Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

Large foundation models can exhibit unique capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barely overlap. For example, visual-language models (VLMs) are trained on Internet-scale image captions, but large language models (LMs) are further trained on Internet-scale text with no images (e.g. from spreadsheets, to SAT questions). As a result, these models store different forms of commonsense knowledge across different domains. In this work, we show that this model diversity is symbiotic, and can be leveraged to build AI systems with structured Socratic dialogue -- in which new multimodal tasks are formulated as a guided language-based exchange between different pre-existing foundation models, without additional finetuning. In the context of egocentric perception, we present a case study of Socratic Models (SMs) that can provide meaningful results for complex tasks such as generating free-form answers to contextual questions about egocentric video, by formulating video Q&A as short story Q&A, i.e. summarizing the video into a short story, then answering questions about it. Additionally, SMs can generate captions for Internet images, and are competitive with state-of-the-art on zero-shot video-to-text retrieval with 42.8 R@1 on MSR-VTT 1k-A. SMs demonstrate how to compose foundation models zero-shot to capture new multimodal functionalities, without domain-specific data collection. Prototypes are available at socraticmodels.github.io.Comment: https://socraticmodels.github.io

Enhancing visual embeddings through weakly supervised captioning for zero-shot learning

Visual features designed for image classification have shown to be useful in zero-shot learning (ZSL) when generalizing towards classes not seen during training. In this paper, we argue that a more effective way of building visual features for ZSL is to extract them through captioning, in order not just to classify an image but, instead, to describe it. However, modern captioning models rely on a massive level of supervision, e.g up to 15 extended descriptions per instance provided by humans, which is simply not available for ZSL benchmarks. In the latter in fact, the available annotations inform about the presence/absence of attributes within a fixed list only. Worse, attributes are seldom annotated at the image level, but rather, at the class level only: because of this, the annotation cannot be visually grounded. In this paper, we deal with such a weakly supervised regime to train an end-to-end LSTM captioner, whose backbone CNN image encoder can provide better features for ZSL. Our enhancement of visual features, called 'VisEn', is compatible with any generic ZSL method, without requiring changes in its pipeline (a part from adapting hyper-parameters). Experimentally, VisEn is capable of sharply improving recognition performance on unseen classes, as we demonstrate thorough an ablation study which encompasses different ZSL approaches. Further, on the challenging fine-grained CUB dataset, VisEn improves by margin state-of-the-art methods, by using visual descriptors of one order of magnitude smaller