Paper Citation Record · LEDGER

Gradient Weighted Superpixels for Interpretability in CNNs

As of 16 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:1908.08997.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.08997 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:03:13.666763Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact0
verified fuzzy10
unresolved10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ea7b76eb-d5a8-4ee7-a09e-1ca772546c19 · outbound

This paper cites Slic superpixels compared to state-of-the-art superpixel methods.

Gradient Weighted Superpixels for Interpretability in CNNs Slic superpixels compared to state-of-the-art superpixel methods

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccea477b-5ae5-4d56-8cdb-760e4163c164 · outbound

This paper cites On pixel-wise explanations for non-linear clas- siﬁer decisions by layer-wise relevance propagation.

Gradient Weighted Superpixels for Interpretability in CNNs On pixel-wise explanations for non-linear clas- siﬁer decisions by layer-wise relevance propagation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T13:03:13.563673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 772a8511-90cb-4813-bfe4-d18746e31c7e · outbound

This paper cites Quo vadis, action recognition? A new model and the kinetics dataset.

Gradient Weighted Superpixels for Interpretability in CNNs Quo vadis, action recognition? A new model and the kinetics dataset

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:03:14.132512Z

Source-reported events for the cited work

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Observation 69548d65-6a80-49cc-a3eb-dff5a2dbe844 · outbound

This paper cites Balasubra- manian.

Gradient Weighted Superpixels for Interpretability in CNNs Balasubra- manian

Reference 4

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Source-reported events for the cited work

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Observation 039236f1-8d32-44c7-8b84-f8f70b9c91a5 · outbound

This paper cites Interpretable explanations of black boxes by mean- ingful perturbation.

Gradient Weighted Superpixels for Interpretability in CNNs Interpretable explanations of black boxes by mean- ingful perturbation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:03:14.115787Z

Source-reported events for the cited work

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Observation cdc2a1df-5bd6-4240-8f81-a553d6d219d3 · outbound

This paper cites Deep residual learning for image recognition.

Gradient Weighted Superpixels for Interpretability in CNNs Deep residual learning for image recognition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:03:14.099061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 052ec7aa-a4e1-4688-a7a0-2cb282da9865 · outbound

This paper cites The Kinetics Human Action Video Dataset.

Gradient Weighted Superpixels for Interpretability in CNNs The Kinetics Human Action Video Dataset

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T13:03:13.589772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86f43ab4-221b-4221-ae84-66a13cc7c0a6 · outbound

This paper cites Explaining nonlinear classiﬁcation decisions with deep taylor decomposition.

Gradient Weighted Superpixels for Interpretability in CNNs Explaining nonlinear classiﬁcation decisions with deep taylor decomposition

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:03:14.082163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4c38969f-1b27-4aa9-8ff2-8b4304eb9071 · outbound

This paper cites Automatic differentiation in pytorch.

Gradient Weighted Superpixels for Interpretability in CNNs Automatic differentiation in pytorch

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T13:03:13.600376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bff0c4dd-afad-4eb4-b166-c1eca903a031 · outbound

This paper cites Why should I trust you?.

Gradient Weighted Superpixels for Interpretability in CNNs Why should I trust you?

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:03:14.055385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5970f221-22d9-4c34-b7d8-36def323667d · outbound

This paper cites Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra.

Gradient Weighted Superpixels for Interpretability in CNNs Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:03:14.038018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 053ef7f9-ef48-4707-afd9-d37bbc80379c · outbound

This paper cites Learning important fea- tures through propagating activation differences.

Gradient Weighted Superpixels for Interpretability in CNNs Learning important fea- tures through propagating activation differences

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:03:14.019964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9c4c4b64-f848-4483-a9fb-1c873b3127f6 · outbound

This paper cites Simonyan and A.

Gradient Weighted Superpixels for Interpretability in CNNs Simonyan and A

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T13:03:13.620350Z

Source-reported events for the cited work

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Observation a3e1c4b3-bfd8-4b4b-8be3-7041688ba5c1 · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Gradient Weighted Superpixels for Interpretability in CNNs Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T13:03:13.626043Z

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Observation 38a2b1c1-b41e-4776-94f6-3aefebb74178 · outbound

This paper cites Striving for Simplicity: The All Convolutional Net.

Gradient Weighted Superpixels for Interpretability in CNNs Striving for Simplicity: The All Convolutional Net

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T13:03:13.631193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 230b6a77-684d-4a1b-bd74-0f43263caa42 · outbound

This paper cites Axiomatic attribution for deep net- works.

Gradient Weighted Superpixels for Interpretability in CNNs Axiomatic attribution for deep net- works

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T13:03:13.636578Z

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Unavailable: canonical work link unavailable.

Observation 48ecd5d4-00ea-4e51-b4fd-ee60e2347eda · outbound

This paper cites Learning spatiotemporal features with 3d convolutional networks.

Gradient Weighted Superpixels for Interpretability in CNNs Learning spatiotemporal features with 3d convolutional networks

Reference 17

Resolution: malformed identifier; no resolver link, observed 2026-08-14T13:03:13.641884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a844010-e6b1-4a51-b5e0-c05a8b36b4d1 · outbound

This paper cites A closer look at spatiotemporal convolutions for action recognition.

Gradient Weighted Superpixels for Interpretability in CNNs A closer look at spatiotemporal convolutions for action recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:03:13.990911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2147c9a5-7c4a-49fe-9a53-c455c0b903b1 · outbound

This paper cites Quick shift and kernel methods for mode seeking.

Gradient Weighted Superpixels for Interpretability in CNNs Quick shift and kernel methods for mode seeking

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:03:13.973621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c831f3b0-fe05-45eb-b196-9120d4bff2b1 · outbound

This paper cites Visualizing and understanding convolutional net- works.

Gradient Weighted Superpixels for Interpretability in CNNs Visualizing and understanding convolutional net- works

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:03:13.956874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8ff79c16-59eb-4671-8fe4-368d421e88e8 · outbound

This paper cites Top-down neural attention by excitation backprop.

Gradient Weighted Superpixels for Interpretability in CNNs Top-down neural attention by excitation backprop

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T13:03:13.661954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 371115d3-858f-402a-9f29-adca16e4e01b · outbound

This paper cites an unresolved cited work.

Gradient Weighted Superpixels for Interpretability in CNNs Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:03:13.927646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.