Systems, methods, and non-transitory computer-readable media can receive a compressed convolutional neural network (CNN). A media content item to be processed can be acquired. The compressed CNN to can be utilized to apply a media processing technique to the media content item to produce information about the media content item. It can be determined, based on at least some of the information about the media content item, whether to transmit at least a portion of the media content item to one or more remote servers for additional media processing.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving, by a computing system, a compressed convolutional neural network (CNN); acquiring, by the computing system, a media content item to be processed; utilizing, by the computing system, the compressed CNN to apply a media processing technique to the media content item to produce information about the media content item; and determining, by the computing system, based on at least some of the information about the media content item, whether to transmit at least a portion of the media content item to one or more remote servers for additional media processing. 2. The computer-implemented method of claim 1, further comprising: transmitting at least the portion of the media content item to the one or more remote servers for the additional media processing; enabling one or more objects depicted in at least the portion of the media content item to be recognized based on the additional media processing; and receiving information associated with the one or more objects recognized based on the additional media processing. 3. The computer-implemented method of claim 1, wherein the compressed CNN is generated based on a compression process performed remotely from the computing system. 4. The computer-implemented method of claim 3, wherein the compression process is at least one of selected or configured based on one or more properties associated with the computing system. 5. The computer-implemented method of claim 3, wherein the compression process utilizes a matrix factorization method. 6. The computer-implemented method of claim 3, wherein the compression process utilizes a vector quantization method. 7. The computer-implemented method of claim 6, wherein the vector quantization method is associated with at least one of binarization, scalar quantization, product quantization, or residual quantization. 8. The computer-implemented method of claim 1, wherein the information about the media content item includes a score indicating a level of confidence associated with recognizing, by the media processing technique, one or more objects of interest depicted in the media content item. 9. The computer-implemented method of claim 8, wherein determining whether to transmit at least the portion of the media content item to the one or more remote servers for the additional media processing further comprises determining whether the score at least meets a specified confidence threshold. 10. The computer-implemented method of claim 1, wherein the information about the media content item is produced in real-time based on utilizing the compressed CNN to apply the media processing technique to the media content item. 11. A system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: receiving a compressed convolutional neural network (CNN); acquiring a media content item to be processed; utilizing the compressed CNN to apply a media processing technique to the media content item to produce information about the media content item; and determining, based on at least some of the information about the media content item, whether to transmit at least a portion of the media content item to one or more remote servers for additional media processing. 12. The system of claim 11, wherein the instructions cause the system to further perform: transmitting at least the portion of the media content item to the one or more remote servers for the additional media processing; enabling one or more objects depicted in at least the portion of the media content item to be recognized based on the additional media processing; and receiving information associated with the one or more objects recognized based on the additional media processing. 13. The system of claim 11, wherein the compressed CNN is generated based on a compression process performed remotely from the system. 14. The system of claim 13, wherein the compression process utilizes a matrix factorization method. 15. The system of claim 13, wherein the compression process utilizes a vector quantization method. 16. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising: receiving a compressed convolutional neural network (CNN); acquiring a media content item to be processed; utilizing the compressed CNN to apply a media processing technique to the media content item to produce information about the media content item; and determining, based on at least some of the information about the media content item, whether to transmit at least a portion of the media content item to one or more remote servers for additional media processing. 17. The non-transitory computer-readable storage medium of claim 16, wherein the instructions cause the computing system to further perform: transmitting at least the portion of the media content item to the one or more remote servers for the additional media processing; enabling one or more objects depicted in at least the portion of the media content item to be recognized based on the additional media processing; and receiving information associated with the one or more objects recognized based on the additional media processing. 18. The non-transitory computer-readable storage medium of claim 16, wherein the compressed CNN is generated based on a compression process performed remotely from the computing system. 19. The non-transitory computer-readable storage medium of claim 18, wherein the compression process utilizes a matrix factorization method. 20. The non-transitory computer-readable storage medium of claim 18, wherein the compression process utilizes a vector quantization method.