Seeing Clearly, Reasoning Confidently: Plug-and-Play Remedies for Vision Language Model Blindness

TL;DR

This paper presents a plug-and-play module that enhances vision language models' reasoning on rare objects by refining visual tokens and enriching prompts, without requiring finetuning, leading to significant performance improvements.

Contribution

It introduces a lightweight, plug-and-play approach that leverages prior knowledge and synonym-augmented descriptions to improve rare object reasoning in VLMs without finetuning.

Findings

Significant gains in rare object recognition and reasoning

Improved focus on relevant image regions

Enhanced fine-grained object details

Abstract

Vision language models (VLMs) have achieved remarkable success in broad visual understanding, yet they remain challenged by object-centric reasoning on rare objects due to the scarcity of such instances in pretraining data. While prior efforts alleviate this issue by retrieving additional data or introducing stronger vision encoders, these methods are still computationally intensive during finetuning VLMs and don't fully exploit the original training data. In this paper, we introduce an efficient plug-and-play module that substantially improves VLMs' reasoning over rare objects by refining visual tokens and enriching input text prompts, without VLMs finetuning. Specifically, we propose to learn multi-modal class embeddings for rare objects by leveraging prior knowledge from vision foundation models and synonym-augmented text descriptions, compensating for limited training examples. These embeddings refine the visual tokens in VLMs through a lightweight attention-based enhancement module that improves fine-grained object details. In addition, we use the learned embeddings as object-aware detectors to generate informative hints, which are injected into the text prompts to help guide the VLM's attention toward relevant image regions. Experiments on two benchmarks show consistent and substantial gains for pretrained VLMs in rare object recognition and reasoning. Further analysis reveals how our method strengthens the VLM's ability to focus on and reason about rare objects.