Search Results for author:
Found 16 papers, 9 papers with code
Fine-Grained Image-Text Correspondence with Cost Aggregation for Open-Vocabulary Part Segmentation
Open-Vocabulary Part Segmentation (OVPS) is an emerging field for recognizing fine-grained parts in unseen categories.
Learning to Transfer Human Hand Skills for Robot Manipulations
We present a method for teaching dexterous manipulation tasks to robots from human hand motion demonstrations.
MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
Referring Image Segmentation (RIS) is an advanced vision-language task that involves identifying and segmenting objects within an image as described by free-form text descriptions.
Ranked #6 on
Referring Expression Segmentation
on RefCOCO testB
Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation
Additionally, we employ a dual-branch structure to mitigate performance degradation caused by data imbalance.
Ranked #1 on
Semi-Supervised Semantic Segmentation
on nuScenes
LIDAR Semantic Segmentation
Semi-Supervised Semantic Segmentation
Understanding Multi-Granularity for Open-Vocabulary Part Segmentation
Open-vocabulary part segmentation (OVPS) is an emerging research area focused on segmenting fine-grained entities using diverse and previously unseen vocabularies.
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
Self-Supervised Vision Transformers Are Efficient Segmentation Learners for Imperfect Labels
This study demonstrates a cost-effective approach to semantic segmentation using self-supervised vision transformers (SSVT).
Weakly Supervised Semantic Segmentation for Driving Scenes
Notably, the proposed method achieves 51. 8\% mIoU on the Cityscapes test dataset, showcasing its potential as a strong WSSS baseline on driving scene datasets.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation
Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level weak supervision have several limitations: sparse object coverage, inaccurate object boundaries, and co-occurring pixels from non-target objects.
Ranked #24 on
Weakly-Supervised Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets
In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.
Evaluating Weakly Supervised Object Localization Methods Right
In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.
Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation
We propose the resource-optimized neural architecture search method which can be applied to 3D medical segmentation tasks in a short training time (1. 39 days for 1GB dataset) using a small amount of computation power (one RTX 2080Ti, 10. 8GB GPU memory).
Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading
We explore deep Reinforcement Learning(RL) algorithms for scalping trading and knew that there is no appropriate trading gym and agent examples.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
Multiple profiles sensor-based monitoring and anomaly detection
However, there are several challenges in developing an effective process monitoring system: (i) data streams generated by multiple sensors are high-dimensional profiles; (ii) sensor signals are affected by noise due to system-inherent variations; (iii) signals of different sensors have cluster-wise features; and (iv) an anomaly may cause only sparse changes of sensor signals.
Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning
Multivariate functional data from a complex system are naturally high-dimensional and have complex cross-correlation structure.
