Paper Citation Record · LEDGER

Performance Analysis of Traditional VQA Models Under Limited Computational Resources

As of 10 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2502.05738.

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

pith.paper-citation-record.v1
2502.05738 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:10:38.360640Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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 exact6
verified fuzzy1
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 93978d07-bc26-455b-8dd6-472c274992bb · outbound

This paper cites Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-08T18:10:38.619877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 54be280f-3706-4b34-9961-c3a2669e43b8 · outbound

This paper cites Learning Convolutional Text Representations for Visual Question Answering.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources Learning Convolutional Text Representations for Visual Question Answering

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-08T18:10:38.594649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4ef1d1db-b1c5-4d74-9e93-0b166b93b7e1 · outbound

This paper cites Structured Attentions for Visual Question Answering.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources Structured Attentions for Visual Question Answering

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-08T18:10:38.579059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 36ec7fbb-05a3-481e-8938-e2da7d1e3f1c · outbound

This paper cites iVQA: Inverse Visual Question Answering.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources iVQA: Inverse Visual Question Answering

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-08T18:10:38.563465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0cbe02dc-f50d-47f4-b404-8ea3b1e126d1 · outbound

This paper cites Self-critical Sequence Training for Image Captioning.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources Self-critical Sequence Training for Image Captioning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.281868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9b0ba65-3614-475f-a3e6-74181f19a943 · outbound

This paper cites X-Linear Attention Networks for Image Captioning.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources X-Linear Attention Networks for Image Captioning

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-08T18:10:38.520079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dee30146-85d3-47fd-849a-dbc2a2bdfa95 · outbound

This paper cites LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.299321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99ef28dd-9510-4c9b-9edb-cf56bd770c80 · outbound

This paper cites Beyond a pre-trained object detector: Cross-modal textual and visual context for image captioning,.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources Beyond a pre-trained object detector: Cross-modal textual and visual context for image captioning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:10:38.632483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 11fe739e-246c-4a01-afcd-f6a528289fe1 · outbound

This paper cites Transformer-Based Multi-modal Proposal and Re-Rank for Wikipedia Image-Caption Matching.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources Transformer-Based Multi-modal Proposal and Re-Rank for Wikipedia Image-Caption Matching

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-08T18:10:38.494657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f6c2cb35-f96e-4aea-9037-75b6b9e78df8 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.311056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f24e703-7482-4897-8b24-d8eca8e55813 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.315617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed3f86ac-51f9-49e3-a784-ef6b44ceaaf6 · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources Training data-efficient image transformers & distillation through attention

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.319474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b875199-5513-4653-99ad-c25625dfa6a5 · outbound

This paper cites ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.323443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e23c349d-d4e3-4c1b-b770-a7998624d357 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources Distilling the Knowledge in a Neural Network

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.327827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5d66f28-4c3d-4039-9f6d-cc525750cd45 · outbound

This paper cites TinyBERT: Distilling BERT for Natural Language Understanding.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources TinyBERT: Distilling BERT for Natural Language Understanding

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.356476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3851e900-dd65-4cdd-8426-fa4fe8fdbd91 · outbound

This paper cites Learning both Weights and Connections for Efficient Neural Networks.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources Learning both Weights and Connections for Efficient Neural Networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.335940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50dcd689-b2e8-488a-af13-aaa88a2cbecd · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.353136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d01917d4-6eed-4a4c-b8c1-de86dd67f13e · outbound

This paper cites MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.360640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4d9f3a9-d543-4878-8903-1c708158c3f5 · outbound

This paper cites Show, Attend and Tell: Neural Image Caption Generation with Visual Attention.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.278124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d0941e8-7218-4156-b52d-027e66c44e7a · outbound

This paper cites Bidirectional Attention Flow for Machine Comprehension.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources Bidirectional Attention Flow for Machine Comprehension

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.256348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa2525b9-c4fe-4f2a-90b8-d18bc7ab16ff · outbound

This paper cites Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.344144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2385e11e-2888-4327-9185-b82d5e498178 · outbound

This paper cites Meshed-Memory Transformer for Image Captioning.

Performance Analysis of Traditional VQA Models Under Limited Computational Resources Meshed-Memory Transformer for Image Captioning

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-08T18:10:38.290999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.