VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation | Paper Citation Record

Observation 9149ea59-ec67-4e60-979c-162b6e87f035 · outbound

This paper cites Class- conditional domain adaptation for semantic seg- mentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Class- conditional domain adaptation for semantic seg- mentation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:27.116622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a486aa45-d82b-4e7e-9358-4ca74ab5ffd4 · outbound

This paper cites On exploring weakly supervised domain adaptation strate- gies for semantic segmentation using synthetic data.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation On exploring weakly supervised domain adaptation strate- gies for semantic segmentation using synthetic data

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:27.100884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f51fbd8b-17dd-493f-a540-6c20245e7ae2 · outbound

This paper cites Taming diffusion model for exemplar-based image translation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Taming diffusion model for exemplar-based image translation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:27.086291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7521d30b-7ff8-4ba3-9d56-304a55653042 · outbound

This paper cites Learning layout generation for virtual worlds.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Learning layout generation for virtual worlds

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:27.071778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8b144e39-207a-4df7-8db5-e7366b3ae242 · outbound

This paper cites Adap- tive sampling and reconstruction for gradient- domain rendering.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Adap- tive sampling and reconstruction for gradient- domain rendering

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:27.056591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d8d75f75-630b-454b-832a-84c92f809a95 · outbound

This paper cites Multi3D: 3D-aware multimodal image synthesis.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Multi3D: 3D-aware multimodal image synthesis

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:27.042424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7b72e773-d576-428f-80fd-27944d0c2ee7 · outbound

This paper cites Biased Class disagreement: detection of out of distribution instances by using differ- ently biased semantic segmentation models.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Biased Class disagreement: detection of out of distribution instances by using differ- ently biased semantic segmentation models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:27.027519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7ff878b8-8b43-4009-8a9e-5eb49371f223 · outbound

This paper cites Cross-modal learning using privileged informa- tion for long-tailed image classification.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Cross-modal learning using privileged informa- tion for long-tailed image classification

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:27.012117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e737a53d-b053-48c3-bccc-0d1110102e99 · outbound

This paper cites Don't Stop Learning: Towards Continual Learning for the CLIP Model.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Don't Stop Learning: Towards Continual Learning for the CLIP Model

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T17:15:25.482676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 767a15c0-3427-4baf-a01c-c875e005cd81 · outbound

This paper cites Generative Negative Text Replay for Continual Vision-Language Pretraining.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Generative Negative Text Replay for Continual Vision-Language Pretraining

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:15:26.146190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9c8b6349-1cef-4307-9739-a8c9ca83da1a · outbound

This paper cites Extract Free Dense Labels from CLIP.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Extract Free Dense Labels from CLIP

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.996595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 486f52ff-4091-41e2-878a-ec25699459c6 · outbound

This paper cites CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T17:15:25.498363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93377c40-2659-42c4-849a-5087eb1df94c · outbound

This paper cites CAT-Seg: Cost Aggre- gation for Open-Vocabulary Semantic Segmen- tation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation CAT-Seg: Cost Aggre- gation for Open-Vocabulary Semantic Segmen- tation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.978848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b5dc3c7d-0697-4586-bc38-ef0acab4cde1 · outbound

This paper cites COCO-Stuff: Thing and Stuff Classes in Context.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation COCO-Stuff: Thing and Stuff Classes in Context

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.963128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation df96cb00-6c92-4ac8-b1f9-a2697ed59021 · outbound

This paper cites Early Convolutions Help Transform- ers See Better.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Early Convolutions Help Transform- ers See Better

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.947151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation aecef7a2-c7a5-4447-8bf9-08434a37cd46 · outbound

This paper cites Incorporating Convolution Designs Into Visual Transformers.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Incorporating Convolution Designs Into Visual Transformers

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.930092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5d325a2c-9e92-4b45-bc19-bac10efe29a1 · outbound

This paper cites Pyramid Geometric Consistency Learning For Seman- tic Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Pyramid Geometric Consistency Learning For Seman- tic Segmentation

Reference 17

Resolution: malformed identifier; no resolver link, observed 2026-08-11T17:15:25.522902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1021ca6c-0dc0-4969-88e3-474147237285 · outbound

