TROPE : TR aining-Free Object-Part Enhancement for Seamlessly Improving Fine-Grained Zero-Shot Image Captioning (2024)

Abstract: Zero-shot inference, where pre-trained models perform tasks without specific training data, is an exciting emergent ability of large models like CLIP. Although there has been considerable exploration into enhancing zero-shot abilities in image captioning (IC) for popular datasets such as MSCOCO and Flickr8k, these approaches fall short with fine-grained datasets like CUB, FLO, UCM-Captions, and Sydney-Captions. These datasets require captions to discern between visually and semantically similar classes, focusing on detailed object parts and their attributes. To overcome this challenge, we introduce TRaining-Free Object-Part Enhancement (TROPE). TROPE enriches a base caption with additional object-part details using object detector proposals and natural language processing techniques. It complements rather than alters the base caption, allowing seamless integration with other captioning methods and offering users enhanced flexibility. Our evaluations show that TROPE consistently boosts performance across all tested zero-shot IC approaches and achieves state-of-the-art results on fine-grained IC datasets.

## introduction object parts and their attributes have been shown to play a critical role in distinguishing between classes in tasks like fine-grained classification @xcite @xcite . despite their importance, previous works in image captioning (ic) have instead focused primarily on objects, their attributes, and their interactions, as seen in by common utilized semantic structures like scene graphs @xcite @xcite . this focus is partly because ic is often applied to general domain datasets 1 trope source codes and data: https://github.com/ joshuafeinglass/trope . like mscoco @xcite and flickr8k @xcite , where images typically contain various object classes, and captions provide a highlevel scene description. existing training-free zero-shot methods, such as zerocap @xcite and conzic @xcite , enhance captions by substituting words in a base caption with more a contextually appropriate ones, utilizing scores from clip @xcite and a large language model (llm). however, since these models are pre-trained on general domain data, the resulting captions often lack fine-grained detail. this is especially problematic for fine-grained datasets, such as bird species in cub @xcite , flower species in flo @xcite , or aerial scenes in ucm-captions @xcite and sydney-captions @xcite , which require distinguishing between visually and semantically similar classes. @xcite showed that that visual and semantic feature representations from these fine-grained datasets differ significantly from those of general domain datasets, leading to poor performance in domain generalization benchmarks due to task misalignment @xcite . we conjecture that effective zero-shot ic in finegrained contexts necessitates robust primitives that are consistent across both the training and test domains. following this line of reasoning, we proposed training-free object-part enhancement (trope), which adapts pre-trained models to finegrained datasets by supplementing captions with object part information. trope effectively augments the base captions of existing zero-shot ic methods with fine-grained details as shown in figure 1. our evaluations demonstrate that adding information from object part semantic proposals consistently enhances ic performance across all tested methods, datasets, and metrics. precisionrecall curves indicate that trope significantly improves recall with a minimal impact on precision, particularly in datasets where there is substantial overlap between the object detector's vocabulary and the terms commonly used by human annotators. we also present examples of trope's application to an enterprise captioner, gpt4, and discuss two failure cases: one involving a lack of recognizable objects and another featuring redundant or incorrect part information. to further explore the bias of general domain datasets, we conducted an analytical study on the frequency of terms in human-annotated and machine-generated texts across both general domain and fine-grained datasets. we found that semantic indicator words, such as "with", "has", and "have", which introduce object part descriptions, are much more common in fine-grained datasets. this finding underscores the strong relationship between the semantic structure of images and the captions used to describe them, reinforcing the need to adapt models trained on general domain datasets to fine-grained settings using techniques like trope. contributions: our work introduces the setting of fine-grained zero-shot captioning, extending zeroshot capabilities to four fine-grained captioning datasets. our analyses reveal that existing zeroshot benchmarks cater predominantly to general domains and fail to meet the specific needs of finegrained settings. we propose trope as a solution to enhance zero-shot captioning performance by incorporating detailed information from a pre-trained object detector, consistently enriching caption detail and improving performance across all methods, evaluation metrics, and datasets. ## related work the detection of objects and attributes, facilitated by large datasets of human-labeled regions @xcite , has historically been a cornerstone for various visionlanguage tasks @xcite . previous works have integrated this object and attribute