ROME : Evaluating Pre-trained Vision-Language Models on Reasoning beyond Visual Common Sense (2023)

Abstract: Humans possess a strong capability for reasoning beyond common sense. For example, given an unconventional image of a goldfish laying on the table next to an empty fishbowl, a human would effortlessly determine that the fish is not inside the fishbowl. The case, however, may be different for a vision-language model, whose reasoning could gravitate towards the common scenario that the fish is inside the bowl, despite the visual input. In this paper, we introduce a novel probing dataset named ROME (reasoning beyond commonsense knowledge) to evaluate whether the state-of-the-art pre-trained vision-language models have the reasoning capability to correctly interpret counter-intuitive content. ROME contains images that defy commonsense knowledge with regards to color, shape, material, size and positional relation. Experiments on the state-of-the-art pre-trained vision-language models reveal that most of these models are still largely incapable of interpreting counter-intuitive scenarios. We hope that ROME will spur further investigations on reasoning beyond commonsense knowledge in vision-language research.

## introduction humans possess not only commonsense knowledge such as fish are usually found inside (rather than outside) a fishbowl, but also the ability to handle counter-intuitive scenarios and reason beyond common sense. for example, when presented with the image on the left in figure 1 and asked "is the fish inside the fishbowl?" humans would have no problem answering "no." however, it is not clear whether recent pre-trained vision-language models (vlms) such as llava @xcite and instructblip @xcite , which exhibit impressive vision-language understanding abilities, have the capacity to reason beyond visual common sense. in this paper, we aim to empirically answer this research question by evaluating recent vlms on a new probing dataset we introduce, called rome (reasoning beyond commonsense knowledge). such empirical evaluation is important because humans' ability to "think outside the box" is a critical source of creativity and, thus, also a desirable ability for ai models. unfortunately, most existing benchmarks to evaluate vlms do not challenge them with unconventional or counterintuitive situations. our rome dataset consists of 1,563 images generated by dall-e-2 depicting counter-intuitive scenarios. instead of relying on a small group of people's notion of counter-intuitive scenarios, we largely leverage a recent visual common sense dataset, vicomte, that was systematically derived from visual genome @xcite . we automatically convert commonsense assertions related to objects' color, shape, material and size in vicomte into descriptions of counter-intuitive scenarios, and use dall-e-2 to generate images accordingly. in addition, we manually use bing image creator to generate counter-intuitive images related to objects' positional relations to supplement the data above. to assess both a vlm's commonsense knowledge and its abilities to reason beyond common sense, we design two groups of probing tasks in the form of binary (yes/no) visual questions: (1) commonsense questions, which are related to the common attribute values of various objects, or the typical relative size or positional relationship between two objects. (2) counter-intuitive questions, which ask about the objects, their attributes, and their relative size or positional relation in counterintuitive images.we then define four evaluation metrics based on these probing questions. we use the rome dataset and the probing questions to evaluate six pre-trained vision-language models: albef @xcite , blip-2 @xcite , llava @xcite , minigpt-4 @xcite , mplug-owl @xcite , and instructblip @xcite . among these models, blip-2, llava, minigpt-4, mplug-owl, and instructblip are the latest models that are built on top of pre-trained large language models (llms), e.g., llama. they can be directly used by end-users through natural language conversations such as to perform zero-shot visual question answering (vqa) , making them readily available for wide adoption in end-user applications. therefore, we focus on the evaluation of these state-of-the-art models. in contrast, although earlier vlms such as clip @xcite and albef also demonstrated strong zero-shot capabilities on tasks including object detection @xcite and vqa @xcite , they usually require careful prompt engineering to work properly, and thus we do not focus on these earlier models. nevertheless, we still include one of them, namely, albef pre-trained with vqav2 foot_0 , in order to have a comprehensive representation of vlms. our experiments demonstrate that, although these state-of-the-art vlms are generally effective at counter-intuitive object recognition, most of them perform poorly in terms of counter-intuitive attribute and relation recognition. meanwhile, interestingly, the models perform poorly on our commonsense questions, often giving inconsistent answers to a pair of questions asking about the same commonsense knowledge. this suggests that many of the latest pre-trained