ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations | Paper Citation Record

Observation 60d41486-0e03-4083-8dd7-6de7fc5fc742 · outbound

This paper cites Deep ViT Features as Dense Visual Descriptors.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Deep ViT Features as Dense Visual Descriptors

Reference 1

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Observation 540bfb82-538b-4521-b225-b1de2879b4f7 · outbound

This paper cites A faster secure content-based image retrieval using clustering for cloud.Expert Systems with Applications, 189:116070, 2022.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations A faster secure content-based image retrieval using clustering for cloud.Expert Systems with Applications, 189:116070, 2022

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:21.796219Z

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Observation 74280c08-bb87-4a1d-818a-33f71c11d3c6 · outbound

This paper cites Clustering- based real-time anomaly detection—a breakthrough in big data technologies.Transactions on Emerging Telecommunications Technologies, 33(8):e3647, 2022.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Clustering- based real-time anomaly detection—a breakthrough in big data technologies.Transactions on Emerging Telecommunications Technologies, 33(8):e3647, 2022

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:21.709593Z

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Observation f9eb6a9e-e7aa-45fd-804c-09f2390c2057 · outbound

This paper cites Cascade r-cnn: Delving into high quality object detection.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Cascade r-cnn: Delving into high quality object detection

Reference 4

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Observation c5142043-0bc8-4343-98dc-530f7669f8ed · outbound

This paper cites End-to-end object detection with transformers.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations End-to-end object detection with transformers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:11.760048Z

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Observation 538cba33-a7fc-4876-91ea-3db9051ef526 · outbound

This paper cites Ovarnet: Towards open-vocabulary object attribute recognition.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Ovarnet: Towards open-vocabulary object attribute recognition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:21.525735Z

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Observation de1d17b4-a60a-4ac2-a360-ae5fe7bfa399 · outbound

This paper cites Spann: Highly-efficient billion-scale approximate nearest neighborhood search.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Spann: Highly-efficient billion-scale approximate nearest neighborhood search

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:21.372353Z

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Observation a8c12b2f-eaf7-480f-b3ef-b222b3b5d635 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations A simple framework for contrastive learning of visual representations

Reference 8

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Observation 981745d6-05d0-4784-8ade-f4b8f92b6a76 · outbound

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

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Yolo- world: Real-time open-vocabulary object detection

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:12.128348Z

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Observation 87723fa0-76ff-4811-b403-f9688512e209 · outbound

This paper cites A new subspace clustering strategy for ai-based data analysis in iot system.IEEE Internet of Things Journal, 8 (16):12540–12549, 2021.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations A new subspace clustering strategy for ai-based data analysis in iot system.IEEE Internet of Things Journal, 8 (16):12540–12549, 2021

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:21.253415Z

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Observation f13376ab-338c-4c74-9109-8f2c38f86399 · outbound

This paper cites The Faiss library.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations The Faiss library

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:12.315800Z

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Observation e1a026dc-0635-4d93-9bcc-048842d5f324 · outbound

This paper cites Everingham, L.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Everingham, L

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:21.044860Z

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Observation aefc5831-1b60-4209-94a6-7bb8049ed108 · outbound

This paper cites Imagebind: One embedding space to bind them all.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Imagebind: One embedding space to bind them all

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:12.539177Z

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Observation 060b8075-6f88-45d7-b6ce-6b904d94d56b · outbound

This paper cites Open-vocabulary object detection via vision and language knowledge distillation.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Open-vocabulary object detection via vision and language knowledge distillation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:20.894297Z

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Observation 28eafe4d-c5fe-4926-baf4-5dea7fe0f98d · outbound

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

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Ow-detr: Open-world detection transformer

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:20.739768Z

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Observation 81b46498-35f9-4dfb-973e-abc010d9b5c2 · outbound

This paper cites Morgan kaufmann, 2022.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Morgan kaufmann, 2022

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:12.788301Z

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Observation 16dc3243-f86d-413b-8ba3-28839a3a0a7e · outbound

This paper cites Mask r-cnn.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Mask r-cnn

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:12.897361Z

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Observation ee7896e7-aaf1-47ee-a63c-982fdf6e4664 · outbound

This paper cites Open-set image tagging with multi-grained text supervision.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Open-set image tagging with multi-grained text supervision

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:20.594625Z

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Observation bd5484f9-5395-466a-a19a-df57d1a95617 · outbound

