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Paper Citation Record · LEDGER

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models

As of 11 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2501.07396.

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

pith.paper-citation-record.v1
2501.07396 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

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measured 55 of 55 standing notices

One-hop event checks from named stored sources.

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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

55 of 55 outbound references displayed

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External citation measurements

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Outbound references

Observation dc4496b5-fd0c-46ab-91f9-26c200c68b4c · outbound

This paper cites Automatic target recognition: State of the art survey,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Automatic target recognition: State of the art survey,

Reference 1

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Observation deb2b37f-8c78-4fad-99bb-31a88772cdd6 · outbound

This paper cites The automatic target-recognition system in saip,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models The automatic target-recognition system in saip,

Reference 2

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Observation 628ebbc3-5f95-40f0-9ee2-26aa53bcd266 · outbound

This paper cites Automatic target recognition based on simultaneous sparse representation,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Automatic target recognition based on simultaneous sparse representation,

Reference 3

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Observation 4e6c73a9-c868-4b61-b8ca-88bb210ff7cb · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Distilling the Knowledge in a Neural Network

Reference 4

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Observation 46d17d76-732e-4fda-8706-f359b60fa1d1 · outbound

This paper cites Accelerating very deep convo- lutional networks for classification and detection,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Accelerating very deep convo- lutional networks for classification and detection,

Reference 5

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Observation 2ac91e57-5e08-4285-abd8-0f06487b36e1 · outbound

This paper cites Object recognition and detection with deep learning for autonomous driving applications,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Object recognition and detection with deep learning for autonomous driving applications,

Reference 6

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Observation 2ee9df0e-93e5-4706-a3d3-096251c60349 · outbound

This paper cites Review of current aided/automatic target acquisition technology for military target acquisition tasks,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Review of current aided/automatic target acquisition technology for military target acquisition tasks,

Reference 7

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Observation d55f1fb0-da79-459d-b3b2-b17762f17f2c · outbound

This paper cites Ar- tificial intelligence for national security: the predictability problem,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Ar- tificial intelligence for national security: the predictability problem,

Reference 8

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Observation fa2075fe-048a-4597-be49-ce93699e3c89 · outbound

This paper cites Autonomous vehicles and intelligent automation: Applications, challenges, and opportuni- ties,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Autonomous vehicles and intelligent automation: Applications, challenges, and opportuni- ties,

Reference 9

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Observation 3b11ccae-5cf1-469f-a63c-e7a9d281d582 · outbound

This paper cites Concrete Problems in AI Safety.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Concrete Problems in AI Safety

Reference 10

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Observation b605cf6d-5f96-4a3f-887d-4278d402d013 · outbound

This paper cites Unsolved Problems in ML Safety.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Unsolved Problems in ML Safety

Reference 11

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Observation 7e493f4a-57f0-4958-b834-c7f3cf6430cd · outbound

This paper cites Generalized out-of-distribution detection: A survey,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Generalized out-of-distribution detection: A survey,

Reference 12

Resolution
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Observation c93f806d-e52f-4c3d-9c85-3652a3a3388a · outbound

This paper cites Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey

Reference 13

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Observation 61e44d9c-526c-44a1-980c-317f19e760b2 · outbound

This paper cites Meta-uda: Unsupervised domain adaptive thermal object detection using meta- learning,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Meta-uda: Unsupervised domain adaptive thermal object detection using meta- learning,

Reference 14

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Observation 887be8d0-66c2-46fd-b644-e42173d6e812 · outbound

This paper cites On the Validity of Bayesian Neural Networks for Uncertainty Estimation.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models On the Validity of Bayesian Neural Networks for Uncertainty Estimation

Reference 15

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Observation 956611e8-b9d7-424e-b118-db9623b8799b · outbound

This paper cites Knowing the unknown: Open-world recognition for biodiversity datasets,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Knowing the unknown: Open-world recognition for biodiversity datasets,

Reference 16

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Observation afb4a997-c6af-4c7b-ab59-a2d84a370710 · outbound

This paper cites The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

Reference 17

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Observation 9f8c79a3-6e31-425a-a1cf-bcf827769365 · outbound

This paper cites The impact of cooperative perception on decision making and planning of autonomous vehicles,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models The impact of cooperative perception on decision making and planning of autonomous vehicles,

Reference 18

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Observation 18650a64-b00d-4e38-9f95-5c14fa8f8f0d · outbound

This paper cites Towards open world object detection,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Towards open world object detection,

Reference 19

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Observation 3557120f-3e8a-499c-9c20-9c72cd041094 · outbound

This paper cites Unidentified video objects: A benchmark for dense, open-world segmentation,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Unidentified video objects: A benchmark for dense, open-world segmentation,

Reference 20

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Observation ce836303-24dc-43b6-a461-a341b50e7740 · outbound

This paper cites Breaking the closed world assumption in text classification,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Breaking the closed world assumption in text classification,

Reference 21

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Observation ea92b599-17d7-46a9-a322-c5ba712bc5dc · outbound

This paper cites Dynamic few-shot visual learning with- out forgetting,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Dynamic few-shot visual learning with- out forgetting,

Reference 22

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Observation 8bcc5b1c-f476-4ad6-b3b0-f362cd51556f · outbound

This paper cites Online incremental learning algorithm for anomaly detection and prediction in health care,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Online incremental learning algorithm for anomaly detection and prediction in health care,

Reference 23

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Observation 9cc440da-812c-4e8e-81a6-f3494dcd66f7 · outbound

This paper cites Detecting everything in the open world: Towards universal object detection,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Detecting everything in the open world: Towards universal object detection,

Reference 24

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Observation 395b5ec9-e5df-4c32-8794-52872d63dba4 · outbound

This paper cites Lifelong machine learning: a paradigm for continuous learn- ing,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Lifelong machine learning: a paradigm for continuous learn- ing,

Reference 25

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Observation d5228723-27a2-4274-85bc-936f8796d8c8 · outbound

This paper cites Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems,

Reference 26

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Observation 6e3661ee-815a-40ac-8a20-e8605608929b · outbound

This paper cites Vision-language models for vision tasks: A survey,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Vision-language models for vision tasks: A survey,

Reference 27

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Observation 3028b6c9-a367-4e3b-8cfd-16f1f431f59b · outbound

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Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models A Survey of Vision-Language Pre-Trained Models

Reference 28

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Observation b8fdba8b-e753-4669-865b-9acaaae85c12 · outbound

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Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Learning transferable visual models from natural language supervision,

Reference 29

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Observation 7288af76-c6fe-4578-8b7d-3f916fb0e767 · outbound

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Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Clip and complementary meth- ods,

Reference 30

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Observation e7909ff5-7b6c-4221-ab62-1b96b31c740a · outbound

This paper cites On the Vulnerability of LLM/VLM-Controlled Robotics.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models On the Vulnerability of LLM/VLM-Controlled Robotics

Reference 31

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Observation 453d7c37-b7d8-4046-85dd-a25282ffcad3 · outbound

This paper cites Applications of large language models for robot navigation and scene understanding,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Applications of large language models for robot navigation and scene understanding,

Reference 32

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Observation a2476a74-ef01-4a2f-9994-a2999e1149e1 · outbound

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Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs

Reference 33

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Observation d96ccc21-8e9b-461b-94f0-36234a8aeebb · outbound

This paper cites Yolo-world: Real-time open-vocabulary object detection,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Yolo-world: Real-time open-vocabulary object detection,

Reference 34

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Observation d7c1a4f4-a43d-42f7-9a0e-0e1039cb037c · outbound

This paper cites Towards open world recognition,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Towards open world recognition,

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation b9d88c8b-1109-4117-bf61-8bdc8c25937b · outbound

This paper cites Ow-detr: Open-world detection transformer,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Ow-detr: Open-world detection transformer,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:46:18.319089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:46:17.585082Z digest=sha256:2bd567dee050fc227d9392b29f853abb3c6222939f8ad4541451a017a5d217e7

Observation 0b954148-e704-47c9-ad46-6d563de1407c · outbound

This paper cites Exploring vision-language foundation model for novel object captioning,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Exploring vision-language foundation model for novel object captioning,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:46:18.298902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:46:17.590635Z digest=sha256:35c2b97be52c258f74497568101b640973cdfe93b7bf6bf10edf3e7cb367720a

Observation 76597959-bab1-413f-94ff-ce60661f6881 · outbound

This paper cites Improved open world object detection using class-wise feature space learning,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Improved open world object detection using class-wise feature space learning,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:46:18.278186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:46:17.595627Z digest=sha256:dc70481917ea1e7d77912d4adff9c3bff61e398d1f240340f1aff34434c04422

Observation efbc1feb-d4b8-474f-ad30-a67c0c1e6cfb · outbound

This paper cites Self-Supervised Features Improve Open-World Learning.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Self-Supervised Features Improve Open-World Learning

Reference 39

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unresolved
no resolver link, observed 2026-08-10T20:46:17.601016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:46:17.601016Z digest=sha256:32de34846739aa03f9f1fc63ec6c5444c3f193fc9c5ec8894d6ad7e0bd4047e8

Observation b127b10a-ac6d-4846-8f55-25336d39a4df · outbound

This paper cites Can Foundation Models Wrangle Your Data?.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Can Foundation Models Wrangle Your Data?

