A data driven method for feature transformation

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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation (2013, background): “This hypothesis is grounded, for example, in the wide range of papers that attempt to boost detection accuracy with work along four axes: (1) rich structured models [20, 42]; (2) multiple feature learning [38, 41]; (3) learned histogram-based features [11, 29, 32]; or (4) unsupervised feature learni”
Joint Deep Learning for Pedestrian Detection (2013, background): “The approach in [7] learns features and a part-based model sequentially but not jointly.”
Histograms of Sparse Codes for Object Detection (2013, methodology): “Just as in the use of contrast-sensitive gradients in HOG, and the cosine and absolute cosine metrics in [9], we need signed values of x i to differentiate white-on-black and black-on-white patterns.”
Modeling Image Patches with a Generic Dictionary of Mini-epitomes (2014, unknown): “The power of learned patch-level features has also been demonstrated recently in [5, 9, 24].”