Composition Vision-Language Understanding via Segment and Depth Anything Model

TL;DR

The paper tackles the challenge of deepening vision-language understanding in real-world scenes by bridging segmentation, depth estimation, and language reasoning. It introduces a modular pipeline that fuses the Segment Anything Model (SAM) for instance-level segmentation, the Depth Anything Model (DAM) for per-instance depth, and GPT-4V for language-based reasoning, forming a symbolic knowledge layer for zero-shot tasks. Key contributions include: (i) symbolic knowledge gathering that combines neural cues with depth to produce intrinsic/extrinsic instance information, (ii) a discriminative, pairwise composition reasoning module over 8 spatial relations and 6 interactions, and (iii) a Visual Language Answering boosting strategy that leverages a two-branch architecture to improve zero-shot VQA. The approach demonstrates improved multimodal understanding on in-the-wild images and enables dataset generation opportunities, with practical impact on robust visual reasoning for downstream applications and future research in open-set, multimodal perception. $8$ fundamental spatial relations and $6$ complex interactions are explicitly modeled to capture rich scene composition, advancing zero-shot compositionality in vision-language systems.

Abstract

We introduce a pioneering unified library that leverages depth anything, segment anything models to augment neural comprehension in language-vision model zero-shot understanding. This library synergizes the capabilities of the Depth Anything Model (DAM), Segment Anything Model (SAM), and GPT-4V, enhancing multimodal tasks such as vision-question-answering (VQA) and composition reasoning. Through the fusion of segmentation and depth analysis at the symbolic instance level, our library provides nuanced inputs for language models, significantly advancing image interpretation. Validated across a spectrum of in-the-wild real-world images, our findings showcase progress in vision-language models through neural-symbolic integration. This novel approach melds visual and language analysis in an unprecedented manner. Overall, our library opens new directions for future research aimed at decoding the complexities of the real world through advanced multimodal technologies and our code is available at \url{https://github.com/AnthonyHuo/SAM-DAM-for-Compositional-Reasoning}.

Figures (5)

• Figure 1: Illustration of the image understanding library. In this paper, we create an library that leverages multi large vision models to extract the rich information for an image input, then using GPT-4V to extract and summary more high level information for the vision language understanding, this pipline can be used in vqa, reasoning and many other tasks.
• Figure 2: Illustration of the synergy between the Depth Anything Model (DAM) and Segment Anything Model (SAM) in enhancing understanding of images captured in natural settings. By utilizing SAM, we differentiate between instances through the application of masks and bounding boxes, while DAM is employed to produce 2.5D depth information, facilitating the comprehension of 3D signals and contributing to a richer understanding of scene composition. Ultimately, by integrating the insights from GPT-4V, we construct a comprehensive response to queries or delineate the intrinsic and extrinsic characteristics of objects of interest using text prompts.
• Figure 3: Quantitative results on in-the-wild images for composition reasoning.
• Figure 4: The ability of zero-shot symbolic visual question answering for image understanding library, leveraging the ablities of large vision models, image understanding can outperforms GPT-4V on understanding the symbolic information of one scene, like counting numbers of one object.
• Figure 5: Illustration of the enhancement for the vision-language model by using the image understanding library. The image understanding library can extract the spatial information from an image, then after combining the gathered spatial information, the GPT-4V can enhance the original vision-language understanding with more complex spatial information expression.