作者: filial 時間: 2025-3-21 21:12 作者: Inexorable 時間: 2025-3-22 00:28 作者: Comedienne 時間: 2025-3-22 05:15
,Pose Forecasting in?Industrial Human-Robot Collaboration,ions, taking place during the human-cobot interaction. We test SeS-GCN on CHICO for two important perception tasks in robotics: human pose forecasting, where it reaches an average error of?85.3?mm (MPJPE) at 1 sec in the future with a run time of 2.3 ms, and collision detection, by comparing the for作者: 北京人起源 時間: 2025-3-22 10:34 作者: badinage 時間: 2025-3-22 15:36 作者: badinage 時間: 2025-3-22 19:41
,Domain Knowledge-Informed Self-supervised Representations for?Workout Form Assessment,ngles, clothes, and illumination to learn powerful representations. To facilitate our self-supervised pretraining, and supervised finetuning, we curated a new exercise dataset, . (.), comprising of three exercises: BackSquat, BarbellRow, and OverheadPress. It has been annotated by expert trainers fo作者: forthy 時間: 2025-3-22 23:42
,Responsive Listening Head Generation: A Benchmark Dataset and?Baseline,ation, listening head generation takes as input both the audio and visual signals from the speaker, and gives non-verbal feedbacks (.., head motions, facial expressions) in a real-time manner. Our dataset supports a wide range of applications such as human-to-human interaction, video-to-video transl作者: Eulogy 時間: 2025-3-23 04:16
,Towards Scale-Aware, Robust, and?Generalizable Unsupervised Monocular Depth Estimation by?Integratitainty measure, which is non-trivial for unsupervised methods. By leveraging IMU during training, DynaDepth not only learns an absolute scale, but also provides a better generalization ability and robustness against vision degradation such as illumination change and moving objects. We validate the e作者: Coterminous 時間: 2025-3-23 08:55
TIPS: Text-Induced Pose Synthesis,pose transfer framework where we also introduce a new dataset DF-PASS, by adding descriptive pose annotations for the images of the DeepFashion dataset. The proposed method generates promising results with significant qualitative and quantitative scores in our experiments.作者: BLAND 時間: 2025-3-23 10:54
,Addressing Heterogeneity in?Federated Learning via?Distributional Transformation,s shows that . outperforms state-of-the-art FL methods and data augmentation methods under various settings and different degrees of client distributional heterogeneity (e.g., for CelebA and 100% heterogeneity . has accuracy of 80.4% vs. 72.1% or lower for other SOTA approaches).作者: Tonometry 時間: 2025-3-23 17:51 作者: OPINE 時間: 2025-3-23 18:18
,Colorization for?, Marine Plankton Images,ments and comparisons with state-of-the-art approaches are presented to show that our method achieves a substantial improvement over previous methods on color restoration of scientific plankton image data.作者: 知識分子 時間: 2025-3-24 01:26 作者: Between 時間: 2025-3-24 03:16
,A Cloud 3D Dataset and?Application-Specific Learned Image Compression in?Cloud 3D,hich makes it feasible to reduce the model complexity to accelerate compression computation. We evaluated our models on six gaming image datasets. The results show that our approach has similar rate-distortion performance as a state-of-the-art learned image compression algorithm, while obtaining abo作者: 健忘癥 時間: 2025-3-24 07:44
,AutoTransition: Learning to?Recommend Video Transition Effects,k. Then we propose a model to learn the matching correspondence from vision/audio inputs to video transitions. Specifically, the proposed model employs a multi-modal transformer to fuse vision and audio information, as well as capture the context cues in sequential transition outputs. Through both q作者: 組成 時間: 2025-3-24 11:10 作者: Aesthete 時間: 2025-3-24 16:35 作者: inconceivable 時間: 2025-3-24 20:09 作者: 共同生活 時間: 2025-3-25 00:00
