2024年1月29日 · We use a new attention mechanism called GVA to create a caption that describes an image. This second-level attention mechanism gives importance scores to candidate feature vectors and normalizes them using the softmax function to get weights.

2024年1月29日 · To tackle this issue, we propose an innovative and well-designed Guided Visual Attention (GVA) approach, incorporating an additional attention mechanism to re-adjust the attentional weights on the visual feature vectors and feed the resulting context vector to the language LSTM.

To address this limitation, we propose a novel geometric variation adaptive change detector (GVA-CD), which adaptively adjusts the shape and size of neighboring areas based on the geometrical structure of ground objects. More specifically, we design a new geometric variation adaptive module (GVAM) as a component of GVA-CD.

In this paper, we present a novel method that facilitates the creation of vivid 3D Gaussian avatars from monocular video inputs (GVA). Our innovation lies in addressing the intricate challenges of delivering high-fidelity human body reconstructions and aligning 3D Gaussians with human skin surfaces accurately.

Automated image caption generation with attention mechanisms focuses on visual features including objects, attributes, actions, and scenes of the image to understand and provide more detailed captions, which attains great attention in the multimedia field.

A detailed review of prevailing image captioning methods using deep learning techniques. Multimed. Tools Appl. (2022). https://doi.org/10.1007/s11042-021-11293-1

Article "GVA: guided visual attention approach for automatic image caption generation" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as "JST").

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Automated image caption generation with attention mechanisms focuses on visual features including objects, attributes, actions, and scenes of the image to understand and

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