This paper cites Learning Trans- ferable Visual Models From Natural Language Supervision.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Learning Trans- ferable Visual Models From Natural Language Supervision

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.913689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3819e4ee-3112-4217-b1f4-2c855330d37e · outbound

This paper cites SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.897654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 84a9685b-dd57-40d8-a992-d22580527b0d · outbound

This paper cites Open-Vocabulary Panop- tic Segmentation with MaskCLIP.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Open-Vocabulary Panop- tic Segmentation with MaskCLIP

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.882282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d4e26e95-0ffd-4089-8949-1bf12d5e809d · outbound

This paper cites Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.866946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a3912096-d51f-4a9c-9ba7-9bf41dd8d088 · outbound

This paper cites Collaborating Foundation Models for Domain Generalized Semantic Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Collaborating Foundation Models for Domain Generalized Semantic Segmentation

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:15:26.028527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation abc3e6b0-a1ca-4196-86d5-69d9471b459e · outbound

This paper cites Side Adapter Network for Open-Vocabulary Seman- tic Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Side Adapter Network for Open-Vocabulary Seman- tic Segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.851192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 435554b0-4d22-4902-afa0-ba417e3b0708 · outbound

This paper cites CLIP-SP: Vision-language model with adap- tive prompting for scene parsing.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation CLIP-SP: Vision-language model with adap- tive prompting for scene parsing

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.836352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4050d6f8-9303-440e-8bb2-22a9c3bf4cc6 · outbound

This paper cites Exploring Visual Interpretability for Contrastive Language-Image Pre-training.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Exploring Visual Interpretability for Contrastive Language-Image Pre-training

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T17:15:25.570313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f31e2cfa-d541-440d-9969-83287a02daed · outbound

This paper cites A Closer Look at the Explainability of Contrastive Language-Image Pre-training.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation A Closer Look at the Explainability of Contrastive Language-Image Pre-training

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T17:15:25.576074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 442e3d55-7a7a-4a81-b7a6-3c2102367c01 · outbound

This paper cites SemiVL: Semi-Supervised Semantic Segmen- tation with Vision-Language Guidance.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation SemiVL: Semi-Supervised Semantic Segmen- tation with Vision-Language Guidance

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.821824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1e112731-ae78-47cb-b957-5e97b126731d · outbound

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

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Open- vocabulary semantic segmentation with mask- adapted clip

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.804916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5674b553-33b2-4779-b909-3aa2e6eddbcd · outbound

This paper cites Decoupling Zero- Shot Semantic Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Decoupling Zero- Shot Semantic Segmentation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.789514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e2a3ae04-bfa9-4d55-9875-c128fcf818af · outbound

This paper cites A Simple Baseline for Open Vocab- ulary Semantic Segmentation with Pre-trained Vision-language Model.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation A Simple Baseline for Open Vocab- ulary Semantic Segmentation with Pre-trained Vision-language Model

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.774403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 28854943-f192-499a-86dd-9e29ac7ada30 · outbound

This paper cites Discovering latent target subdomains for domain adaptive seman- tic segmentation via style clustering.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Discovering latent target subdomains for domain adaptive seman- tic segmentation via style clustering

Reference 31

Resolution: verified exact; doi, observed 2026-08-11T17:15:25.841888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 01bf8997-ae71-48d2-896f-fbc57c5f04d0 · outbound

This paper cites Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Vi- sual Perception in Automated Driving.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Vi- sual Perception in Automated Driving

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.759534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d16cb663-908a-4bf8-abaf-94bfc2bec18e · outbound

This paper cites Per-Class Curriculum for Unsupervised Domain Adaptation in Se- mantic Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Per-Class Curriculum for Unsupervised Domain Adaptation in Se- mantic Segmentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.745176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation da1c4118-ff16-4793-9f4c-ff5ee452a7bb · outbound

This paper cites Pseudo-Label : The Simple and Ef- ficient Semi-Supervised Learning Method for Deep Neural Networks.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Pseudo-Label : The Simple and Ef- ficient Semi-Supervised Learning Method for Deep Neural Networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.731158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7ac10ccb-3a07-46eb-8c83-e343617b6556 · outbound

