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A method for alleviating double forgetting of visual language model and an evaluation method

PendingCN122174881Amaintain abilitySolving double forgetfulnessInference methodsNeural learning methodsLinguistic modelContinual learning
This invention proposes a continuous learning method and evaluation method to mitigate the double forgetting of visual language models. The method includes: adding two parallel paths to the backbone network of a pre-trained visual language model—one a task-agnostic expert path and the other a task-related expert group path—to obtain a model based on heterogeneous expert hybridization; training the model using a two-stage training strategy, including: a first stage, freezing the task-related expert group path and training only the task-agnostic expert path, using contrastive learning to solidify the pre-trained general knowledge of the visual language model; a second stage, freezing the task-agnostic expert path and training only the task-related expert group path, using a cross-entropy loss function to learn task-specific knowledge; and an inference stage, dynamically fusing the outputs of the two parallel paths through a hierarchical routing mechanism to achieve collaborative learning of pre-trained knowledge and task-specific knowledge.
Owner:NANJING UNIV +1