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Wei Jiang (Columbia University), “Advanced Techniques for Semantic Concept Detection in General Videos” (2010). Advisor: Prof. Shih-Fu Chang
The thesis investigates semantic concept detection in general videos focusing on two advanced directions: multi-concept learning and multi-modality learning. For multi-concept learning, we provide an in-depth study of jointly detecting multiple concepts in general domains, where concept relations are hard to compute. Specifically, we propose two methods with the frameworks of two-layer Context-Based Concept Fusion (CBCF) and single-layer multi-label classification, respectively. These methods use semantic context information to enhance detection of individual concepts. For multi-modality learning, we conduct an in-depth investigation of combining audio and visual cues to help detect generic concepts. We develop methods with two strategies: global fusion of features or models from audio and visual modalities, and construction of the local audio-visual atomic representation to enforce a moderate-level audio-visual synchronization. Audio and visual modalities complement each other and their simultaneous processing can uncover relations that are otherwise unavailable when considering the modalities separately.
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