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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+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

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+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

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+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

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+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

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+00:00.

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