作者: Oratory 時間: 2025-3-21 23:44
978-3-030-58522-8Springer Nature Switzerland AG 2020作者: Gustatory 時間: 2025-3-22 00:57 作者: Madrigal 時間: 2025-3-22 04:35
The Ebola Pandemic in Sierra Leonehods are not able to robustly estimate pose and shape of animals, particularly for social animals such as birds, which are often occluded by each other and objects in the environment. To address this problem, we first introduce a model and multi-view optimization approach, which we use to capture th作者: 使殘廢 時間: 2025-3-22 11:08
The Ebola Pandemic in Sierra Leoneropose an approach for learning . on videos, inspired by human learning. Our model combines visual features as input with natural language supervision to generate high-level representations of similarities across a set of videos. This allows our model to perform cognitive tasks such as . (which gene作者: 精密 時間: 2025-3-22 13:46
A. M. Romaní,S. Sabater,I. Mu?ozimal supervision is an important problem in computer vision. Contrary to prior work, the whole training process (i) uses a differentiable shape model surface and (ii) is trained end-to-end by jointly optimizing all parameters of a single, self-contained objective that can be solved with slightly mod作者: 精密 時間: 2025-3-22 17:50 作者: Extemporize 時間: 2025-3-22 21:11 作者: 相符 時間: 2025-3-23 02:07 作者: periodontitis 時間: 2025-3-23 07:46 作者: Aromatic 時間: 2025-3-23 12:09 作者: Antigen 時間: 2025-3-23 15:40
Idealist and Materialist Explanations,fold of camera poses. In highly ambiguous environments, which can easily arise due to symmetries and repetitive structures in the scene, computing one plausible solution (what most state-of-the-art methods currently regress) may not be sufficient. Instead we predict multiple camera pose hypotheses a作者: 地名詞典 時間: 2025-3-23 19:40 作者: 鄙視讀作 時間: 2025-3-24 00:31 作者: conservative 時間: 2025-3-24 05:31
https://doi.org/10.1007/978-1-349-24924-4g a computational model for this purpose is challenging due to semantic ambiguity and a lack of labeled data: current datasets only tell you where people ., not where they .. We tackle this problem by leveraging information from existing datasets, without additional labeling. We first augment the se作者: 圓柱 時間: 2025-3-24 08:35
https://doi.org/10.1007/978-1-349-24924-4at uses attention to localize and group sound sources, and optical flow to aggregate information over time. We demonstrate the effectiveness of the audio-visual object embeddings that our model learns by using them for four downstream speech-oriented tasks: (a) multi-speaker sound source separation,作者: CHASE 時間: 2025-3-24 12:14 作者: Carcinoma 時間: 2025-3-24 15:04 作者: DOLT 時間: 2025-3-24 20:06 作者: Arthr- 時間: 2025-3-25 02:06
https://doi.org/10.1007/978-1-349-24924-4 optical extinction coefficient, as a function of altitude in a cloud. Cloud droplets become larger as vapor condenses on them in an updraft. Reconstruction of the volumetric structure of clouds is important for climate research. Data for such reconstruction is multi-view images of each cloud taken 作者: 痛恨 時間: 2025-3-25 04:45 作者: DRILL 時間: 2025-3-25 08:38
https://doi.org/10.1007/978-1-349-24924-4y of metric learning losses, which prescribe what the proximity of image and text should be, in the learned space. However, most prior methods have focused on the case where image and text convey redundant information; in contrast, real-world image-text pairs convey complementary information with li作者: 人類 時間: 2025-3-25 13:18 作者: Melatonin 時間: 2025-3-25 16:17
Conference proceedings 2020n, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with top作者: 慎重 時間: 2025-3-25 22:10
0302-9743 uter Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers dea作者: 無節(jié)奏 時間: 2025-3-26 02:11 作者: 讓你明白 時間: 2025-3-26 05:36 作者: 多嘴多舌 時間: 2025-3-26 10:54
The Ebola Pandemic in Sierra Leonemmonalities among sets. We compare our model to several baseline algorithms and show that significant improvements result from explicitly learning relational abstractions with semantic supervision. Code and models are available online (Project website: .).作者: dyspareunia 時間: 2025-3-26 13:08 作者: irritation 時間: 2025-3-26 20:52
https://doi.org/10.1007/978-1-349-24924-4a contextual adversarial loss. Using this strategy, we demonstrate a model that learns to predict a walkability map from a single image. We evaluate our model on the Waymo and Cityscapes datasets, demonstrating superior performance compared to baselines and state-of-the-art models.作者: filial 時間: 2025-3-26 23:32 作者: insincerity 時間: 2025-3-27 03:14
https://doi.org/10.1007/978-1-349-24924-4ove the reconstruction quality. The stochastic tomography is based on Monte-Carlo (MC) radiative transfer. It is formulated and implemented in a coarse-to-fine form, making it scalable to large fields.作者: 擔憂 時間: 2025-3-27 05:26
https://doi.org/10.1007/978-1-349-24924-4h does not necessarily align with visual coherency. Our method ensures that not only are paired images and texts close, but the expected image-image and text-text relationships are also observed. Our approach improves the results of cross-modal retrieval on four datasets compared to five baselines.作者: 迅速飛過 時間: 2025-3-27 10:20 作者: violate 時間: 2025-3-27 16:06
