Search Results for author:
Found 24 papers, 6 papers with code
CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning
Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets.
Ranked #86 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
Vehicle Re-Identification Based on Complementary Features
Top performance in City-Scale Multi-Camera Vehicle Re-Identification demonstrated the advantage of our methods, and we got 5-th place in the vehicle Re-ID track of AIC2020.
Show, Deconfound and Tell: Image Captioning With Causal Inference
In the encoding stage, the IOD is able to disentangle the region-based visual features by deconfounding the visual confounder.
A variational autoencoder approach for choice set generation and implicit perception of alternatives in choice modeling
This paper derives the generalized extreme value (GEV) model with implicit availability/perception (IAP) of alternatives and proposes a variational autoencoder (VAE) approach for choice set generation and implicit perception of alternatives.
Efficient Truncated Linear Regression with Unknown Noise Variance
In this paper, we provide the first computationally and statistically efficient estimators for truncated linear regression when the noise variance is unknown, estimating both the linear model and the variance of the noise.
Efficient Eye Typing with 9-direction Gaze Estimation
According to the results from our experiments, our CNN model is able to accurately estimate different people's gaze under various lighting conditions by different devices.
Efficient Dense Labeling of Human Activity Sequences from Wearables using Fully Convolutional Networks
We conduct extensive experiments and demonstrate that our proposed approach is able to outperform the state-of-the-arts in terms of classification and label misalignment measures on three challenging datasets: Opportunity, Hand Gesture, and our new dataset.
Part-Based Visual Tracking with Online Latent Structural Learning
Despite many advances made in the area, deformable targets and partial occlusions continue to represent key problems in visual tracking.
Video Object Segmentation and Tracking: A Survey
Second, we provide a detailed discussion and overview of the technical characteristics of the different methods.
Facial Action Unit Detection via Adaptive Attention and Relation
Specifically, we propose an adaptive attention regression network to regress the global attention map of each AU under the constraint of attention predefinition and the guidance of AU detection, which is beneficial for capturing both specified dependencies by landmarks in strongly correlated regions and facial globally distributed dependencies in weakly correlated regions.
Experiments on route choice set generation using a large GPS trajectory set
A modified link penalty method, which accounts for preference of using higher hierarchical roads, provides a route set with 97% coverage (80% overlap threshold).
Deep Active Learning for Solvability Prediction in Power Systems
Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism.
CycleSegNet: Object Co-segmentation with Cycle Refinement and Region Correspondence
Image co-segmentation is an active computer vision task that aims to segment the common objects from a set of images.
Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning with Region-of-Interest Active Sampling
Frequency-constrained unit commitment (FCUC) is proposed to address this challenge.
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control
Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
TextDCT: Arbitrary-Shaped Text Detection via Discrete Cosine Transform Mask
Arbitrary-shaped scene text detection is a challenging task due to the variety of text changes in font, size, color, and orientation.
Semi-Analytical Electromagnetic Transient Simulation Using Differential Transformation
For electromagnetic transient (EMT) simulation of a power system, a state-space-based approach needs to solve state-space EMT equations by using numerical integration methods, e. g., the Euler method, Runge-Kutta methods, and trapezoidal-rule method, at small time steps.
A general equilibrium model for multi-passenger ridesharing systems with stable matching
This paper is one of the first studies that explicitly integrates the ridesharing platform multi-passenger matching problem into the model.
CT-Net: Arbitrary-Shaped Text Detection via Contour Transformer
Contour based scene text detection methods have rapidly developed recently, but still suffer from inaccurate frontend contour initialization, multi-stage error accumulation, or deficient local information aggregation.
How would mobility-as-a-service (MaaS) platform survive as an intermediary? From the viewpoint of stability in many-to-many matching
This study investigates the assignment and pricing problem for a MaaS platform as an intermediary in a multi-modal transportation network, which purchases capacity from service operators and sells multi-modal trips to travelers.
Dynamic V2X Autonomous Perception from Road-to-Vehicle Vision
Vehicle-to-everything (V2X) perception is an innovative technology that enhances vehicle perception accuracy, thereby elevating the security and reliability of autonomous systems.
Efficient Decoder for End-to-End Oriented Object Detection in Remote Sensing Images
To address these limitations, we propose an end-to-end oriented detector equipped with an efficient decoder, which incorporates two technologies, Rotated RoI attention (RRoI attention) and Selective Distinct Queries (SDQ).
A Semi-Analytical Approach for State-Space Electromagnetic Transient Simulation Using the Differential Transformation
Case studies are conducted on EMT models of the IEEE 39-bus system and a synthetic 390-bus system to demonstrate the merits of the new simulation approach against traditional methods.
Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization
Moreover, the proposed framework is applicable to other NN-based choice models such as TasteNets.
