Search Results for author:
Found 14 papers, 4 papers with code
WorDepth: Variational Language Prior for Monocular Depth Estimation
To test this, we focus on monocular depth estimation, the problem of predicting a dense depth map from a single image, but with an additional text caption describing the scene.
AugUndo: Scaling Up Augmentations for Unsupervised Depth Completion
The sparse depth modality have seen even less as intensity transformations alter the scale of the 3D scene, and geometric transformations may decimate the sparse points during resampling.
Sub-token ViT Embedding via Stochastic Resonance Transformers
We term our method ``Stochastic Resonance Transformer" (SRT), which we show can effectively super-resolve features of pre-trained ViTs, capturing more of the local fine-grained structures that might otherwise be neglected as a result of tokenization.
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots
We introduce a method to segment the visual field into independently moving regions, trained with no ground truth or supervision.
Surprising Instabilities in Training Deep Networks and a Theoretical Analysis
We discover restrained numerical instabilities in current training practices of deep networks with stochastic gradient descent (SGD).
On the Viability of Monocular Depth Pre-training for Semantic Segmentation
We explore how pre-training a model to infer depth from a single image compares to pre-training the model for a semantic task, e. g. ImageNet classification, for the purpose of downstream transfer to semantic segmentation.
Accelerated PDEs for Construction and Theoretical Analysis of an SGD Extension
We introduce a recently developed framework PDE Acceleration, which is a variational approach to accelerated optimization with partial differential equations (PDE), in the context of optimization of deep networks.
Flow-Guided Video Inpainting with Scene Templates
We consider the problem of filling in missing spatio-temporal regions of a video.
Channel-Directed Gradients for Optimization of Convolutional Neural Networks
We introduce optimization methods for convolutional neural networks that can be used to improve existing gradient-based optimization in terms of generalization error.
Phase Consistent Ecological Domain Adaptation
We introduce two criteria to regularize the optimization involved in learning a classifier in a domain where no annotated data are available, leveraging annotated data in a different domain, a problem known as unsupervised domain adaptation.
Minimum Delay Object Detection From Video
We consider the problem of detecting objects, as they come into view, from videos in an online fashion.
Extending Layered Models to 3D Motion
We consider the problem of inferring a layered representa-tion, its depth ordering and motion segmentation from a video in whichobjects may undergo 3D non-planar motion relative to the camera.
Minimum Delay Moving Object Detection
Our method is designed to detect the object(s) with minimum delay, i. e., frames after the object moves, constraining the false alarms.
Quickest Moving Object Detection
We present a general framework and method for simultaneous detection and segmentation of an object in a video that moves (or comes into view of the camera) at some unknown time in the video.
