Paper Citation Record · LEDGER

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements

As of 18 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 2 inbound Pith citation observations for arXiv:2411.12044.

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

pith.paper-citation-record.v1
2411.12044 v2

Coverage vector

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Reference resolution

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External citation measurements

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Outbound references

Observation 211d8a3e-c8e6-4690-8246-333147dd6420 · outbound

This paper cites Cdul: Clip-driven unsupervised learning for multi-label image classification.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Cdul: Clip-driven unsupervised learning for multi-label image classification

Reference 1

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Observation b6ea9513-2029-4441-8569-f4895551b810 · outbound

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Flamingo: a visual language model for few-shot learning

Reference 2

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This paper cites Self- supervised multimodal versatile networks.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Self- supervised multimodal versatile networks

Reference 3

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Observation da910b16-5db1-4bf5-a5b7-051b5d23c11a · outbound

This paper cites Single-stage semantic segmentation from image labels.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Single-stage semantic segmentation from image labels

Reference 4

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Observation 481c2f86-fb96-4dae-b2d3-1901bb82c67d · outbound

This paper cites Fossil: Free open-vocabulary semantic seg- mentation through synthetic references retrieval.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Fossil: Free open-vocabulary semantic seg- mentation through synthetic references retrieval

Reference 5

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Observation dff6d89b-1641-4696-843d-b213366b72ab · outbound

This paper cites Grounding everything: Emerging localiza- tion properties in vision-language transformers.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Grounding everything: Emerging localiza- tion properties in vision-language transformers

Reference 6

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This paper cites Brown, Benjamin Mann, Nick Ryder, Melanie Sub- biah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Brown, Benjamin Mann, Nick Ryder, Melanie Sub- biah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al

Reference 7

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Coco- stuff: Thing and stuff classes in context

Reference 8

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This paper cites Cambridge dictionary.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Cambridge dictionary

Reference 9

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Observation 6f44682f-14fd-47f9-b54a-a974ef599626 · outbound

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Learn- ing to generate text-grounded mask for open-world semantic segmentation from only image-text pairs

Reference 10

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Observation 8c13f81f-b621-4f41-8757-12e61577acf3 · outbound

This paper cites Reproducible scal- ing laws for contrastive language-image learning.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Reproducible scal- ing laws for contrastive language-image learning

Reference 11

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This paper cites The cityscapes dataset for semantic urban scene understanding.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements The cityscapes dataset for semantic urban scene understanding

Reference 12

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This paper cites Imagenet: A large-scale hierarchical image database.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Imagenet: A large-scale hierarchical image database

Reference 13

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Observation c21f8ba0-67ec-4ace-a42f-393c5e3a02af · outbound

This paper cites Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 14

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 15

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements De- coupling zero-shot semantic segmentation

Reference 16

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Observation 29ad03de-4071-454d-8fe6-17bcb00fcf8c · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 17

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This paper cites Learning to prompt for open-vocabulary ob- ject detection with vision-language model.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Learning to prompt for open-vocabulary ob- ject detection with vision-language model

Reference 18

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This paper cites The Llama 3 Herd of Models.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements The Llama 3 Herd of Models

Reference 19

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements The pascal visual object classes (voc) challenge

Reference 20

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This paper cites Improving clip training with language rewrites.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Improving clip training with language rewrites

Reference 21

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This paper cites In- terpreting clip’s image representation via text-based decom- position.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements In- terpreting clip’s image representation via text-based decom- position

Reference 22

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Scal- ing open-vocabulary image segmentation with image-level labels

Reference 23

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This paper cites Open-vocabulary object detection via vision and language knowledge distillation.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Open-vocabulary object detection via vision and language knowledge distillation

Reference 24

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation

Reference 25

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This paper cites Open-vocabulary semantic segmentation with decou- pled one-pass network.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Open-vocabulary semantic segmentation with decou- pled one-pass network

Reference 26

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This paper cites Deep residual learning for image recognition.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Deep residual learning for image recognition

Reference 27

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This paper cites Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 28

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ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Open- clip, July 2021

Reference 29

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Scaling up visual and vision-language representation learning with noisy text supervision

Reference 30

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This paper cites Learning mask-aware clip representations for zero-shot segmentation.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Learning mask-aware clip representations for zero-shot segmentation

Reference 31

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ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Segment any- thing

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This paper cites ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference

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ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Language-driven semantic seg- mentation

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This paper cites Align before fuse: Vision and language representation learn- ing with momentum distillation.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Align before fuse: Vision and language representation learn- ing with momentum distillation

Reference 35

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This paper cites Cascade-clip: Cascaded vision-language embeddings alignment for zero-shot semantic segmentation.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Cascade-clip: Cascaded vision-language embeddings alignment for zero-shot semantic segmentation

Reference 36

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements A Closer Look at the Explainability of Contrastive Language-Image Pre-training

Reference 37

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ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Open-vocabulary object segmentation with diffusion models

Reference 38

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ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Open-vocabulary semantic segmentation with mask-adapted clip

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ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Microsoft coco: Common objects in context

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This paper cites Clip is also an ef- ficient segmenter: A text-driven approach for weakly super- vised semantic segmentation.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Clip is also an ef- ficient segmenter: A text-driven approach for weakly super- vised semantic segmentation

Reference 41

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ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Tagclip: A local-to-global framework to enhance open-vocabulary multi-label classification of clip without training

Reference 42

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ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Object- centric learning with slot attention

Reference 43

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ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Segclip: Patch aggregation with learn- able centers for open-vocabulary semantic segmentation

Reference 44

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This paper cites Segment anything model for medical image analysis: an experimental study.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Segment anything model for medical image analysis: an experimental study

Reference 45

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ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements MetaICL: Learning to learn in context

Reference 46

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ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements The role of context for object detection and se- mantic segmentation in the wild

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This paper cites Peters, Mark Neumann, Mohit Iyyer, Matt Gard- ner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Peters, Mark Neumann, Mohit Iyyer, Matt Gard- ner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer

Reference 48

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This paper cites Learn- ing transferable visual models from natural language super- vision.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Learn- ing transferable visual models from natural language super- vision

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This paper cites Improving language understanding by gen- erative pre-training.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Improving language understanding by gen- erative pre-training

Reference 50

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 51

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This paper cites Per- ceptual grouping in contrastive vision-language models.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Per- ceptual grouping in contrastive vision-language models

Reference 52

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This paper cites Laion-5b: An open large-scale dataset for train- ing next generation image-text models.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Laion-5b: An open large-scale dataset for train- ing next generation image-text models

Reference 53

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This paper cites Ex- plore the potential of clip for training-free open vocabulary semantic segmentation.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Ex- plore the potential of clip for training-free open vocabulary semantic segmentation

Reference 54

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This paper cites Edadet: Open-vocabulary ob- ject detection using early dense alignment.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Edadet: Open-vocabulary ob- ject detection using early dense alignment

Reference 55

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This paper cites Reco: Retrieve and co-segment for zero-shot transfer.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Reco: Retrieve and co-segment for zero-shot transfer

Reference 56

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Observation 275e2ec4-c1d3-415d-9948-5d5671d3821c · outbound

This paper cites Going denser with open-vocabulary part segmentation.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Going denser with open-vocabulary part segmentation

Reference 57

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Observation b5a22c4c-0561-46b1-80bb-9ae040ee9baf · outbound

This paper cites Clip as rnn: Segment countless visual concepts without training endeavor.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Clip as rnn: Segment countless visual concepts without training endeavor

Reference 58

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Observation bcc374ff-4d67-4c54-96a5-7b86198e0e83 · outbound

This paper cites Galip: Generative adversarial clips for text-to-image synthe- sis.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Galip: Generative adversarial clips for text-to-image synthe- sis

Reference 59

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Observation f8581781-ecde-49f0-966c-776d4b62e2f6 · outbound

This paper cites Attention is all you need.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Attention is all you need

Reference 60

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This paper cites SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference

Reference 61

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This paper cites CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP

Reference 62

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Observation 02361cb0-5e06-4ee1-ad18-e1e7941c7d63 · outbound

This paper cites Clip-diy: Clip dense infer- ence yields open-vocabulary semantic segmentation for-free.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Clip-diy: Clip dense infer- ence yields open-vocabulary semantic segmentation for-free

Reference 63

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Observation a58c2522-69dc-41e7-a956-422f5e3d408a · outbound

This paper cites Demystifying CLIP Data.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Demystifying CLIP Data

Reference 64

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Groupvit: Semantic segmentation emerges from text supervision

Reference 65

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This paper cites Learning open-vocabulary semantic segmentation models from natural language supervision.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Learning open-vocabulary semantic segmentation models from natural language supervision

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:02:15.525991Z

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Observation 3f61d6fc-b9ee-49aa-a0e4-e03d8475c2ab · outbound

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Open-vocabulary panop- tic segmentation with text-to-image diffusion models

Reference 67

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

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements A simple baseline for open- vocabulary semantic segmentation with pre-trained vision- language model

Reference 68

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This paper cites Learning deep features for discrimi- native localization.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Learning deep features for discrimi- native localization

Reference 69

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Observation f0908021-ee5c-4ca8-b8e1-68d43e7cfc9b · outbound

This paper cites Extract free dense labels from clip.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Extract free dense labels from clip

Reference 70

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This paper cites thing” classes and one explicit background class, our method fails to distinguish foreground classes from the background when the word “background.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements thing” classes and one explicit background class, our method fails to distinguish foreground classes from the background when the word “background

Reference 71

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Pith citing papers

Observation 5b4f4557-10d4-48b9-b2a5-b444dc26a802 · inbound

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation cites this paper.

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements

Reference 42

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SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation cites this paper.

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:44:02.856656Z

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