshipcts. However, these two-stage “enumerate-and-select” methods suffer object feature ambiguity brought by dense proposals and low detection efficiency caused by the proposal enumeration procedure. In this study, we propose a sparse proposal evolution (SPE) approach, which advances WSOD from the two-st作者: 光明正大 時(shí)間: 2025-3-23 14:36 作者: 油膏 時(shí)間: 2025-3-23 19:31 作者: 發(fā)微光 時(shí)間: 2025-3-24 02:03 作者: 膽大 時(shí)間: 2025-3-24 04:36
Robert D. Tollison,Richard E. Wagnertuned for object detection without needing to redesign a hierarchical backbone for pre-training. With minimal adaptations for fine-tuning, our plain-backbone detector can achieve competitive results. Surprisingly, we observe: (i) it is sufficient to build a simple feature pyramid from a single-scale作者: 預(yù)防注射 時(shí)間: 2025-3-24 07:51 作者: 新星 時(shí)間: 2025-3-24 14:45
https://doi.org/10.1007/978-1-349-17708-0 of a lightweight detector for deployment is often significantly different from a high-capacity detector. Thus, we investigate KD among heterogeneous teacher-student pairs for a wide application. We observe that the core difficulty for heterogeneous KD?(hetero-KD) is the significant semantic gap bet作者: Hangar 時(shí)間: 2025-3-24 15:27 作者: 山崩 時(shí)間: 2025-3-24 21:36
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234261.jpg作者: 單片眼鏡 時(shí)間: 2025-3-25 02:28 作者: DEVIL 時(shí)間: 2025-3-25 05:46 作者: 不公開 時(shí)間: 2025-3-25 09:42 作者: depreciate 時(shí)間: 2025-3-25 13:41 作者: CURB 時(shí)間: 2025-3-25 18:59 作者: 表狀態(tài) 時(shí)間: 2025-3-25 23:36 作者: In-Situ 時(shí)間: 2025-3-26 01:22
Robert D. Tollison,Richard E. Wagnermethods that were all based on hierarchical backbones, reaching up to 61.3 AP. on the COCO dataset using only ImageNet-1K pre-training. We hope our study will draw attention to research on plain-backbone detectors. Code for ViTDet is available (.).作者: Fracture 時(shí)間: 2025-3-26 06:53 作者: 聽覺 時(shí)間: 2025-3-26 09:19 作者: 輕快來事 時(shí)間: 2025-3-26 13:40 作者: 積習(xí)已深 時(shí)間: 2025-3-26 19:12 作者: 血友病 時(shí)間: 2025-3-26 21:10
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: 逢迎白雪 時(shí)間: 2025-3-27 05:06
,Category-Level 6D Object Pose and?Size Estimation Using Self-supervised Deep Prior Deformation Networe specifically, we apply two rigid transformations to each object observation in parallel, and feed them into DPDN respectively to yield dual sets of predictions; on top of the parallel learning, an inter-consistency term is employed to keep cross consistency between dual predictions for improving作者: insurrection 時(shí)間: 2025-3-27 07:49 作者: 譏諷 時(shí)間: 2025-3-27 12:17
,Domain Adaptive Hand Keypoint and?Pixel Localization in?the?Wild,sy predictions during self-training. In this paper, we propose to utilize the divergence of two predictions to estimate the confidence of the target image for both tasks. These predictions are given from two separate networks, and their divergence helps identify the noisy predictions. To integrate o作者: Ceremony 時(shí)間: 2025-3-27 15:32 作者: follicular-unit 時(shí)間: 2025-3-27 18:44 作者: BOGUS 時(shí)間: 2025-3-28 01:40
,Multimodal Object Detection via?Probabilistic Ensembling, hold, e.g., fusing outputs from other fusion methods (both off-the-shelf and trained in-house). We validate ProbEn on two benchmarks containing both aligned (KAIST) and unaligned (FLIR) multimodal images, showing that ProbEn outperforms prior work by more than . in relative performance!作者: Trypsin 時(shí)間: 2025-3-28 05:17 作者: 果仁 時(shí)間: 2025-3-28 09:37
,CPO: Change Robust Panorama to?Point Cloud Localization,r gradient-based optimization. CPO is lightweight and achieves effective localization in all tested scenarios, showing stable performance despite scene changes, repetitive structures, or featureless regions, which are typical challenges for visual localization with perspective cameras.作者: 忙碌 時(shí)間: 2025-3-28 12:05 作者: deadlock 時(shí)間: 2025-3-28 16:23 作者: Cleave 時(shí)間: 2025-3-28 19:03 作者: 膠水 時(shí)間: 2025-3-28 22:57
