Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation | Paper Citation Record

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This paper cites Flamingo: a visual language model for few-shot learning.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Flamingo: a visual language model for few-shot learning

Reference 1

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Observation 9da6e0c7-d4d4-4573-aab5-d66a620e0fbd · outbound

This paper cites Yolact: Real-time instance segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Yolact: Real-time instance segmentation

Reference 2

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This paper cites Lan- guage models are few-shot learners.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Lan- guage models are few-shot learners

Reference 3

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Coco- stuff: Thing and stuff classes in context

Reference 4

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This paper cites Cascade r-cnn: Delv- ing into high quality object detection.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Cascade r-cnn: Delv- ing into high quality object detection

Reference 5

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Observation 234daceb-41a8-453a-bc27-e6e23aacc44f · outbound

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation End-to- end object detection with transformers

Reference 6

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Observation 99e41fc5-af82-4dcc-ab4e-9446c9c0874b · outbound

This paper cites Hybrid task cascade for instance seg- mentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Hybrid task cascade for instance seg- mentation

Reference 7

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Observation d66f6102-df1f-40f0-977d-e87c70b01b1a · outbound

This paper cites Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Reference 8

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This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 9

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Observation e17ee739-1306-48d8-95ce-c5a4a67ead0c · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 10

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Observation b08237e9-efe2-4efe-a916-b3266748e198 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 11

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Observation 87305927-b1d6-4780-96dd-a86a87e1917f · outbound

This paper cites Scaling Wide Residual Networks for Panoptic Segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Scaling Wide Residual Networks for Panoptic Segmentation

Reference 12

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Observation 7c1f5756-20fe-4506-ad05-1f66da3ea466 · outbound

This paper cites Uniter: Universal image-text representation learning.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Uniter: Universal image-text representation learning

Reference 13

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Observation b64e0bf5-b913-433f-a822-e1748ce36c6c · outbound

This paper cites Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation

Reference 14

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Observation e55bde3c-278c-4834-8028-3bef5c78a77d · outbound

This paper cites Per- pixel classification is not all you need for semantic segmen- tation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Per- pixel classification is not all you need for semantic segmen- tation

Reference 15

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Observation 29a6deb3-65a9-4dc0-aaf8-7b46ad5e60dd · outbound

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Masked-attention mask transformer for universal image segmentation

Reference 16

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Cat- seg: Cost aggregation for open-vocabulary semantic seg- mentation

Reference 17

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Observation 3ad12b4c-fedb-4092-9b6c-ac97ab336997 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 18

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Observation d969e514-a726-48c4-86c5-eefa0c7b6699 · outbound

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation De- coupling zero-shot semantic segmentation

Reference 19

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Observation 21243f6c-26b9-4bca-8d8c-803728e83eeb · outbound

This paper cites Open- vocabulary universal image segmentation with maskclip.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Open- vocabulary universal image segmentation with maskclip

Reference 20

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Observation 8b9c1c51-15c2-4810-9b33-1efbf780ab13 · outbound

This paper cites The pascal visual object classes challenge: A retrospective.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation The pascal visual object classes challenge: A retrospective

Reference 21

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Observation a88faa8b-a639-48b2-aca2-6125d38d419e · outbound

This paper cites Dual attention network for scene seg- mentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Dual attention network for scene seg- mentation

Reference 22

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Scal- ing open-vocabulary image segmentation with image-level labels

Reference 23

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This paper cites Multi-scale high-resolution vision transformer for se- mantic segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Multi-scale high-resolution vision transformer for se- mantic segmentation

Reference 24

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This paper cites Deep residual learning for image recognition.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Deep residual learning for image recognition

Reference 25

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This paper cites Mask r-cnn.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Mask r-cnn

Reference 26

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This paper cites Oneformer: One transformer to rule universal image segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Oneformer: One transformer to rule universal image segmentation

Reference 27

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This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 28

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Observation 97530eb1-ae01-4671-b6c5-3bca3fd0972b · outbound

This paper cites Instancecut: from edges to instances with multicut.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Instancecut: from edges to instances with multicut

Reference 29

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This paper cites Panoptic segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Panoptic segmentation

Reference 30

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This paper cites Segment Anything.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Segment Anything

Reference 31

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This paper cites Language-driven semantic seg- mentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Language-driven semantic seg- mentation

Reference 32

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This paper cites Mask dino: Towards a unified transformer-based framework for object detection and segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Mask dino: Towards a unified transformer-based framework for object detection and segmentation

