Search Results for author:
Found 13 papers, 10 papers with code
Exploring Simple Open-Vocabulary Semantic Segmentation
Open-vocabulary semantic segmentation models aim to accurately assign a semantic label to each pixel in an image from a set of arbitrary open-vocabulary texts.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+1
ActiveNeRF: Learning where to See with Uncertainty Estimation
In this paper, we present a novel learning framework, ActiveNeRF, aiming to model a 3D scene with a constrained input budget.
Learning to Weight Samples for Dynamic Early-exiting Networks
Intuitively, easy samples, which generally exit early in the network during inference, should contribute more to training early classifiers.
Extreme Masking for Learning Instance and Distributed Visual Representations
The paper presents a scalable approach for learning spatially distributed visual representations over individual tokens and a holistic instance representation simultaneously.
Domain Adaptation via Prompt Learning
Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain, where only unlabeled samples are given.
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition
Recent works have shown that the computational efficiency of video recognition can be significantly improved by reducing the spatial redundancy.
Video Autoencoder: self-supervised disentanglement of static 3D structure and motion
Relying on temporal continuity in videos, our work assumes that the 3D scene structure in nearby video frames remains static.
The Functional Correspondence Problem
For example, what are the correspondences between a bottle and shoe for the task of pounding or the task of pouring.
MAST: A Memory-Augmented Self-supervised Tracker
Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods.
Ranked #4 on
Unsupervised Video Object Segmentation
on DAVIS 2017 (val)
(using extra training data)
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+2
Self-supervised Learning for Video Correspondence Flow
Fourth, in order to shed light on the potential of self-supervised learning on the task of video correspondence flow, we probe the upper bound by training on additional data, \ie more diverse videos, further demonstrating significant improvements on video segmentation.
Self-Supervised Learning
Semi-Supervised Video Object Segmentation
+4
Anytime Stereo Image Depth Estimation on Mobile Devices
Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints.
Ranked #1 on
Stereo Depth Estimation
on KITTI2012
Neural Allocentric Intuitive Physics Prediction from Real Videos
In this framework, real images are first converted to a synthetic domain representation that reduces complexity arising from lighting and texture.
End-to-End Refinement Guided by Pre-trained Prototypical Classifier
We propose imitation refinement, a novel approach to refine imperfect input patterns, guided by a pre-trained classifier incorporating prior knowledge from simulated theoretical data, such that the refined patterns imitate the ideal data.