vlms still lack strong commonsense reasoning capabilities and capabilities to reason beyond common sense, which, in turn, indicates that further improvements are needed in both directions. ## related work visual common sense. with the rapid development of foundation models, increasing attention is paid to their commonsense reasoning capabilities. recent studies have investigated the existence of commonsense knowledge in pre-trained language models (e.g., @xcite . others have focused on visual common sense (e.g., vedantam et al. (2015); zellers et al. (2021); liu et al. (2022) ). in particular, @xcite studied commonsense knowledge pertaining to typical visual attributes of different objects. we follow their notion of visual common sense and use their dataset as our main source of commonsense knowledge. note that this notion of visual common sense is different from the notion of visual commonsense reasoning in the well-known vcr dataset @xcite . while most previous work has focused on commonsense reasoning, we focus on evaluating reasoning capabilities beyond common sense. @xcite introduced whoops, a dataset of weird/unconventional images, where the selection of "weird" scenarios is based on a (relatively) small group of 30 designers' impression of common sense and weirdness. in contrast, our selection of counter-intuitive scenarios is based on the visual commonsense dataset by @xcite that was systematically constructed. whoops covers a wide range of weird scenarios, many of which involve non-visual common sense, such as social norms, cultural knowledge, and celebrities. on the other hand, our dataset is centered on five categories of common sense that are purely visual. these types of common sense are also more primitive (compared to whoops, where the reasons for weirdness are often complex, involving multiple steps of reasoning). also, whoops focuses on explanation, while our dataset is framed around the models' ability to overcome common sense and correctly recognitze objects, attributes and spatial relations in counter-intuitive scenarios. another dataset that contains some counter-intuitive images is the winoground dataset @xcite , but their focus is compositional reasoning and not all images in winoground are counter-intuitive. pre-trained vision-language models. after the success of bert (kenton and toutanova, 2019), the community started developing pre-trained vision-language models, such as albef @xcite , x 2 -vlm @xcite and clip @xcite , which are trained on web-scale image-text pairs and exhibit powerful zero-shot and few-shot transfer capabilities for downstream tasks. recently, given the impressive power of llms (such as chatgpt) to perform various tasks through natural language conversations, a new line of work leverages llms to build vlms with parameter-efficient fine-tuning (e.g., blip-2 @xcite , llava @xcite and instructblip @xcite ). these models usually train an alignment module to convert visual inputs into embeddings acceptable by a pre-trained llm. evaluation of vlms. recently, there have been various attempts to evaluate vlms on different aspects. @xcite constructed the winoground dataset to assess the ability of vlms to conduct visio-linguistic compositional reasoning. @xcite studied the existence of social bias in vlms. @xcite considered the hallucination problem, i.e., the inclusion of objects that are inconsistent with the given images in the generated text descriptions. @xcite compared different llm backbones and model designs of vlms to explore the influence of diversified prompts on the instruction-following ability of these models. in contrast, we evaluate state-of-theart vlms for reasoning beyond common sense. ## creation of counter-intuitive images in this section, we explain how we create counterintuitive images. ## probing questions and evaluation metrics our main research question is whether state-of-theart pre-trained vlms can handle counter-intuitive images well, i.e., whether they are able to reason beyond common sense. however, we should first address a more fundamental question, which is whether these pre-trained vlms possess visual common sense in the first place. we therefore create two groups of probing questions: (1) questions testing pre-trained vlms' visual common sense, and (2) questions testing pre-trained vlms' reasoning abilities beyond visual common sense. our preliminary investigations showed that the latest pre-trained vlms, such as llava, tend to generate verbose answers to open-ended questions. (see appendix c for examples.) this makes it hard to automatically process the generated answers. we hence design only binary questions, whose answers can be automatically processed by checking whether they start with "yes" or "no." ## conclusion in this work, we construct the rome dataset and propose a probing framework for evaluating the capability of pre-trained vision-language models (vlms) to reason beyond common sense. using rome and a set of metrics we define, we show that state-of-the-art vlms are lacking in this aspect. we hope rome will spur further research in the important direction of reasoning beyond common sense in the nlp and vision communities.