Search Results for author:
Found 18 papers, 9 papers with code
Apple Intelligence Foundation Language Models
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute.
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference
On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences.
UPSCALE: Unconstrained Channel Pruning
However, for multi-branch segments of a model, channel removal can introduce inference-time memory copies.
AutoFocusFormer: Image Segmentation off the Grid
To achieve this, we propose AutoFocusFormer (AFF), a local-attention transformer image recognition backbone, which performs adaptive downsampling by learning to retain the most important pixels for the task.
Ranked #3 on
Instance Segmentation
on Cityscapes val
Generative Multiplane Images: Making a 2D GAN 3D-Aware
What is really needed to make an existing 2D GAN 3D-aware?
FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction
In this paper, we present FvOR, a learning-based object reconstruction method that predicts accurate 3D models given a few images with noisy input poses.
VideoPose: Estimating 6D object pose from videos
We introduce a simple yet effective algorithm that uses convolutional neural networks to directly estimate object poses from videos.
MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutions
Deep learning has made significant impacts on multi-view stereo systems.
Semantic MapNet: Building Allocentric Semantic Maps and Representations from Egocentric Views
We study the task of semantic mapping - specifically, an embodied agent (a robot or an egocentric AI assistant) is given a tour of a new environment and asked to build an allocentric top-down semantic map ("what is where?")
Cross-channel Communication Networks
Convolutional neural networks process input data by sending channel-wise feature response maps to subsequent layers.
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts
We develop new representations and algorithms for three-dimensional (3D) object detection and spatial layout prediction in cluttered indoor scenes.
Ranked #24 on
3D Object Detection
on SUN-RGBD val
Embodied Visual Recognition
Passive visual systems typically fail to recognize objects in the amodal setting where they are heavily occluded.
3D Scene Reconstruction with Multi-layer Depth and Epipolar Transformers
We tackle the problem of automatically reconstructing a complete 3D model of a scene from a single RGB image.
A Fusion Approach for Multi-Frame Optical Flow Estimation
To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account.
3D Object Detection With Latent Support Surfaces
We develop a 3D object detection algorithm that uses latent support surfaces to capture contextual relationships in indoor scenes.
Ranked #26 on
3D Object Detection
on SUN-RGBD val
Cascaded Scene Flow Prediction using Semantic Segmentation
Given two consecutive frames from a pair of stereo cameras, 3D scene flow methods simultaneously estimate the 3D geometry and motion of the observed scene.
Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients
We develop new representations and algorithms for three-dimensional (3D) object detection and spatial layout prediction in cluttered indoor scenes.
Ranked #27 on
3D Object Detection
on SUN-RGBD val
Image Segmentation by Cascaded Region Agglomeration
We propose a hierarchical segmentation algorithm that starts with a very fine oversegmentation and gradually merges regions using a cascade of boundary classifiers.
