CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models

Recent advances in Large Vision-Language Models (LVLMs) have sparked significant progress in general-purpose vision tasks through visual instruction tuning. While some works have demonstrated the capability of LVLMs to generate segmentation masks that align phrases with natural language descriptions in a single image, they struggle with segmentation-grounded comparisons across multiple images, particularly at finer granularities such as object parts. In this paper, we introduce the new task of part-focused semantic co-segmentation, which seeks to identify and segment common and unique objects and parts across images. To address this task, we present CALICO, the first LVLM that can segment and reason over multiple masks across images, enabling object comparison based on their constituent parts. CALICO features two proposed components, a novel Correspondence Extraction Module, which captures semantic-rich information to identify part-level correspondences between objects, and a Correspondence Adaptation Module, which embeds this information into the LVLM to facilitate multi-image understanding in a parameter-efficient manner. To support training and evaluation, we curate MixedParts, a comprehensive multi-image segmentation dataset containing $\sim$2.4M samples across $\sim$44K images with diverse object and part categories. Experimental results show CALICO, finetuned on only 0.3% of its architecture, achieves robust performance in part-focused semantic co-segmentation.

Face Verification and Forgery Detection for Ophthalmic Surgery Images

Although modern face verification systems are accessible and accurate, they are not always robust to pose variance and occlusions. Moreover, accurate models require a large amount of data to train. We structure our experiments to operate on small amounts of data obtained from an NGO that funds ophthalmic surgeries. We set up our face verification task as that of verifying pre-operation and post-operation images of a patient that undergoes ophthalmic surgery, and as such the post-operation images have occlusions like an eye patch. In this paper, we present a system that performs the face verification task using one-shot learning. To this end, our paper uses deep convolutional networks and compares different model architectures and loss functions. Our best model achieves 85% test accuracy. During inference time, we also attempt to detect image forgeries in addition to performing face verification. To achieve this, we use Error Level Analysis. Finally, we propose an inference pipeline that demonstrates how these techniques can be used to implement an automated face verification and forgery detection system.