Multi-scale recognition with DAG-CNNs
icon We explore multi-scale convolutional neural nets (CNNs) for image classification. Contemporary approaches extract features from a single output layer. By extracting features from multiple layers, one can simultaneously reason about high, mid, and low-level features during classification. The resulting multi-scale architecture can itself be seen as a feed-forward model that is structured as a directed acyclic graph (DAG-CNNs). We use DAG-CNNs to learn a set of multiscale features that can be effectively shared between coarse and fine-grained classification tasks. While finetuning such models helps performance, we show that even “off-the-self” multiscale features perform quite well. We present extensive analysis and demonstrate state-of-the-art classification performance on three standard scene benchmarks (SUN397, MIT67, and Scene15). In terms of the heavily benchmarked MIT67 and Scene15 datasets, our results reduce the lowest previously-reported error by 23.9% and 9.5%, respectively.

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Text Reference

Songfan Yang and Deva Ramanan. Multi-scale recognition with dag-cnns. In IEEE International Conference on Computer Vision. 2015.

BibTeX Reference

    author = "Yang, Songfan and Ramanan, Deva",
    booktitle = "IEEE International Conference on Computer Vision",
    title = "Multi-scale recognition with DAG-CNNs",
    year = "2015",
    tag = "object_recognition"