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Sparselet Models for Efficient Multiclass Object Detection. Present by Guilin Liu. Key Idea. Use sparse coding of part filters to represent each filter as a sparse linear combination of shared dictionary elements. - PowerPoint PPT Presentation
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masc.cs.gmu.edu
Sparselet Models for Efficient Multiclass Object Detection
Present by Guilin Liu
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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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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Overview
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System pipeline
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Overview
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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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1. Sparse reconstruction
Solve the optimization problem busing the Orthogonal Matching Pursuit algorithm(OMP)Two steps:a.Fixed D, optimize αb.Fixex α, optimize D
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2. Precomputation & efficient reconstruction
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2. Precomputation & efficient reconstruction
1. Precompute convolutions for all sparselets2. Approximate t convolution response by linear
combination of the activation vectors from step 1.
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3. Implementation(CPU, GPU)
The independence and parallelizablity of:Convolution, HOG computation and distance transforms
1. CPU implementation: CPU cach miss limited the overall speedup
2. GPU implementation: a. Compute image pyramids and HOG featuresb. Compute filter responses to root, part or part basis
filter
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4. Experiments
1. Reconstruction error
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4. Experiments
2. held-out evaluation
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4. Experiments
3. Average precision
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