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Found 4 papers, 1 papers with code
Perception Tokens Enhance Visual Reasoning in Multimodal Language Models
For example, in a depth-related task, an MLM augmented with perception tokens can reason by generating a depth map as tokens, enabling it to solve the problem effectively.
Gene-Level Representation Learning via Interventional Style Transfer in Optical Pooled Screening
Optical pooled screening (OPS) combines automated microscopy and genetic perturbations to systematically study gene function in a scalable and cost-effective way.
Data Alignment for Zero-Shot Concept Generation in Dermatology AI
Our goal is to use these models to generate caption text that aligns well with both the clinical lexicon and with the natural human language used in CLIP's pre-training data.
MIMIC: Masked Image Modeling with Image Correspondences
We train multiple models with different masked image modeling objectives to showcase the following findings: Representations trained on our automatically generated MIMIC-3M outperform those learned from expensive crowdsourced datasets (ImageNet-1K) and those learned from synthetic environments (MULTIVIEW-HABITAT) on two dense geometric tasks: depth estimation on NYUv2 (1. 7%), and surface normals estimation on Taskonomy (2. 05%).
