Abstract
Accurate quantification of fish feeding intensity is crucial for precision feeding in aquaculture, as it directly affects feed utilization and farming efficiency. Although multimodal fusion has proven to be an effective solution, existing methods often overlook the inconsistencies in responses and decision conflicts between different modalities, thus limiting the reliability of the quantification results. To address this issue, this paper proposes a Progressive Multimodal Interaction Network (PMIN) that integrates image, audio, and water-wave data for fish feeding intensity quantification. Specifically, a unified feature extraction framework is first constructed to map inputs from different modalities into a structurally consistent feature space, thereby reducing representational discrepancies across modalities. Then, an auxiliary-modality reinforcement primary-modality mechanism is designed to facilitate the fusion of cross-modal information, which is achieved through channel aware re