Zero-shot classification with multi-model information interaction and its application in Mars exploration scenarios

To address the issue that traditional Mars image classification methods based on images do not perform well in the face of the complex and unknown environment of Mars and the frequent occurrence of unseen categories, a zero-shot classification algorithm of multi-modal information interaction was studied and the migration and optimization of the algorithm to the field of deep space exploration was explored. The research content was mainly divided into two aspects: dataset construction and algorithm research. In terms of dataset construction, the zero-shot classification dataset for Mars exploration was integrated and reconstructed. For algorithm research, a zero-shot scene classification algorithm based on multi-modal feature interaction was proposed. Then combined with knowledge distillation, the model was compressed and optimized, which greatly reduced the number of parameters and complexity of the model while ensuring the performance of zero-shot classification. To verify the effectiveness of the algorithm, a large number of comparative experiments and visualization analysis are carried out, and the results prove the feasibility and effectiveness of the proposed algorithm.