WorldGPT: Empowering LLM as Multimodal World Model

World models are progressively being employed across diverse fields, extending from basic environment simulation to complex scenario construction. However, existing models are mainly trained on domain-specific states and actions, and confined to single-modality state representations. In this paper, We introduce WorldGPT, a generalist world model built upon Multimodal Large Language Model (MLLM). WorldGPT acquires an understanding of world dynamics through analyzing millions of videos across various domains. To further enhance WorldGPT's capability in specialized scenarios and long-term tasks, we have integrated it with a novel cognitive architecture that combines memory offloading, knowledge retrieval, and context reflection. As for evaluation, we build WorldNet, a multimodal state transition prediction benchmark encompassing varied real-life scenarios. Conducting evaluations on WorldNet directly demonstrates WorldGPT's capability to accurately model state transition patterns, affirming its effectiveness in understanding and predicting the dynamics of complex scenarios. We further explore WorldGPT's emerging potential in serving as a world simulator, helping multimodal agents generalize to unfamiliar domains through efficiently synthesising multimodal instruction instances which are proved to be as reliable as authentic data for fine-tuning purposes. The project is available on \url{https://github.com/DCDmllm/WorldGPT}.

De-fine: Decomposing and Refining Visual Programs with Auto-Feedback

Visual programming, a modular and generalizable paradigm, integrates different modules and Python operators to solve various vision-language tasks. Unlike end-to-end models that need task-specific data, it advances in performing visual processing and reasoning in an unsupervised manner. Current visual programming methods generate programs in a single pass for each task where the ability to evaluate and optimize based on feedback, unfortunately, is lacking, which consequentially limits their effectiveness for complex, multi-step problems. Drawing inspiration from benders decomposition, we introduce De-fine, a general framework that automatically decomposes complex tasks into simpler subtasks and refines programs through auto-feedback. This model-agnostic approach can improve logical reasoning performance by integrating the strengths of multiple models. Our experiments across various visual tasks show that De-fine creates more accurate and robust programs, setting new benchmarks in the field.

Improving Vision Anomaly Detection with the Guidance of Language Modality

Recent years have seen a surge of interest in anomaly detection for tackling industrial defect detection, event detection, etc. However, existing unsupervised anomaly detectors, particularly those for the vision modality, face significant challenges due to redundant information and sparse latent space. Conversely, the language modality performs well due to its relatively single data. This paper tackles the aforementioned challenges for vision modality from a multimodal point of view. Specifically, we propose Cross-modal Guidance (CMG), which consists of Cross-modal Entropy Reduction (CMER) and Cross-modal Linear Embedding (CMLE), to tackle the redundant information issue and sparse space issue, respectively. CMER masks parts of the raw image and computes the matching score with the text. Then, CMER discards irrelevant pixels to make the detector focus on critical contents. To learn a more compact latent space for the vision anomaly detector, CMLE learns a correlation structure matrix from the language modality, and then the latent space of vision modality will be learned with the guidance of the matrix. Thereafter, the vision latent space will get semantically similar images closer. Extensive experiments demonstrate the effectiveness of the proposed methods. Particularly, CMG outperforms the baseline that only uses images by 16.81%. Ablation experiments further confirm the synergy among the proposed methods, as each component depends on the other to achieve optimal performance.