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Knowledge Preservation on VQAv2, GQA, VizWiz, SQA, TextVQA, POPE, MM-Bench, MM-Bench-CN

80.7VQAv2 AccuracyPre-trained Model
68.32471.53774.7577.963Jun 23, 2026

Evaluation results

MethodLinks
Pre-trained ModelBackbone=Qwen-2.5VL-3B, Target Task=Flickr30k2026.0680.758.965.982.775.687.677.776.675.7
CGMBackbone=Qwen-2.5VL-3B, Target Task=Flickr30k2026.0679.656.163.58273.286.978.176.874.5
CGM†Backbone=Qwen-2.5VL-3B, Target Task=Flickr30k2026.0676.9536182.272.486.578.276.773.3
DAREBackbone=Qwen-2.5VL-3B, Target Task=Flickr30k2026.0673.447.854.278.26486.673.75766.9
Fine-tuned ModelBackbone=Qwen-2.5VL-3B, Target Task=Flickr30k2026.0672.946.85779.266.283.975.66368.1
WandaBackbone=Qwen-2.5VL-3B, Target Task=Flickr30k2026.0672.846.857.279.366.183.975.463.168.1
MagnitudeBackbone=Qwen-2.5VL-3B, Target Task=Flickr30k2026.067245.856.479.965.783.276.263.767.8
TailorBackbone=Qwen-2.5VL-3B, Target Task=Flickr30k2026.0670.346.456.179.761.284.675.660.766.8
GraftingBackbone=Qwen-2.5VL-3B, Target Task=Flickr30k2026.0668.844.359.380.562.47676.770.167.2