23 citations · 26 across the 5 of their papers we have counts for
6 papers
LookME: Lookup-Based Multimodal Embeddings for Layer Injection in Vision-Language Models
Zeyu Xu, Xingzhong Hou, Pengkai Guo +6
Vision-Language Models (VLMs) have achieved strong progress in multimodal understanding. However, scaling dense or sparse Mixture-of-Experts (MoE) models to improve performance lim…
Learning Geo-Temporal Image Features
Menghua Zhai, Tawfiq Salem, Connor Greenwell +3
We propose to implicitly learn to extract geo-temporal image features, which are mid-level features related to when and where an image was captured, by explicitly optimizing for a…
Learning to Look around Objects for Top-View Representations of Outdoor Scenes
Samuel Schulter, Menghua Zhai, Nathan Jacobs +1
Given a single RGB image of a complex outdoor road scene in the perspective view, we address the novel problem of estimating an occlusion-reasoned semantic scene layout in the top-…
A Unified Model for Near and Remote Sensing
Scott Workman, Menghua Zhai, David J. Crandall +1
We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use. In our approach, we combine ov…
Predicting Ground-Level Scene Layout from Aerial Imagery
Menghua Zhai, Zachary Bessinger, Scott Workman +1
We introduce a novel strategy for learning to extract semantically meaningful features from aerial imagery. Instead of manually labeling the aerial imagery, we propose to predict (…
Detecting Vanishing Points using Global Image Context in a Non-Manhattan World
Menghua Zhai, Scott Workman, Nathan Jacobs
We propose a novel method for detecting horizontal vanishing points and the zenith vanishing point in man-made environments. The dominant trend in existing methods is to first find…