作者: oxidize 時間: 2025-3-22 00:18 作者: 集聚成團 時間: 2025-3-22 04:25 作者: 撫慰 時間: 2025-3-22 05:18 作者: Limousine 時間: 2025-3-22 12:27
https://doi.org/10.1057/9781137547439 all object classes to be given in the training set. Such a closed setting limits the practical application of SGG. In this paper, we introduce . scene graph generation, a novel, realistic and challenging setting, in which a model is trained on a set of base object classes but is required to infer r作者: 污穢 時間: 2025-3-22 13:48 作者: 污穢 時間: 2025-3-22 19:36
https://doi.org/10.1057/9781137032690n how to reason about structure not only from observation, but also by interacting with the world around us – by taking things apart and putting them back together again. The ability to reason about structure and compositionality allows us to not only build things, but also understand and reverse-en作者: 迅速成長 時間: 2025-3-23 00:26 作者: POINT 時間: 2025-3-23 04:10 作者: PRISE 時間: 2025-3-23 08:32
Anton Basson,Keyan Tomaselli,Gerda Dullaarteo restoration for each weather using a different custom-designed architecture. This approach has many limitations. First, a custom-designed architecture for each weather condition requires domain-specific knowledge. Second, disparate network architectures across weather conditions prevent easy know作者: abject 時間: 2025-3-23 13:47 作者: deriver 時間: 2025-3-23 16:56
Learning with Images in the Digital Ageds which employ an external LM. The conditional independence of the external LM on the input image may cause it to erroneously rectify correct predictions, leading to significant inefficiencies. Our method, PARSeq, learns an ensemble of internal AR LMs with shared weights using Permutation Language 作者: Modify 時間: 2025-3-23 21:50
https://doi.org/10.1057/9781137070388quences from formula images with the attention mechanism. However, such methods may fail to accurately read formulas with complicated structure or generate long markup sequences, as the attention results are often inaccurate due to the large variance of writing styles or spatial layouts. To alleviat作者: organism 時間: 2025-3-24 01:30 作者: Hyperalgesia 時間: 2025-3-24 04:31
Preface to the Post-War Surgenceat the detection stage, which is used as the input of the text recognition stage. We observe that when using tight text bounding boxes as input, a text recognizer frequently fails to achieve optimal performance due to the inconsistency between bounding boxes and deep representations of text recognit作者: Retrieval 時間: 2025-3-24 09:55 作者: 同來核對 時間: 2025-3-24 14:38 作者: Carcinogen 時間: 2025-3-24 16:40 作者: 拉開這車床 時間: 2025-3-24 22:58
THE INDUSTRIAL REVOLUTION AND AFTERocus on irregular text while have not explored artistic text specifically. The challenges of artistic text recognition include the various appearance with special-designed fonts and effects, the complex connections and overlaps between characters, and the severe interference from background patterns作者: arbiter 時間: 2025-3-24 23:41 作者: 遵循的規(guī)范 時間: 2025-3-25 03:37 作者: Redundant 時間: 2025-3-25 07:46
978-3-031-19814-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 思想 時間: 2025-3-25 14:40 作者: 飛來飛去真休 時間: 2025-3-25 15:57
Conference proceedings 2022ning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..作者: 去世 時間: 2025-3-25 20:23 作者: 空氣 時間: 2025-3-26 00:15 作者: 暴發(fā)戶 時間: 2025-3-26 08:05
Anton Basson,Keyan Tomaselli,Gerda Dullaartnal networks. To fill this gap, we propose a framework to unify the multitude of continuous convolutions. This enables our main contribution, variance-aware weight initialization. We show that this initialization can avoid batch normalization while achieving similar and, in some cases, better performance.作者: Dri727 時間: 2025-3-26 08:59
https://doi.org/10.1057/9781137032690nal overhead. Experimental results show that the proposed architecture achieved a new state-of-the-art record by outperforming other approaches with large margin in both FlyingThings3D and KITTI benchmarks. Codes are available at ..作者: 我的巨大 時間: 2025-3-26 15:22 作者: 平息 時間: 2025-3-26 17:12
The Growth of Schools 1889–1898conduct three tasks to detect the onomatopoeia region and capture its intended meaning: text detection, text recognition, and link prediction. Through extensive experiments, we analyze the characteristics of the COO. Our data and code are available at ..作者: Obvious 時間: 2025-3-26 22:37 作者: 填料 時間: 2025-3-27 04:44
,Bi-PointFlowNet: Bidirectional Learning for?Point Cloud Based Scene Flow Estimation,nal overhead. Experimental results show that the proposed architecture achieved a new state-of-the-art record by outperforming other approaches with large margin in both FlyingThings3D and KITTI benchmarks. Codes are available at ..作者: 冷淡周邊 時間: 2025-3-27 08:23 作者: 管理員 時間: 2025-3-27 12:17
