1. Sparselet Models for Efficient Multiclass Object Detection
Present by Guilin Liu
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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
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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
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4. Overview
System pipeline
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5. Overview
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6. 1. Sparse reconstruction
Find a generic dictionary approximate the part filters pooled from a set of training models, subject to a sparsity constraint
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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
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8. 2. Precomputation & efficient reconstruction
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9. 2. Precomputation & efficient reconstruction
Precompute convolutions for all sparselets
Approximate t convolution response by linear combination of the activation vectors from step 1.
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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
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11. 4. Experiments
Reconstruction error
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12. 4. Experiments
2. held-out evaluation
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13. 4. Experiments
3. Average precision
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