ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation | Paper Citation Record

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This paper cites GPT-4 Technical Report.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation GPT-4 Technical Report

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Flamingo: a visual language model for few-shot learning

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This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Qwen2.5-VL Technical Report

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Fossil: Free open-vocabulary semantic seg- mentation through synthetic references retrieval

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This paper cites Training-free open- vocabulary segmentation with offline diffusion-augmented prototype generation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Training-free open- vocabulary segmentation with offline diffusion-augmented prototype generation

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Grounding everything: Emerging localiza- tion properties in vision-language transformers

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

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Coco- stuff: Thing and stuff classes in context

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This paper cites Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Emerg- ing properties in self-supervised vision transformers

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

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Learn- ing to generate text-grounded mask for open-world semantic segmentation from only image-text pairs

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Exploring open-vocabulary semantic segmentation from clip vision encoder distillation only

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

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Cat- seg: Cost aggregation for open-vocabulary semantic seg- mentation

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation The cityscapes dataset for semantic urban scene understanding

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation De- coupling zero-shot semantic segmentation

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

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation The pascal visual object classes (voc) challenge

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This paper cites Scaling laws of synthetic images for model training.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Scaling laws of synthetic images for model training

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Data filtering networks

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Efficient graph-based image segmentation

Reference 20

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Dat- acomp: In search of the next generation of multimodal datasets

Reference 21

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Scal- ing open-vocabulary image segmentation with image-level labels

Reference 22

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation kNN-CLIP: Retrieval enables training-free segmentation on continually expanding large vocabularies

Reference 23

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Robustifying token attention for vision transformers

Reference 24

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This paper cites Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Scaling up visual and vision-language representa- tion learning with noisy text supervision

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Diffusion models for open-vocabulary segmen- tation

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Segment any- thing

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Efficient inference in fully connected crfs with gaussian edge potentials

Reference 29

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation LISA: Reasoning segmentation via large language model

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Veclip: Improving clip training via visual-enriched captions

Reference 31

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Proxyclip: Proxy at- tention improves clip for open-vocabulary segmentation

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentation

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 34

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation A Closer Look at the Explainability of Contrastive Language-Image Pre-training

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Open-vocabulary semantic segmentation with mask-adapted clip

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Microsoft coco: Common objects in context

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Visual instruction tuning

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Segclip: Patch aggregation with learnable centers for open-vocabulary semantic segmentation

Reference 39

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Emergent open-vocabulary semantic segmenta- tion from off-the-shelf vision-language models

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Sieve: Multimodal dataset pruning using image captioning models

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Towards interactive 3d surgical scene reconstruction: An incremental training and monitoring framework

Reference 42

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation The role of context for object detection and semantic segmentation in the wild

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Open vocabulary semantic segmentation with patch aligned con- trastive learning

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic segmentation

Reference 45

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Improving multimodal datasets with image captioning

Reference 46

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Unresolved cited work

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Kosmos-2: Grounding Multimodal Large Language Models to the World

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation and et al

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Filtering, distil- lation, and hard negatives for vision-language pre-training

Reference 50

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

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Learning transferable visual models from natural language supervi- sion

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation SAM 2: Segment Anything in Images and Videos

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Zero- guidance segmentation using zero segment labels

Reference 53

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation High-resolution image synthesis with latent diffusion models

Reference 54

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 55

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation LAION-5b: An open large-scale dataset for train- ing next generation image-text models

Reference 56

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation

Reference 57

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Reco: Re- trieve and co-segment for zero-shot transfer

Reference 58

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Is synthetic data all we need? benchmarking the robustness of models trained with synthetic images

Reference 59

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Clip as rnn: Segment countless visual concepts without training endeavor

Reference 60

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Attention is all you need

Reference 61

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Sclip: Rethink- ing self-attention for dense vision-language inference

Reference 62

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Sam-clip: Merging vision foundation models to- wards semantic and spatial understanding

Reference 63

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter

Reference 64

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Use: Universal segment embeddings for open-vocabulary image segmentation

Reference 65

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Image-to-image matching via foundation models: A new perspective for open-vocabulary semantic segmentation

Reference 66

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Probabilistic pixel-adaptive refinement networks

Reference 67

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Image-text co- decomposition for text-supervised semantic segmentation

Reference 68

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Clip-diy: Clip dense infer- ence yields open-vocabulary semantic segmentation for-free

Reference 69

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Florence-2: Advancing a unified representation for a variety of vision tasks

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Rewrite caption semantics: Bridging seman- tic gaps for language-supervised semantic segmentation.Ad- vances in Neural Information Processing Systems, 36, 2024

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Groupvit: Semantic segmentation emerges from text supervision

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Learning open-vocabulary semantic segmentation models from natural language supervision

Reference 73

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation A simple baseline for open- vocabulary semantic segmentation with pre-trained vision- language model

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Side adapter network for open-vocabulary semantic segmentation

Reference 75

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation V AC-CNN: A visual analytics system for compar- ative studies of deep convolutional neural networks

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Suny: A visual interpretation framework for convolutional neural networks from a necessary and suf- ficient perspective

Reference 77

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation SLIM: Spuriousness mitigation with minimal human annotations

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation AttributionScanner: A visual analytics system for model validation with metadata-free slice finding

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation VISTA: A visual analytics framework to enhance foundation model-generated data labels

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Vislix: An xai framework for val- idating vision models with slice discovery and analysis

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation A simple framework for text- supervised semantic segmentation

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation CorrCLIP: Reconstructing Patch Correlations in CLIP for Open-Vocabulary Semantic Segmentation

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Tip- adapter: Training-free adaption of clip for few-shot classi- fication

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Labelvizier: Interactive validation and relabeling for technical text annotations

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Semantic under- standing of scenes through the ade20k dataset

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Extract free dense labels from clip

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:36:51.084290Z

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ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions

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