DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception | Paper Citation Record

Observation e9aae3a4-dfd5-425b-a1c0-f4b125ccb202 · outbound

This paper cites MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations

Reference 1

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Observation 0d225a0d-3206-4e4e-baf4-31be6a13ea0d · outbound

This paper cites Multi-label cluster discrimination for vi- sual representation learning.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Multi-label cluster discrimination for vi- sual representation learning

Reference 2

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Observation 8e370ba9-bba1-4df9-99a1-ff676b8cd86a · outbound

This paper cites Coco- stuff: Thing and stuff classes in context.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Coco- stuff: Thing and stuff classes in context

Reference 3

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Observation ddff29ea-de4b-457b-8fa2-c63ee79e8cf6 · outbound

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

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception End- to-end object detection with transformers

Reference 4

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Observation 68d404bc-9099-4d88-9636-7966d69ec71f · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Emerg- ing properties in self-supervised vision transformers

Reference 5

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Observation 76596e1e-6444-4b2c-9117-238ba3aec9db · outbound

This paper cites Learn- ing to generate text-grounded mask for open-world semantic segmentation from only image-text pairs.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Learn- ing to generate text-grounded mask for open-world semantic segmentation from only image-text pairs

Reference 6

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Observation 14c75a3c-c75e-4228-a46c-ce05fae03dba · outbound

This paper cites Enhanced training of query- based object detection via selective query recollection.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Enhanced training of query- based object detection via selective query recollection

Reference 7

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Observation cff71e60-1f2a-42aa-9505-a23304f5a18b · outbound

This paper cites RTGen: Generating Region-Text Pairs for Open-Vocabulary Object Detection.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception RTGen: Generating Region-Text Pairs for Open-Vocabulary Object Detection

Reference 8

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Observation 3234b041-7c1f-4ed1-8a03-273534d7e542 · outbound

This paper cites Exploring open-vocabulary semantic segmentation from clip vision encoder distilla- tion only.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Exploring open-vocabulary semantic segmentation from clip vision encoder distilla- tion only

Reference 9

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Observation 67be98ee-a142-40a7-9ae1-288be7e3f3f7 · outbound

This paper cites An empiri- cal study of training self-supervised vision transformers.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception An empiri- cal study of training self-supervised vision transformers

Reference 10

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Observation 913c4eda-21fd-4fcb-811f-1c337e0cd8ee · outbound

This paper cites FrozenSeg: Harmonizing Frozen Foundation Models for Open-Vocabulary Segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception FrozenSeg: Harmonizing Frozen Foundation Models for Open-Vocabulary Segmentation

Reference 11

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Observation 22383667-7fda-4fb1-ba59-4866674e2b1a · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Masked-attention mask transformer for universal image segmentation

Reference 12

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Observation 37fefd88-d736-4261-8e80-34cf398d7143 · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Reproducible scaling laws for contrastive language-image learning

Reference 13

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Observation af632cf0-cd46-430d-9272-85831784784b · outbound

This paper cites Cat- seg: Cost aggregation for open-vocabulary semantic seg- mentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Cat- seg: Cost aggregation for open-vocabulary semantic seg- mentation

Reference 14

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Observation 93b8383a-ba90-4fe1-8a6a-c45407bfc0a8 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception The cityscapes dataset for semantic urban scene understanding

Reference 15

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Observation cebce2ac-f561-4ac4-8bed-4a939b6c9558 · outbound

This paper cites Vision Transformers Need Registers.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Vision Transformers Need Registers

Reference 16

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Observation 0853771a-d97a-4169-8fb0-2fdafe56c009 · outbound

This paper cites De- coupling zero-shot semantic segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception De- coupling zero-shot semantic segmentation

Reference 17

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Observation 468d10fa-c3e1-4c66-998d-0c539eaca17a · outbound

This paper cites De- coupling zero-shot semantic segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception De- coupling zero-shot semantic segmentation

Reference 18

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Observation 462fd581-3f34-45dc-86ad-0bc55051bca9 · outbound

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

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 19

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Observation 1c5b858b-92a4-44d0-afcc-25ae57cb1028 · outbound

This paper cites Learning to prompt for open-vocabulary ob- ject detection with vision-language model.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Learning to prompt for open-vocabulary ob- ject detection with vision-language model

Reference 20

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Observation c0517833-1b5b-45fd-bfcf-38deb8eac989 · outbound

This paper cites The pascal visual object classes (voc) challenge.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception The pascal visual object classes (voc) challenge

Reference 21

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Observation 3ac2733e-ef95-41d3-877d-7094c283477d · outbound

This paper cites Eva-02: A visual representation for neon genesis.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Eva-02: A visual representation for neon genesis

Reference 22

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Observation c08ac922-58bf-42fc-803e-c748758dac55 · outbound

This paper cites Scal- ing open-vocabulary image segmentation with image-level labels.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Scal- ing open-vocabulary image segmentation with image-level labels

Reference 23

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Observation 47bcd4df-4d2a-4635-b4a7-f89b9941b288 · outbound

This paper cites Open-vocabulary Object Detection via Vision and Language Knowledge Distillation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Open-vocabulary Object Detection via Vision and Language Knowledge Distillation

Reference 24

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Observation 8eb4ec01-fd45-4696-97fb-123fc54c215c · outbound

This paper cites Lvis: A dataset for large vocabulary instance segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Lvis: A dataset for large vocabulary instance segmentation

Reference 25

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Observation a0f84489-692e-4350-9eed-fea9e5e69ea2 · outbound

This paper cites Global knowledge calibration for fast open-vocabulary segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Global knowledge calibration for fast open-vocabulary segmentation

Reference 26

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Observation f64f60f8-04a9-471d-8a57-7e3846ed7a53 · outbound

This paper cites Deep residual learning for image recognition.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Deep residual learning for image recognition

Reference 27

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Observation bf60fed1-5ef8-4462-afca-ab085d1bc05c · outbound

This paper cites Mask r-cnn.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Mask r-cnn

Reference 28

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Observation 3ed427ba-2453-4f56-9234-df67cb096604 · outbound

This paper cites Proxydet: Synthesizing proxy novel classes via classwise mixup for open-vocabulary object de- tection.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Proxydet: Synthesizing proxy novel classes via classwise mixup for open-vocabulary object de- tection

Reference 29

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Observation ebb4b513-25b4-48b5-bcac-f67570b5ba01 · outbound

This paper cites Learning mask-aware clip representations for zero-shot segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Learning mask-aware clip representations for zero-shot segmentation

Reference 30

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Observation f94252f9-ed42-49dd-9e8c-01c8b515070b · outbound

This paper cites Collaborative vision-text rep- resentation optimizing for open-vocabulary segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Collaborative vision-text rep- resentation optimizing for open-vocabulary segmentation

Reference 31

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Observation cb8c8593-2c30-4bd9-98bc-f2227ce10143 · outbound

This paper cites Diffusion Models for Open-Vocabulary Segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Diffusion Models for Open-Vocabulary Segmentation

Reference 32

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This paper cites Con- trastive feature masking open-vocabulary vision transformer.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Con- trastive feature masking open-vocabulary vision transformer

Reference 33

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Observation ae6c345f-e92a-478b-903f-87db34582ba0 · outbound

This paper cites Region- aware pretraining for open-vocabulary object detection with vision transformers.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Region- aware pretraining for open-vocabulary object detection with vision transformers

Reference 34

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Observation a51bfe8b-5b8f-4a98-b5b7-9cf36b8bc568 · outbound

This paper cites Region- aware pretraining for open-vocabulary object detection with vision transformers.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Region- aware pretraining for open-vocabulary object detection with vision transformers

Reference 35

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Observation fcc4b647-7b24-401e-8fe6-f41b9e059f3a · outbound

This paper cites Segment anything.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Segment anything

Reference 36

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This paper cites F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models

Reference 37

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This paper cites ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference

Reference 38

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This paper cites Language-driven Semantic Segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Language-driven Semantic Segmentation

Reference 39

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This paper cites Mask dino: To- wards a unified transformer-based framework for object de- tection and segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Mask dino: To- wards a unified transformer-based framework for object de- tection and segmentation

Reference 40

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This paper cites Scaling language-image pre-training via masking.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Scaling language-image pre-training via masking

Reference 41

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Observation ab33abad-a8d4-4cf6-ae36-2fa3fabfc689 · outbound

This paper cites Open-vocabulary semantic segmentation with mask-adapted clip.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Open-vocabulary semantic segmentation with mask-adapted clip

Reference 42

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This paper cites Microsoft coco: Common objects in context.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Microsoft coco: Common objects in context

Reference 43

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Observation 070c7d6e-30f8-46f9-adae-1813a64fbbac · outbound

This paper cites DAB-DETR: Dynamic anchor boxes are better queries for DETR.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception DAB-DETR: Dynamic anchor boxes are better queries for DETR

Reference 44

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This paper cites A convnet for the 2020s.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception A convnet for the 2020s

Reference 45

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Observation 042192fd-e0c5-4792-9083-4632687909b9 · outbound

This paper cites Decoupled Weight Decay Regularization.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Decoupled Weight Decay Regularization

Reference 46

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Observation c7f0a4dd-4d11-4f02-ac1b-17b5f2374b62 · outbound

This paper cites Codet: Co-occurrence guided region-word alignment for open-vocabulary object detection.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Codet: Co-occurrence guided region-word alignment for open-vocabulary object detection

Reference 47

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Observation 7b4cee9c-5873-404f-8894-f8ecd200a4fb · outbound

This paper cites The role of context for object detection and se- mantic segmentation in the wild.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception The role of context for object detection and se- mantic segmentation in the wild

Reference 48

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Observation a62eadd3-09d0-49d1-836c-f645f3b83aec · outbound

This paper cites Open vocabulary semantic segmentation with patch aligned contrastive learning.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Open vocabulary semantic segmentation with patch aligned contrastive learning

Reference 49

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Observation ce90d100-740b-4c16-8035-2ba832ba0e4a · outbound

This paper cites Silc: Improving vision language pretraining with self-distillation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Silc: Improving vision language pretraining with self-distillation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:52.154332Z

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Observation 7bd8280a-6ee1-45c4-8a30-c073dbcf4121 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception DINOv2: Learning Robust Visual Features without Supervision

Reference 51

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Observation 2f56c0a5-d4ce-42f2-aca4-bf6f2be1012b · outbound

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

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Learning transferable visual models from natural language supervision

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:52.139163Z

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Observation 170ef4b5-5d35-4290-99ae-6ccd44fbea49 · outbound

This paper cites Am-radio: Agglomerative vision foundation model reduce all domains into one.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Am-radio: Agglomerative vision foundation model reduce all domains into one

Reference 53

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Observation 3a30c56d-7ebd-48d1-ae0b-e7a096b9db1a · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception SAM 2: Segment Anything in Images and Videos

Reference 54

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Observation 422c6cf0-7904-48a9-b9d9-e71487fabc2b · outbound

This paper cites Faster r-cnn: Towards real-time object detection with re- gion proposal networks.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Faster r-cnn: Towards real-time object detection with re- gion proposal networks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:52.108194Z

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 25a96dca-2d6e-44ae-b053-bc33d5675659 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception High-resolution image synthesis with latent diffusion models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:52.092729Z

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

Observation d8817179-a197-4cde-abe8-bf19f003de73 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception U- net: Convolutional networks for biomedical image segmen- tation

Reference 57

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Observation 8482aed8-b069-40bc-b817-91a598cdbb7e · outbound

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

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Objects365: A large-scale, high-quality dataset for object detection

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:52.068781Z

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Observation d9f9e63a-d1cc-47ae-bdbd-ccbcf5716fc0 · outbound

This paper cites Explore the potential of clip for training-free open vocab- ulary semantic segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Explore the potential of clip for training-free open vocab- ulary semantic segmentation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:52.054043Z

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Observation 109f509b-4cac-4186-ba97-88c6d5e40419 · outbound

This paper cites Reco: Re- trieve and co-segment for zero-shot transfer.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Reco: Re- trieve and co-segment for zero-shot transfer

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:52.038339Z

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Observation ed593250-ddfb-47a2-af9e-4f127fa1458e · outbound

This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 61

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Observation b3861b70-d2a1-419c-8138-fc70d2464651 · outbound

This paper cites Attention is all you need.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Attention is all you need

Reference 62

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Observation ccdf75bc-5191-4cec-915c-76003b377e57 · outbound

This paper cites SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference

Reference 63

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Observation 0c73180c-46c6-40f0-8c18-72c72bb0c4dc · outbound

This paper cites Sam-clip: Merging vision foundation models to- wards semantic and spatial understanding.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Sam-clip: Merging vision foundation models to- wards semantic and spatial understanding

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:52.012924Z

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Observation 5fca5207-427c-42a8-9253-3cd19660c357 · outbound

This paper cites OV-DQUO: Open-Vocabulary DETR with Denoising Text Query Training and Open-World Unknown Objects Supervision.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception OV-DQUO: Open-Vocabulary DETR with Denoising Text Query Training and Open-World Unknown Objects Supervision

Reference 65

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This paper cites Object-aware distillation pyramid for open-vocabulary object detection.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Object-aware distillation pyramid for open-vocabulary object detection

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.996871Z

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DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Aligning bag of regions for open- vocabulary object detection

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.982056Z

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Observation 7b10b19b-314a-4c9f-9dea-0e13204c3cd2 · outbound

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DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception CLIPSelf: Vision transformer distills itself for open-vocabulary dense predic- tion

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.967945Z

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Observation f4c999de-617b-4fa2-b726-47d0f1d480fb · outbound

This paper cites Clim: Contrastive language- image mosaic for region representation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Clim: Contrastive language- image mosaic for region representation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.954236Z

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Observation c000e75c-b8f3-4568-b730-fe922edf3b20 · outbound

This paper cites Cora: Adapting clip for open-vocabulary detection with region prompting and anchor pre-matching.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Cora: Adapting clip for open-vocabulary detection with region prompting and anchor pre-matching

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.939787Z

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This paper cites CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation

Reference 71

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DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Sed: A simple encoder-decoder for open- vocabulary semantic segmentation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.925047Z

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Observation 02720cb8-6d13-47c0-8461-f19b3856e426 · outbound

This paper cites Groupvit: Semantic segmentation emerges from text supervision.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Groupvit: Semantic segmentation emerges from text supervision

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.909842Z

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Observation 04d6b0f0-068d-4cbd-9697-8e7e9eda7003 · outbound

This paper cites Open-vocabulary panoptic segmentation with text-to-image diffusion models.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Open-vocabulary panoptic segmentation with text-to-image diffusion models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.895581Z

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Observation 956e1fba-9e92-4576-997a-88adeabd0c96 · outbound

This paper cites A simple baseline for open- vocabulary semantic segmentation with pre-trained vision- language model.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception A simple baseline for open- vocabulary semantic segmentation with pre-trained vision- language model

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.880802Z

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Observation ea888fa1-9dde-4126-b0ad-794a184a5432 · outbound

This paper cites Side adapter network for open-vocabulary semantic segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Side adapter network for open-vocabulary semantic segmentation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.865949Z

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Observation 8d8167ef-10b8-466a-a158-959384331fb4 · outbound

This paper cites Masq- clip for open-vocabulary universal image segmentation.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Masq- clip for open-vocabulary universal image segmentation

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.850027Z

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Observation 789a0f28-0753-4a92-aa35-f3bf09d4703e · outbound

This paper cites Convolutions die hard: Open-vocabulary seg- mentation with single frozen convolutional clip.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Convolutions die hard: Open-vocabulary seg- mentation with single frozen convolutional clip

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.835151Z

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Observation f71d9dc9-c45a-47ad-802e-ccc086dacf8e · outbound

This paper cites Open-vocabulary sam: Segment and recognize twenty-thousand classes interactively.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Open-vocabulary sam: Segment and recognize twenty-thousand classes interactively

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.819800Z

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Observation 88bcfc3d-56d5-46b7-84a3-dc8ffb1a88d6 · outbound

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DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Open-vocabulary seman- tic segmentation using test-time distillation

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.805520Z

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Observation 54eb1a4f-ef56-4c71-bb1d-64036837bf99 · outbound

This paper cites Open-vocabulary detr with conditional matching.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Open-vocabulary detr with conditional matching

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.790280Z

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Observation 477675a1-284f-42c7-89af-12bacbd4bf94 · outbound

This paper cites Open-vocabulary object detection using captions.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Open-vocabulary object detection using captions

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.775352Z

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Observation 66ab4757-6d03-4830-af07-df3f4c335b6d · outbound

This paper cites Exploring region- word alignment in built-in detector for open-vocabulary ob- ject detection.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Exploring region- word alignment in built-in detector for open-vocabulary ob- ject detection

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.760220Z

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

Observation e15bea48-ff31-4fdf-9203-3a459b7a28e7 · outbound

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DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:34:51.743586Z

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Observation 484fd19c-1cdf-4d69-97d7-6c1aaa231ae5 · outbound

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

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Regionclip: Region- based language-image pretraining

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.728407Z

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

Observation 83fb5320-1d59-459f-a561-a82457df43e8 · outbound

This paper cites Semantic under- standing of scenes through the ade20k dataset.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Semantic under- standing of scenes through the ade20k dataset

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.713386Z

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

Observation 84c38a45-464a-49a4-9eae-896fcb682c38 · outbound

This paper cites Extract free dense labels from clip.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Extract free dense labels from clip

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.698402Z

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 f2bb49e9-a457-4e16-b11f-cbe6639ad7fe · outbound

This paper cites iBOT: Image BERT Pre-Training with Online Tokenizer.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception iBOT: Image BERT Pre-Training with Online Tokenizer

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-15T23:34:51.194148Z

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Observation 2b00f19d-44af-42a6-9c2d-4795dd0d353a · outbound

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

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Detecting twenty-thousand classes using image-level supervision

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.683129Z

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

Observation 3f943471-4305-40b5-9d58-4f1dd6ccf77f · outbound

This paper cites A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-15T23:34:51.204889Z

Source-reported events for the cited work

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Observation d6afd2c4-e3ea-48ff-a9d4-61892665c171 · outbound

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DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 91

Resolution: malformed identifier; no resolver link, observed 2026-08-15T23:34:51.210503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ee1cfe7-f31e-466c-aae0-b93426701e27 · outbound

This paper cites global view.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception global view

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:51.667175Z

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 38f4ac70-acbb-429c-9f3c-d6485fd9ce36 · outbound

This paper cites an unresolved cited work.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Unresolved cited work

Reference 93

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:34:51.652065Z

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 7398ef4e-35c6-4f80-938d-02a4af4db702 · outbound

This paper cites bird” rather than to be “background.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception bird” rather than to be “background

Reference 94

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T23:34:51.636451Z

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 9c4a0f9c-9bff-4ac8-b256-a86025346a41 · outbound

This paper cites an unresolved cited work.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Unresolved cited work

Reference 95

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:34:51.618973Z

Source-reported events for the cited work

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Observation 6d86f5c9-9661-4a08-a392-a29cc178fbaa · outbound

This paper cites an unresolved cited work.

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception Unresolved cited work

Reference 96

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T23:34:51.603267Z

Source-reported events for the cited work

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Coverage vector

Reference resolution

Outbound references

Pith citing papers