Sparselet Models for Efficient Multiclass Object Detection Present by Guilin Liu masc.cs.gmu.edu

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 masc.cs.gmu.edu

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 masc.cs.gmu.edu

Overview System pipeline masc.cs.gmu.edu

Overview masc.cs.gmu.edu

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

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

2. Precomputation & efficient reconstruction masc.cs.gmu.edu

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

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 masc.cs.gmu.edu

4. Experiments Reconstruction error masc.cs.gmu.edu

4. Experiments 2. held-out evaluation masc.cs.gmu.edu

4. Experiments 3. Average precision masc.cs.gmu.edu

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