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Titlebook: Computer Vision – ECCV 2018; 15th European Confer Vittorio Ferrari,Martial Hebert,Yair Weiss Conference proceedings 2018 Springer Nature Sw

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51#
發(fā)表于 2025-3-30 11:39:16 | 只看該作者
Deep Feature Pyramid Reconfiguration for Object Detectiondesigns for feature pyramids are still inefficient to integrate the semantic information over different scales. In this paper, we begin by investigating current feature pyramids solutions, and then reformulate the feature pyramid construction as the feature reconfiguration process. Finally, we propo
52#
發(fā)表于 2025-3-30 14:36:57 | 只看該作者
Goal-Oriented Visual Question Generation via Intermediate Rewardsabout images is proven to be an inscrutable challenge. Towards this end, we propose a Deep Reinforcement Learning framework based on three new intermediate rewards, namely ., . and . that encourage the generation of succinct questions, which in turn uncover valuable information towards the overall g
53#
發(fā)表于 2025-3-30 17:53:56 | 只看該作者
54#
發(fā)表于 2025-3-30 23:23:22 | 只看該作者
55#
發(fā)表于 2025-3-31 04:49:35 | 只看該作者
56#
發(fā)表于 2025-3-31 08:22:55 | 只看該作者
Joint Map and Symmetry Synchronizationair is unique. This assumption, however, easily breaks when visual objects possess self-symmetries. In this paper, we study the problem of jointly optimizing symmetry groups and pair-wise maps among a collection of symmetric objects. We introduce a lifting map representation for encoding both symmet
57#
發(fā)表于 2025-3-31 09:44:54 | 只看該作者
MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamicse leverage this structure and present a novel . for learning motion sequence generation. Our model jointly learns a feature embedding for motion modes (that the motion sequence can be reconstructed from) and a feature transformation that represents the transition of one motion mode to the next motio
58#
發(fā)表于 2025-3-31 15:50:27 | 只看該作者
Rethinking the Form of Latent States in Image CaptioningExisting captioning models usually represent latent states as vectors, taking this practice for granted. We rethink this choice and study an alternative formulation, namely using two-dimensional maps to encode latent states. This is motivated by the curiosity about a question: . Our study on MSCOCO
59#
發(fā)表于 2025-3-31 20:10:11 | 只看該作者
https://doi.org/10.1007/978-3-030-01228-13D; artificial intelligence; computer vision; data security; image coding; image processing; image reconst
60#
發(fā)表于 2025-3-31 22:03:45 | 只看該作者
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