Toward Interactive Regional Understanding in Vision-Large Language Models

TL;DR

RegionVLM tackles the limitation of Vision-Language Pretraining models in fine-grained regional understanding by enabling explicit, user-indicated region grounding without architectural changes. It leverages the Localized Narratives dataset and a simple tokenization of scribbles to condition a Q-Former, guiding a frozen LLM to generate region-specific captions while preserving global image comprehension. The approach delivers interactive dialogue, strong zero-shot regional understanding across RIS, VCR, and VQA tasks, and improved zero-shot captioning, demonstrating a practical path toward region-aware, generalist VL models. This work suggests fruitful avenues for further enhancement via instruction tuning and broader regional grounding capabilities.

Abstract

Recent Vision-Language Pre-training (VLP) models have demonstrated significant advancements. Nevertheless, these models heavily rely on image-text pairs that capture only coarse and global information of an image, leading to a limitation in their regional understanding ability. In this work, we introduce \textbf{RegionVLM}, equipped with explicit regional modeling capabilities, allowing them to understand user-indicated image regions. To achieve this, we design a simple yet innovative architecture, requiring no modifications to the model architecture or objective function. Additionally, we leverage a dataset that contains a novel source of information, namely Localized Narratives, which has been overlooked in previous VLP research. Our experiments demonstrate that our single generalist model not only achieves an interactive dialogue system but also exhibits superior performance on various zero-shot region understanding tasks, without compromising its ability for global image understanding.

Figures (8)

• Figure 1: Conceptual comparison between BLIP-2 and our model. While BLIP-2 generates a single caption based on the entire image, our model can generate multiple captions corresponding to regions explicitly indicated by users.
• Figure 2: Examples of trajectories and their corresponding captions provided by the Localized Narratives dataset.
• Figure 3: Overall architecture of our proposed model. Our model converts a set of trajectory points from Localized Narratives into word tokens. The word tokens and visual features are passed to the Q-Former, generating a soft prompt. This allows the frozen LLM to generate captions corresponding to the indicated regions.
• Figure 4: Examples of cross-attention maps between learnable queries $Z$ and image features $I$ by varying the $W$ for a single image. The examples demonstrate that the queries successfully attend to the regions indicated by $W$, as denoted by yellow stars.
• Figure 5: Selected examples of interactive dialogue using our model. The regions indicated by a user are noted as yellow stars. The examples illustrate a wide range of abilities for interacting with users, reasoning, guessing, question answering, etc. Note that the series of dialogues in one column is obtained from a single process.