Stephan Neuhaus,Bernhard Plattnerctive for the probe’s future performance, ameliorating the sales forecasts of all state-of-the-art models on the recent VISUELLE fast-fashion dataset. We also show that POP reflects the ground-truth popularity of new styles (ensembles of clothing items) on the Fashion Forward benchmark, demonstratin作者: 旋轉(zhuǎn)一周 時間: 2025-3-25 04:55
https://doi.org/10.1007/978-3-642-39498-0ions, taking place during the human-cobot interaction. We test SeS-GCN on CHICO for two important perception tasks in robotics: human pose forecasting, where it reaches an average error of?85.3?mm (MPJPE) at 1 sec in the future with a run time of 2.3 ms, and collision detection, by comparing the for作者: MIRE 時間: 2025-3-25 07:40
A Closer Look at Information Security Costsormance to fully supervised approaches. Additionally, we extend the model to multi-actor settings to recognize group activities while localizing the multiple, plausible actors. We also show that it generalizes to out-of-domain data with limited performance degradation.作者: 多嘴多舌 時間: 2025-3-25 13:57 作者: Palate 時間: 2025-3-25 15:50
A Closer Look at Information Security Costsngles, clothes, and illumination to learn powerful representations. To facilitate our self-supervised pretraining, and supervised finetuning, we curated a new exercise dataset, . (.), comprising of three exercises: BackSquat, BarbellRow, and OverheadPress. It has been annotated by expert trainers fo作者: 織物 時間: 2025-3-25 20:05 作者: 奴才 時間: 2025-3-26 04:12 作者: SEVER 時間: 2025-3-26 05:51 作者: placebo-effect 時間: 2025-3-26 11:28
Thomas Marschak,Stefan Reichelsteins shows that . outperforms state-of-the-art FL methods and data augmentation methods under various settings and different degrees of client distributional heterogeneity (e.g., for CelebA and 100% heterogeneity . has accuracy of 80.4% vs. 72.1% or lower for other SOTA approaches).作者: 引起 時間: 2025-3-26 15:14 作者: forager 時間: 2025-3-26 19:11 作者: 落葉劑 時間: 2025-3-26 22:52 作者: Consequence 時間: 2025-3-27 04:56 作者: Highbrow 時間: 2025-3-27 07:48 作者: 鞭子 時間: 2025-3-27 10:42
Charles B. Blankart,Friedrich Schneiderves state-of-the-art performance in the open-set semantic segmentation task on the SemanticKITTI and nuScenes datasets, and alleviate the catastrophic forgetting problem with a large margin during incremental learning.作者: LEVY 時間: 2025-3-27 15:37
Regulation of the UK Insurance Industryy drawn one) in addition to text considerably increases retrieval recall compared to traditional text-based image retrieval. To evaluate our approach, we collect 5,000 hand-drawn sketches for images in the test set of the COCO dataset. The collected sketches are available a ..作者: 難管 時間: 2025-3-27 20:09 作者: 客觀 時間: 2025-3-27 23:18 作者: PHAG 時間: 2025-3-28 04:40 作者: 表主動 時間: 2025-3-28 07:44
,Online Segmentation of?LiDAR Sequences: Dataset and?Algorithm,e total latency. Helix4D reaches accuracy on par with the best segmentation algorithms on HelixNet and SemanticKITTI with a reduction of over . in terms of latency and . in model size. The code and data are available at: ..作者: 賄賂 時間: 2025-3-28 10:41
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: 嘴唇可修剪 時間: 2025-3-28 15:13
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; reinforc作者: 橡子 時間: 2025-3-28 20:22 作者: Redundant 時間: 2025-3-29 01:00 作者: 相互影響 時間: 2025-3-29 05:49
,An Efficient Person Clustering Algorithm for?Open Checkout-free Groceries,ial since it faces challenges of recognizing the dynamic and massive flow of people. In particular, a clustering method that can efficiently assign each snapshot to the corresponding customer is essential for the system. In order to address the unique challenges in the open checkout-free grocery, we作者: 厚臉皮 時間: 2025-3-29 08:11 作者: Duodenitis 時間: 2025-3-29 11:46 作者: Albumin 時間: 2025-3-29 19:34
