Balancing framework: Enhanced performance through contrastive masked encoders and gradient feature

• Parallel branches resolve conflicts: masked autoencoder & edge-aware contrastive path. • Random coordinate attention fuses cross-branch features via gradient-modulated keys. • Gradient histogram alignment balances objectives, boosting classification and detection. • Validated on ImageNet, COCO, VOC, Birdsnap, Leaf Disease datasets shows SOTA performance.