Search Results for author:
Found 31 papers, 9 papers with code
Joint-Task Regularization for Partially Labeled Multi-Task Learning
Multi-task learning has become increasingly popular in the machine learning field, but its practicality is hindered by the need for large, labeled datasets.
R^2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding
Video temporal grounding (VTG) is a fine-grained video understanding problem that aims to ground relevant clips in untrimmed videos given natural language queries.
$R^2$-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding
Video temporal grounding (VTG) is a fine-grained video understanding problem that aims to ground relevant clips in untrimmed videos given natural language queries.
Ranked #2 on
Highlight Detection
on QVHighlights
Blurry Video Compression: A Trade-off between Visual Enhancement and Data Compression
Existing video compression (VC) methods primarily aim to reduce the spatial and temporal redundancies between consecutive frames in a video while preserving its quality.
How Does Artificial Intelligence Improve Human Decision-Making? Evidence from the AI-Powered Go Program
We study how humans learn from AI, exploiting an introduction of an AI-powered Go program (APG) that unexpectedly outperformed the best professional player.
Sound Source Localization is All about Cross-Modal Alignment
However, prior arts and existing benchmarks do not account for a more important aspect of the problem, cross-modal semantic understanding, which is essential for genuine sound source localization.
Active anomaly detection based on deep one-class classification
In an active learning framework, a model queries samples to be labeled by experts and re-trains the model with the labeled data samples.
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination
Therefore, it has been a common practice to learn normality under the assumption that anomalous data are absent in a training dataset, which we call normality assumption.
Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite Images
AED-RS Dataset contains satellite images of normal and abnormal situations of 8 open public places from all over the world.
ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation
We address a weakly-supervised low-shot instance segmentation, an annotation-efficient training method to deal with novel classes effectively.
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics
One-class classification has been a prevailing method in building deep anomaly detection models under the assumption that a dataset consisting of normal samples is available.
ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing for Open Compound Domain Adaptation in Semantic Segmentation
Open compound domain adaptation (OCDA) considers the target domain as the compound of multiple unknown homogeneous subdomains.
Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selection
In this work, we propose a new domain adaptation framework for semantic segmentation with annotated points via active selection.
Audio-Visual Fusion Layers for Event Type Aware Video Recognition
Human brain is continuously inundated with the multisensory information and their complex interactions coming from the outside world at any given moment.
Learning Sound Localization Better From Semantically Similar Samples
Thus, these semantically correlated pairs, "hard positives", are mistakenly grouped as negatives.
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination
In this paper, we propose Normality-Calibrated Autoencoder (NCAE), which can boost anomaly detection performance on the contaminated datasets without any prior information or explicit abnormal samples in the training phase.
FoxInst: A Frustratingly Simple Baseline for Weakly Few-shot Instance Segmentation
We propose the first weakly-supervised few-shot instance segmentation task and a frustratingly simple but strong baseline model, FoxInst.
Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-Identification
However, this achievement requires large-scale and well-annotated datasets.
Restoration of Video Frames from a Single Blurred Image with Motion Understanding
We formulate video restoration from a single blurred image as an inverse problem by setting clean image sequence and their respective motion as latent factors, and the blurred image as an observation.
Motion-blurred Video Interpolation and Extrapolation
Abrupt motion of camera or objects in a scene result in a blurry video, and therefore recovering high quality video requires two types of enhancements: visual enhancement and temporal upsampling.
Optical Flow Estimation from a Single Motion-blurred Image
A flow estimator network is then used to estimate optical flow from the decoded features in a coarse-to-fine manner.
Learning to Localize Sound Sources in Visual Scenes: Analysis and Applications
Visual events are usually accompanied by sounds in our daily lives.
Visuomotor Understanding for Representation Learning of Driving Scenes
We postulate that success on this task requires the network to learn semantic and geometric knowledge in the ego-centric view.
Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
We take an approach to learn a generalizable embedding space for novel tasks.
Learning to Localize Sound Source in Visual Scenes
We show that even with a few supervision, false conclusion is able to be corrected and the source of sound in a visual scene can be localized effectively.
Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition
Recent advances in visual recognition show overarching success by virtue of large amounts of supervised data.
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions.
Ranked #1 on
Lane Detection
on Caltech Lanes Washington
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks
We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN).
Ranked #73 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
Globally Optimal Manhattan Frame Estimation in Real-Time
Given a set of surface normals, we pose a Manhattan Frame (MF) estimation problem as a consensus set maximization that maximizes the number of inliers over the rotation search space.
Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time
Most man-made environments, such as urban and indoor scenes, consist of a set of parallel and orthogonal planar structures.
