Efficient Model Evaluation with Bilinear Separation Model

In this paper, we investigate the issue of evaluating efficiently a large set of models on an input image in detection and classification tasks. We show that by formulating the visual task as a large matrix multiplication problem, something that is possible for a broad set of modern detectors and classifiers, we are able to dramatically reduce the rate of growth of computation as the number of models increases. The approach, based on a bilinear separation model, combines standard matrix factorization with a task dependent term which ensures that the resulting smaller size problem maintains performance on the original task. Experiments show that we are able to maintain, or even exceed, the level of performance compared to the default approach of using all the models directly, in both detection and classification tasks. This approach is complementary to other efforts in the literature on speeding up computation through GPU implementation, fast matrix operations, or quantization, in that any of these optimizations can be incorporated.