End-to-End Instance Segmentation with Recurrent Attention

By Mengye Ren, Richard S. Zemel. 2016

312 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

End-to-End Object Detection with Transformers (2020, neutral): “Closest to our approach are end-to-end set predictions for object detection [43] and instance segmentation [41,30,36,42].”
Object-Centric Learning with Slot Attention (2020, methodology): “[78] Mengye Ren and Richard S Zemel.”
Recurrent Neural Networks for Semantic Instance Segmentation (2017, methodology): “Similarly to other works [36, 34], we use the soft intersection over union loss (sIoU) as the cost function between the mask predicted by our network m1 and the ground truth mask m2:”
Pix2seq: A Language Modeling Framework for Object Detection (2021, background): “Our work is also related to recurrent models in object detection (Stewart et al., 2016; Park & Berg, 2015; Romera-Paredes & Torr, 2016; Salvador et al., 2017; Ren & Zemel, 2017), in which the system learns to predict one object at a time.”
Representation Learning by Learning to Count (2017, background): “Some recent works count common objects in the scene without relying on object localization [6, 37].”
Path Aggregation Network for Instance Segmentation (2018, methodology): “RNN was utilized in [53, 50] to propose one instance in each time step.”
Proposal-Free Network for Instance-Level Object Segmentation (2015, neutral): “[27] introduced an integrated recurrent network with an attention mechanism, which sequentially employs a box proposal network, a segmentation network and a scoring network to obtain instance-level predictions.”
On the Binding Problem in Artificial Neural Networks (2020, methodology): “, 2015), instance segmentation (Ren and Zemel, 2017), or (multi-)object tracking (Kosiorek et al.”
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features (2017, background): “For example, [63, 61] propose recurrent neural networks to sequentially segment an instance at a time.”
Learning to Count Objects in Natural Images for Visual Question Answering (2018, methodology): “Ren & Zemel (2017) circumvent the problem by limiting attention to only work within one bounding box at a time, remotely similar to our approach of using object proposal features.”
Recurrent Pixel Embedding for Instance Grouping (2017, background): “Alternately, recurrent models that sequentially produce a list of instances [76, 73] offer another approach to address variable sized output structures in a uniﬁed manner.”
Strip Pooling: Rethinking Spatial Pooling for Scene Parsing (2020, methodology): “One way to improve the capability of modeling the longrange dependencies in CNNs is to adopt self-attention or non-local modules [51, 23, 7, 45, 21, 53, 66, 62, 61, 28].”
SSAP: Single-Shot Instance Segmentation With Affinity Pyramid (2019, background): “Recurrent Neural Networks (RNNs) is adopted in several approaches [47, 45] to generate one instance mask at each time.”
DeeperLab: Single-Shot Image Parser (2019, methodology): “We note that Covering has been used in several instance segmentation works [92, 74, 76, 93, 78].”