Feature extraction includes determining a reference model for feature extraction and fine-tuning the reference model for different tasks. The method also includes storing a set of weight differences calculated during the fine-tuning. Each set may correspond to a different task.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of feature extraction, comprising: determining a reference model for feature extraction; fine-tuning the reference model for a plurality of different tasks; and storing a set of weight differences calculated during the fine-tuning, each set corresponding to a different task. 2. The method of claim 1, in which the reference model comprises a localization model. 3. The method of claim 1, in which the reference model comprises a feature learning model. 4. The method of claim 1, in which the storing comprises storing only non-zero weight differences. 5. The method of claim 1, in which the fine-tuning comprises applying a task specific classifier. 6. An apparatus for feature extraction, comprising: a memory; and at least one processor coupled to the memory, the at least one processor configured: to determine a reference model for feature extraction; to fine-tune the reference model for a plurality of different tasks; and to store a set of weight differences calculated during fine-tuning, each set corresponding to a different task. 7. The apparatus of claim 6, in which the reference model comprises a localization model. 8. The apparatus of claim 6, in which the reference model comprises a feature learning model. 9. The apparatus of claim 6, in which the at least one processor is further configured to store only non-zero weight differences. 10. The apparatus of claim 6, in which the at least one processor is further configured to apply a task specific classifier. 11. An apparatus for feature extraction, comprising: means for determining a reference model for feature extraction; means for fine-tuning the reference model for a plurality of different tasks; and means for storing a set of weight differences calculated during fine-tuning, each set corresponding to a different task. 12. The apparatus of claim 11, in which the reference model comprises a localization model. 13. The apparatus of claim 11, in which the reference model comprises a feature learning model. 14. The apparatus of claim 11, in which the means for storing stores only non-zero weight differences. 15. The apparatus of claim 11, further including means for applying a task specific classifier. 16. A non-transitory computer-readable medium having encoded thereon program code to be executed by a processor, the program code comprising: program code to determine a reference model for feature extraction; program code to fine-tune the reference model for a plurality of different tasks; and program code to store a set of weight differences calculated during fine-tuning, each set corresponding to a different task. 17. The computer-readable medium of claim 16, in which the reference model comprises a localization model. 18. The computer-readable medium of claim 16, in which the reference model comprises a feature learning model. 19. The computer-readable medium of claim 16, further comprising program code to store only non-zero weight differences. 20. The computer-readable medium of claim 16, further comprising program code to apply a task specific classifier.