| <Poster Width="1734" Height="1041"> |
| <Panel left="20" right="144" width="441" height="550"> |
| <Text>Introduction</Text> |
| <Text>IFine-grained Recognition</Text> |
| <Text>anna’s hummingbird</Text> |
| <Figure left="38" right="215" width="188" height="91" no="1" OriWidth="0" OriHeight="0 |
| " /> |
| <Text>ruby-throated hummingbird</Text> |
| <Figure left="258" right="215" width="187" height="94" no="2" OriWidth="0" OriHeight="0 |
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| <Text>Human Attribute PredictionI</Text> |
| <Figure left="30" right="332" width="427" height="141" no="3" OriWidth="0" OriHeight="0 |
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| <Text>Pose-normalized representations [1]I</Text> |
| <Figure left="49" right="498" width="384" height="183" no="4" OriWidth="0" OriHeight="0 |
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| <Panel left="19" right="699" width="442" height="298"> |
| <Text>Deformable Part Model (DPM)</Text> |
| <Text>Weakly supervised DPMI</Text> |
| <Text>I Fix-sized part filters initialized by</Text> |
| <Text>heuristics.</Text> |
| <Text>I Components initialized by clustering</Text> |
| <Text>aspect ratio.</Text> |
| <Text>I Strongly supervised DPM [2]</Text> |
| <Text>I Semantic part filters initialized by</Text> |
| <Text>part annotations.</Text> |
| <Text>I Clusters pose information to initialize</Text> |
| <Text>the components.</Text> |
| <Text>Computational efficient DPM detections [3].I</Text> |
| <Text>I Strong DPM provides semantic part localizations for</Text> |
| <Text>pose-normalized representations.</Text> |
| <Text>I What about simpler weak DPM without pose annotations?</Text> |
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| <Panel left="487" right="144" width="750" height="467"> |
| <Text>Method</Text> |
| <Text>Deformable part descriptors (DPD)</Text> |
| <Text>Test Image</Text> |
| <Text>Part Localization</Text> |
| <Text>Pose-normalization</Text> |
| <Text>Classification</Text> |
| <Figure left="492" right="250" width="743" height="271" no="6" OriWidth="0.802768" OriHeight="0.256239 |
| " /> |
| <Text>The first descriptor (top row) applies a strong DPM for part localization then pool features from these inherently</Text> |
| <Text>semantic parts.I</Text> |
| <Text>I The second descriptor employs a weakly supervised DPM for part localization and then used a learned semantic</Text> |
| <Text>correspondence weights to pool features from the latent parts into semantic regions.</Text> |
| </Panel> |
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| <Panel left="483" right="618" width="510" height="205"> |
| <Text>How Weights Get Computed</Text> |
| <Text>(j)</Text> |
| <Text>Iw</Text> |
| <Text>il∈ W of size |P| × |R| × |C|.</Text> |
| <Text>part of component c (j). rl : semantic region.</Text> |
| <Text>keypoints or other semantic labels.</Text> |
| <Text>I ρkl ∈ [0, 1]: relevance of ak to region rl .</Text> |
| <Text>I Ijk : training images with ak and component c (j).(j)</Text> |
| <Text>I p : i-th</Text> |
| <Text>i</Text> |
| <Text>I ak ∈ A:</Text> |
| <Figure left="762" right="663" width="231" height="142" no="7" OriWidth="0.532872" OriHeight="0.24153267 |
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| </Panel> |
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| <Panel left="1003" right="619" width="234" height="207"> |
| <Text>Pooling/Classification</Text> |
| <Text>1Pose-normalized representationI</Text> |
| <Text>36Pooled image feature for</Text> |
| <Text>semantic region Ψ(l, rl ).8</Text> |
| <Text>1 vs all linear SVM using Ψpn for</Text> |
| <Text>final classification.</Text> |
| </Panel> |
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| <Panel left="487" right="831" width="749" height="166"> |
| <Text>Example Results and Failure</Text> |
| <Text>TORSOCases</Text> |
| <Text>Top scored people with long hair.</Text> |
| <Figure left="505" right="887" width="224" height="92" no="8" OriWidth="0" OriHeight="0 |
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| <Text>LEGS0.0%0.2%32.1%0.2%10.6%18.7%8.2%29.9%Top scored people wearing long sleeves.</Text> |
| <Figure left="763" right="886" width="222" height="92" no="9" OriWidth="0" OriHeight="0 |
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| <Text>Most confused failure case of males.</Text> |
| <Figure left="1015" right="887" width="202" height="85" no="10" OriWidth="0" OriHeight="0 |
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| </Panel> |
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| <Panel left="1262" right="143" width="442" height="404"> |
| <Text>Experimental Results</Text> |
| <Text>Fine-grained Recognition</Text> |
| <Figure left="1286" right="211" width="177" height="116" no="11" OriWidth="0.317762" OriHeight="0.137255 |
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| <Text> Results on CUB200-2010 dataset .</Text> |
| <Figure left="1493" right="210" width="200" height="104" no="12" OriWidth="0.310265" OriHeight="0.118984 |
| " /> |
| <Text> Results on CUB200-2011 dataset.</Text> |
| <Text>Human Attribute Prediction</Text> |
| <Figure left="1296" right="372" width="377" height="143" no="13" OriWidth="0.687428" OriHeight="0.162656 |
| " /> |
| <Text> Results on the Human Attributes dataset.</Text> |
| </Panel> |
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| <Panel left="1262" right="551" width="442" height="368"> |
| <Text>Localization Results of strong DPM</Text> |
| <Text> Samples of correct part localizations.</Text> |
| <Figure left="1269" right="593" width="428" height="154" no="14" OriWidth="0" OriHeight="0 |
| " /> |
| <Text> Failure cases of part localizations.</Text> |
| <Figure left="1269" right="762" width="431" height="145" no="15" OriWidth="0" OriHeight="0 |
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| </Panel> |
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| <Panel left="1262" right="924" width="440" height="78"> |
| <Text>References</Text> |
| <Text>[1] Ning Zhang, Ryan Farrell and Trevor Darrell. Pose Pooling Kernels for Sub-Category Recognition. In CVPR 2012.</Text> |
| <Text>[2] Hossein Azizpour and Ivan Laptev. Object Detection Using Strongly-Supervised Deformable Part Models. In ECCV 2012.</Text> |
| <Text>[3] Charles Dubout and Franc¸ois Fleuret. Exact Acceleration of Linear Object Detectors. In ECCV 2012.</Text> |
| <Text>[4] Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng and Trevor Darrell. DeCAF: A Deep Convolutional</Text> |
| <Text>Activation Feature for Generic Visual Recognition. On Arxiv.</Text> |
| </Panel> |
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| </Poster> |
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