Communication breakdown: On the low mutual intelligibility between human and neural captioning (2022)

Abstract: We compare the 0-shot performance of a neural caption-based image retriever when given as input either human-produced captions or captions generated by a neural captioner. We conduct this comparison on the recently introduced ImageCoDe data-set (Krojer et al. 2022), which contains hard distractors nearly identical to the images to be retrieved. We find that the neural retriever has much higher performance when fed neural rather than human captions, despite the fact that the former, unlike the latter, were generated without awareness of the distractors that make the task hard. Even more remarkably, when the same neural captions are given to human subjects, their retrieval performance is almost at chance level. Our results thus add to the growing body of evidence that, even when the “language” of neural models resembles English, this superficial resemblance might be deeply misleading.

## introduction neural vision-and-language models have achieved impressive results in tasks such as visual commonsense reasoning and question answering (e.g., @xcite . however, @xcite recently showed, in the context of captionbased image retrieval, that state-of-the-art multimodal models still perform poorly when the candidate pool contains very similar distractor images (such as close frames from the same video). here, we show that, when the best pre-trained image retrieval system of @xcite is fed captions produced by an out-of-the box neural caption generator, its performance makes a big jump forward. 0-shot image retrieval accuracy improves by almost 6% compared to the highest previously reported human-caption-based performance by the same model, with fine-tuning and various ad-hoc architectural adaptations. this is remarkable, because the off-the-shelf caption generator we use (unlike the humans who wrote the original captions in the data-set) is not taking the set of distractor images into account. even more remarkably, we show that, when human subjects are tasked with retrieving the right image using the same neural captions that help the model so much, their performance is only marginally above chance level. ## results and analysis neural vs. human caption performance as shown in a concrete sense of the differences between the two types of captions is given by the examples in fig. 1 . the examples in this figure are picked randomly. based on manual inspection of a larger set, we are confident they are representative of the full data. clearly, neural captions are shorter (avg. length at 11.4 tokens vs. 23.2 for human captions) and more plainly descriptive (although the description is mostly only vaguely related to what's actually depicted). since there is no way to make the out-of-the-box clipcap system distractoraware, the neural captions are not highlighting discriminative aspects of a target image compared to the distractors. human captions, on the other hand, use very articulated language to highlight what is unique about the target compared to the closest distractors (often focusing on rather marginal aspects of the image, because of their discriminativeness, e.g., for the first example in the figure, the fact that the blue backpack is hardly visible). it is not surprising that a generic image retriever, that was not trained to handle this highly context-based linguistic style, would not get much useful information out of the human captions. it is interesting, however, that this generic system performs relatively well with the neural captions, given how off-the-mark and non-discriminative the latter typically are. as more quantitative cues of the differences between caption types, we observe that human captions are making more use of both rare lemmas and function words (see frequency plots in appendix b). 2 extracting the lemmas that are statistically most strongly associated to the human caption set (see appendix c for method and full top list), we observe "meta-visual" words such as visible and see, pronouns and determiners cuing anaphoric structure (the, her, his), and function words signaling a more complex sentence structure, such as auxiliaries, negation and connectives. among the most typical neural lemmas, we find instead general terms for concrete entities such as people, woman, table and food. are neural captions really discriminative? by looking at figure 1 , we see that neural captions might be (very noisily) descriptive of the target, but they seem hardly discriminative with respect to the nearest distractors. recall that each imagecode set contains a sequence of 10 frames from the same scene. in general, the frames that are farther away in time might be easier to discriminate than the closest ones (consider the full candidate set examples in appendix e: it is in general much easier to tell apart the first and last frames than two adjacent images). it could be, then, that the non-random but still low performance of the image retriever with neural captions is due to the combination of two factors. on the one hand, the neural captions might suffice for the retriever to exclude the farthest distractors. on the other, its performance at telling the closest frames apart is actually random. to rule out this explanation, we repeated the retrieval experiment in the most challenging setup, in which we excluded all but the distractors immediately preceding and following the target frame (if the target is the first/last image, we pick the two frames following/preceding it, respectively). the retriever using neural captions still reaches 48.7% accuracy, well above chance level (33.3%). we must thus conclude that neural captions such as those in fig. 1 do carry a non-negligible degree of discriminative power for a neural image retriever. on a related point, neural captions such as those in the figure seem so generic that one could imagine the neural caption generator would produce the same caption for close-by frames. this is not the case. we consider the 678 imagecode cases in which a candidate set is repeated across multiple data points, with only the target changing, and in which the targets are adjacent or one-frame apart. in 93.7% of such cases, clipcap generated at least two non-identical captions. for example, the frame on the left of the center-bottom pair in fig. 1 is also used as a target, and for it clipcap generates the caption "a person holding a water bottle with a dog in it." looking at the right-bottom pair, the frame on the right is also a target, and clipcap generated the following caption for it: "a picture of a kitchen with a bunch of televisions on it." future research should ascertain to what extent these intuitively uninformative variations in frame description actually contain cues that are systematically discriminative for the retrieval system. human performance on neural captions looking again at examples such as those in fig. 1 (all cases in which the image retriever correctly identified the target), we might conjecture that the neural captions are more informative for the neural retriever model than they are for humans. to verify this hypothesis, we organized a crowd-sourcing experiment in which human subjects had to perform the same 10-image-set target discrimination task we submitted to the neural image retriever. more precisely, we selected a subset of imagecode that is balanced in terms of image retriever performance as follows. we used all 522 sets where the retriever guessed the target, and we randomly added the same number of sets where the retriever got it wrong (so that its accuracy, on this subset, is at 50% by construction). we collected human discrimination decisions for this subset of 1,044 items, when either human or neural captions retriever captions human neural human 54.3 12.8 neural 16.3 50.0 we thus confirm that neural captions such as those presented in fig. 1 , despite being apparently vague and inaccurate descriptions of the target image "in plain english", carry significantly more discriminative value for the neural retriever than they do for humans (recall that the examples in this figure were selected among the cases where the neural caption allowed the neural retriever to guess the right target, while human subjects failed the task). ## conclusion previous research has shown that neural caption generators occasionally produce highly counterintuitive or irrelevant image descriptions (e.g., @xcite . we provide here evidence that such descriptions might only be misleading or uninformative for humans, while still being relatively "understandable" to neural models. we discuss below the limitations that delimit the scope of our finding. still, we can tentatively conclude that, even when they are trained on english, deep nets might pack and retrieve information from token sequences that are different from those an english speaker would encode in and extract from them. a better understanding of this behaviour could help design higher-performance systems. for example, we could implement a module translating human captions to the "machine code" that neural models prefer, leading to better caption-based retrieval; or, from a model-to-model communication perspective @xcite , optimize caption generation directly for neural model understanding, instead of imitating human captions. similar ideas have recently proposed in the context of textual information retrieval (e.g., @xcite . from a less optimistic perspective, our results can be interpreted as another cautionary tale about the degree to which neural models truly "understand language" @xcite , and suggest that a good grasp of their counter-intuitive behaviour should be a priority of current research, or else malicious agents could rely on the models' opaque behaviour for adversarial attacks @xcite and other types of model misuse.