FOR: Finetuning for Object Level Open Vocabulary Image Retrieval | Paper Citation Record

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This paper cites Label-embedding for image classification.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Label-embedding for image classification

Reference 1

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This paper cites Bottom-up and top-down attention for image captioning and visual question answering.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Bottom-up and top-down attention for image captioning and visual question answering

Reference 2

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Pseudo-labeling and confirmation bias in deep semi-supervised learning

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This paper cites Bridg- ing the gap between object and image-level representations for open-vocabulary detection.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Bridg- ing the gap between object and image-level representations for open-vocabulary detection

Reference 4

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Zero-shot object detection

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This paper cites Mixmatch: A holistic approach to semi-supervised learning.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Mixmatch: A holistic approach to semi-supervised learning

Reference 6

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This paper cites Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom

Reference 7

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval X-detr: A versatile architecture for instance-wise vision- language tasks

Reference 8

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

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval End-to- end object detection with transformers

Reference 9

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Big self-supervised mod- els are strong semi-supervised learners

Reference 10

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval UNITER: universal image-text representation learning

Reference 11

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This paper cites Proba- bilistic embeddings for cross-modal retrieval.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Proba- bilistic embeddings for cross-modal retrieval

Reference 12

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This paper cites Imagenet: A large-scale hierarchical image database.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Imagenet: A large-scale hierarchical image database

Reference 13

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This paper cites Finding beans in burgers: Deep semantic- visual embedding with localization.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Finding beans in burgers: Deep semantic- visual embedding with localization

Reference 14

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This paper cites Fleet, Jamie Ryan Kiros, and Sanja Fidler.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Fleet, Jamie Ryan Kiros, and Sanja Fidler

Reference 15

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This paper cites De- vise: A deep visual-semantic embedding model.Advances in neural information processing systems (NeurIPS), 26, 2013.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval De- vise: A deep visual-semantic embedding model.Advances in neural information processing systems (NeurIPS), 26, 2013

Reference 16

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This paper cites Understanding the diffi- culty of training deep feedforward neural networks.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Understanding the diffi- culty of training deep feedforward neural networks

Reference 17

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This paper cites Improving image-sentence embeddings using large weakly annotated photo collections.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Improving image-sentence embeddings using large weakly annotated photo collections

Reference 18

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

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Open-vocabulary object detection via vision and language knowledge distillation

Reference 19

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Girshick

Reference 20

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Generative multi-label zero-shot learning

Reference 21

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Mean average precision map@k metric explained code

Reference 22

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Instance-aware im- age and sentence matching with selective multimodal LSTM

Reference 23

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers

Reference 24

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval A shared multi-attention framework for multi-label zero-shot learning

Reference 25

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Saliency-guided attention network for image-sentence matching

Reference 26

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 27

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Billion- scale similarity search with GPUs

Reference 28

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Deep fragment embeddings for bidirectional image sentence map- ping

Reference 29

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Kingma and Jimmy Ba

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Shamma, Michael S

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Temporal ensembling for semi-supervised learning

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Pseudo-label: The simple and effi- cient semi-supervised learning method for deep neural net- works

Reference 36

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Stacked cross attention for image-text match- ing

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Object- centric open-vocabulary image retrieval with aggregated fea- tures

Reference 38

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Blip: Bootstrapping language-image pre-training for uni- fied vision-language understanding and generation

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Selvaraju, Akhilesh Gotmare, Shafiq R

Reference 41

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Adapting CLIP For Phrase Localization Without Further Training

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Oscar: Object-semantics aligned pre-training for vision-language tasks

Reference 44

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C

Reference 45

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval OVIS: open-vocabulary visual instance search via visual-semantic aligned representation learning

Reference 46

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks

Reference 47

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Simple open-vocabulary object detection with vi- sion transformers

Reference 48

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Gritsenko, and Neil Houlsby

Reference 49

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Zero-Shot Learning by Convex Combination of Semantic Embeddings

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Meta pseudo labels

Reference 52

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This paper cites Learning transferable visual models from natural language supervi- sion.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Learning transferable visual models from natural language supervi- sion

Reference 53

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

Observation 8279678d-be3a-4a0f-907b-8b02f10f7380 · outbound

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

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Learning transferable visual models from natural language supervision

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:44.812922Z

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

Observation 32507526-2ecc-4074-846f-32b1062fe148 · outbound

This paper cites Denseclip: Language-guided dense prediction with context- aware prompting.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Denseclip: Language-guided dense prediction with context- aware prompting

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:44.802015Z

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Observation 344bb416-94ed-4c4b-afe8-07253d2c8211 · outbound

This paper cites Regularization with stochastic transformations and perturba- tions for deep semi-supervised learning.

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Reference 56

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Observation f9390e8f-d0e3-4351-a377-a724965289cc · outbound

This paper cites Objects365: A large-scale, high-quality dataset for object detection.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Objects365: A large-scale, high-quality dataset for object detection

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:44.783806Z

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Observation 709102e8-1cd6-4c97-8075-a6903002902b · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 58

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Observation 0a9d0bf0-888d-426d-89a1-2fc076229288 · outbound

This paper cites A Simple Semi-Supervised Learning Framework for Object Detection.

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Reference 59

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Observation ea46336a-b1d4-4448-9650-8cdf05a0cf7b · outbound

This paper cites Dualcoop: Fast adaptation to multi-label recognition with limited annotations.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Dualcoop: Fast adaptation to multi-label recognition with limited annotations

Reference 60

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Observation 4b43bf7a-9756-4073-aead-773778dcfe73 · outbound

This paper cites LXMERT: learning cross- modality encoder representations from transformers.

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Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:44.755377Z

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Observation c7ad6b8d-16d5-47a5-a1e2-e70438683180 · outbound

This paper cites GIT: A generative image-to-text transformer for vision and language.

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Reference 62

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Observation ef0f87d3-b8a2-405e-a4c5-f16b7fdc7551 · outbound

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Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:44.733306Z

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Observation a81d8b9c-e971-4e63-a19f-8ba16a6494e7 · outbound

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Reference 64

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This paper cites Aligning bag of regions for open- vocabulary object detection.

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Reference 65

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Reference 66

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

Observation 81b8bdef-5eb0-46d9-9bba-cabc7ba1b374 · outbound

This paper cites CLIM: contrastive language-image mosaic for region representation.

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Reference 67

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

Observation 580337d6-a7cf-4fc9-b58b-182f5997df2c · outbound

This paper cites CORA: adapting CLIP for open-vocabulary detection with region prompting and anchor pre-matching.

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Reference 68

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Observation 700ef036-57bb-4c77-ab99-8e0878c5917f · outbound

This paper cites Unsupervised data augmentation for consistency training.

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Reference 69

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Observation b744b86f-6194-4436-b81d-dd6704429373 · outbound

This paper cites Self-training with noisy student improves imagenet classification.

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Reference 70

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Observation 048912da-d9df-44e4-b709-f355f2b41bd4 · outbound

This paper cites From image descriptions to visual denotations: New similarity metrics for semantic inference over event descrip- tions.

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Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:44.657502Z

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

Observation bd62beab-7379-4888-8130-214531072f82 · outbound

This paper cites Coca: Contrastive captioners are image-text foundation models.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Coca: Contrastive captioners are image-text foundation models

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:44.647759Z

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

Observation b4d89a9c-6988-460f-b56c-92e848b601fd · outbound

This paper cites Florence: A New Foundation Model for Computer Vision.

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Reference 73

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Observation d208642c-8131-4507-af4f-51998798543d · outbound

This paper cites Lit: Zero-shot transfer with locked-image text tuning.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Lit: Zero-shot transfer with locked-image text tuning

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:44.637917Z

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

Observation ef404a21-badb-4213-b54b-cac7d18684fa · outbound

This paper cites Fast zero- shot image tagging.

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Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:44.627313Z

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Observation ee36940d-78f8-4565-a92f-1b331c27776f · outbound

This paper cites Exploiting unlabeled data with vision and language models for object detection.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Exploiting unlabeled data with vision and language models for object detection

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:44.616505Z

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Observation e2e330fd-2ab1-4fbe-94ce-3da4a3e6a41e · outbound

This paper cites Regionclip: Region-based language-image pretraining.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Regionclip: Region-based language-image pretraining

Reference 77

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

Observation b62ee730-0a93-42a7-87f4-f0bb529cb6f6 · outbound

This paper cites Extract free dense labels from CLIP.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Extract free dense labels from CLIP

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:44.594224Z

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Observation eb1e4021-4501-4b31-ad38-015d563c7db8 · outbound

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

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Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:44.583536Z

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Observation 95111b72-017c-45d8-bace-6ee30db8b557 · outbound

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Reference 80

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

Observation 351aa591-742d-4d2d-89d0-32ec6b46c002 · outbound

This paper cites Rethinking pre- training and self-training.

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Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:44.562509Z

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 e59c07a9-5278-4433-89ee-a27e483a05aa · outbound

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Reference 85

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Observation bf8ebb87-d2c6-425d-a6f2-0de489193ef3 · outbound

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FOR: Finetuning for Object Level Open Vocabulary Image Retrieval Unresolved cited work

Reference 137

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

Observation c7121743-9217-492c-aba3-3e4c4fe05e1a · outbound

This paper cites 1, 2, 3, 4, 6, 8, 13.

FOR: Finetuning for Object Level Open Vocabulary Image Retrieval 1, 2, 3, 4, 6, 8, 13

Reference 608

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:32:45.076156Z

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 17f61c72-0b65-4ef8-9c9d-831b1e11f4cd · outbound

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Reference 6086

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