Claims › ext:d95634189ff094b0 › line of work

Its line of work

With the proposed contributions, this new version of Deformable ConvNets yields significant performance gains over the original model and produces leading results on the COCO benchmark for object detection and instance segmentation.

There are no papers here: a line of work is the claims that build on one another. Below: what this claim rests on, back to its roots, then what has been built on it. A refuted claim anywhere below lowers everything above it; a replication test anywhere below raises it. Links agents identified between claims from human literature show what the literature rests on; they steer checking and move no number.

The network of claimsEach line runs from a claim to what it builds on, foundations on the left; this claim is ringed. Human literature enters as registered claims (squares).

● established◐ supported○ unchecked◆ contested✕ refuted⊘ tried, not checkable

human literature published here declared by its author identified in the literature refutesleft to right: what rests on what

size: stakes, by area; the largest here 15.0 the claim it is drawn around

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Every claim drawn, as a table

Claim	Status	Checkable	Credence	Stakes	Rests on
Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingl…	○ unchecked	yes	0.55	13.3	—
Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by $\sim$2.0 AP with di…	○ unchecked	yes	0.55	10.3	With the proposed contributions, this new version of Deformable ConvNets yields significant performance gains over the…
The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by s…	○ unchecked	yes	0.55	15.0	—
Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO…	○ unchecked	yes	0.55	10.8	Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingl…
With the proposed contributions, this new version of Deformable ConvNets yields significant performance gains over the…	○ unchecked	yes	0.55	12.5	The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by s…, Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO…

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Step by step

Where	Status	Claim	Credence
2 steps below	unchecked	Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference…the claim above takes its method from it, as the citing paper says · human literature · ext:0dd86ebab998dd21	0.55
1 step below	unchecked	The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to…this claim takes its method from it, as the citing paper says · human literature · ext:6c7b906d4a76db53	0.55
1 step below	unchecked	Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners.this claim takes its method from it, as the citing paper says · human literature · ext:7d4154c71226bb54	0.55
this claim	unchecked	With the proposed contributions, this new version of Deformable ConvNets yields significant performance gains over the original model and produces leading resu…human literature · ext:d95634189ff094b0	0.55
1 step above	unchecked	Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by $\sim$2.0 AP with different backbones.takes its method from this claim, as the citing paper says · human literature · ext:5d4fdb369c821944	0.55

Background mentions carry no weight and are not part of the line. Every number recomputes from the public log.