作者: biosphere 時間: 2025-3-22 00:04 作者: 無關(guān)緊要 時間: 2025-3-22 00:30
The Neo-Classical Model and Some Extensions,deep-learning models from learning good representations. While pre-training methods for representation learning exist in computer vision and natural language processing, they still require large-scale data. It is hard to replicate their success in trajectory forecasting due to the inadequate traject作者: 障礙 時間: 2025-3-22 08:06
Peter Kooreman,Sophia Wunderinkver, semantic segmentation of images captured in such conditions remains a challenging task for current state-of-the-art (.) methods trained on broad daylight images, due to the associated distribution shift. On the other hand, domain adaptation techniques developed for the purpose rely on the avail作者: BALK 時間: 2025-3-22 12:21 作者: frivolous 時間: 2025-3-22 16:40
Growth, Social Innovation and Time Use,he local surroundings, the task is to identify the location of the ground camera within the satellite patch. Related work addressed this task for range-sensors (LiDAR, Radar), but for vision, only as a secondary regression step after an initial cross-view image retrieval step. Since the local satell作者: frivolous 時間: 2025-3-22 17:16
Steps to Be Taken to Calculate Fair Pricess. We present a robust cooperative perception framework with V2X communication using a novel vision Transformer. Specifically, we build a holistic attention model, namely V2X-ViT, to effectively fuse information across on-road agents (i.e., vehicles and infrastructure). V2X-ViT consists of alternati作者: CHURL 時間: 2025-3-22 23:09 作者: JIBE 時間: 2025-3-23 02:45
https://doi.org/10.1007/978-3-030-59166-3 of predicting future pedestrian trajectories in a first-person view setting with a moving camera. To that end, we propose a novel action-based contrastive learning loss, that utilizes pedestrian action information to improve the learned trajectory embeddings. The fundamental idea behind this new lo作者: 上漲 時間: 2025-3-23 05:49
Chisato Yoshida,Alan D. Woodlandather. Yet, they currently lack sufficient spatial resolution for semantic scene understanding. In this paper, we present Radatron, a system capable of accurate object detection using mmWave radar as a stand-alone sensor. To enable Radatron, we introduce a first-of-its-kind, high-resolution automoti作者: 小步走路 時間: 2025-3-23 13:10 作者: 不能妥協(xié) 時間: 2025-3-23 15:21
Chisato Yoshida,Alan D. Woodlandentations to improve the performance of point cloud semantic segmentation. However, these works fail to maintain the balance among performance, efficiency, and memory consumption, showing incapability to integrate sparsity and geometry appropriately. To address these issues, we propose the Geometry-作者: 善變 時間: 2025-3-23 19:36 作者: WAIL 時間: 2025-3-24 00:45 作者: 脾氣暴躁的人 時間: 2025-3-24 05:33
The Economics of Illegal Immigrationlier exposure, or external reconstruction models. However, previous uncertainty approaches that directly associate high uncertainty to anomaly may sometimes lead to incorrect anomaly predictions, and external reconstruction models tend to be too inefficient for real-time self-driving embedded system作者: 掙扎 時間: 2025-3-24 09:01
Chisato Yoshida,Alan D. Woodlandtion shift in training v.s. deployment and allowing training to be scaled both safely and cheaply. However, there is a lack of understanding of how to build effective training benchmarks for closed-loop training. In this work, we present the first empirical study which analyzes the effects of differ作者: 強所 時間: 2025-3-24 11:07 作者: 向下 時間: 2025-3-24 16:57
?rn B. Bodvarsson,Hendrik Van den Bergd achieve reasonable performance on the seen object categories that have been observed in training environments. However, this setting is somewhat limited in real world scenario, where navigating to unseen object categories is generally unavoidable. In this paper, we focus on the problem of navigati作者: Hyperlipidemia 時間: 2025-3-24 20:24
Hispanic Immigration to the United States poses many challenges. These include occlusion of the target object by the agent’s arm, noisy object detection and localization, and the target frequently going out of view as the agent moves around in the scene. We propose Manipulation via Visual Object Location Estimation (m-VOLE), an approach th作者: 博識 時間: 2025-3-25 00:10 作者: Apoptosis 時間: 2025-3-25 06:10
