51 citations · 338 across the 55 of their papers we have counts for
58 papers
Unsupervised Adversarial Detection without Extra Model: Training Loss Should Change
Chien Cheng Chyou, Hung-Ting Su, Winston H. Hsu
Adversarial robustness poses a critical challenge in the deployment of deep learning models for real-world applications. Traditional approaches to adversarial training and supervis…
MiniSUPERB: Lightweight Benchmark for Self-supervised Speech Models
Yu-Hsiang Wang, Huang-Yu Chen, Kai-Wei Chang +2
SUPERB was proposed to evaluate the generalizability of self-supervised learning (SSL) speech models across various tasks. However, it incurs high computational costs due to the la…
Language Models are Causal Knowledge Extractors for Zero-shot Video Question Answering
Hung-Ting Su, Yulei Niu, Xudong Lin +2
Causal Video Question Answering (CVidQA) queries not only association or temporal relations but also causal relations in a video. Existing question synthesis methods pre-trained qu…
MuRAL: Multi-Scale Region-based Active Learning for Object Detection
Yi-Syuan Liou, Tsung-Han Wu, Jia-Fong Yeh +2
Obtaining large-scale labeled object detection dataset can be costly and time-consuming, as it involves annotating images with bounding boxes and class labels. Thus, some specializ…
BIRD-PCC: Bi-directional Range Image-based Deep LiDAR Point Cloud Compression
Chia-Sheng Liu, Jia-Fong Yeh, Hao Hsu +3
The large amount of data collected by LiDAR sensors brings the issue of LiDAR point cloud compression (PCC). Previous works on LiDAR PCC have used range image representations and f…
Free-form 3D Scene Inpainting with Dual-stream GAN
Ru-Fen Jheng, Tsung-Han Wu, Jia-Fong Yeh +1
Nowadays, the need for user editing in a 3D scene has rapidly increased due to the development of AR and VR technology. However, the existing 3D scene completion task (and datasets…
Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling
Hsin-Ying Lee, Hung-Ting Su, Bing-Chen Tsai +3
While recent large-scale video-language pre-training made great progress in video question answering, the design of spatial modeling of video-language models is less fine-grained t…
Coarse-to-Fine Point Cloud Registration with SE(3)-Equivariant Representations
Cheng-Wei Lin, Tung-I Chen, Hsin-Ying Lee +2
Point cloud registration is a crucial problem in computer vision and robotics. Existing methods either rely on matching local geometric features, which are sensitive to the pose di…
CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection
Ching-Yu Tseng, Yi-Rong Chen, Hsin-Ying Lee +3
To achieve accurate 3D object detection at a low cost for autonomous driving, many multi-camera methods have been proposed and solved the occlusion problem of monocular approaches.…
Orbeez-SLAM: A Real-time Monocular Visual SLAM with ORB Features and NeRF-realized Mapping
Chi-Ming Chung, Yang-Che Tseng, Ya-Ching Hsu +6
A spatial AI that can perform complex tasks through visual signals and cooperate with humans is highly anticipated. To achieve this, we need a visual SLAM that easily adapts to new…
Fair Robust Active Learning by Joint Inconsistency
Tsung-Han Wu, Hung-Ting Su, Shang-Tse Chen +1
Fairness and robustness play vital roles in trustworthy machine learning. Observing safety-critical needs in various annotation-expensive vision applications, we introduce a novel…
CFVS: Coarse-to-Fine Visual Servoing for 6-DoF Object-Agnostic Peg-In-Hole Assembly
Bo-Siang Lu, Tung-I Chen, Hsin-Ying Lee +1
Robotic peg-in-hole assembly remains a challenging task due to its high accuracy demand. Previous work tends to simplify the problem by restricting the degree of freedom of the end…
GenISP: Neural ISP for Low-Light Machine Cognition
Igor Morawski, Yu-An Chen, Yu-Sheng Lin +3
Object detection in low-light conditions remains a challenging but important problem with many practical implications. Some recent works show that, in low-light conditions, object…
MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer
Kuan-Chih Huang, Tsung-Han Wu, Hung-Ting Su +1
Monocular 3D object detection is an important yet challenging task in autonomous driving. Some existing methods leverage depth information from an off-the-shelf depth estimator to…
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation
Tsung-Han Wu, Yi-Syuan Liou, Shao-Ji Yuan +4
In the field of domain adaptation, a trade-off exists between the model performance and the number of target domain annotations. Active learning, maximizing model performance with…
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark
Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis +22
Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor. Human-centered segmentation, which i…
Stage Conscious Attention Network (SCAN) : A Demonstration-Conditioned Policy for Few-Shot Imitation
Jia-Fong Yeh, Chi-Ming Chung, Hung-Ting Su +2
In few-shot imitation learning (FSIL), using behavioral cloning (BC) to solve unseen tasks with few expert demonstrations becomes a popular research direction. The following capabi…
3rd Place Solution for NeurIPS 2021 Shifts Challenge: Vehicle Motion Prediction
Ching-Yu Tseng, Po-Shao Lin, Yu-Jia Liou +2
Shifts Challenge: Robustness and Uncertainty under Real-World Distributional Shift is a competition held by NeurIPS 2021. The objective of this competition is to search for methods…
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data
Guan-Rong Lu, Yueh-Cheng Liu, Tung-I Chen +3
Anomaly awareness is an essential capability for safety-critical applications such as autonomous driving. While recent progress of robotics and computer vision has enabled anomaly…
Multi-Stream Attention Learning for Monocular Vehicle Velocity and Inter-Vehicle Distance Estimation
Kuan-Chih Huang, Yu-Kai Huang, Winston H. Hsu
Vehicle velocity and inter-vehicle distance estimation are essential for ADAS (Advanced driver-assistance systems) and autonomous vehicles. To save the cost of expensive ranging se…
NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset
Igor Morawski, Yu-An Chen, Yu-Sheng Lin +1
Recent work indicates that, besides being a challenge in producing perceptually pleasing images, low light proves more difficult for machine cognition than previously thought. In o…
Multivariate and Propagation Graph Attention Network for Spatial-Temporal Prediction with Outdoor Cellular Traffic
Chung-Yi Lin, Hung-Ting Su, Shen-Lung Tung +1
Spatial-temporal prediction is a critical problem for intelligent transportation, which is helpful for tasks such as traffic control and accident prevention. Previous studies rely…
TrUMAn: Trope Understanding in Movies and Animations
Hung-Ting Su, Po-Wei Shen, Bing-Chen Tsai +3
Understanding and comprehending video content is crucial for many real-world applications such as search and recommendation systems. While recent progress of deep learning has boos…
ODIP: Towards Automatic Adaptation for Object Detection by Interactive Perception
Tung-I Chen, Jen-Wei Wang, Winston H. Hsu
Object detection plays a deep role in visual systems by identifying instances for downstream algorithms. In industrial scenarios, however, a slight change in manufacturing systems…