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Investigate some of Panoptic Segmentation techniques

Guideline: Investigate some of Panoptic Segmentation techniques and explore advantages and disadvantages between different techniques by testing the model on the COCO dataset.

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Investigate some of Panoptic Segmentation techniques

Investigate some of Panoptic Segmentation techniques and explore advantages and disadvantages between different techniques by testing the model on the COCO dataset.

Please include Abstract, Keywords, Introduction, Main
Content, Conclusion, Acknowledgement and Reference
The similarity with other sources should less than 20%.

More Details:

Understanding the scene in which an autonomous robot operates is critical for its competent functioning.

Such scene comprehension necessitates recognizing instances of traffic participants along with general scene semantics which can be effectively addressed by the panoptic segmentation task.

In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which efficiently encodes and fuses semantically rich multi-scale features.

We incorporate a new semantic head that aggregates fine and contextual features coherently and a new variant of Mask R-CNN as the instance head.

Also, we also propose a novel panoptic fusion module that congruously integrates the output

Likewise, logits from both the heads of our EfficientPS architecture to yield the final panoptic segmentation output.

Additionally, we introduce the KITTI panoptic segmentation dataset that contains panoptic annotations for the popularly challenging KITTI benchmark.

Also, extensive evaluations on Cityscapes, KITTI, Mapillary Vistas and Indian Driving Dataset

demonstrate that our proposed architecture consistently sets the new state-of-the-art on

all these four benchmarks while being the most efficient and fast panoptic segmentation architecture to date.

More so, holistic scene understanding plays a pivotal role in enabling intelligent behavior.

Humans from an early age are able to effortlessly comprehend complex visual scenes

which forms the bases for learning more advanced capabilities (Bremner and Slater 2008).

Lastly, we propose and study a novel ‘Panoptic Segmentation’ (PS) task. Panoptic

segmentation unifies the traditionally distinct tasks of instance segmentation

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