We introduce Groma, a Multimodal Large Language Model (MLLM) with grounded and fine-grained visual perception ability. Beyond holistic image understanding, Groma is adept at region-level tasks such as region captioning and visual grounding. Such capabilities are built upon a localized visual tokenization mechanism, where an image input is decomposed into regions of interest and subsequently encoded into region tokens. By integrating region tokens into user instructions and model responses, we seamlessly enable Groma to understand user-specified region inputs and ground its textual output to images. Besides, to enhance the grounded chat ability of Groma, we curate a visually grounded instruction dataset by leveraging the powerful GPT-4V and visual prompting techniques. Compared with MLLMs that rely on the language model or external module for localization, Groma consistently demonstrates superior performances in standard referring and grounding benchmarks, highlighting the advantages of embedding localization into image tokenization. Project page: https://groma-mllm.github.io/.

Deep neural networks (DNNs) are at the forefront of cutting-edge technology, and have been achieving remarkable performance in a variety of complex tasks. Nevertheless, their integration into safety-critical systems, such as in the aerospace or automotive domains, poses a significant challenge due to the threat of adversarial inputs: perturbations in inputs that might cause the DNN to make grievous mistakes. Multiple studies have demonstrated that even modern DNNs are susceptible to adversarial inputs, and this risk must thus be measured and mitigated to allow the deployment of DNNs in critical settings. Here, we present gRoMA (global Robustness Measurement and Assessment), an innovative and scalable tool that implements a probabilistic approach to measure the global categorial robustness of a DNN. Specifically, gRoMA measures the probability of encountering adversarial inputs for a specific output category. Our tool operates on pre-trained, black-box classification DNNs, and generates input samples belonging to an output category of interest. It measures the DNN's susceptibility to adversarial inputs around these inputs, and aggregates the results to infer the overall global categorial robustness of the DNN up to some small bounded statistical error. We evaluate our tool on the popular Densenet DNN model over the CIFAR10 dataset. Our results reveal significant gaps in the robustness of the different output categories. This experiment demonstrates the usefulness and scalability of our approach and its potential for allowing DNNs to be deployed within critical systems of interest.