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234246.jpg作者: incite 時間: 2025-3-25 11:01 作者: 織物 時間: 2025-3-25 15:29 作者: Loathe 時間: 2025-3-25 17:24 作者: adjacent 時間: 2025-3-25 21:28
Conference proceedings 2022on, 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; reinforcement lear作者: commensurate 時間: 2025-3-26 02:03
,Why Inflation is “A Bad Thing”,set and comparable performance on TuSimple dataset. In addition, our model runs at 46?fps on multi-frame data while using few parameters, indicating the feasibility and practicability in real-time self-driving applications of our proposed method.作者: Mast-Cell 時間: 2025-3-26 08:21 作者: 演講 時間: 2025-3-26 10:50 作者: 淡紫色花 時間: 2025-3-26 15:59
0302-9743 ruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-19841-0978-3-031-19842-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 蘆筍 時間: 2025-3-26 17:28 作者: 縮影 時間: 2025-3-26 22:04 作者: 寒冷 時間: 2025-3-27 03:27 作者: 細(xì)微的差異 時間: 2025-3-27 05:24
SpatialDETR: Robust Scalable Transformer-Based 3D Object Detection From Multi-view Camera Images Wixploits arbitrary receptive fields to integrate cross-sensor data and therefore global context. Extensive experiments on the nuScenes benchmark demonstrate the potential of global attention and result in state-of-the-art performance. Code available at ..作者: Frequency 時間: 2025-3-27 13:03 作者: 憤慨一下 時間: 2025-3-27 14:39
,PreTraM: Self-supervised Pre-training via?Connecting Trajectory and?Map,ctories and maps to a shared embedding space with cross-modal contrastive learning, 2) Map Contrastive Learning, where we enhance map representation with contrastive learning on large quantities of HD-maps. On top of popular baselines such as AgentFormer and Trajectron++, PreTraM reduces their error作者: 遺傳學(xué) 時間: 2025-3-27 20:36
,Master of?All: Simultaneous Generalization of?Urban-Scene Segmentation to?, Adverse Weather Conditi, given a pre-trained model and its parameters, . enforces edge consistency prior at the inference stage and updates the model based on (a) a single test sample at a time (.), or (b) continuously for the whole test domain (.). Not only the target data, . also does not need access to the source data 作者: 缺乏 時間: 2025-3-28 01:28
,LESS: Label-Efficient Semantic Segmentation for?LiDAR Point Clouds,g step, we leverage prototype learning to get more descriptive point embeddings and use multi-scan distillation to exploit richer semantics from temporally aggregated point clouds to boost the performance of single-scan models. Evaluated on the SemanticKITTI and the nuScenes datasets, we show that o作者: 禁止 時間: 2025-3-28 02:08
,Visual Cross-View Metric Localization with?Dense Uncertainty Estimates,e compare against a state-of-the-art regression baseline that uses global image descriptors. Quantitative and qualitative experimental results on the recently proposed VIGOR and the Oxford RobotCar datasets validate our design. The produced probabilities are correlated with localization accuracy, an作者: Excise 時間: 2025-3-28 09:17 作者: Nonconformist 時間: 2025-3-28 13:08
,DevNet: Self-supervised Monocular Depth Learning via?Density Volume Construction,sponding rays. During the training process, novel regularization strategies and loss functions are introduced to mitigate photometric ambiguities and overfitting. Without obviously enlarging model parameters size or running time, DevNet outperforms several representative baselines on both the KITTI-作者: Allodynia 時間: 2025-3-28 14:46
,Action-Based Contrastive Learning for?Trajectory Prediction,les. Additional synthetic trajectory samples are generated using a trained Conditional Variational Autoencoder (CVAE), which is at the core of several models developed for trajectory prediction. Results show that our proposed contrastive framework employs contextual information about pedestrian beha作者: 開花期女 時間: 2025-3-28 21:41 作者: 2否定 時間: 2025-3-28 23:01
Efficient Point Cloud Segmentation with Geometry-Aware Sparse Networks,n, we propose deep sparse supervision in the training phase to help convergence and alleviate the memory consumption problem. Our GASN achieves state-of-the-art performance on both SemanticKITTI and Nuscenes datasets while running significantly faster and consuming less memory.作者: anus928 時間: 2025-3-29 06:21 作者: 消極詞匯 時間: 2025-3-29 08:57 作者: Decrepit 時間: 2025-3-29 12:33 作者: FICE 時間: 2025-3-29 16:31
SLiDE: Self-supervised LiDAR De-snowing Through Reconstruction Difficulty,ow points without any label. Our method achieves the state-of-the-art performance among label-free approaches and is comparable to the fully-supervised method. Moreover, we demonstrate that our method can be exploited as a pretext task to improve label-efficiency of supervised training of de-snowing作者: 價值在貶值 時間: 2025-3-29 23:39
,Generative Meta-Adversarial Network for?Unseen Object Navigation,enerator and an environmental meta discriminator, aiming to generate features for unseen objects and new environments in two steps. The former generates the initial features of the unseen objects based on the semantic embedding of the object category. The latter enables the generator to further lear作者: jovial 時間: 2025-3-30 02:04
,Object Manipulation via?Visual Target Localization,tor, and our analysis shows that our agent is robust to noise in depth perception and agent localization. Importantly, our proposed approach relaxes several assumptions about idealized localization and perception that are commonly employed by recent works in navigation and manipulation – an importan作者: 可忽略 時間: 2025-3-30 06:09
Inflation and Financial Systems,ter and inter-proposal separation, ...., sharpening the discriminativeness of proposal representations across semantic classes and object instances. The generalizability and transferability of . are verified on various 3D detectors (...., PV-RCNN, CenterPoint, PointPillars and PointRCNN) and dataset作者: Palpitation 時間: 2025-3-30 11:31 作者: STALE 時間: 2025-3-30 15:22
Peter Kooreman,Sophia Wunderink, given a pre-trained model and its parameters, . enforces edge consistency prior at the inference stage and updates the model based on (a) a single test sample at a time (.), or (b) continuously for the whole test domain (.). Not only the target data, . also does not need access to the source data 作者: 策略 時間: 2025-3-30 18:52 作者: 收集 時間: 2025-3-30 21:53
Growth, Social Innovation and Time Use,e compare against a state-of-the-art regression baseline that uses global image descriptors. Quantitative and qualitative experimental results on the recently proposed VIGOR and the Oxford RobotCar datasets validate our design. The produced probabilities are correlated with localization accuracy, an作者: perimenopause 時間: 2025-3-31 02:05
Steps to Be Taken to Calculate Fair Pricesand OpenCDA. Extensive experimental results demonstrate that V2X-ViT sets new state-of-the-art performance for 3D object detection and achieves robust performance even under harsh, noisy environments. The code is available at ..作者: gait-cycle 時間: 2025-3-31 05:56 作者: overshadow 時間: 2025-3-31 12:13
https://doi.org/10.1007/978-3-030-59166-3les. Additional synthetic trajectory samples are generated using a trained Conditional Variational Autoencoder (CVAE), which is at the core of several models developed for trajectory prediction. Results show that our proposed contrastive framework employs contextual information about pedestrian beha作者: ferment 時間: 2025-3-31 15:41 作者: 庇護(hù) 時間: 2025-3-31 19:05
Chisato Yoshida,Alan D. Woodlandn, we propose deep sparse supervision in the training phase to help convergence and alleviate the memory consumption problem. Our GASN achieves state-of-the-art performance on both SemanticKITTI and Nuscenes datasets while running significantly faster and consuming less memory.作者: mortgage 時間: 2025-4-1 01:22
https://doi.org/10.1057/9780230514881ablish two new large-scale datasets to this field by collecting lidar-scanned point clouds from public autonomous driving datasets and annotating the collected data through novel pseudo-labeling. Extensive experiments on both public and proposed datasets show that our method outperforms prior state-作者: OWL 時間: 2025-4-1 05:36 作者: predict 時間: 2025-4-1 08:58
