Search Results for author:
Found 9 papers, 5 papers with code
Cropper: Vision-Language Model for Image Cropping through In-Context Learning
The goal of image cropping is to identify visually appealing crops within an image.
Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction
We propose 4 insights that help to significantly improve the performance of deep learning models that predict surface normals and semantic labels from a single RGB image.
Ranked #1 on
Semantic Segmentation
on ScanNetV2
(Pixel Accuracy metric)
Efficient Hierarchical Graph-Based Segmentation of RGBD Videos
We present an efficient and scalable algorithm for segmenting 3D RGBD point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based approach.
Object category learning and retrieval with weak supervision
We consider the problem of retrieving objects from image data and learning to classify them into meaningful semantic categories with minimal supervision.
Let's Dance: Learning From Online Dance Videos
We present a comparison of numerous state-of-the-art techniques on our dataset using three different representations (video, optical flow and multi-person pose data) in order to analyze these approaches.
Semantic Instance Labeling Leveraging Hierarchical Segmentation
Most of the approaches for indoor RGBD semantic la- beling focus on using pixels or superpixels to train a classi- fier.
An Energy Minimization Approach to 3D Non-Rigid Deformable Surface Estimation Using RGBD Data
In particular, we show that our method is able to estimate the configuration of a textureless nonrigid object with no correspondences available.
Eyemotion: Classifying facial expressions in VR using eye-tracking cameras
One of the main challenges of social interaction in virtual reality settings is that head-mounted displays occlude a large portion of the face, blocking facial expressions and thereby restricting social engagement cues among users.
Predicting Daily Activities From Egocentric Images Using Deep Learning
We collected a dataset of 40, 103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities.