This paper cites LLMs meet VLMs: Boost open vocabulary object detection with fine-grained descriptors.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations LLMs meet VLMs: Boost open vocabulary object detection with fine-grained descriptors

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:20.415552Z

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Observation 578db368-a385-4c0a-be60-58535355c99d · outbound

This paper cites Billion-scale similarity search with GPUs.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Billion-scale similarity search with GPUs

Reference 20

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Observation 1aa9cfc4-1021-4e37-9586-833ea0aa5f24 · outbound

This paper cites Towards Open World Object Detection.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Towards Open World Object Detection

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:03:17.081587Z

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Observation aa3a1b33-d0a6-4327-b079-5578d77ffc18 · outbound

This paper cites Clustering-based anomaly detection in multivariate time series data.Applied Soft Computing, 100:106919, 2021.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Clustering-based anomaly detection in multivariate time series data.Applied Soft Computing, 100:106919, 2021

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:20.211377Z

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Observation f77cb5f4-4852-45e8-a7d9-0c8ecf0fefcc · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:20.059465Z

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Observation abb161e9-bad1-4c94-8162-2cac43802b79 · outbound

This paper cites Grounded language-image pre-training.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Grounded language-image pre-training

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:13.531762Z

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Observation 5d24c68b-862c-4798-a19b-273d4146aa27 · outbound

This paper cites Clusterfomer: clustering as a universal visual learner.Advances in neural information processing systems, 36, 2024.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Clusterfomer: clustering as a universal visual learner.Advances in neural information processing systems, 36, 2024

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:19.840545Z

Source-reported events for the cited work

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Observation 8a15c77e-0a3b-4e79-a8c6-ae6c2ea0f192 · outbound

This paper cites Generative region-language pretraining for open-ended object detection.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Generative region-language pretraining for open-ended object detection

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:13.698571Z

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Observation 8d4e44ee-ea96-4404-9dd3-db69857d794a · outbound

This paper cites Microsoft coco: Common objects in context.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Microsoft coco: Common objects in context

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:13.788934Z

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Observation 8428d942-67c7-4008-9c20-2815960f3bcf · outbound

This paper cites Visual instruction tuning.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Visual instruction tuning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:13.887583Z

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Observation 1ea43bff-ba98-43d6-a060-c04361f36e5f · outbound

This paper cites Class-agnostic Object Detection with Multi-modal Transformer.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Class-agnostic Object Detection with Multi-modal Transformer

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:13.984531Z

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Observation 7d0409be-734d-417a-b16d-99c6f0885f32 · outbound

This paper cites Simple open-vocabulary object detection.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Simple open-vocabulary object detection

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:19.727960Z

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Observation 2990f0b3-d617-42c5-a9b8-432b55fe07bf · outbound

This paper cites Clustergan: Latent space clustering in generative adversarial networks.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Clustergan: Latent space clustering in generative adversarial networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:19.597813Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a3f889bf-aba8-4e55-8b84-d7dd7dccf214 · outbound

This paper cites Nagy, Patricia A.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Nagy, Patricia A

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:19.489883Z

Source-reported events for the cited work

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Observation ac0e5d5e-c9fa-4c1c-b95d-4b1bad49540b · outbound

This paper cites Spice: Semantic pseudo-labeling for image clustering.IEEE Transactions on Image Processing, 31:7264–7278, 2022.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Spice: Semantic pseudo-labeling for image clustering.IEEE Transactions on Image Processing, 31:7264–7278, 2022

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:19.365778Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d0ae824c-ff32-4200-a14f-c93764a918ea · outbound

This paper cites Data clustering: application and trends.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Data clustering: application and trends

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:19.236739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0ee63f41-589e-4001-83ba-8bddc826b17c · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Learning Transferable Visual Models From Natural Language Supervision

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:14.554505Z

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Observation 94ef4eda-7650-4841-b59c-c83db87dc8de · outbound

This paper cites Learning transferable visual models from natural language supervision.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Learning transferable visual models from natural language supervision

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:14.683850Z

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Observation bc5e5f6e-adf1-4645-9e2d-692a20db0ea4 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.IEEE transactions on pattern analysis and machine intelligence, 39(6):1137–1149, 2016.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Faster r-cnn: Towards real-time object detection with region proposal networks.IEEE transactions on pattern analysis and machine intelligence, 39(6):1137–1149, 2016

