Fine-grained CLIP fine-tuning with self-annotated region alignment | Paper Citation Record

Observation 8bd193dc-b30d-4078-8177-d7083e37a48e · outbound

This paper cites Learning transferable visual models from natural language supervision.

Fine-grained CLIP fine-tuning with self-annotated region alignment Learning transferable visual models from natural language supervision

Reference 1

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Observation 63d26381-afdc-4909-bda2-548169a26959 · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

Fine-grained CLIP fine-tuning with self-annotated region alignment Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 2

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This paper cites Domain generalization by mutual-information regularization with pre-trained models.

Fine-grained CLIP fine-tuning with self-annotated region alignment Domain generalization by mutual-information regularization with pre-trained models

Reference 3

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This paper cites Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning.Neurocomputing, 508:293–304, 2022.

Fine-grained CLIP fine-tuning with self-annotated region alignment Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning.Neurocomputing, 508:293–304, 2022

Reference 4

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This paper cites A simple baseline for zeroshot semantic segmentation with pre-trained vision-language model.ECCV, 2022.

Fine-grained CLIP fine-tuning with self-annotated region alignment A simple baseline for zeroshot semantic segmentation with pre-trained vision-language model.ECCV, 2022

Reference 5

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Observation 6de4a91c-378c-4c3d-9f61-982631c05f86 · outbound

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

Fine-grained CLIP fine-tuning with self-annotated region alignment Open-vocabulary semantic segmentation with mask-adapted clip

Reference 6

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Observation 11bf04f8-b573-4897-8467-4c55297b54c3 · outbound

This paper cites Open-vocabulary object detection via vision and language knowledge distillation.ICLR, 2022.

Fine-grained CLIP fine-tuning with self-annotated region alignment Open-vocabulary object detection via vision and language knowledge distillation.ICLR, 2022

Reference 7

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Observation 1b1ce2ea-0b29-4882-b758-c8d3ec22c2e1 · outbound

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

Fine-grained CLIP fine-tuning with self-annotated region alignment Learning to prompt for open-vocabulary object detection with vision-language model

Reference 8

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Observation 565de053-86d3-4a2e-8d37-626adad563dc · outbound

This paper cites F-vlm: Open-vocabulary object detection upon frozen vision and language models.

Fine-grained CLIP fine-tuning with self-annotated region alignment F-vlm: Open-vocabulary object detection upon frozen vision and language models

Reference 9

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Observation b8dcd1f4-4855-4adf-963d-405c14e8163e · outbound

This paper cites Cora: Adapting clip for open-vocabulary detection with region prompting and anchor pre-matching.

Fine-grained CLIP fine-tuning with self-annotated region alignment Cora: Adapting clip for open-vocabulary detection with region prompting and anchor pre-matching

Reference 10

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Observation d6a36991-b888-41d2-8104-2e835b78156e · outbound

This paper cites Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip.NeurIPS, 36, 2024.

Fine-grained CLIP fine-tuning with self-annotated region alignment Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip.NeurIPS, 36, 2024

Reference 11

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Observation eb9e5c88-b18c-48dd-b5fb-419445fe7bb6 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

Fine-grained CLIP fine-tuning with self-annotated region alignment SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 12

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Observation 35d417ee-a6ef-4c79-9ebc-15a5b96c56dd · outbound

This paper cites Fg-clip: Fine-grained visual and textual alignment.

Fine-grained CLIP fine-tuning with self-annotated region alignment Fg-clip: Fine-grained visual and textual alignment

Reference 13

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Observation 4025cb0e-223d-4a04-9a14-b3b58f99fea0 · outbound

This paper cites Improving fine-grained understanding in image-text pre-training.

Fine-grained CLIP fine-tuning with self-annotated region alignment Improving fine-grained understanding in image-text pre-training

Reference 14

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Observation 77529e6f-7493-4b61-9f75-161c2dccf433 · outbound

This paper cites Position-guided text prompt for vision-language pre-training.

Fine-grained CLIP fine-tuning with self-annotated region alignment Position-guided text prompt for vision-language pre-training

Reference 15

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Observation 6a4a53db-8026-4009-8207-31fb8c29c6fe · outbound

This paper cites Regionclip: Region-based language-image pretraining.

Fine-grained CLIP fine-tuning with self-annotated region alignment Regionclip: Region-based language-image pretraining

Reference 16

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Observation f74a3a0a-d803-456f-874a-7f819ad08ca3 · outbound

This paper cites Densevlm: A retrieval and decoupled alignment framework for open-vocabulary dense prediction.

Fine-grained CLIP fine-tuning with self-annotated region alignment Densevlm: A retrieval and decoupled alignment framework for open-vocabulary dense prediction

Reference 17

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Observation 2fc4abb7-f457-4d88-90c9-7a70dce8e874 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.NeurIPS, 28, 2015.

