Learning to recognize occluded and small objects with partial inputs
Hasib Zunair A. Ben Hamza
Recognizing multiple objects in an image is challenging due to occlusions,
and becomes even more so when the objects are small. While promising, existing
multi-label image recognition models do not explicitly learn context-based
representations, and hence struggle to correctly recognize small and occluded
objects. Intuitively, recognizing occluded objects requires knowledge of
partial input, and hence context. Motivated by this intuition, we propose
Masked Supervised Learning (MSL), a single-stage, model-agnostic learning
paradigm for multi-label image recognition. The key idea is to learn
context-based representations using a masked branch and to model label
co-occurrence using label consistency. Experimental results demonstrate the
simplicity, applicability and more importantly the competitive performance of
MSL against previous state-of-the-art methods on standard multi-label image
recognition benchmarks. In addition, we show that MSL is robust to random
masking and demonstrate its effectiveness in recognizing non-masked objects.
Code and pretrained models are available on GitHub.