Toward Interactive Regional Understanding in Vision-Large Language Models (2024)

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 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.

## introduction vision-language pre-training (vlp) models @xcite @xcite have shown significant progress in recent years. a notable advancement is the emergence of zero-shot capabilities, which turn vlp models into generalist models, particularly when combined with large language models (llms). these models are now capable of solving various vision-language (vl) downstream tasks, including visual question answering (vqa) and image captioning, without the need for task-specific finetuning. the general knowledge enabling such zeroshot capabilities can be attained through training with massive image-text pair datasets @xcite @xcite . however, vlp models still face a significant challenge: their limited ability to comprehend the fine-grained semantics of specific regions within an image. this stems from the nature of their training datasets. existing image-text pairs, typically obtained by web crawling, tend to focus on the salient information of the image and fail to provide an explicit indication of the area of the image the text is describing. as a result, existing vlp models tend to focus on the implicit global information of the image, lacking the ability to understand the image region explicitly indicated by a user. in this paper, we introduce regionvlm, equipped with the regional understanding capability with explicit, or interactive, indications from users. we argue its significance for the following reasons. first, the regional understanding ability broadens the versatility of vlp models. this facilitates the execution of additional vision-language tasks that require explicit indications of regions, such as referring image segmentation @xcite and visual commonsense reasoning @xcite . second, we can tackle inherent ambiguity (or multiplicity) in vl tasks @xcite @xcite ) by employing regional understanding. an image can inherently be described by numerous text descriptions, e.g., describing the visual attribute of a brown dog who is jumping, running, catching frisbees and playing with the lady. a lady in pink top and blue shorts holding frisbees in her hand is playing with a brown dog in a park. a man wearing black shirt and shorts standing beside a white car is watching the dog and the lady playing with frisbees. the salient object, explaining the background, etc. however, current vlp models are trained to associate an image with a single given caption rather than encompassing all possible explanations. by empowering a model to focus on specific regions with explicit indications, we can better manage this ambiguity. third, integrating regional understanding enhances the interactivity between the model and users. as demonstrated by commercial services such as gpt-4v @xcite , enabling users to specify regions of interest in an image can lead to more precise and relevant interactions. there have been several attempts to make vlp models endow region understanding ability. one possible direction is to develop a specialized model based on clip @xcite for segmentation and detection by directly using fully supervised labels for each task @xcite @xcite . several recent studies aim to develop a generalist model with the capability of region understanding by leveraging datasets containing image regions and their corresponding captions @xcite @xcite . however, the datasets used for these methods exhibit inherent drawbacks. for example, the visual grounding dataset @xcite ) contains regiontext pairs only for a limited set of object classes; the captions of visual genome @xcite are relatively short and only depict a limited relationship between objects. to achieve generality and scalability in vlp models, we need a dataset containing diverse regions with various open-world objects as well as expressive captions. in the paper, we propose to exploit regional textual information from diverse narratives of images. specifically, we utilize the localized narratives (ln) datasets @xcite , which provide narrative descriptions by annotators and their mouse trajectory over the described region. the ln dataset includes expres-sive free-form captions depicting multiple openworld objects in a single image (see figure 2 ), and thus, it can provide general and meaningful regional information to the vlp model. as shown in figure 1 , unlike blip-2 which can generate captions only for the entire image, our model can generate captions for multiple regions of the image through explicit indications. we introduce a simple technique allowing a model to accept the regional information of ln. more specifically, we directly convert the 2d coordinates of the trajectory points to the sequence of strings (e.g., "[", "19", "44", "]", "[", "23", "55", "]", as shown in figure 4 ) and simply use them as the input of vlp models. finally, our model is trained to generate a caption corresponding to image regions associated with each trajectory, resulting in an ability to understand regional information. our approach does not require architectural modifications or redefinition of the objective function, ensuring seamless alignment with the original scheme that takes the entire image and text as input. our regionvlm can incorporate various appealing aspects into the existing model while preserving its original capabilities. our experiments demonstrate that our generalist model can achieve the interactive dialogue system by understanding the explicit region indication from a user. in addition, we show that our model can perform various zeroshot regional understanding tasks that were beyond the capability of the conventional blip-2. furthermore, our model achieves better performance than the recent state-of-the-art methods. ## proposed method in this section, we describe our training dataset and the proposed model, which addresses the limitations of previous methods introduced in section 2. we first revisit our base model, blip-2 @xcite in section 3.1. we then introduce our dataset and model in sections 3.2 and 3.3, respectively. finally, we present how our method performs various vl downstream tasks in section 3.4. ## conclusion in this study, we have addressed the limited region understanding ability of existing vision-language pre-training models. we proposed a model that can input the indication of the region, which isseamlessly integrated into the existing model. in addition, we utilized localized narratives to learn the general knowledge of image regions. our experiments showcase the superior performance of our generalist model across a diverse set of zero-shot region understanding tasks, without compromising its ability for global image comprehension tasks. as a generalist model, we foresee significant potential for further enhancement through instruction tuning @xcite , establishing a promising direction for future research. a appendix dataset details. as mentioned in section 4.1, we use a combination of image localized narratives (pont-tuset et al., 2020), video localized narratives @xcite , visual genome @xcite , and laion-400m filtered by @xcite . we present statistics of each dataset in failure case analysis for ris. as demonstrated in limitations. in contrast to global image-text pair datasets, which can be automatically collected from the web, our dataset may have limitations in terms of scalability. to address this, we can generate pseudo region captions using our trained model, as blip-2 utilizes the pseudo captions produced by blip @xcite captioning model. additionally, our current evaluations focus mainly on zero-shot downstream tasks. it is also worth to explore the possibility of our method for transfer learning @xcite , semi-supervised learning @xcite , few-shot learning @xcite , and weakly supervised learning @xcite . potential risks. since our model is based on frozen llms, it shares similar potential risks to llms, such as generating offensive output, vulnerability to attacks, and leaking personal sensitive data. to address this, we may need an additional filtering module to prevent such output from being conveyed to users.