85 research outputs found
SeGAN: Segmenting and Generating the Invisible
Objects often occlude each other in scenes; Inferring their appearance beyond
their visible parts plays an important role in scene understanding, depth
estimation, object interaction and manipulation. In this paper, we study the
challenging problem of completing the appearance of occluded objects. Doing so
requires knowing which pixels to paint (segmenting the invisible parts of
objects) and what color to paint them (generating the invisible parts). Our
proposed novel solution, SeGAN, jointly optimizes for both segmentation and
generation of the invisible parts of objects. Our experimental results show
that: (a) SeGAN can learn to generate the appearance of the occluded parts of
objects; (b) SeGAN outperforms state-of-the-art segmentation baselines for the
invisible parts of objects; (c) trained on synthetic photo realistic images,
SeGAN can reliably segment natural images; (d) by reasoning about occluder
occludee relations, our method can infer depth layering.Comment: Accepted to CVPR18 as spotligh
Detecting Semantic Parts on Partially Occluded Objects
In this paper, we address the task of detecting semantic parts on partially
occluded objects. We consider a scenario where the model is trained using
non-occluded images but tested on occluded images. The motivation is that there
are infinite number of occlusion patterns in real world, which cannot be fully
covered in the training data. So the models should be inherently robust and
adaptive to occlusions instead of fitting / learning the occlusion patterns in
the training data. Our approach detects semantic parts by accumulating the
confidence of local visual cues. Specifically, the method uses a simple voting
method, based on log-likelihood ratio tests and spatial constraints, to combine
the evidence of local cues. These cues are called visual concepts, which are
derived by clustering the internal states of deep networks. We evaluate our
voting scheme on the VehicleSemanticPart dataset with dense part annotations.
We randomly place two, three or four irrelevant objects onto the target object
to generate testing images with various occlusions. Experiments show that our
algorithm outperforms several competitors in semantic part detection when
occlusions are present.Comment: Accepted to BMVC 2017 (13 pages, 3 figures
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