Cross-modal-pest-Identifying
Cross-modal pest Identifying
A typical cross-modal pest recognition model can be presented as follows: Cross-modal pest recognition utilizes two different modalities of data, namely image and text, to more accurately identify pests. The workflow is as follows. Multimodal data collection: Collect both image data and text description data of pests labeled with category information, image data can be labeled with salient features, and text data can be labeled with information such as shape, color and features. Image model training: CNN model is used to train the feature extraction and classification model for image data only. Text model training: LISTM model is used to extract features only for text data to obtain text feature representation. Multimodal feature fusion: two modal feature representations are spliced or fused to obtain a multimodal joint feature representation. Multi-task learning: learn the tasks of image classification and text classification on the fused features simultaneously to achieve the joint cross-modal representation and model optimization. Compared with single modality, the cross-modal model can more fully represent the pest features and combine visual and semantic information for inference, so as to improve the recognition accuracy. I can give some specific network structure design solutions if needed. Please let me know your ideas.