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