Fine-grained CLIP fine-tuning with self-annotated region alignment Faster r-cnn: Towards real-time object detection with region proposal networks.NeurIPS, 28, 2015

Reference 18

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Observation 32a894cb-2843-4da8-a076-3556dcf10c95 · outbound

This paper cites Fineclip: Self-distilled region-based clip for better fine-grained understanding.Advances in Neural Information Processing Systems, 37:27896–27918, 2024.

Fine-grained CLIP fine-tuning with self-annotated region alignment Fineclip: Self-distilled region-based clip for better fine-grained understanding.Advances in Neural Information Processing Systems, 37:27896–27918, 2024

Reference 19

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Observation c1be3bf9-5335-4ab8-b12e-af7abd41c793 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Fine-grained CLIP fine-tuning with self-annotated region alignment Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 20

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Observation f86e41b0-c080-4a67-b931-7a6d6092232a · outbound

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

Fine-grained CLIP fine-tuning with self-annotated region alignment CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction

Reference 21

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Observation 6fea05e2-1c49-4392-903e-2e2ec146ea25 · outbound

This paper cites Atas: Any-to-any self-distillation for enhanced open-vocabulary dense prediction.

Fine-grained CLIP fine-tuning with self-annotated region alignment Atas: Any-to-any self-distillation for enhanced open-vocabulary dense prediction

Reference 22

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Observation e2969ff4-6d48-4357-b08b-10d160aea840 · outbound

This paper cites Clim: Contrastive language-image mosaic for region representation.

Fine-grained CLIP fine-tuning with self-annotated region alignment Clim: Contrastive language-image mosaic for region representation

Reference 23

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Observation 2ec8a351-5b2b-41af-a3d7-29ee243695a9 · outbound

This paper cites Learning to prompt for vision-language models.

Fine-grained CLIP fine-tuning with self-annotated region alignment Learning to prompt for vision-language models

Reference 24

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Observation f34aa329-6d56-427e-be60-15064d712336 · outbound

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

Fine-grained CLIP fine-tuning with self-annotated region alignment CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 25

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Observation 0818cb07-8563-4be6-83b6-66b9ae21c98c · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Fine-grained CLIP fine-tuning with self-annotated region alignment Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 26

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Observation ea92005a-c474-4d63-8c91-8cf61cc49883 · outbound

This paper cites Region-aware pretraining for open-vocabulary object detection with vision transformers.

Fine-grained CLIP fine-tuning with self-annotated region alignment Region-aware pretraining for open-vocabulary object detection with vision transformers

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T04:34:22.348171Z

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Observation b348c575-dc87-487b-b4c1-837e80beb3cb · outbound

This paper cites Open-vocabulary object detection upon frozen vision and language models.

Fine-grained CLIP fine-tuning with self-annotated region alignment Open-vocabulary object detection upon frozen vision and language models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T04:34:22.421378Z

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Observation ee5b9da2-0c64-4109-bc6c-6afea2ff4193 · outbound

This paper cites Simple open-vocabulary object detection.

Fine-grained CLIP fine-tuning with self-annotated region alignment Simple open-vocabulary object detection

Reference 29

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Observation 461b57f4-e6e2-4136-8ece-e49634b213ad · outbound

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

Fine-grained CLIP fine-tuning with self-annotated region alignment Scaling open-vocabulary image segmentation with image-level labels

Reference 30

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Observation 597862d6-c43b-4b4e-8c4d-21297c1af748 · outbound

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

Fine-grained CLIP fine-tuning with self-annotated region alignment Cascade-clip: Cascaded vision-language embeddings alignment for zero-shot semantic segmentation

Reference 31

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Observation 0dddf0e2-4cd3-4e4c-8838-73e619e389cc · outbound

This paper cites Visualizing and understanding convolutional networks.

Fine-grained CLIP fine-tuning with self-annotated region alignment Visualizing and understanding convolutional networks

Reference 32

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Observation fe6d806c-2905-435d-8059-6219fb50845b · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Fine-grained CLIP fine-tuning with self-annotated region alignment Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 33

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This paper cites why should i trust you?.

Fine-grained CLIP fine-tuning with self-annotated region alignment why should i trust you?

Reference 34

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Observation 515bce79-e3d9-414a-8667-04882cf09d0a · outbound

This paper cites RISE: Randomized Input Sampling for Explanation of Black-box Models.

Fine-grained CLIP fine-tuning with self-annotated region alignment RISE: Randomized Input Sampling for Explanation of Black-box Models

Reference 35

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Observation 9a1c5731-fc8d-4aec-9dff-2fcb024282b4 · outbound

This paper cites Odam: Gradient-based instance-specific visual explanations for object detection.

Fine-grained CLIP fine-tuning with self-annotated region alignment Odam: Gradient-based instance-specific visual explanations for object detection

Reference 36

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Observation 600f2382-940b-4e1c-99e6-583c91c474da · outbound

This paper cites Attcat: Explaining transformers via attentive class activation tokens.NeurIPS, 2022.

Fine-grained CLIP fine-tuning with self-annotated region alignment Attcat: Explaining transformers via attentive class activation tokens.NeurIPS, 2022

Reference 37

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Observation 2fcd5a22-9bb6-4959-906a-6f43fa45edc7 · outbound

This paper cites Vit-cx: Causal explanation of vision transformers.

Fine-grained CLIP fine-tuning with self-annotated region alignment Vit-cx: Causal explanation of vision transformers

Reference 38

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This paper cites X-pruner: explainable pruning for vision transformers.

Fine-grained CLIP fine-tuning with self-annotated region alignment X-pruner: explainable pruning for vision transformers

Reference 39

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Observation 444c21dc-5c62-448f-9e75-d2e059dc324b · outbound

This paper cites Gradient-based visual explanation for transformer-based clip.

Fine-grained CLIP fine-tuning with self-annotated region alignment Gradient-based visual explanation for transformer-based clip

Reference 40

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Observation c9b6e25d-0459-4d2d-a378-0d1fce8d4c8d · outbound

This paper cites Extract free dense labels from clip.

Fine-grained CLIP fine-tuning with self-annotated region alignment Extract free dense labels from clip

Reference 41

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Observation 94584bb7-1049-4471-ae94-be1cd70f3a1b · outbound

This paper cites O’Reilly Media, Inc., 2009.

Fine-grained CLIP fine-tuning with self-annotated region alignment O’Reilly Media, Inc., 2009

Reference 42

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Observation 99c64763-8baf-433a-992d-4803d6f90527 · outbound

This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

Fine-grained CLIP fine-tuning with self-annotated region alignment EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 43

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This paper cites Microsoft coco: Common objects in context.

Fine-grained CLIP fine-tuning with self-annotated region alignment Microsoft coco: Common objects in context

Reference 44

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This paper cites Scene parsing through ade20k dataset.

Fine-grained CLIP fine-tuning with self-annotated region alignment Scene parsing through ade20k dataset

Reference 45

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This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Fine-grained CLIP fine-tuning with self-annotated region alignment Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 46

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Observation 60a84edb-86d0-41fd-ba53-cab7dbf6fd26 · outbound

This paper cites Lvis: A dataset for large vocabulary instance segmentation.

Fine-grained CLIP fine-tuning with self-annotated region alignment Lvis: A dataset for large vocabulary instance segmentation

Reference 47

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Observation 3aedc7db-11ac-459e-878e-0cb683dd086c · outbound

This paper cites Open-vocabulary object detection using captions.

Fine-grained CLIP fine-tuning with self-annotated region alignment Open-vocabulary object detection using captions

Reference 48

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Observation 37a13a99-87f3-4a96-8828-89617d4ca713 · outbound

This paper cites Detecting twenty-thousand classes using image-level supervision.

Fine-grained CLIP fine-tuning with self-annotated region alignment Detecting twenty-thousand classes using image-level supervision

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Observation 70f1f7f9-eba6-42e5-9a90-bbe7c2f29837 · outbound

This paper cites Learning Object-Language Alignments for Open-Vocabulary Object Detection.

Fine-grained CLIP fine-tuning with self-annotated region alignment Learning Object-Language Alignments for Open-Vocabulary Object Detection

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Observation 4015e22e-3554-412d-a9ef-d73385683845 · outbound

This paper cites Everingham, L.

Fine-grained CLIP fine-tuning with self-annotated region alignment Everingham, L

Reference 51

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Observation 5291320a-5457-4b97-80fa-605b2501cc42 · outbound

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

Fine-grained CLIP fine-tuning with self-annotated region alignment The role of context for object detection and semantic segmentation in the wild

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

Fine-grained CLIP fine-tuning with self-annotated region alignment Cat-seg: Cost aggregation for open-vocabulary semantic segmentation

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Resolution: unresolved; no resolver link, observed 2026-08-02T04:34:23.912590Z

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Observation deaab706-55ba-4d15-928f-54ad94fe9b78 · outbound

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

Fine-grained CLIP fine-tuning with self-annotated region alignment Coco-stuff: Thing and stuff classes in context

Reference 54

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This paper cites Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models.

Fine-grained CLIP fine-tuning with self-annotated region alignment Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models

Reference 55

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