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ENTITY Graph convolutional networks for video grounding

Graph convolutional networks for video grounding

PulseAugur coverage of Graph convolutional networks for video grounding — every cluster mentioning Graph convolutional networks for video grounding across labs, papers, and developer communities, ranked by signal.

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RECENT · PAGE 1/1 · 6 TOTAL
  1. TOOL · CL_27994 ·

    iPay framework uses multimodal AI for transit payment recognition

    Researchers have developed iPay, a new framework for recognizing payment actions in transit surveillance footage. This system utilizes a multimodal mixture-of-experts architecture, combining RGB and skeleton data stream…

  2. RESEARCH · CL_27731 ·

    New ES-VAE model improves skeletal pose trajectory analysis

    Researchers have developed an Elastic Shape Variational Autoencoder (ES-VAE) designed to model skeletal pose trajectories more effectively. This new model uses a geometry-aware representation to isolate intrinsic shape …

  3. TOOL · CL_20744 ·

    New ALDA4Rec method improves recommendation systems with graph-based learning

    Researchers have developed a new method called ALDA4Rec to improve recommendation systems by addressing noise and static representations in graph-based models. The approach constructs an item-item graph, filters noise u…

  4. TOOL · CL_16082 ·

    Researchers explore privacy-utility trade-offs in Graph Convolutional Networks

    Researchers have developed a theoretical framework to understand differential privacy in Graph Convolutional Networks (GCNs) by examining subsampling stability. The study derives upper bounds on misclassification rates,…

  5. RESEARCH · CL_10109 ·

    Deep Graph Networks improve crime hotspot prediction accuracy to 78%

    Researchers have developed a new framework using Deep Graph Convolutional Networks (GCNs) to predict crime hotspots. This approach models crime data as a graph, where grid cells are nodes and proximity defines edges, al…

  6. RESEARCH · CL_05418 ·

    MixTGFormer achieves state-of-the-art 3D human pose estimation

    Researchers have developed a new method called MixTGFormer for 3D human pose estimation, which aims to improve upon existing Transformer-based approaches. This novel network integrates Graph Convolutional Networks (GCN)…