information into training @xcite , labels @xcite , and text generation @xcite . trope builds on this foundation by extracting hierarchical object relationships to improve the detail of image captions. enhancing the level of detail presented in image captions is a popular and multi-faceted topic. entity-aware captioning seeks to replace generic nouns with context-specific entities from wiki text based either on a base (template) caption @xcite @xcite or optimized generation @xcite . similarly, stylized captioning is also performed by either modifying a base caption @xcite or optimized generation @xcite . lastly, scene graphs have been used to either enhance a base caption @xcite or as an additional feature for optimized generation @xcite . zero-shot captioning presents distinct challenges, as it operates without direct access to imagetext pairs for training, relying instead on the intrinsic capabilities of pre-trained models like clip @xcite and simctg @xcite . several works have tried to enhance zeroshot performance using training-free @xcite methods or text-only training @xcite @xcite @xcite strategies that assume access to target dataset captions. @xcite introduced a domain generalization ic benchmark spanning general and finegrained datasets, where a large gap in performance on general datasets and fine-grained datasets could be observed. trope aims to bridge the gap between general and fine-grained datasets by adding detailed object descriptions without requiring additional training data, improving the applicability and effectiveness of zero-shot captioning methods in more challenging environments. ## experiments to validate the effectiveness of trope for enhancing the detail of generated image captions, we conduct experiments on our proposed fine-grained ic benchmark in section 5.3, where trope demonstrates consistent improvement in fine-grained image captioning performance for standard metrics. to further motivate the use of trope, we then explore the bias of general domain datasets in a word frequency study in section 5.4. flickr8k @xcite is a popular dataset comprised of 8000 images with 5 human annotated captions for each image. the images were crawled from social media postings and like ms-coco, are primarily common objects. ## discussion based on our results and the apparent spectrum of fine-grained dataset characteristics, trope's effectiveness appears widely applicable to numerous image datasets. however, its performance varies depending on each dataset's structure. a significant factor influencing trope's success is the alignment between the common terminology used by human captioners and the vocabulary of the object detector employed. for instance, while vinvl effectively covers common terms related to bird parts (e.g., head, tail, wing), flower parts (e.g., petal, leaf), and aerial views (e.g., airplane, airport), it lacks specialized terms frequently used in flower descriptions (e.g., stamen, pistil, veins). this gap is notable in our results: trope shows state-of-theart performance on cub, ucm, and sc, but somewhat underperforms in the flo dataset compared to domain generalization techniques, particularly as additional part proposals are integrated, which dramatically affects precision. furthermore, as the precision of object detectors improves, we anticipate that methods like trope will yield even greater improvements in image captioning performance. trope's strength lies in significantly boosting recall with minimal reductions in precision. in cases of poor detector performance, the typical outcome is no change to the base caption, whereas a mismatch between the detector's vocabulary and human captions can lead to redundant or irrelevant descriptions, thereby decreasing precision. our analysis also indicates that different caption evaluation metrics prioritize different aspects of trope's semantic components (see considering these insights, the optimal number of semantic part proposals to add to a base caption depends on the specific needs and goals of the research. for applications requiring high accuracy, such as assistive technologies, we recommend adding only a single proposal. conversely, for purposes like training generative models or enhancing retrieval systems, incorporating multiple proposals may be beneficial as it enhances the discriminative information available, despite the potential for introducing irrelevant details. researchers should select evaluation metrics that best align with their objectives and tailor their approach accordingly. ## conclusion and broader impact we have introduced trope, a training-free method for zero-shot captioning that enhances base captions by adding semantic part proposals to key object instances. this approach has demonstrated state-of-the-art performance in fine-grained zeroshot image captioning (ic), consistently improving captions across all tested models, metrics, and datasets. given the foundational role of ic in a variety of vision-language tasks, trope holds potential for enhancing fine-grained performance in applications such as text-to-image generation, text-to-image retrieval, and image-to-text retrieval. future work could also focus on extending the prin-ciples underlying trope to other modalities, such as audio or video. this would involve adapting trope to work with relevant pre-trained models tailored to these modalities, potentially opening new avenues for multimodal integration and captioning enhancements.