Joint Optimization for Multi-person Shape Models from Markerless 3D-Scans, sufficient to achieve competitive performance on the challenging FAUST surface correspondence benchmark. The training and evaluation code will be made available for research purposes to facilitate end-to-end shape model training on novel datasets with minimal setup cost.作者: CUR 時間: 2025-3-27 21:24
Hidden Footprints: Learning Contextual Walkability from 3D Human Trails,a contextual adversarial loss. Using this strategy, we demonstrate a model that learns to predict a walkability map from a single image. We evaluate our model on the Waymo and Cityscapes datasets, demonstrating superior performance compared to baselines and state-of-the-art models.作者: Narcissist 時間: 2025-3-28 01:27
Self-supervised Learning of Audio-Visual Objects from Video,applying it to non-human speakers, including cartoons and puppets. Our model significantly outperforms other self-supervised approaches, and obtains performance competitive with methods that use supervised face detection.作者: 詞匯記憶方法 時間: 2025-3-28 04:40 作者: 思想 時間: 2025-3-28 07:50
Preserving Semantic Neighborhoods for Robust Cross-Modal Retrieval,h does not necessarily align with visual coherency. Our method ensures that not only are paired images and texts close, but the expected image-image and text-text relationships are also observed. Our approach improves the results of cross-modal retrieval on four datasets compared to five baselines.作者: placebo 時間: 2025-3-28 12:16 作者: Concerto 時間: 2025-3-28 16:22
https://doi.org/10.1007/978-1-137-54471-1construct shapes with rich geometry . appearance. Our method is supervised and trained on a public dataset of shapes from common object categories. Quantitative results indicate that our method significantly outperforms previous work, while qualitative results demonstrate the high quality of our reconstructions.作者: Evolve 時間: 2025-3-28 19:15
Medicine: The Secularisation of Hope,ure the edited image produced by the model closely aligns with the originally provided image. Qualitative and quantitative results on three different artistic datasets demonstrate the effectiveness of the proposed framework on both image generation and editing tasks.作者: Indicative 時間: 2025-3-28 23:24 作者: MERIT 時間: 2025-3-29 03:04
Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images,construct shapes with rich geometry . appearance. Our method is supervised and trained on a public dataset of shapes from common object categories. Quantitative results indicate that our method significantly outperforms previous work, while qualitative results demonstrate the high quality of our reconstructions.作者: Prostaglandins 時間: 2025-3-29 10:05 作者: Keratin 時間: 2025-3-29 14:47 作者: micturition 時間: 2025-3-29 18:49 作者: 享樂主義者 時間: 2025-3-29 23:02 作者: 高深莫測 時間: 2025-3-30 00:28 作者: 稱贊 時間: 2025-3-30 04:07 作者: 具體 時間: 2025-3-30 10:28 作者: Expurgate 時間: 2025-3-30 16:16 作者: generic 時間: 2025-3-30 18:11 作者: 真繁榮 時間: 2025-3-30 22:16
An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds,nd other multi-frame approaches by 1.2% while using less memory and computation per frame. To the best of our knowledge, this is the first work to use an LSTM for 3D object detection in sparse point clouds.作者: 倒轉(zhuǎn) 時間: 2025-3-31 00:54 作者: 有斑點 時間: 2025-3-31 07:29 作者: Accomplish 時間: 2025-3-31 12:00 作者: Traumatic-Grief 時間: 2025-3-31 14:01 作者: fatty-acids 時間: 2025-3-31 20:17
Political Aspects of MNE Theory,n connectomics. With the framework, we curate, to our best knowledge, the largest connectomics dataset with dense synapses and mitochondria annotation. On this new dataset, our method outperforms previous state-of-the-art methods by 3.1% for synapse and 3.8% for mitochondria in terms of region-of-in作者: 一大群 時間: 2025-3-31 22:52 作者: Harbor 時間: 2025-4-1 03:13
https://doi.org/10.1007/978-1-349-24924-4e created an annotated dataset and benchmarked seven state-of-the-art deep learning classification methods in three categories, namely: (1) point clouds, (2) volumetric representation in voxel grids, and (3) view-based representation.作者: 聽寫 時間: 2025-4-1 06:12
https://doi.org/10.1007/978-1-349-24924-4ated garment model can be easily retargeted to another body, enabling garment customization. In addition, a large garment appearance dataset is provided for use in garment reconstruction, garment capturing, and other applications. We demonstrate that our generative model has high reconstruction accu作者: 制定法律 時間: 2025-4-1 13:20 作者: 現(xiàn)代 時間: 2025-4-1 16:04
https://doi.org/10.1007/978-1-349-24924-4nd other multi-frame approaches by 1.2% while using less memory and computation per frame. To the best of our knowledge, this is the first work to use an LSTM for 3D object detection in sparse point clouds.作者: 革新 時間: 2025-4-1 21:46
https://doi.org/10.1007/978-1-349-24924-4sed Co-Attention assisted ranking network shows superior performance even over the supervised(The term “supervised” refers to the approach with access to the manual ground-truth annotations for training.) approach. The effectiveness of our Contrastive Attention module is also demonstrated by the per
