换话题
发表于 2025-3-26 22:46:52
Udo Thiedekest, only the foreground objects are significantly brightened by the flash as they are relatively nearer to the camera than the background. Second, the brightness variations introduced by the flash provide hints to surface orientation changes. Accordingly, the first observation is explored to form th
蘑菇
发表于 2025-3-27 05:09:51
Karin Dollhausen,Josef Wehnerover the 3D page shape by exploiting the intrinsic vector fields of the image. Based on the assumption that the curled page shape is a general cylindrical surface, we estimate the parameters related to the camera and the 3D shape model through weighted majority voting on the vector fields. Then the
Adulterate
发表于 2025-3-27 08:51:20
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学术讨论会
发表于 2025-3-27 10:38:18
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擦试不掉
发表于 2025-3-27 13:58:09
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Bravado
发表于 2025-3-27 20:16:20
Ute Hoffmann, which specify classes that observations do . belong to. Let . and . be the true and complementary labels, respectively. We first model the annotation of complementary labels via transition probabilities ., where . is the number of classes. Previous methods implicitly assume that ., are identical,
拥挤前
发表于 2025-3-28 01:58:14
Bettina Heintzy, lawn) and the corresponding words in sentences allows to capture fine-grained interplay between vision and language, and makes image-text matching more interpretable. Prior work either simply aggregates the similarity of all possible pairs of regions and words without attending differentially to
Inexorable
发表于 2025-3-28 06:01:05
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荧光
发表于 2025-3-28 06:25:06
Robin B. Hamman statement is true only after one has compensated for camera rotation, individual object motion, and image position. We address these issues in the paper). It is natural for a learning agent to associate image patterns with the magnitude of their displacement over time: as the agent moves, faraway m
Thyroxine
发表于 2025-3-28 13:03:49
Gerit Götzenbrucker,Bernd Lögermate goal of indoor mapping research. The challenge lies in the processing of RGBD streams spanning a large 3D space. The proposed neural architecture, dubbed FloorNet, effectively processes the data through three neural network branches: (1) PointNet with 3D points, exploiting 3D information; (2) C