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  • Waffling around for Performance: Visual Classification with Random . . .
    Waffling around for Performance: Visual Classification with Random Words and Broad Concepts Karsten Roth, Jae Myung Kim, A Sophia Koepke, Oriol Vinyals, Cordelia Schmid, Zeynep Akata
  • Waffling around for Performance: Visual Classification with Random . . .
    In this work, we study this behaviour in detail and propose Waffle CLIP, a framework for zero-shot visual classification which achieves similar performance gains on a large number of visual classification tasks by simply replacing LLM-generated descriptors with random character and word descriptors without querying external models
  • Waffling around for Performance: Visual Classification with Random . . .
    In this work, we critically study this behavior and propose Waffle CLIP, 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
  • Abstract - arXiv. org
    fleCLIP Across benchmarks and architectures (see Tab 8), we observe dichotomies in performance between either random word or random character sequences, often performing either best or worst on a specific benchmark and backbone, while the joint usage of random words and char-acter sequences strikes a consistent and best transferable average
  • EvolvingInterpretableVisualClassifierswith LargeLanguageModels
    Abstract Multimodal pre-trained models, such as CLIP, are popu-lar for zero-shot classification due to their open-vocabulary flexibility and high performance However, vision-language models, which compute similarity scores between images and class labels, are largely black-box, with limited interpretability, risk for bias, and inability to discover new visual concepts not written down
  • Abstract arXiv:2306. 07282v1 [cs. CV] 12 Jun 2023
    2 Related Work Image classification with VLMs such as CLIP [47] has gained popularity particularly in low-data regimes As in-put prompts have a significant impact on the performance, recent research has focused on the exploration of learnable prompts for the text encoder [67, 66, 33, 54], the visual en-coder [1, 7, 61, 32] or for both encoders jointly [62] Al-ternatively synthetic images
  • Evolving Interpretable Visual Classifiers with Large Language Models
    Abstract Multimodal pre-trained models, such as CLIP, are popular for zero-shot classification due to their open-vocabulary flexibility and high performance However, vision-language models, which compute similarity scores between images and class labels, are largely black-box, with limited interpretability, risk for bias, and inability to discover new visual concepts not written down
  • Real Classification by Description: Extending CLIP’s Limits of Part . . .
    Specifically, the ’WaffleCLIP’ study [18] introduced a paradigm in which LLM-generated descriptors were replaced with random words placed together with the object class name, but achieved comparable zero shot visual classification results, challenging the presumed necessity of semantic depth that is additional to class name in these models
  • Enhancing Visual Classification using Comparative Descriptors
    Waffling around for performance: Visual classification with random words and broad concepts In IEEE CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023, pages 15700–15711
  • Vocabulary-free Fine-grained Visual Recognition via Enriched . . .
    These results indicate that our approach benefits not only classification performance but also visual-semantic alignment, as it better captures the relationship between fine-grained attributes and image regions through contextual grounding





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