This paper cites DAFormer: Im- proving Network Architectures and Training Strategies for Domain-Adaptive Semantic Seg- mentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation DAFormer: Im- proving Network Architectures and Training Strategies for Domain-Adaptive Semantic Seg- mentation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.715830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a73cdaf8-a9a6-4571-9f3d-f032f99c897e · outbound

This paper cites HRDA: Context- Aware High-Resolution Domain-Adaptive Se- mantic Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation HRDA: Context- Aware High-Resolution Domain-Adaptive Se- mantic Segmentation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.700891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5b3a775c-17ca-4b47-980e-65a58af2f424 · outbound

This paper cites CDAC: Cross-domain Attention Consistency in Transformer for Domain Adaptive Seman- tic Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation CDAC: Cross-domain Attention Consistency in Transformer for Domain Adaptive Seman- tic Segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.685464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5e88715c-2eaa-43af-b3c1-1b737f79cb57 · outbound

This paper cites CoN- Mix for Source-free Single and Multi-target Do- main Adaptation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation CoN- Mix for Source-free Single and Multi-target Do- main Adaptation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.671193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f76192ae-c57b-480f-9cda-988256f2756b · outbound

This paper cites MIC: Masked Image Consistency for Context- Enhanced Domain Adaptation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation MIC: Masked Image Consistency for Context- Enhanced Domain Adaptation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.656452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dd22711a-5e7e-4713-9552-d7dee29ab4ba · outbound

This paper cites Mean teachers are bet- ter role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Mean teachers are bet- ter role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.641917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 07545c57-d634-4cf2-be3e-f9b933d50bb6 · outbound

This paper cites Research On Data Model Migration In Image Semantic Segmenta- tion Based On Deep Learning.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Research On Data Model Migration In Image Semantic Segmenta- tion Based On Deep Learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.627693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a94136a9-f0b0-438a-9f11-dc6e8a350fa8 · outbound

This paper cites Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.613234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3cafea80-4c00-4a50-8706-3134ca23f6cc · outbound

This paper cites Characterizations of semantic do- mains for randomized algorithms.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Characterizations of semantic do- mains for randomized algorithms

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.597989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b55572fa-9ad3-44f5-a248-52543895e974 · outbound

This paper cites Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.582940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 64c48310-2a06-4787-833c-9e6c95269ff9 · outbound

This paper cites Structured Domain Randomization: Bridg- ing the Reality Gap by Context-Aware Synthetic Data.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Structured Domain Randomization: Bridg- ing the Reality Gap by Context-Aware Synthetic Data

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.568102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 88cf7b5d-c678-444d-860f-88300e649e80 · outbound

This paper cites Domain randomization for neural network classification.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Domain randomization for neural network classification

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.552622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5992a64b-bf03-44e0-8caa-355209d842f0 · outbound

This paper cites DACS: Domain Adaptation via Cross-domain Mixed Sampling.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation DACS: Domain Adaptation via Cross-domain Mixed Sampling

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.537414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d9afe163-a5c2-484e-bfc4-2f6e4e79360b · outbound

This paper cites CLIP-Flow: Decoding images encoded in CLIP space.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation CLIP-Flow: Decoding images encoded in CLIP space

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.522918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b9d13039-40e4-4b70-80b6-c49b5a1c41dc · outbound

This paper cites Attention is All you Need.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Attention is All you Need

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.506027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 57daf410-4a92-4bfa-8b15-7a0ed06837db · outbound

This paper cites Universal Language Model Fine-tuning for Text Classification.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Universal Language Model Fine-tuning for Text Classification

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.489729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ad0a3f32-a9d6-46d9-a3ca-a115df562a8b · outbound

This paper cites Fast End-to- End Trainable Guided Filter.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Fast End-to- End Trainable Guided Filter

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.475682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 946699b8-0412-43bd-aefc-32bfd0f6213b · outbound

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

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.460564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0eaf6ebe-3ad3-4ff9-b12d-b9fe6c4137a3 · outbound

This paper cites Convolu- tional neural network architecture for geometric matching.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Convolu- tional neural network architecture for geometric matching

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.445032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ee99c6da-1570-4f5a-97f0-9f3004af3e5f · outbound

This paper cites Cost Ag- gregation with 4D Convolutional Swin Trans- former for Few-Shot Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Cost Ag- gregation with 4D Convolutional Swin Trans- former for Few-Shot Segmentation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.429888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e4267177-df75-4d99-8e9c-24aa3bdace12 · outbound

This paper cites The Cityscapes Dataset for Seman- tic Urban Scene Understanding.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation The Cityscapes Dataset for Seman- tic Urban Scene Understanding

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.415708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cc7ac1b9-f856-4855-b629-896a71dd096f · outbound

This paper cites Language Models are Few-Shot Learners.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Language Models are Few-Shot Learners

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.401412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3118b4e5-bcfd-475e-bbab-f467fc675065 · outbound

This paper cites The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.387200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 436c1927-08eb-4dbc-af8c-449af3b1ea81 · outbound

This paper cites Domain randomization for trans- ferring deep neural networks from simulation to the real world.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Domain randomization for trans- ferring deep neural networks from simulation to the real world

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.373047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 011dfbdd-e6c6-4612-8767-bc73a6854d2c · outbound

This paper cites Semantic understanding of scenes through the ade20k dataset.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Semantic understanding of scenes through the ade20k dataset

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.359071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 54f0feef-b3ed-485e-b15b-366646ca2bf8 · outbound

This paper cites The Role of Con- text for Object Detection and Semantic Segmen- tation in the Wild.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation The Role of Con- text for Object Detection and Semantic Segmen- tation in the Wild

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.345301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 42c4fa23-0bf1-4a6a-9f61-484cfa2de2d8 · outbound

This paper cites The Pascal Visual Object Classes Challenge: A Retrospec- tive.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation The Pascal Visual Object Classes Challenge: A Retrospec- tive

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.330538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 51ae5699-3bf9-439b-9d63-74b342430223 · outbound

This paper cites The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Seg- mentation of Urban Scenes.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Seg- mentation of Urban Scenes

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.316114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 60493020-d06b-44f5-bb87-dfa79f3abc42 · outbound

This paper cites Swin Transformer V2: Scaling Up Capacity and Res- olution.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Swin Transformer V2: Scaling Up Capacity and Res- olution

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.300556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 574e5fd7-daf4-4b76-97f7-5c9f8f8a388c · outbound

This paper cites Segment Anything.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Segment Anything

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.285025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 98f0da41-ea53-4989-b3ce-2dbb1ed1b582 · outbound

This paper cites FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.270050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7602ddb6-7910-4283-b577-be153846f188 · outbound

This paper cites MasQCLIP for Open-Vocabulary Universal Image Segmenta- tion.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation MasQCLIP for Open-Vocabulary Universal Image Segmenta- tion

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.254557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 57bb45db-d711-4a9b-9c6f-8253a3c2273f · outbound

This paper cites ZegCLIP: Towards Adapting CLIP for Zero-Shot Seman- tic Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation ZegCLIP: Towards Adapting CLIP for Zero-Shot Seman- tic Segmentation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.238424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2146108f-ccf2-43ab-9664-ca82fc8a31f7 · outbound

This paper cites Hierarchical Open-vocabulary Uni- versal Image Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Hierarchical Open-vocabulary Uni- versal Image Segmentation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.223697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c502d873-d5d6-4b36-9a7c-9b554184eb09 · outbound

This paper cites Transferring Multi-Modal Domain Knowledge to Uni-Modal Domain for Urban Scene Segmenta- tion.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Transferring Multi-Modal Domain Knowledge to Uni-Modal Domain for Urban Scene Segmenta- tion

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.208408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 699f93c9-4776-4a4b-8684-3765462b3551 · outbound

This paper cites DiGA: Distil To Generalize and Then Adapt for Domain Adaptive Semantic Segmentation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation DiGA: Distil To Generalize and Then Adapt for Domain Adaptive Semantic Segmentation

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.193234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 767cf4bb-09d9-4858-b149-96bff05e92a8 · outbound

This paper cites Transferring to Real- World Layouts: A Depth-aware Framework for Scene Adaptation.

VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation Transferring to Real- World Layouts: A Depth-aware Framework for Scene Adaptation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:15:26.177467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers