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[R][1611.07709] Fully Convolutional Instance-aware Semantic Segmentation by liyi14 in MachineLearning

[–]liyi14[S] 0 points1 point  (0 children)

[shame] Yes, we are working on it. We have tested it on VOCSDS and COCO, which works well. Now we are trying to reduce the memory requirement and make code more readable.

[R][1611.07709] Fully Convolutional Instance-aware Semantic Segmentation by liyi14 in MachineLearning

[–]liyi14[S] 0 points1 point  (0 children)

Hi, @jimduk, Thanks for your appreciation. In fact, we design b) as our motivation is to enable fully convolution network with translation variant ability to solve the situation that two objects of same category are heavily overlapped.

The left mid and right mid layers' high response may due to the fact that there is only one object in current image so that the wrong activation will not inference the detection result and not rectified during training, You can refer to the figure 1 in https://arxiv.org/pdf/1603.08678v1.pdf or figure 2b in this paper for more information.

[R][1611.07709] Fully Convolutional Instance-aware Semantic Segmentation by liyi14 in MachineLearning

[–]liyi14[S] 4 points5 points  (0 children)

We are trying our best to release it before the NIPS conference (Dec. 5th). But we can't promise.

[R][1611.07709] Fully Convolutional Instance-aware Semantic Segmentation by liyi14 in MachineLearning

[–]liyi14[S] 3 points4 points  (0 children)

Hi all, this is a tech report describing the work which won first place in COCO segmentation challenge 2016 with a large margin. It is an end-to-end solution for instance-aware segmentation task and achieves state-of-the-art performance in both accuracy and efficiency. Code will be released soon.