Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance).
( Image credit: Detectron2 )
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We posit that the effectiveness of recurrent vision models is bottlenecked by the widespread algorithm used for training them, "back-propagation through time" (BPTT), which has O(N) memory-complexity for training an N step model.
We view this work as a notable step towards building a simple procedure to harness unlabeled video sequences to surpass state-of-the-art performance on core computer vision tasks.
In this paper, we introduce a novel perception task denoted as multi-object panoptic tracking (MOPT), which unifies the conventionally disjoint tasks of semantic segmentation, instance segmentation, and multi-object tracking.
We introduce a Bidirectional Graph Reasoning Network (BGRNet), which incorporates graph structure into the conventional panoptic segmentation network to mine the intra-modular and intermodular relations within and between foreground things and background stuff classes.
Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously.
In this work, we propose an Efficient Panoptic Segmentation Network (EPSNet) to tackle the panoptic segmentation tasks with fast inference speed.
#8 best model for Panoptic Segmentation on COCO test-dev
In this paper, we attempt to remove this constraint by factorizing 2D self-attention into two 1D self-attentions.
Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly.