Paper Citation Record · LEDGER

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks

As of 18 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.18675.

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

pith.paper-citation-record.v1
2507.18675 v2

Coverage vector

measured 36 of 36 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-15T18:16:15.771212Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

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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

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation 5c906201-ce95-4b06-8361-e5bf22231b03 · outbound

This paper cites Leveraging Vision -Language Models for Improving Domain Generalization in Image Classification.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Leveraging Vision -Language Models for Improving Domain Generalization in Image Classification

Reference 1

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Observation b42e8a9c-025b-42ac-a052-645e77104a56 · outbound

This paper cites Are Visual-Language Models Effective Action Recognition? A Comparative Study.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Are Visual-Language Models Effective Action Recognition? A Comparative Study

Reference 2

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Observation 5091d515-2eca-4f6b-b38b-2a0d3f6841d2 · outbound

This paper cites Vision–language model for visual question answering in medical imagery.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Vision–language model for visual question answering in medical imagery

Reference 3

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Observation c64017ab-cd13-4b9a-a5f9-3f23b1f9ff1f · outbound

This paper cites When deep learners change their mind: Learning dynamics for active learning.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks When deep learners change their mind: Learning dynamics for active learning

Reference 4

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Observation 7116ff25-bad7-432e-913f-b73209cc6692 · outbound

This paper cites An Introduction to Vision-Language Modeling.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks An Introduction to Vision-Language Modeling

Reference 5

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Observation 5a18d4b3-1509-478e-b4d8-5816046489a7 · outbound

This paper cites PracticalDG: Perturbation Distillation on Vision- Language Models for Hybrid Domain Generalization.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks PracticalDG: Perturbation Distillation on Vision- Language Models for Hybrid Domain Generalization

Reference 6

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Observation 4cb39969-b1da-4a9e-a20f-d0374c62e8fe · outbound

This paper cites Pub - medclip: How much does clip benefit visual question answering in the medical domain?.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Pub - medclip: How much does clip benefit visual question answering in the medical domain?

Reference 7

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Observation a01ce183-c5e8-4d54-9b26-c00771e8b294 · outbound

This paper cites Clipsyntel: clip and llm synergy for multi - modal question summarization in healthcare.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Clipsyntel: clip and llm synergy for multi - modal question summarization in healthcare

Reference 8

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Observation 8599834e-4962-45b3-afce-5fc894760d96 · outbound

This paper cites Learn2augment: learning to composite videos for data augmentation in action recognition.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Learn2augment: learning to composite videos for data augmentation in action recognition

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:16:16.253854Z

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Observation 99c5f218-63d8-4483-a43b-e727a90ef1ea · outbound

This paper cites Class-Specific Noise Injection for Improved Road Segmentation.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Class-Specific Noise Injection for Improved Road Segmentation

Reference 10

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Observation 9c393a4b-7fef-4d1f-aaf6-e1540c4185f7 · outbound

This paper cites Temporal Modeling Approach for Video Action Recognition Based on Vision-language Models.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Temporal Modeling Approach for Video Action Recognition Based on Vision-language Models

Reference 11

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Observation e05f7981-d6fa-474f-97f9-3828fb363c00 · outbound

This paper cites Perturbation- based methods for explaining deep neural networks: A survey.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Perturbation- based methods for explaining deep neural networks: A survey

Reference 12

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Observation a1a36eec-d4d2-4eaf-b7f6-39708a0b1ad7 · outbound

This paper cites A dversarial attack on yolov5 for traffic and road sign detection.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks A dversarial attack on yolov5 for traffic and road sign detection

Reference 13

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Observation 2814c903-1eea-4b62-8ec2-a39b84838e12 · outbound

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

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 14

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Observation e99755b9-7c87-4a02-8f9c-c7eebdc838d3 · outbound

This paper cites Language augmentation in clip for improved anatomy detection on multi-modal medical images.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Language augmentation in clip for improved anatomy detection on multi-modal medical images

