EBind: a practical approach to space binding

We simplify space binding by focusing on two core components, a single encoder per modality and high-quality data; enabling training state-of-the-art models on a single GPU in a few hours as opposed to multiple days. We present EBind, an Easy, data-centric, and parameter-efficient method to Bind the embedding spaces of multiple contrastive models. We demonstrate that a simple 1.8B-parameter image-text-video-audio-3D model can outperform models 4 to 17x the size. The key to achieving this is a carefully curated dataset of three complementary data sources: i) 6.7M fully-automated multimodal quintuples sourced via SOTA retrieval models, ii) 1M diverse, semi-automated triples annotated by humans as negative, partial, or positive matches, and iii) 3.4M pre-existing captioned data items. We use 13 different evaluations to demonstrate the value of each data source. Due to limitations with existing benchmarks, we further introduce the first high-quality, consensus-annotated zero-shot classification benchmark between audio and PCs. In contrast to related work, we will open-source our code, model weights, and datasets.

Continuous Online Adaptation Driven by User Interaction for Medical Image Segmentation

Interactive segmentation models use real-time user interactions, such as mouse clicks, as extra inputs to dynamically refine the model predictions. After model deployment, user corrections of model predictions could be used to adapt the model to the post-deployment data distribution, countering distribution-shift and enhancing reliability. Motivated by this, we introduce an online adaptation framework that enables an interactive segmentation model to continuously learn from user interaction and improve its performance on new data distributions, as it processes a sequence of test images. We introduce the Gaussian Point Loss function to train the model how to leverage user clicks, along with a two-stage online optimization method that adapts the model using the corrected predictions generated via user interactions. We demonstrate that this simple and therefore practical approach is very effective. Experiments on 5 fundus and 4 brain MRI databases demonstrate that our method outperforms existing approaches under various data distribution shifts, including segmentation of image modalities and pathologies not seen during training.