Learning Aligned Cross-Modal Representations from Weakly Aligned Data

Castrejon, Lluis, Aytar, Yusuf, Vondrick, Carl, Pirsiavash, Hamed, Torralba, Antonio·2016·2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2016.321

Cited by (188)

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  27. Model-Based Transfer Learning
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  28. Transfer Learning Theory
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  31. Semantic convex matrix factorisation for cross‐media retrieval
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  32. Transfer Learning in Activity Recognition
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  33. Transfer Learning in Computer Vision
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  34. Transitive Transfer Learning
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  35. Robust Cross-Modal Retrieval by Adversarial Training
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  39. Efficient Cross-modal Retrieval via Discriminative Deep Correspondence Model
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  42. Multi-task Learning
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  43. Relation-Based Transfer Learning
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  44. Weakly-Supervised Action Detection Guided by Audio Narration
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  53. Introduction
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  54. Domain Adaptive Computational Models for Computer Vision
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  55. CROSS-MODAL COMPUTER GAMES AS AN INTERACTIVE LEARNING MEDIUM
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  56. Heterogeneous transfer learning
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  57. Transfer Learning for Cross-domain Visual Recognition
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  58. 2D-Convolution Based Feature Fusion for Cross-Modal Correlation Learning
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  60. Learning visual attributes from contextual explanations
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  61. An Improvement on Audio-to-MIDI Alignment Using Triplet Pair
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  62. Task-Assisted Domain Adaptation with Anchor Tasks
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  63. A Theoretical Analysis of Multi-Modal Representation Learning with Regular Functions
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  64. Transfer Learning in Dialogue Systems
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  65. Deep learning-empowered wavefront shaping in scattering media
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  66. Feature-Based Transfer Learning
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  68. Heterogeneous Transfer Learning
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  69. Transfer Learning in Urban Computing
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  70. Concluding Remarks
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  71. Preface
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  72. Adversarial Transfer Learning
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  73. Pose-Aware Embedding Networks and Multi-Modal Image-Language Retrieval
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  74. Index
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  75. Pose-Aware Embedding Networks and Multi-Modal Image-Language Retrieval
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  87. Attribute-Enhanced Prompt Learning for Zero-Shot Cross-Modal Retrieval
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  88. Semantic Invariant Representation Learning and Data-Driven Label Prototype Modeling for Incomplete Multi-view Multi-label Classification
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