,Actor-Centered Representations for?Action Localization in?Streaming Videos, tackle the problem of learning . representations through the notion of . to . actions in streaming videos . the need for training labels and outlines for the objects in the video. We propose a framework driven by the notion of hierarchical predictive learning to construct . features by attention-ba作者: vascular 時間: 2025-3-29 20:02 作者: CHAFE 時間: 2025-3-30 00:42
,Domain Knowledge-Informed Self-supervised Representations for?Workout Form Assessment,ally requires estimating human’s body pose. However, off-the-shelf pose estimators struggle to perform well on the videos recorded in gym scenarios due to factors such as camera angles, occlusion from gym equipment, illumination, and clothing. To aggravate the problem, the errors to be detected in t作者: monochromatic 時間: 2025-3-30 04:24
,Responsive Listening Head Generation: A Benchmark Dataset and?Baseline,rsation. As the indispensable complement to talking heads generation, listening head generation has seldomly been studied in literature. Automatically synthesizing listening behavior that actively responds to a talking head, is critical to applications such as digital human, virtual agents and socia作者: JADED 時間: 2025-3-30 09:06 作者: agnostic 時間: 2025-3-30 16:20 作者: Inculcate 時間: 2025-3-30 19:04 作者: 抵押貸款 時間: 2025-3-30 21:00
,Where in?the?World Is This Image? Transformer-Based Geo-localization in?the?Wild,The challenges include huge diversity of images due to different environmental scenarios, drastic variation in the appearance of the same location depending on the time of the day, weather, season, and more importantly, the prediction is made from a single image possibly having only a few geo-locati作者: 生氣地 時間: 2025-3-31 04:40 作者: 組成 時間: 2025-3-31 07:05 作者: GLUE 時間: 2025-3-31 10:29
,A Sketch is Worth a?Thousand Words: Image Retrieval with?Text and?Sketch,r image retrieval using a text description and a sketch as input. We argue that both input modalities complement each other in a manner that cannot be achieved easily by either one alone. TASK-former follows the late-fusion dual-encoder approach, similar to CLIP [.], which allows efficient and scala作者: urethritis 時間: 2025-3-31 16:44 作者: 聽覺 時間: 2025-3-31 17:51
,AutoTransition: Learning to?Recommend Video Transition Effects,ging for non-professionals to choose best transitions due to the lack of cinematographic knowledge and design skills. In this paper, we present the premier work on performing automatic video transitions recommendation (VTR): given a sequence of raw video shots and companion audio, recommend video tr作者: 防御 時間: 2025-3-31 22:26
,Online Segmentation of?LiDAR Sequences: Dataset and?Algorithm,mentation operate on . frames, causing an acquisition latency incompatible with real-time applications. To address this issue, we first introduce HelixNet, a 10 billion point dataset with fine-grained labels, timestamps, and sensor rotation information necessary to accurately assess the real-time re作者: Tonometry 時間: 2025-4-1 04:48
,Open-world Semantic Segmentation for?LIDAR Point Clouds,sed-set assumption makes the network only able to output labels of trained classes, even for objects never seen before, while a static network cannot update its knowledge base according to what it has seen. Therefore, in this work, we propose the . task for LIDAR point clouds, which aims to 1) ident作者: 愛社交 時間: 2025-4-1 06:25 作者: hematuria 時間: 2025-4-1 10:48
Computer Vision – ECCV 2022978-3-031-19839-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Ascendancy 時間: 2025-4-1 16:35
https://doi.org/10.1007/978-3-031-19839-7artificial intelligence; autonomous vehicles; computer vision; image coding; image processing; image reco作者: 亞麻制品 時間: 2025-4-1 22:24
