1,649 research outputs found

    三蘇小說研究(1950 年代)

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    本研究會從兩個方向進行討論︰一、)作為報人、知識份子、文化人的高雄與五十 年代報章與文壇(通俗文化及嚴肅文學兩個場域)的關係。二、)以經紀拉、旦仃、石 狗公、史得等筆名寫連載小說的三蘇,其作品與社會、時代的關係。在第一部分的討論 中,除了探討作家與媒體、文化場域的關係外,也會個別討論到作品所涉及的特有問題, 如「三及第」文體於小說創作,及時代文化的意涵。而第二部分的討論,則集中對五十 年代兩類連載小說進行分析,透過文本細讀的方法,結合時代、社會背景進行比對,從 而探討作品在通俗文學的類別中所跨越及達到了的意義和貢獻。最後,就現存有關三蘇 作品資料整理的部分,進行必要的修正及補充。 三蘇(1918-1981),原名高德雄(或名高德熊),香港五、六十年代知名作家,先 後為多家報紙寫社論及連載小說,有筆名三蘇、經紀拉、石狗公、小生姓高、旦仃、周 弓、史得、許德、吳起、凌侶、區品器、禹伯等。對五、六年代的香港社會而言,報張 除了作為一種資訊傳播的媒介,更是流行文化的載體。於一定程度上,更是當時社會東 西文化交流、漸趨國際化的具體表徵。報紙連載便是這個時期特殊的文化產物,深受普 羅大眾歡迎。通俗文學作家中,作品題材之廣、產量之多、語言風格之奇尤以三蘇為箇 中佼佼者。他的創作由四十年代末一直持續到八十年代初,見證香港報業的黃金時代, 同時也是作家本人創作的高峰期

    CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields

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    Neural Radiance Fields (NeRF) have the potential to be a major representation of media. Since training a NeRF has never been an easy task, the protection of its model copyright should be a priority. In this paper, by analyzing the pros and cons of possible copyright protection solutions, we propose to protect the copyright of NeRF models by replacing the original color representation in NeRF with a watermarked color representation. Then, a distortion-resistant rendering scheme is designed to guarantee robust message extraction in 2D renderings of NeRF. Our proposed method can directly protect the copyright of NeRF models while maintaining high rendering quality and bit accuracy when compared among optional solutions.Comment: 11 pages, 6 figures, accepted by iccv 2023 non-camera-ready versio

    MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection

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    Accurate and reliable 3D detection is vital for many applications including autonomous driving vehicles and service robots. In this paper, we present a flexible and high-performance 3D detection framework, named MPPNet, for 3D temporal object detection with point cloud sequences. We propose a novel three-hierarchy framework with proxy points for multi-frame feature encoding and interactions to achieve better detection. The three hierarchies conduct per-frame feature encoding, short-clip feature fusion, and whole-sequence feature aggregation, respectively. To enable processing long-sequence point clouds with reasonable computational resources, intra-group feature mixing and inter-group feature attention are proposed to form the second and third feature encoding hierarchies, which are recurrently applied for aggregating multi-frame trajectory features. The proxy points not only act as consistent object representations for each frame, but also serve as the courier to facilitate feature interaction between frames. The experiments on large Waymo Open dataset show that our approach outperforms state-of-the-art methods with large margins when applied to both short (e.g., 4-frame) and long (e.g., 16-frame) point cloud sequences. Code is available at https://github.com/open-mmlab/OpenPCDet.Comment: Accepted by ECCV 202

    Learning Rays via Deep Neural Network in a Ray-based IPDG Method for High-Frequency Helmholtz Equations in Inhomogeneous Media

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    We develop a deep learning approach to extract ray directions at discrete locations by analyzing highly oscillatory wave fields. A deep neural network is trained on a set of local plane-wave fields to predict ray directions at discrete locations. The resulting deep neural network is then applied to a reduced-frequency Helmholtz solution to extract the directions, which are further incorporated into a ray-based interior-penalty discontinuous Galerkin (IPDG) method to solve the Helmholtz equations at higher frequencies. In this way, we observe no apparent pollution effects in the resulting Helmholtz solutions in inhomogeneous media. Our 2D and 3D numerical results show that the proposed scheme is very efficient and yields highly accurate solutions.Comment: 30 page

    Return migration and re-migration of Brazilian-Japanese and the role of identity in their migration

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    published_or_final_versionInternational and Public AffairsMasterMaster of International and Public Affair

    TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses

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    3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots. With the commonly used tracking-by-detection paradigm, 3D MOT has made important progress in recent years. However, these methods only use the detection boxes of the current frame to obtain trajectory-box association results, which makes it impossible for the tracker to recover objects missed by the detector. In this paper, we present TrajectoryFormer, a novel point-cloud-based 3D MOT framework. To recover the missed object by detector, we generates multiple trajectory hypotheses with hybrid candidate boxes, including temporally predicted boxes and current-frame detection boxes, for trajectory-box association. The predicted boxes can propagate object's history trajectory information to the current frame and thus the network can tolerate short-term miss detection of the tracked objects. We combine long-term object motion feature and short-term object appearance feature to create per-hypothesis feature embedding, which reduces the computational overhead for spatial-temporal encoding. Additionally, we introduce a Global-Local Interaction Module to conduct information interaction among all hypotheses and models their spatial relations, leading to accurate estimation of hypotheses. Our TrajectoryFormer achieves state-of-the-art performance on the Waymo 3D MOT benchmarks.Comment: 10 pages, 3 figure
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