COO: Comic Onomatopoeia Dataset for Recognizing Arbitrary or Truncated Texts,conduct three tasks to detect the onomatopoeia region and capture its intended meaning: text detection, text recognition, and link prediction. Through extensive experiments, we analyze the characteristics of the COO. Our data and code are available at ..作者: Budget 時間: 2025-3-27 16:42
0302-9743 ruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-19814-4978-3-031-19815-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 易怒 時間: 2025-3-27 18:22 作者: 薄膜 時間: 2025-3-27 23:45
https://doi.org/10.1007/978-1-349-00531-4earning and allow for parallel decoding, dynamic length change and good interpretability. The quantitative experiments clearly demonstrate that LevOCR achieves state-of-the-art performances on standard benchmarks and the qualitative analyses verify the effectiveness and advantage of the proposed LevOCR algorithm.作者: 立即 時間: 2025-3-28 02:34
,Towards Open-Vocabulary Scene Graph Generation with?Prompt-Based Finetuning,is able to support inference over completely unseen object classes, which existing methods are incapable of handling. On extensive experiments on three benchmark datasets, Visual Genome, GQA and Open-Image, our method significantly outperforms recent, strong SGG methods on the setting of Ov-SGG, as well as on the conventional closed SGG.作者: 生來 時間: 2025-3-28 09:37 作者: Original 時間: 2025-3-28 11:18 作者: 爭論 時間: 2025-3-28 17:20 作者: CLAMP 時間: 2025-3-28 21:21
,Weakly Supervised 3D Scene Segmentation with?Region-Level Boundary Awareness and?Instance Discriminfident predictions of the network to discriminate the intermediate feature embeddings in multiple stages. In the limited reconstruction case, our proposed approach, termed WS3D, has pioneer performance on the large-scale ScanNet on semantic segmentation and instance segmentation. Also, our proposed 作者: verdict 時間: 2025-3-29 02:29 作者: 踉蹌 時間: 2025-3-29 06:29 作者: BROW 時間: 2025-3-29 11:09 作者: FOVEA 時間: 2025-3-29 11:41
Scene Text Recognition with Permuted Autoregressive Sequence Models,ructure and parallel token processing. Due to its extensive use of attention, it is robust on arbitrarily-oriented text, which is common in real-world images. Code, pretrained weights, and data are available at: ..作者: 大氣層 時間: 2025-3-29 18:08
,When Counting Meets HMER: Counting-Aware Network for?Handwritten Mathematical Expression Recognitio that both joint optimization and counting results are beneficial for correcting the prediction errors of encoder-decoder models, and CAN consistently outperforms the state-of-the-art methods. In particular, compared with an encoder-decoder model for HMER, the extra time cost caused by the proposed 作者: HERTZ 時間: 2025-3-29 23:17 作者: 鞭子 時間: 2025-3-30 01:33
Optimal Boxes: Boosting End-to-End Scene Text Recognition by Adjusting Annotated Bounding Boxes viarecognition systems can be improved when using the adjusted bounding boxes as the ground truths for training. Specifically, on several benchmark datasets for scene text understanding, the proposed method outperforms state-of-the-art text spotters by an average of 2.0% F-Score on end-to-end text reco作者: ARCH 時間: 2025-3-30 04:19 作者: definition 時間: 2025-3-30 11:56
Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detectioual features for learning effective scene text representations. With the learning of textual features, the pre-trained model can attend texts in images well with character awareness. Besides, these designs enable the learning from weakly annotated texts (i.e. partial texts in images without text bou作者: indenture 時間: 2025-3-30 15:22
,Toward Understanding WordArt: Corner-Guided Transformer for?Scene Text Recognition,n a character contrastive loss to model the character-level feature, improving the feature representation for character classification. Thirdly, we utilize Transformer to learn the global feature on image-level and model the global relationship of the corner points, with the assistance of a corner-q作者: 笨拙的我 時間: 2025-3-30 19:06 作者: doxazosin 時間: 2025-3-30 21:45 作者: 琺瑯 時間: 2025-3-31 03:40
The Education of a Circus Clownfident predictions of the network to discriminate the intermediate feature embeddings in multiple stages. In the limited reconstruction case, our proposed approach, termed WS3D, has pioneer performance on the large-scale ScanNet on semantic segmentation and instance segmentation. Also, our proposed 作者: Sigmoidoscopy 時間: 2025-3-31 05:22 作者: Biguanides 時間: 2025-3-31 09:38 作者: 平 時間: 2025-3-31 15:50 作者: 抒情短詩 時間: 2025-3-31 18:44
Learning with Images in the Digital Ageructure and parallel token processing. Due to its extensive use of attention, it is robust on arbitrarily-oriented text, which is common in real-world images. Code, pretrained weights, and data are available at: ..作者: accordance 時間: 2025-3-31 22:13
