Does VLM Classification Benefit from LLM Description Semantics?

• URL: http://arxiv.org/abs/2412.11917v3
• Date: Thu, 19 Dec 2024 17:57:59 GMT
• Title: Does VLM Classification Benefit from LLM Description Semantics?
• Authors: Pingchuan Ma, Lennart Rietdorf, Dmytro Kotovenko, Vincent Tao Hu, Björn Ommer,
• Abstract summary: We propose a training-free method for selecting discriminative descriptions that work independently of classname-ensembling effects. Our approach identifies descriptions that effectively differentiate classes within a local CLIP label neighborhood, improving classification accuracy across seven datasets.
• Score: 26.743684911323857
• License:
• Abstract: Accurately describing images with text is a foundation of explainable AI. Vision-Language Models (VLMs) like CLIP have recently addressed this by aligning images and texts in a shared embedding space, expressing semantic similarities between vision and language embeddings. VLM classification can be improved with descriptions generated by Large Language Models (LLMs). However, it is difficult to determine the contribution of actual description semantics, as the performance gain may also stem from a semantic-agnostic ensembling effect, where multiple modified text prompts act as a noisy test-time augmentation for the original one. We propose an alternative evaluation scenario to decide if a performance boost of LLM-generated descriptions is caused by such a noise augmentation effect or rather by genuine description semantics. The proposed scenario avoids noisy test-time augmentation and ensures that genuine, distinctive descriptions cause the performance boost. Furthermore, we propose a training-free method for selecting discriminative descriptions that work independently of classname-ensembling effects. Our approach identifies descriptions that effectively differentiate classes within a local CLIP label neighborhood, improving classification accuracy across seven datasets. Additionally, we provide insights into the explainability of description-based image classification with VLMs.

Related papers

• Scene Graph Generation with Role-Playing Large Language Models [50.252588437973245]
  Current approaches for open-vocabulary scene graph generation (OVSGG) use vision-language models such as CLIP. We propose SDSGG, a scene-specific description based OVSGG framework. To capture the complicated interplay between subjects and objects, we propose a new lightweight module called mutual visual adapter.
  arXiv  Detail & Related papers  (2024-10-20T11:40:31Z)
• Finetuning CLIP to Reason about Pairwise Differences [52.028073305958074]
  We propose an approach to train vision-language models such as CLIP in a contrastive manner to reason about differences in embedding space. We first demonstrate that our approach yields significantly improved capabilities in ranking images by a certain attribute. We also illustrate that the resulting embeddings obey a larger degree of geometric properties in embedding space.
  arXiv  Detail & Related papers  (2024-09-15T13:02:14Z)
• Can Better Text Semantics in Prompt Tuning Improve VLM Generalization? [28.041879000565874]
  We introduce a prompt-tuning method that leverages class descriptions obtained from Large Language Models. Our approach constructs part-level description-guided image and text features, which are subsequently aligned to learn more generalizable prompts. Our comprehensive experiments conducted across 11 benchmark datasets show that our method outperforms established methods.
  arXiv  Detail & Related papers  (2024-05-13T16:52:17Z)
• CLAMP: Contrastive LAnguage Model Prompt-tuning [89.96914454453791]
  We show that large language models can achieve good image classification performance when adapted this way. Our approach beats state-of-the-art mLLMs by 13% and slightly outperforms contrastive learning with a custom text model.
  arXiv  Detail & Related papers  (2023-12-04T05:13:59Z)
• LLMs as Visual Explainers: Advancing Image Classification with Evolving Visual Descriptions [13.546494268784757]
  We propose a framework that integrates large language models (LLMs) and vision-language models (VLMs) to find the optimal class descriptors. Our training-free approach develops an LLM-based agent with an evolutionary optimization strategy to iteratively refine class descriptors.
  arXiv  Detail & Related papers  (2023-11-20T16:37:45Z)
• SILC: Improving Vision Language Pretraining with Self-Distillation [113.50400246862056]
  We introduce SILC, a novel framework for vision language pretraining. SILC improves image-text contrastive learning with the simple addition of local-to-global correspondence learning by self-distillation. We show that distilling local image features from an exponential moving average (EMA) teacher model significantly improves model performance on dense predictions tasks like detection and segmentation.
  arXiv  Detail & Related papers  (2023-10-20T08:44:47Z)
• Text Descriptions are Compressive and Invariant Representations for Visual Learning [63.3464863723631]
  We show that an alternative approach, in line with humans' understanding of multiple visual features per class, can provide compelling performance in the robust few-shot learning setting. In particular, we introduce a novel method, textit SLR-AVD (Sparse Logistic Regression using Augmented Visual Descriptors). This method first automatically generates multiple visual descriptions of each class via a large language model (LLM), then uses a VLM to translate these descriptions to a set of visual feature embeddings of each image, and finally uses sparse logistic regression to select a relevant subset of these features to classify
  arXiv  Detail & Related papers  (2023-07-10T03:06:45Z)
• Waffling around for Performance: Visual Classification with Random Words and Broad Concepts [121.60918966567657]
  WaffleCLIP is a framework for zero-shot visual classification which simply replaces LLM-generated descriptors with random character and word descriptors. We conduct an extensive experimental study on the impact and shortcomings of additional semantics introduced with LLM-generated descriptors.
  arXiv  Detail & Related papers  (2023-06-12T17:59:48Z)
• Visual Classification via Description from Large Language Models [23.932495654407425]
  Vision-language models (VLMs) have shown promising performance on a variety of recognition tasks. We present an alternative framework for classification with VLMs, which we call classification by description.
  arXiv  Detail & Related papers  (2022-10-13T17:03:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.