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Found 23 papers, 13 papers with code
Fast Bilingual Grapheme-To-Phoneme Conversion
Autoregressive transformer (ART)-based grapheme-to-phoneme (G2P) models have been proposed for bi/multilingual text-to-speech systems.
Image-Object-Specific Prompt Learning for Few-Shot Class-Incremental Learning
To create these IOS classifiers, we encode a bias prompt into the classifiers using our specially designed module, which harnesses key-prompt pairs to pinpoint the IOS features of classes in each session.
Cognitive TransFuser: Semantics-guided Transformer-based Sensor Fusion for Improved Waypoint Prediction
Sensor fusion approaches for intelligent self-driving agents remain key to driving scene understanding given visual global contexts acquired from input sensors.
Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model
We investigate the effectiveness of zero-shot and few-shot cross-lingual transfer for phrase break prediction using a pre-trained multilingual language model.
Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) presents the primary challenge of balancing underfitting to a new session's task and forgetting the tasks from previous sessions.
Incremental Few-Shot Object Detection via Simple Fine-Tuning Approach
In this paper, we explore incremental few-shot object detection (iFSD), which incrementally learns novel classes using only a few examples without revisiting base classes.
Doubly Contrastive End-to-End Semantic Segmentation for Autonomous Driving under Adverse Weather
Road scene understanding tasks have recently become crucial for self-driving vehicles.
Self-supervised 3D Object Detection from Monocular Pseudo-LiDAR
There have been attempts to detect 3D objects by fusion of stereo camera images and LiDAR sensor data or using LiDAR for pre-training and only monocular images for testing, but there have been less attempts to use only monocular image sequences due to low accuracy.
Deep Q-Network for AI Soccer
Reinforcement learning has shown an outstanding performance in the applications of games, particularly in Atari games as well as Go.
Dual Task Learning by Leveraging Both Dense Correspondence and Mis-Correspondence for Robust Change Detection With Imperfect Matches
Moreover, we design an evaluation protocol which reflects performance in real-world settings.
Writing in The Air: Unconstrained Text Recognition from Finger Movement Using Spatio-Temporal Convolution
Our dataset consists of five sub-datasets in two languages (Korean and English) and amounts to 209, 926 video instances from 122 participants.
GSECnet: Ground Segmentation of Point Clouds for Edge Computing
This paper proposes the GSECnet - Ground Segmentation network for Edge Computing, an efficient ground segmentation framework of point clouds specifically designed to be deployable on a low-power edge computing unit.
Revisiting Self-Supervised Monocular Depth Estimation
Self-supervised learning of depth map prediction and motion estimation from monocular video sequences is of vital importance -- since it realizes a broad range of tasks in robotics and autonomous vehicles.
ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments
We present a challenging dataset, ChangeSim, aimed at online scene change detection (SCD) and more.
Ranked #2 on
Scene Change Detection
on ChangeSim
RDIS: Random Drop Imputation with Self-Training for Incomplete Time Series Data
In RDIS, we generate extra missing values by applying a random drop on the observed values in incomplete data.
Continual Unsupervised Domain Adaptation for Semantic Segmentation
Our novel ETM framework contains Target-specific Memory (TM) for each target domain to alleviate catastrophic forgetting.
A Real-Time Predictive Pedestrian Collision Warning Service for Cooperative Intelligent Transportation Systems Using 3D Pose Estimation
To achieve the goal, pedestrian orientation recognition and prediction of pedestrian's crossing or not-crossing intention play a central role.
SimVODIS: Simultaneous Visual Odometry, Object Detection, and Instance Segmentation
Intelligent agents need to understand the surrounding environment to provide meaningful services to or interact intelligently with humans.
Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste Inspection
In this paper, we propose a convolutional recurrent reconstructive network (CRRN), which decomposes the anomaly patterns generated by the printer defects, from SPI data.
3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents
We claim the following characteristics for a versatile environment model: accuracy, applicability, usability, and scalability.
A Stabilized Feedback Episodic Memory (SF-EM) and Home Service Provision Framework for Robot and IoT Collaboration
The service provision with these two main components in a Smart Home environment requires: 1) learning and reasoning algorithms and 2) the integration of robot and IoT systems.
I-Keyboard: Fully Imaginary Keyboard on Touch Devices Empowered by Deep Neural Decoder
Current soft keyboards, however, increase the typo rate due to lack of tactile feedback and degrade the usability of mobile devices due to their large portion on screens.
Dense Recurrent Neural Network with Attention Gate
We propose the dense RNN, which has the fully connections from each hidden state to multiple preceding hidden states of all layers directly.