Reference 15

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Observation c0a9211b-c188-4843-a68b-8cbbf12409c7 · outbound

This paper cites PALM: Predicting Actions through Language Models.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks PALM: Predicting Actions through Language Models

Reference 16

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Observation 2a182daa-20e5-425d-a840-eb420f81bad6 · outbound

This paper cites Segment Anything.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Segment Anything

Reference 17

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This paper cites Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare

Reference 18

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Observation 57251939-f85f-4b06-908c-fd9499ee11db · outbound

This paper cites Enhancing clip with gpt-4: Harness- ing visual descriptions as prompts.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Enhancing clip with gpt-4: Harness- ing visual descriptions as prompts

Reference 19

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Observation da2b2fb5-34e3-45a8-afb0-7628970b5995 · outbound

This paper cites Learning transferable visual models from nat- ural language supervision.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Learning transferable visual models from nat- ural language supervision

Reference 20

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Observation 7f484ea5-da3b-4100-8470-099fa43f2cb0 · outbound

This paper cites Vision language models are blind: Failing to translate detailed visual features into words.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Vision language models are blind: Failing to translate detailed visual features into words

Reference 21

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This paper cites How Much Can CLIP Benefit Vision-and-Language Tasks?.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks How Much Can CLIP Benefit Vision-and-Language Tasks?

Reference 22

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This paper cites Test-time prompt tuning for zero-shot general- ization in vision-language models.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Test-time prompt tuning for zero-shot general- ization in vision-language models

Reference 23

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Observation bf7884b7-6150-40d6-b059-befcade4fa8c · outbound

This paper cites Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors

Reference 24

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This paper cites Motionclip: Exposing human motion genera - tion to clip space.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Motionclip: Exposing human motion genera - tion to clip space

Reference 25

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Observation 0b0f2520-2a16-4ca4-b37d-d030f0fcd319 · outbound

This paper cites XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models

Reference 26

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Observation e3929c2c-f003-455f-b096-1c14574783e1 · outbound

This paper cites CLIP with Quality Captions: A Strong Pretraining for Vision Tasks.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks CLIP with Quality Captions: A Strong Pretraining for Vision Tasks

Reference 27

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This paper cites ActionCLIP: A New Paradigm for Video Action Recognition.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks ActionCLIP: A New Paradigm for Video Action Recognition

Reference 28

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This paper cites Actionclip: Adapting language-image pre- trained models for video action recognition.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Actionclip: Adapting language-image pre- trained models for video action recognition

Reference 29

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This paper cites Incorporating Scene Graphs into Pre-trained Vision-Language Models for Multimodal Open-vocabulary Action Recognition.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Incorporating Scene Graphs into Pre-trained Vision-Language Models for Multimodal Open-vocabulary Action Recognition

Reference 30

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

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction

Reference 31

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This paper cites Revisiting classi - fier: Transferring vision-language models for video recognition.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Revisiting classi - fier: Transferring vision-language models for video recognition

Reference 32

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This paper cites Investigating Compositional Challenges in Vision- Language Models for Visual Grounding.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Investigating Compositional Challenges in Vision- Language Models for Visual Grounding

Reference 33

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Observation 9bd7a2d6-847d-41af-9280-a3edbb1c6d6b · outbound

This paper cites PeVL : Pose -Enhanced Vision-Language Model for Fine-Grained Human Action Recognition.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks PeVL : Pose -Enhanced Vision-Language Model for Fine-Grained Human Action Recognition

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:16:16.020935Z

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This paper cites Vision -language models for vision tasks: A survey.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks Vision -language models for vision tasks: A survey

Reference 35

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This paper cites CLIP in Medical Imaging: A Survey.

Advancing Vision-based Human Action Recognition: Exploring Vision-Language CLIP Model for Generalisation in Domain-Independent Tasks CLIP in Medical Imaging: A Survey

Reference 36

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Pith citing papers

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