GazeCLIP: Enhancing Gaze Estimation Through Text-Guided Multimodal Learning Article Swipe
Jun Wang
,
Hao Ruan
,
Mingjie Wang
,
Chuanghui Zhang
,
Chunhua Li
,
Jun Zhou
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.00260
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.00260
Visual gaze estimation, with its wide-ranging application scenarios, has garnered increasing attention within the research community. Although existing approaches infer gaze solely from image signals, recent advances in visual-language collaboration have demonstrated that the integration of linguistic information can significantly enhance performance across various visual tasks. Leveraging the remarkable transferability of large-scale Contrastive Language-Image Pre-training (CLIP) models, we address the open and urgent question of how to effectively apply linguistic cues to gaze estimation. In this work, we propose GazeCLIP, a novel gaze estimation framework that deeply explores text-face collaboration. Specifically, we introduce a meticulously designed linguistic description generator to produce text signals enriched with coarse directional cues. Furthermore, we present a CLIP-based backbone adept at characterizing text-face pairs for gaze estimation, complemented by a fine-grained multimodal fusion module that models the intricate interrelationships between heterogeneous inputs. Extensive experiments on three challenging datasets demonstrate the superiority of GazeCLIP, which achieves state-of-the-art accuracy. Our findings underscore the potential of using visual-language collaboration to advance gaze estimation and open new avenues for future research in multimodal learning for visual tasks. The implementation code and the pre-trained model will be made publicly available.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.00260
- https://arxiv.org/pdf/2401.00260
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390528629
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390528629Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2401.00260Digital Object Identifier
- Title
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GazeCLIP: Enhancing Gaze Estimation Through Text-Guided Multimodal LearningWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-30Full publication date if available
- Authors
-
Jun Wang, Hao Ruan, Mingjie Wang, Chuanghui Zhang, Chunhua Li, Jun ZhouList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.00260Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2401.00260Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2401.00260Direct OA link when available
- Concepts
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Gaze, Computer science, Artificial intelligence, Generator (circuit theory), Prior probability, Estimation, Machine learning, Human–computer interaction, Natural language processing, Bayesian probability, Power (physics), Management, Physics, Economics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.effectively | 67 |
| abstract_inverted_index.estimation, | 2, 121 |
| abstract_inverted_index.estimation. | 73 |
| abstract_inverted_index.experiments | 138 |
| abstract_inverted_index.information | 37 |
| abstract_inverted_index.integration | 34 |
| abstract_inverted_index.large-scale | 51 |
| abstract_inverted_index.performance | 41 |
| abstract_inverted_index.pre-trained | 183 |
| abstract_inverted_index.superiority | 145 |
| abstract_inverted_index.Furthermore, | 108 |
| abstract_inverted_index.Pre-training | 54 |
| abstract_inverted_index.complemented | 122 |
| abstract_inverted_index.demonstrated | 31 |
| abstract_inverted_index.fine-grained | 125 |
| abstract_inverted_index.meticulously | 94 |
| abstract_inverted_index.wide-ranging | 5 |
| abstract_inverted_index.Specifically, | 90 |
| abstract_inverted_index.collaboration | 29, 160 |
| abstract_inverted_index.heterogeneous | 135 |
| abstract_inverted_index.significantly | 39 |
| abstract_inverted_index.Language-Image | 53 |
| abstract_inverted_index.characterizing | 116 |
| abstract_inverted_index.collaboration. | 89 |
| abstract_inverted_index.implementation | 179 |
| abstract_inverted_index.transferability | 49 |
| abstract_inverted_index.visual-language | 28, 159 |
| abstract_inverted_index.state-of-the-art | 150 |
| abstract_inverted_index.interrelationships | 133 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |