GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models

• URL: http://arxiv.org/abs/2410.06154v2
• Date: Mon, 02 Dec 2024 14:49:55 GMT
• Title: GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models
• Authors: M. Jehanzeb Mirza, Mengjie Zhao, Zhuoyuan Mao, Sivan Doveh, Wei Lin, Paul Gavrikov, Michael Dorkenwald, Shiqi Yang, Saurav Jha, Hiromi Wakaki, Yuki Mitsufuji, Horst Possegger, Rogerio Feris, Leonid Karlinsky, James Glass,
• Abstract summary: Large Language Models (LLMs) act as implicits for Vision-Langugage Models (VLMs)<n>Our GLOV meta-prompts an LLM with the downstream task description, querying it for suitable VLM prompts.<n>We evaluate our GLOV on 16 diverse datasets using two families of VLMs.
• Score: 44.82179903133343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose a novel method (GLOV) enabling Large Language Models (LLMs) to act as implicit Optimizers for Vision-Langugage Models (VLMs) to enhance downstream vision tasks. Our GLOV meta-prompts an LLM with the downstream task description, querying it for suitable VLM prompts (e.g., for zero-shot classification with CLIP). These prompts are ranked according to a purity measure obtained through a fitness function. In each respective optimization step, the ranked prompts are fed as in-context examples (with their accuracies) to equip the LLM with the knowledge of the type of text prompts preferred by the downstream VLM. Furthermore, we also explicitly steer the LLM generation process in each optimization step by specifically adding an offset difference vector of the embeddings from the positive and negative solutions found by the LLM, in previous optimization steps, to the intermediate layer of the network for the next generation step. This offset vector steers the LLM generation toward the type of language preferred by the downstream VLM, resulting in enhanced performance on the downstream vision tasks. We comprehensively evaluate our GLOV on 16 diverse datasets using two families of VLMs, i.e., dual-encoder (e.g., CLIP) and encoder-decoder (e.g., LLaVa) models -- showing that the discovered solutions can enhance the recognition performance by up to 15.0% and 57.5% (3.8% and 21.6% on average) for these models.

Related papers

• Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning [17.59802090014789]
  We introduce PrefVLM, a framework that integrates Vision-Language Models (VLMs) with selective human feedback. Our method leverages VLMs to generate initial preference labels, which are then filtered to identify uncertain cases for targeted human annotation. Experiments on Meta-World manipulation tasks demonstrate that PrefVLM achieves comparable or superior success rates to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-02-03T18:50:15Z)
• OLA-VLM: Elevating Visual Perception in Multimodal LLMs with Auxiliary Embedding Distillation [95.78870389271832]
  The standard practice for developing contemporary MLLMs is to feed features from vision encoder(s) into the LLM and train with natural language supervision. We propose OLA-VLM, the first approach distilling knowledge into the LLM's hidden representations from a set of target visual representations. We show that OLA-VLM boosts performance by an average margin of up to 2.5% on various benchmarks, with a notable improvement of 8.7% on the Depth task in CV-Bench.
  arXiv  Detail & Related papers  (2024-12-12T18:55:18Z)
• Rethinking VLMs and LLMs for Image Classification [6.550471260627169]
  Large Language Models (LLMs) are increasingly being merged with Visual Language Models (VLMs) to enable new capabilities. We show that, for object and scene recognition, VLMs that do not leverage LLMs can achieve better performance than VLMs that do. We propose a pragmatic solution: a lightweight fix involving a relatively small LLM that efficiently routes visual tasks to the most suitable model for the task.
  arXiv  Detail & Related papers  (2024-10-03T23:40:21Z)
• LLM-wrapper: Black-Box Semantic-Aware Adaptation of Vision-Language Models for Referring Expression Comprehension [45.856469849910496]
  We propose a method for 'black-box' adaptation of Vision Language Models (VLMs) for the Referring Expression (REC) task using Large Language Models (LLMs) LLMs capitalizes on the reasoning abilities of LLMs, improved with a light fine-tuning, to select the most relevant bounding box matching the referring expression. Our approach offers several advantages: it enables the adaptation of closed-source models without needing access to their internal workings.
  arXiv  Detail & Related papers  (2024-09-18T12:32:25Z)
• CLIP-DPO: Vision-Language Models as a Source of Preference for Fixing Hallucinations in LVLMs [37.98496239547762]
  Large Vision Language Models are prone to hallucinating details like objects and their properties or relations, limiting their real-world deployment. We present CLIP-DPO, a preference optimization method that leverages contrastively pre-trained Vision-Language (VL) embedding models, such as CLIP, for DPO-based optimization of LVLMs.
  arXiv  Detail & Related papers  (2024-08-19T21:56:20Z)
• MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization [73.7779735046424]
  We show that different prompts should be adapted to different Large Language Models (LLM) to enhance their capabilities across various downstream tasks in NLP. We then propose a model-adaptive prompt (MAPO) method that optimize the original prompts for each specific LLM in downstream tasks.
  arXiv  Detail & Related papers  (2024-07-04T18:39:59Z)
• Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance [56.04768229686853]
  Large Vision-Language Models (LVLMs) tend to hallucinate non-existing objects in the images. We introduce a framework called Mitigating hallucinAtion via classifieR-Free guIdaNcE (MARINE) MARINE is both training-free and API-free, and can effectively and efficiently reduce object hallucinations during the generation process.
  arXiv  Detail & Related papers  (2024-02-13T18:59:05Z)
• 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)
• Language Models as Black-Box Optimizers for Vision-Language Models [62.80817942316398]
  Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. We aim to develop a black-box approach to optimize VLMs through natural language prompts.
  arXiv  Detail & Related papers  (2023-09-12T04:03:41Z)
• LLMRec: Benchmarking Large Language Models on Recommendation Task [54.48899723591296]
  The application of Large Language Models (LLMs) in the recommendation domain has not been thoroughly investigated. We benchmark several popular off-the-shelf LLMs on five recommendation tasks, including rating prediction, sequential recommendation, direct recommendation, explanation generation, and review summarization. The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
  arXiv  Detail & Related papers  (2023-08-23T16:32:54Z)
• Guiding Large Language Models via Directional Stimulus Prompting [114.84930073977672]
  We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model to generate an auxiliary directional stimulus prompt for each input instance.
  arXiv  Detail & Related papers  (2023-02-22T17:44:15Z)

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.