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:14.816865Z

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Observation 348bb4b6-ba08-44a2-ad20-a41061a2e6be · outbound

This paper cites Deepdpm: Deep clustering with an unknown number of clusters.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Deepdpm: Deep clustering with an unknown number of clusters

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:19.092507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6fac64f8-4de8-4de7-803e-cc18ff48203d · outbound

This paper cites Enhancing object detection by leveraging large language models for contextual knowledge.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Enhancing object detection by leveraging large language models for contextual knowledge

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:18.902838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c01a72d3-ceff-48d2-bffd-0d8926023589 · outbound

This paper cites A review of clustering techniques and developments.Neurocomputing, 267:664–681, 2017.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations A review of clustering techniques and developments.Neurocomputing, 267:664–681, 2017

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:18.728106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 97459e15-7803-4ff5-9223-869db0e6c6b2 · outbound

This paper cites Stop using the elbow criterion for k-means and how to choose the number of clusters instead.ACM SIGKDD Explorations Newsletter, 25(1):36–42, 2023.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Stop using the elbow criterion for k-means and how to choose the number of clusters instead.ACM SIGKDD Explorations Newsletter, 25(1):36–42, 2023

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:18.568939Z

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Observation 3f5805a5-3257-453e-b4a2-3779417ea75b · outbound

This paper cites Sparse r-cnn: End-to-end object detection with learnable proposals.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Sparse r-cnn: End-to-end object detection with learnable proposals

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:15.268403Z

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Observation a74a3e0d-7a54-41c5-a605-6f697f92fe9f · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations LLaMA: Open and Efficient Foundation Language Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:15.366225Z

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Observation 360889eb-c21d-4b6d-bf0c-d43183d69048 · outbound

This paper cites Scan: Learning to classify images without labels.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Scan: Learning to classify images without labels

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:15.493428Z

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Observation 74f1d7b7-2a43-4c06-bd95-27330d311807 · outbound

This paper cites Searching for best practices in retrieval-augmented generation.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Searching for best practices in retrieval-augmented generation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:15.593141Z

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Observation 0ecb1176-d130-4b1b-8b21-21a7b36a2c6b · outbound

This paper cites Retccl: Clustering-guided contrastive learning for whole-slide image retrieval.Medical image analysis, 83:102645, 2023.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Retccl: Clustering-guided contrastive learning for whole-slide image retrieval.Medical image analysis, 83:102645, 2023

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:18.370597Z

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Observation c3b93f63-99cc-4197-a25c-756ab758730b · outbound

This paper cites The effects of context on incidental vocabulary learning.University of Hawaii National Foreign Language Resource Center, 20(2):232–245, 2008.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations The effects of context on incidental vocabulary learning.University of Hawaii National Foreign Language Resource Center, 20(2):232–245, 2008

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:18.199684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 737c960c-b66b-4380-a3b5-b8e8f273d9c9 · outbound

This paper cites How well does CLIP understand texture?.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations How well does CLIP understand texture?

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:03:16.832753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 01de9b47-a5ca-44ea-a853-2907cab3553d · outbound

This paper cites Hierarchical clustering supported by reciprocal nearest neighbors.Information Sciences, 527:279–292, 2020.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Hierarchical clustering supported by reciprocal nearest neighbors.Information Sciences, 527:279–292, 2020

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:18.018279Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 45c0e843-eb7f-46f3-ac47-2b0045283e64 · outbound

This paper cites Open-vocabulary detr with conditional matching.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Open-vocabulary detr with conditional matching

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:17.810767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation af6893ed-d749-42c3-b8a9-d266a2ecf2b2 · outbound

This paper cites Open-vocabulary object detection using captions.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Open-vocabulary object detection using captions

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:17.565370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation baae690b-501f-4003-bd4b-b06b3901c93b · outbound

This paper cites Recognize Anything: A Strong Image Tagging Model.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Recognize Anything: A Strong Image Tagging Model

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:16.194374Z

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Observation efca2206-da84-462d-93d0-f109e4d9a9ee · outbound

This paper cites Towards open-set object detection and discovery.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Towards open-set object detection and discovery

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:16.369396Z

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Unavailable: canonical work link unavailable.

Observation 68d13b8a-6507-4811-9587-e1352589df79 · outbound

This paper cites Detecting twenty-thousand classes using image-level supervision.

ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotations Detecting twenty-thousand classes using image-level supervision

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:03:17.340327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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