Sparselet Models for Efficient Multiclass Object Detection

1. Sparselet Models for Efficient Multiclass Object Detection Present by Guilin Liu

2. Key Idea Use sparse coding of part filters to represent each filter as a sparse linear combination of shared dictionary elements. Reconstruction of original part filter responses via sparse matrix-vector product GPU implementation

3. Problem/motivation Individual model become redundant as the number of categories grow------Sparse Coding Learn basis parts so reconstructing the response of a target model is efficient

4. Overview System pipeline

5. Overview

6. 1. Sparse reconstruction Find a generic dictionary approximate the part filters pooled from a set of training models, subject to a sparsity constraint

7. 1. Sparse reconstruction Solve the optimization problem busing the Orthogonal Matching Pursuit algorithm(OMP) Two steps: Fixed D, optimize α Fixex α, optimize D

8. 2. Precomputation & efficient reconstruction

9. 2. Precomputation & efficient reconstruction Precompute convolutions for all sparselets Approximate t convolution response by linear combination of the activation vectors from step 1.

10. 3. Implementation(CPU, GPU) • The independence and parallelizablity of: • Convolution, HOG computation and distance transforms • CPU implementation: CPU cach miss limited the overall speedup • GPU implementation: • Compute image pyramids and HOG features • Compute filter responses to root, part or part basis filter

11. 4. Experiments Reconstruction error

12. 4. Experiments 2. held-out evaluation

13. 4. Experiments 3. Average precision