m&m's: A Benchmark to Evaluate Tool-Use for multi-step multi-modal Tasks

Real-world multi-modal problems are rarely solved by a single machine learning model, and often require multi-step computational plans that involve stitching several models. Tool-augmented LLMs hold tremendous promise for automating the generation of such computational plans. However, the lack of standardized benchmarks for evaluating LLMs as planners for multi-step multi-modal tasks has prevented a systematic study of planner design decisions. Should LLMs generate a full plan in a single shot or step-by-step? Should they invoke tools directly with Python code or through structured data formats like JSON? Does feedback improve planning? To answer these questions and more, we introduce m&m's: a benchmark containing 4K+ multi-step multi-modal tasks involving 33 tools that include multi-modal models, (free) public APIs, and image processing modules. For each of these task queries, we provide automatically generated plans using this realistic toolset. We further provide a high-quality subset of 1,565 task plans that are human-verified and correctly executable. With m&m's, we evaluate 6 popular LLMs with 2 planning strategies (multi-step vs. step-by-step planning), 2 plan formats (JSON vs. code), and 3 types of feedback (parsing/verification/execution). Finally, we summarize takeaways from our extensive experiments. Our dataset and code are available on HuggingFace (https://huggingface.co/datasets/zixianma/mnms) and Github (https://github.com/RAIVNLab/mnms).

LesionPaste: One-Shot Anomaly Detection for Medical Images

Due to the high cost of manually annotating medical images, especially for large-scale datasets, anomaly detection has been explored through training models with only normal data. Lacking prior knowledge of true anomalies is the main reason for the limited application of previous anomaly detection methods, especially in the medical image analysis realm. In this work, we propose a one-shot anomaly detection framework, namely LesionPaste, that utilizes true anomalies from a single annotated sample and synthesizes artificial anomalous samples for anomaly detection. First, a lesion bank is constructed by applying augmentation to randomly selected lesion patches. Then, MixUp is adopted to paste patches from the lesion bank at random positions in normal images to synthesize anomalous samples for training. Finally, a classification network is trained using the synthetic abnormal samples and the true normal data. Extensive experiments are conducted on two publicly-available medical image datasets with different types of abnormalities. On both datasets, our proposed LesionPaste largely outperforms several state-of-the-art unsupervised and semi-supervised anomaly detection methods, and is on a par with the fully-supervised counterpart. To note, LesionPaste is even better than the fully-supervised method in detecting early-stage diabetic retinopathy.