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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.

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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