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-10T20:46:17.606731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:46:17.606731Z digest=sha256:2e8d8480d62b773f80734bb799905310d9300b726e2d39f8768c4200932130b6

Observation a2a538d1-006f-4a65-baea-4ef941aa657d · outbound

This paper cites Segment anything,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Segment anything,

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-10T20:46:17.612848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:46:17.612848Z digest=sha256:05b0e2c0ff22d8f05931591f3513b02759917249bb009d1396988c729b774415

Observation 8100238f-25b0-4abc-8038-659a9b11933e · outbound

This paper cites Dremel: interactive analysis of web- scale datasets,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Dremel: interactive analysis of web- scale datasets,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:46:18.242910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:46:17.618638Z digest=sha256:dbe46d90ac6349f09b2e19a6c47aec5945a25b788bb6356d8211b4e5fa1d43b3

Observation 2dd39ac8-f27e-41f9-a95c-b3e7ec337969 · outbound

This paper cites Open-set automatic target recognition,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Open-set automatic target recognition,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:46:18.222306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:46:17.623741Z digest=sha256:279a9d18946ff17e0e9ea6337c4b4593548e8e63ddbe94996953abd48a3011a3

Observation c1b8d13e-7ca8-4b87-bd24-f931dd2c8f65 · outbound

This paper cites Hello gpt-4o,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Hello gpt-4o,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:46:18.201005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:46:17.628432Z digest=sha256:8c423c9afcf1dd9c3125302280bb8165144964c8a029c1d21fa080997982d171

Observation 842953ae-91a4-4f14-891e-1db88ca75f28 · outbound

This paper cites Introducing the next generation of Claude,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Introducing the next generation of Claude,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:46:18.179771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:46:17.633888Z digest=sha256:17d9c977a7ef4a10d67c8e6e18fe4b44efa39ac44eaba101f841806658449b05

Observation b27e26fb-fcc0-49a8-90ed-e73ab01a0593 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-10T20:46:17.639653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:46:17.639653Z digest=sha256:f7b0c80b2ff48d391dadd3555787cf6fbea6c0c769a7ba2a98771aa0678043aa

Observation 850b3a59-bbfa-40ef-8874-53338b3d1d51 · outbound

This paper cites Visual instruction tuning,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Visual instruction tuning,

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-10T20:46:17.645260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:46:17.645260Z digest=sha256:b3779b9f8198de00fb08b81262e1a463b813c80e33ba54518b73703b94d71824

Observation 0f460483-556d-4f6f-9e4a-7d8a4a66c3c7 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-10T20:46:17.650325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:46:17.650325Z digest=sha256:cd699382f20e0534c6d7d6c8219c9507cc3eee0333d2471ef54abb55cff2b65f

Observation abdd08d9-1c36-4410-8b3f-a3c06f7b886e · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-10T20:46:17.656050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:46:17.656050Z digest=sha256:d4911bb1cd9c9c63fdf8d82ffd8ac57d16273bb9704d480434b514fb555a9b71

Observation baf40376-7db4-4934-b7f0-31b2c146b65d · outbound

This paper cites InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-10T20:46:17.661661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:46:17.661661Z digest=sha256:343bb2cd2af0f80a818886b2029ebab4648d8fe785a32a31a3dc7b761f4ce5a0

Observation e70e6963-3c31-4288-8316-446f2b95e8ce · outbound

This paper cites Llava-next: Improved reasoning, ocr, and world knowledge,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Llava-next: Improved reasoning, ocr, and world knowledge,

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-10T20:46:17.667544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:46:17.667544Z digest=sha256:0ac87b46a14cbedab6f468dc5947e1f752b9d9e73630e9fc016ece4333e3569b

Observation 0c7a0eb7-ceb2-47c2-a88a-fabe790fdcc5 · outbound

This paper cites CogVLM: Visual Expert for Pretrained Language Models.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models CogVLM: Visual Expert for Pretrained Language Models

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-10T20:46:17.674626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:46:17.674626Z digest=sha256:e34b3cea8bd45f7a654c325b1b69543391efdcc88ed9ec180c2dbf90135896a7

Observation db8982d9-9f25-44eb-853e-c358b5033992 · outbound

This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-10T20:46:17.682505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:46:17.682505Z digest=sha256:fecbe1ba2022d3ec92cf4b3ac1d292a3297564688b47d3b2de2919ac6c25a41d

Observation e7b3e0cf-a358-4251-af24-08db5e931210 · outbound

This paper cites Instructblip: Towards general-purpose vision- language models with instruction tuning,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Instructblip: Towards general-purpose vision- language models with instruction tuning,

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-10T20:46:17.688475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:46:17.688475Z digest=sha256:359e12612fafb2ba0c649e98b08b2d5ece6f0e78cd31480c91dd7b4f7c37e17d

Observation b119770c-7a56-4c32-8844-bbd146a65641 · outbound

This paper cites Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,.

Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:46:18.123979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T20:46:17.694478Z digest=sha256:02cef6ffb5169693e832d5f2b9b19e59a3dd58b10666ca46da7735715a5aff62

Pith citing papers

No inbound Pith citation observations are available.