Paper Citation Record · LEDGER

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation

As of 11 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2412.14145.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.14145 v1

Coverage vector

measured 47 of 47 reference resolution

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation b5427c4b-9860-4c57-bace-aff9c1397599 · outbound

This paper cites Learning transferable visual models from natural lan- guage supervision,.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Learning transferable visual models from natural lan- guage supervision,

Reference 1

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Observation 50b60ac0-0715-4811-b6fd-3f5bee94b0bd · outbound

This paper cites Scaling open- vocabulary image segmentation with image-level labels,.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Scaling open- vocabulary image segmentation with image-level labels,

Reference 2

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Observation cc893f0c-a7f4-4c98-823d-1eb76eee6c6f · outbound

This paper cites Maskclip: Masked self-distillation advances contrastive language- image pretraining,.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Maskclip: Masked self-distillation advances contrastive language- image pretraining,

Reference 3

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This paper cites A simple framework for text-supervised semantic seg- mentation,.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation A simple framework for text-supervised semantic seg- mentation,

Reference 4

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

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Open-vocabulary semantic segmentation with mask-adapted clip,

Reference 5

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Observation 992b89fe-45a8-4713-aedf-80ea4459adf4 · outbound

This paper cites Segment anything,.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Segment anything,

Reference 6

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Observation 8ca79fc6-8334-484c-98f6-9a6c41bd07be · outbound

This paper cites Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

Reference 7

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Observation ffd22855-5026-4349-b6c4-3fceed9484d2 · outbound

This paper cites PosSAM: Panoptic Open-vocabulary Segment Anything.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation PosSAM: Panoptic Open-vocabulary Segment Anything

Reference 8

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Observation df7f4408-53c8-47de-9c8c-8327f1c0a018 · outbound

This paper cites Diffusion Models for Open-Vocabulary Segmentation.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Diffusion Models for Open-Vocabulary Segmentation

Reference 9

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

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Open-vocabulary panoptic segmentation with text-to- image diffusion models,

Reference 10

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Observation d6b4f834-4c8d-40e6-ad00-57a957021889 · outbound

This paper cites CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation

Reference 11

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Observation 1cdb15f9-1aa4-489c-aa98-652d46c7807e · outbound

This paper cites CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction

Reference 12

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Observation bcacc359-537f-40ee-ba76-661dedc8a5a1 · outbound

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

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation

Reference 13

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This paper cites What do Vision Transformers Learn? A Visual Exploration.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation What do Vision Transformers Learn? A Visual Exploration

Reference 14

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Observation c4766506-8db1-4ba6-a40d-041431829357 · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 15

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This paper cites Visualizing and understanding con- volutional networks,.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Visualizing and understanding con- volutional networks,

Reference 16

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Observation 83ea2eb9-3c3a-410d-8d99-db8510550720 · outbound

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

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Masked-attention mask transformer for universal image segmentation,

Reference 17

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Observation 1c0601f6-8278-40c1-8726-6806869e7b6f · outbound

This paper cites kMaX-DeepLab: k-means Mask Transformer.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation kMaX-DeepLab: k-means Mask Transformer

Reference 18

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Observation d4249441-34d9-4b6a-841e-fc8b08bb3275 · outbound

This paper cites Mean Shift Mask Transformer for Unseen Object Instance Segmentation.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Mean Shift Mask Transformer for Unseen Object Instance Segmentation

Reference 19

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This paper cites Feature pyramid networks for object detection,.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Feature pyramid networks for object detection,

Reference 20

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation U-net: Con- volutional networks for biomedical image segmentation,

Reference 21

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

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Side adapter network for open-vocabulary semantic segmentation,

Reference 22

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Observation b9136e1f-4ba0-40c0-984a-6e0758938a33 · outbound

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

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip,

Reference 23

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This paper cites SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation

Reference 24

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Neural discrete representa- tion learning,

Reference 25

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Taming transformers for high-resolution image synthesis,

Reference 26

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Vector-quantized Image Modeling with Improved VQGAN

Reference 27

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation High-resolution image synthesis with latent diffu- sion models,

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Coco-stuff: Thing and stuff classes in context,

Reference 30

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation The role of context for object detection and semantic segmentation in the wild,

Reference 31

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Scene parsing through ade20k dataset,

Reference 32

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This paper cites PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,

Reference 34

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Spae: Semantic pyramid autoencoder for multi- modal generation with frozen llms,

Reference 35

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

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation A simple baseline for open-vocabulary semantic segmentation with pre-trained vision-language model,

Reference 36

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Denoising Vision Transformers

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This paper cites FeatUp: A Model-Agnostic Framework for Features at Any Resolution.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation FeatUp: A Model-Agnostic Framework for Features at Any Resolution

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Learning to upsample by learning to sample,

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Unlocking Pre-trained Image Backbones for Semantic Image Synthesis

Reference 40

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Semantic image synthesis with spatially-adaptive normalization,

Reference 41

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Perceptual losses for real-time style transfer and super-resolution,

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Microsoft COCO Captions: Data Collection and Evaluation Server

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Microsoft coco: Common objects in context,

Reference 44

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 45

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 46

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This paper cites Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction

Reference 47

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Pith citing papers

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