Reference 33

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This paper cites Unifying training and inference for panoptic segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Unifying training and inference for panoptic segmentation

Reference 34

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This paper cites Panoptic segformer: Delving deeper into panoptic segmen- tation with transformers.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Panoptic segformer: Delving deeper into panoptic segmen- tation with transformers

Reference 35

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Open-vocabulary semantic segmentation with mask-adapted clip

Reference 36

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This paper cites Feature pyra- mid networks for object detection.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Feature pyra- mid networks for object detection

Reference 37

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This paper cites An end-to-end network for panoptic segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation An end-to-end network for panoptic segmentation

Reference 38

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Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Path aggregation network for instance segmentation

Reference 39

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Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 40

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Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 41

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Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation A convnet for the 2020s

Reference 42

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Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 43

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation The role of context for object detection and semantic segmentation in the wild

Reference 44

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This paper cites Detectors: Detecting objects with recursive feature pyramid and switch- able atrous convolution.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Detectors: Detecting objects with recursive feature pyramid and switch- able atrous convolution

Reference 45

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Learning transferable visual models from natural language supervi- sion

Reference 46

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 47

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This paper cites High-resolution image 10 synthesis with latent diffusion models.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation High-resolution image 10 synthesis with latent diffusion models

Reference 48

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation U- net: Convolutional networks for biomedical image segmen- tation

Reference 49

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This paper cites Segmenter: Transformer for semantic segmenta- tion.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Segmenter: Transformer for semantic segmenta- tion

Reference 50

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This paper cites Lxmert: Learning cross-modality encoder representations from transformers.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Lxmert: Learning cross-modality encoder representations from transformers

Reference 51

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Observation c4a9df2a-bfe7-4e79-add8-48d5ab332d58 · outbound

This paper cites Conditional con- volutions for instance segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Conditional con- volutions for instance segmentation

Reference 52

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This paper cites Axial-deeplab: Stand- alone axial-attention for panoptic segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Axial-deeplab: Stand- alone axial-attention for panoptic segmentation

Reference 53

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This paper cites Max-deeplab: End-to-end panoptic segmentation with mask transformers.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Max-deeplab: End-to-end panoptic segmentation with mask transformers

Reference 54

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This paper cites Solov2: Dynamic and fast instance segmenta- tion.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Solov2: Dynamic and fast instance segmenta- tion

Reference 55

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This paper cites Segformer: Simple and efficient design for semantic segmentation with transform- ers.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Segformer: Simple and efficient design for semantic segmentation with transform- ers

Reference 56

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This paper cites Upsnet: A unified panoptic segmentation network.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Upsnet: A unified panoptic segmentation network

Reference 57

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Groupvit: Semantic segmentation emerges from text supervision

Reference 58

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Open-vocabulary panop- tic segmentation with text-to-image diffusion models

Reference 59

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation A simple baseline for zero- shot semantic segmentation with pre-trained vision-language model

Reference 60

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation A simple baseline for open- vocabulary semantic segmentation with pre-trained vision- language model

Reference 61

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Side adapter network for open-vocabulary semantic segmentation

Reference 62

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This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 63

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This paper cites Cmt-deeplab: Clustering mask transformers for panoptic segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Cmt-deeplab: Clustering mask transformers for panoptic segmentation

Reference 64

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This paper cites k-means mask transformer.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation k-means mask transformer

Reference 65

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This paper cites Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP

Reference 66

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

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Florence: A New Foundation Model for Computer Vision

Reference 67

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This paper cites Object- contextual representations for semantic segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Object- contextual representations for semantic segmentation

Reference 68

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This paper cites Open-vocabulary object detection using captions.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Open-vocabulary object detection using captions

Reference 69

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This paper cites Vinvl: Revisiting visual representations in vision-language models.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Vinvl: Revisiting visual representations in vision-language models

Reference 70

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This paper cites Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers

Reference 71

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This paper cites Scene parsing through ade20k dataset.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Scene parsing through ade20k dataset

Reference 72

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This paper cites Extract free dense labels from clip.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Extract free dense labels from clip

Reference 73

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This paper cites Zegclip: Towards adapting clip for zero-shot se- mantic segmentation.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Zegclip: Towards adapting clip for zero-shot se- mantic segmentation

Reference 74

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This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 75

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This paper cites Generalized decoding for pixel, image, and lan- guage.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation Generalized decoding for pixel, image, and lan- guage

Reference 76

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This paper cites MIT CSAIL.

Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation MIT CSAIL

Reference 2023

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