作者: Tdd526 時間: 2025-3-21 23:10
Tatiana V. Nikulina,J. Patrick Kociolekdifficult to estimate the gaze of each person in a crowd accurately and simultaneously with existing image-based eye tracking methods, since the image resolution of each person becomes low when we capture the whole crowd with a distant camera. Therefore, we introduce a new approach for localizing th作者: 平息 時間: 2025-3-22 03:18 作者: 束縛 時間: 2025-3-22 06:51 作者: Spartan 時間: 2025-3-22 11:04 作者: 充滿人 時間: 2025-3-22 13:46
https://doi.org/10.1007/978-1-4684-0409-8eatures for recognition are appeared in the partial regions of human, thus we segment a video frame into spatial regions based on the human body parts to enhance feature representation. We utilize an object detector and a pose estimator to segment four regions, namely full body, left/right arm, and 作者: 充滿人 時間: 2025-3-22 19:57 作者: 套索 時間: 2025-3-22 22:54
https://doi.org/10.1007/978-1-349-03555-7sformation. Above all, a method called Style Transfer is drawing much attention which can integrate two photos into one integrated photo regarding their content and style. Although many extended works including Fast Style Transfer have been proposed so far, all the extended methods including origina作者: fatuity 時間: 2025-3-23 03:17 作者: hazard 時間: 2025-3-23 09:05
https://doi.org/10.1007/978-1-349-03555-7techniques for visual recognition have encouraged new possibilities for computing aesthetics and other related concepts in images. In this paper, we design an approach for recognizing styles in photographs by introducing adapted deep convolutional neural networks that are attentive towards strong ne作者: Fresco 時間: 2025-3-23 12:34 作者: 憤怒歷史 時間: 2025-3-23 14:06
https://doi.org/10.1007/978-1-349-03555-7uire any particular parameterization. The power of the system lies in the fact that it generalizes to both seen and unseen layouts of invoice. The system first breaks down the invoice data into various set of entities to extract and then learns structural and semantic information for each entity to 作者: BOAST 時間: 2025-3-23 21:08 作者: Mortar 時間: 2025-3-23 23:07 作者: travail 時間: 2025-3-24 03:21 作者: 彎腰 時間: 2025-3-24 07:54
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234126.jpg作者: Etching 時間: 2025-3-24 11:38 作者: 機(jī)械 時間: 2025-3-24 18:18 作者: committed 時間: 2025-3-24 22:02 作者: adumbrate 時間: 2025-3-25 03:10 作者: absolve 時間: 2025-3-25 03:20
https://doi.org/10.1007/978-1-4684-0409-8om 2 to 90?years old. Consequently, we demonstrated that the proposed method outperform existing methods based on both conventional machine learning frameworks for gait-based age estimation and a deep learning framework for gait recognition.作者: 反抗者 時間: 2025-3-25 09:31
https://doi.org/10.1007/978-1-349-04387-3 designer-in-loop process of taking a generated image to production level design templates (tech-packs). Here the designers bring their own creativity by adding elements, suggestive from the generated image, to accentuate the overall aesthetics of the final design.作者: 輕信 時間: 2025-3-25 12:04 作者: faction 時間: 2025-3-25 16:56 作者: 規(guī)范要多 時間: 2025-3-25 20:58
Let AI Clothe You: Diversified Fashion Generation designer-in-loop process of taking a generated image to production level design templates (tech-packs). Here the designers bring their own creativity by adding elements, suggestive from the generated image, to accentuate the overall aesthetics of the final design.作者: Insulin 時間: 2025-3-26 02:52
Word-Conditioned Image Style Transfere transfer in addition to a given word. We implemented the propose method by modifying the network for arbitrary neural artistic stylization. By the experiments, we show that the proposed method has ability to change the style of an input image taking account of both a given word.作者: 確認(rèn) 時間: 2025-3-26 07:21 作者: 內(nèi)向者 時間: 2025-3-26 12:18 作者: 離開可分裂 時間: 2025-3-26 16:01 作者: 煉油廠 時間: 2025-3-26 19:34
Paying Attention to Style: Recognizing Photo Styles with Convolutional Attentional Unitsural activations. The proposed convolutional attentional units act as a filtering mechanism that conserves activations in convolutional blocks in order to contribute more meaningfully towards the visual style classes. State-of-the-art results were achieved on two large image style datasets, demonstrating the effectiveness of our method.作者: Androgen 時間: 2025-3-26 23:25 作者: Mnemonics 時間: 2025-3-27 02:45 作者: CUMB 時間: 2025-3-27 09:09
https://doi.org/10.1007/978-1-4684-0409-8upper body. From these regions, we extract dense trajectory features and feed them into a shallow RNN to effectively consider the long-term relationships. The evaluation result shows that our framework outperforms previous approaches on the standard two benchmarks, i.e. J-HMDB and MPII Cooking Activities.作者: 空洞 時間: 2025-3-27 12:53 作者: 外來 時間: 2025-3-27 14:53 作者: 節(jié)省 時間: 2025-3-27 20:57 作者: HAVOC 時間: 2025-3-27 22:55 作者: 重力 時間: 2025-3-28 05:04
https://doi.org/10.1007/978-1-349-03555-7raph Convolutional Network (GCN). The system digs deep to extract table information and provide complete invoice reading upto 27 entities of interest without any template information or configuration with an excellent overall F-measure score of 0.93.作者: 油氈 時間: 2025-3-28 09:24 作者: 單挑 時間: 2025-3-28 13:26
A Thumb Tip Wearable Device Consisting of Multiple Cameras to Measure Thumb Postureelationship between the joint angles of the thumb and the images taken by the cameras. In this paper, we captured the keypoint positions of the thumb with a USB sensor device and calculated the joint angles to construct a dataset. The root mean squared error of the test data was 6.23. and 4.75..作者: 確定無疑 時間: 2025-3-28 16:11
An Invoice Reading System Using a Graph Convolutional Networkraph Convolutional Network (GCN). The system digs deep to extract table information and provide complete invoice reading upto 27 entities of interest without any template information or configuration with an excellent overall F-measure score of 0.93.作者: Radiculopathy 時間: 2025-3-28 19:11 作者: murmur 時間: 2025-3-29 01:42 作者: flaunt 時間: 2025-3-29 05:21 作者: FAZE 時間: 2025-3-29 09:58 作者: CUMB 時間: 2025-3-29 12:05 作者: Asseverate 時間: 2025-3-29 17:05 作者: interpose 時間: 2025-3-29 21:37
https://doi.org/10.1007/978-1-349-03555-7el are unpaired sets of noisy and clean images. This paper explores the use of Generative Adversarial Networks (GAN) to generate denoised versions of the noisy documents. In particular, where paired information is available, we formulate the problem as an image-to-image translation task i.e, transla作者: 歡樂中國 時間: 2025-3-30 03:39 作者: Watemelon 時間: 2025-3-30 06:56 作者: 可卡 時間: 2025-3-30 08:20 作者: INCUR 時間: 2025-3-30 15:18
Learning to Clean: A GAN Perspectiveel are unpaired sets of noisy and clean images. This paper explores the use of Generative Adversarial Networks (GAN) to generate denoised versions of the noisy documents. In particular, where paired information is available, we formulate the problem as an image-to-image translation task i.e, transla作者: 通知 時間: 2025-3-30 19:46
Deep Reader: Information Extraction from Document Images via Relation Extraction and Natural Languagthe entities detected by the deep vision models and the relationships between them. DeepReader has a suite of state-of-the-art vision algorithms which are applied to recognize handwritten and printed text, eliminate noisy effects, identify the type of documents and detect visual entities like tables作者: 卵石 時間: 2025-3-30 22:40
Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixelsn boundary areas. Light field cameras provide hundred of views in a single shot, so the ambiguity can be better analysed using other views. In this paper, we present a novel method for anti-occlusion optical flow estimation in a dynamic light field. We first model the light field superpixel (LFSP) a作者: Ondines-curse 時間: 2025-3-31 03:51
Localizing the Gaze Target of a Crowd of Peopledifficult to estimate the gaze of each person in a crowd accurately and simultaneously with existing image-based eye tracking methods, since the image resolution of each person becomes low when we capture the whole crowd with a distant camera. Therefore, we introduce a new approach for localizing th作者: 哭得清醒了 時間: 2025-3-31 05:13 作者: exquisite 時間: 2025-3-31 12:57
Summarizing Videos with Attentionent soft, self-attention mechanism. Current state of the art methods leverage bi-directional recurrent networks such as BiLSTM combined with attention. These networks are complex to implement and computationally demanding compared to fully connected networks. To that end we propose a simple, self-at作者: Dorsal-Kyphosis 時間: 2025-3-31 13:25 作者: 沙草紙 時間: 2025-3-31 19:21 作者: Focus-Words 時間: 2025-4-1 01:06