,SuperLine3D: Self-supervised Line Segmentation and?Description for?LiDAR Point Cloud, registration, in conditions without initial transformation hints. Experiments have demonstrated that our line-based registration method is highly competitive to state-of-the-art point-based approaches. Our code is available at ..作者: podiatrist 時(shí)間: 2025-3-29 03:56 作者: cancellous-bone 時(shí)間: 2025-3-29 10:50
,HEAD: HEtero-Assists Distillation for?Heterogeneous Object Detectors,e assistant is an additional detection head with the architecture homogeneous to the teacher head attached to the student backbone. Thus, a hetero-KD is transformed into a homo-KD, allowing efficient knowledge transfer from the teacher to the student. Moreover, we extend HEAD into a Teacher-Free HEA作者: 憤憤不平 時(shí)間: 2025-3-29 13:50
,Dense Teacher: Dense Pseudo-Labels for?Semi-supervised Object Detection,作者: 財(cái)主 時(shí)間: 2025-3-29 18:58
Introductory Concepts and Problems,ore specifically, we apply two rigid transformations to each object observation in parallel, and feed them into DPDN respectively to yield dual sets of predictions; on top of the parallel learning, an inter-consistency term is employed to keep cross consistency between dual predictions for improving作者: cataract 時(shí)間: 2025-3-29 20:34
The Economics of Self-Destructive Choicesbetween proposals and ground-truth (GT). Benefiting from these strategies, P2BNet is able to produce high-quality instance-level bags for object detection. P2BNet improves the mean average precision (AP) by more than 50% relative to the previous best PSOD method on the MS COCO dataset. It also demon作者: 搖擺 時(shí)間: 2025-3-30 03:40 作者: formula 時(shí)間: 2025-3-30 06:41 作者: Shuttle 時(shí)間: 2025-3-30 11:13
The Economics of Small Businessesriety of advanced one-stage detection architectures. Specifically, on the COCO dataset, our method achieves between +3.1% and +4.6% AP improvement using ResNet-101 and ResNet-50 as the teacher and student backbones, respectively. On the CrowdHuman dataset, we achieve +3.2% and +2.0% improvements in 作者: dominant 時(shí)間: 2025-3-30 13:24 作者: Bumble 時(shí)間: 2025-3-30 16:39 作者: agnostic 時(shí)間: 2025-3-30 22:57
Small Business in German Manufacturingr gradient-based optimization. CPO is lightweight and achieves effective localization in all tested scenarios, showing stable performance despite scene changes, repetitive structures, or featureless regions, which are typical challenges for visual localization with perspective cameras.作者: PIZZA 時(shí)間: 2025-3-31 02:02 作者: Irrepressible 時(shí)間: 2025-3-31 07:12
https://doi.org/10.1007/978-94-015-7854-7en pixel in the camera view. Finally, using the weight maps, the total scene-level count is obtained as a simple weighted sum of the density maps for the camera views. Experiments are conducted on several multi-view counting datasets, and promising performance is achieved compared to calibrated MVCC作者: 苦笑 時(shí)間: 2025-3-31 10:03
David S. Evans,Linda S. Leighton domain. Benefiting from the end-to-end framework that provides richer information of the pseudo labels, we propose the . strategy to take instance-level pseudo confidences into account and improve the effectiveness of the target-domain training process. Moreover, the . strategy and . are proposed t作者: 纖細(xì) 時(shí)間: 2025-3-31 16:38 作者: daredevil 時(shí)間: 2025-3-31 20:08
Robert D. Tollison,Richard E. Wagneriscriminator (AID) and Consistent Features with Reconstruction (CFR) to ensure a reliable robustness. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection abili作者: 無孔 時(shí)間: 2025-4-1 00:38 作者: 地殼 時(shí)間: 2025-4-1 04:39
,BEVFormer: Learning Bird’s-Eye-View Representation from?Multi-camera Images via?Spatiotemporal Tran this work, we present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal作者: conjunctiva 時(shí)間: 2025-4-1 07:15 作者: EVADE 時(shí)間: 2025-4-1 10:26 作者: 背信 時(shí)間: 2025-4-1 15:54
,Domain Adaptive Hand Keypoint and?Pixel Localization in?the?Wild,we only have labeled images taken under very different conditions (.., indoors). In the real world, it is important that the model trained for both tasks works under various imaging conditions. However, their variation covered by existing labeled hand datasets is limited. Thus, it is necessary to ad
