Waffling around for Performance: Visual Classification with Random Words and Broad Concepts Article Swipe
Karsten Roth
,
Jae Myung Kim
,
A. Sophia Koepke
,
Oriol Vinyals
,
Cordelia Schmid
,
Zeynep Akata
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.07282
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.07282
The visual classification performance of vision-language models such as CLIP has been shown to benefit from additional semantic knowledge from large language models (LLMs) such as GPT-3. In particular, averaging over LLM-generated class descriptors, e.g. "waffle, which has a round shape", can notably improve generalization performance. In this work, we critically study this behavior and propose WaffleCLIP, a framework for zero-shot visual classification which simply replaces LLM-generated descriptors with random character and word descriptors. Without querying external models, we achieve comparable performance gains on a large number of visual classification tasks. This allows WaffleCLIP to both serve as a low-cost alternative, as well as a sanity check for any future LLM-based vision-language model extensions. We conduct an extensive experimental study on the impact and shortcomings of additional semantics introduced with LLM-generated descriptors, and showcase how - if available - semantic context is better leveraged by querying LLMs for high-level concepts, which we show can be done to jointly resolve potential class name ambiguities. Code is available here: https://github.com/ExplainableML/WaffleCLIP.
Related Topics
Concepts
Computer science
Generalization
Class (philosophy)
Context (archaeology)
Natural language processing
Word (group theory)
Artificial intelligence
Semantics (computer science)
Code (set theory)
Machine learning
Programming language
Linguistics
Paleontology
Mathematical analysis
Biology
Set (abstract data type)
Philosophy
Mathematics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.07282
- https://arxiv.org/pdf/2306.07282
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380551062
All OpenAlex metadata
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https://openalex.org/W4380551062Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2306.07282Digital Object Identifier
- Title
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Waffling around for Performance: Visual Classification with Random Words and Broad ConceptsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-12Full publication date if available
- Authors
-
Karsten Roth, Jae Myung Kim, A. Sophia Koepke, Oriol Vinyals, Cordelia Schmid, Zeynep AkataList of authors in order
- Landing page
-
https://arxiv.org/abs/2306.07282Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2306.07282Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2306.07282Direct OA link when available
- Concepts
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Computer science, Generalization, Class (philosophy), Context (archaeology), Natural language processing, Word (group theory), Artificial intelligence, Semantics (computer science), Code (set theory), Machine learning, Programming language, Linguistics, Paleontology, Mathematical analysis, Biology, Set (abstract data type), Philosophy, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.resolve | 158 |
| abstract_inverted_index.shape", | 40 |
| abstract_inverted_index."waffle, | 35 |
| abstract_inverted_index.behavior | 53 |
| abstract_inverted_index.external | 76 |
| abstract_inverted_index.language | 21 |
| abstract_inverted_index.low-cost | 99 |
| abstract_inverted_index.querying | 75, 145 |
| abstract_inverted_index.replaces | 65 |
| abstract_inverted_index.semantic | 17, 139 |
| abstract_inverted_index.showcase | 133 |
| abstract_inverted_index.LLM-based | 110 |
| abstract_inverted_index.available | 137, 165 |
| abstract_inverted_index.averaging | 29 |
| abstract_inverted_index.character | 70 |
| abstract_inverted_index.concepts, | 149 |
| abstract_inverted_index.extensive | 117 |
| abstract_inverted_index.framework | 58 |
| abstract_inverted_index.knowledge | 18 |
| abstract_inverted_index.leveraged | 143 |
| abstract_inverted_index.potential | 159 |
| abstract_inverted_index.semantics | 127 |
| abstract_inverted_index.zero-shot | 60 |
| abstract_inverted_index.WaffleCLIP | 93 |
| abstract_inverted_index.additional | 16, 126 |
| abstract_inverted_index.comparable | 80 |
| abstract_inverted_index.critically | 50 |
| abstract_inverted_index.high-level | 148 |
| abstract_inverted_index.introduced | 128 |
| abstract_inverted_index.WaffleCLIP, | 56 |
| abstract_inverted_index.descriptors | 67 |
| abstract_inverted_index.extensions. | 113 |
| abstract_inverted_index.particular, | 28 |
| abstract_inverted_index.performance | 3, 81 |
| abstract_inverted_index.alternative, | 100 |
| abstract_inverted_index.ambiguities. | 162 |
| abstract_inverted_index.descriptors, | 33, 131 |
| abstract_inverted_index.descriptors. | 73 |
| abstract_inverted_index.experimental | 118 |
| abstract_inverted_index.performance. | 45 |
| abstract_inverted_index.shortcomings | 124 |
| abstract_inverted_index.LLM-generated | 31, 66, 130 |
| abstract_inverted_index.classification | 2, 62, 89 |
| abstract_inverted_index.generalization | 44 |
| abstract_inverted_index.vision-language | 5, 111 |
| abstract_inverted_index.https://github.com/ExplainableML/WaffleCLIP. | 167 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |