  Although traditionally binary visual representations are mainly designed to
reduce computational and storage costs in the image retrieval research, this
paper argues that binary visual representations can be applied to large scale
recognition and detection problems in addition to hashing in retrieval.
Furthermore, the binary nature may make it generalize better than its
real-valued counterparts. Existing binary hashing methods are either two-stage
or hinging on loss term regularization or saturated functions, hence converge
slowly and only emit soft binary values. This paper proposes Approximately
Binary Clamping (ABC), which is non-saturating, end-to-end trainable, with fast
convergence and can output true binary visual representations. ABC achieves
comparable accuracy in ImageNet classification as its real-valued counterpart,
and even generalizes better in object detection. On benchmark image retrieval
datasets, ABC also outperforms existing hashing methods.
