9 citations · 19 across the 19 of their papers we have counts for
20 papers
FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding
Gueter Josmy Faure, Min-Hung Chen, Jia-Fong Yeh +2
Vision-Language Models (VLMs) have demonstrated remarkable capabilities in general video understanding, yet they often struggle with the fine-grained comprehension crucial for real…
ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints
Pei-An Chen, Yong-Ching Liang, Jia-Fong Yeh +4
Intelligent embodied agents should not simply follow instructions, as real-world environments often involve unexpected conditions and exceptions. However, existing methods usually…
VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions
Hung-Ting Su, Ting-Jun Wang, Jia-Fong Yeh +2
Conventional Vision-and-Language Navigation (VLN) benchmarks assume instructions are feasible and the referenced target exists, leaving agents ill-equipped to handle false-premise…
Affordance-Guided Coarse-to-Fine Exploration for Base Placement in Open-Vocabulary Mobile Manipulation
Tzu-Jung Lin, Jia-Fong Yeh, Hung-Ting Su +3
In open-vocabulary mobile manipulation (OVMM), task success often hinges on the selection of an appropriate base placement for the robot. Existing approaches typically navigate to…
MovieCORE: COgnitive REasoning in Movies
Gueter Josmy Faure, Min-Hung Chen, Jia-Fong Yeh +5
This paper introduces MovieCORE, a novel video question answering (VQA) dataset designed to probe deeper cognitive understanding of movie content. Unlike existing datasets that foc…
Context-Aware Replanning with Pre-explored Semantic Map for Object Navigation
Po-Chen Ko, Hung-Ting Su, Ching-Yuan Chen +3
Pre-explored Semantic Maps, constructed through prior exploration using visual language models (VLMs), have proven effective as foundational elements for training-free robotic appl…
Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection
Huang-Yu Chen, Jia-Fong Yeh, Jia-Wei Liao +2
LiDAR-based 3D object detection is a critical technology for the development of autonomous driving and robotics. However, the high cost of data annotation limits its advancement. W…
HERMES: temporal-coHERent long-forM understanding with Episodes and Semantics
Gueter Josmy Faure, Jia-Fong Yeh, Min-Hung Chen +3
Long-form video understanding presents unique challenges that extend beyond traditional short-video analysis approaches, particularly in capturing long-range dependencies, processi…
Shared-unique Features and Task-aware Prioritized Sampling on Multi-task Reinforcement Learning
Po-Shao Lin, Jia-Fong Yeh, Yi-Ting Chen +1
We observe that current state-of-the-art (SOTA) methods suffer from the performance imbalance issue when performing multi-task reinforcement learning (MTRL) tasks. While these meth…
VICtoR: Learning Hierarchical Vision-Instruction Correlation Rewards for Long-horizon Manipulation
Kuo-Han Hung, Pang-Chi Lo, Jia-Fong Yeh +3
We study reward models for long-horizon manipulation tasks by learning from action-free videos and language instructions, which we term the visual-instruction correlation (VIC) pro…
Tracking-Assisted Object Detection with Event Cameras
Ting-Kang Yen, Igor Morawski, Shusil Dangi +5
Event-based object detection has recently garnered attention in the computer vision community due to the exceptional properties of event cameras, such as high dynamic range and no…
AED: Adaptable Error Detection for Few-shot Imitation Policy
Jia-Fong Yeh, Kuo-Han Hung, Pang-Chi Lo +5
We introduce a new task called Adaptable Error Detection (AED), which aims to identify behavior errors in few-shot imitation (FSI) policies based on visual observations in novel en…
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…
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…
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…
Dual-Awareness Attention for Few-Shot Object Detection
Tung-I Chen, Yueh-Cheng Liu, Hung-Ting Su +5
While recent progress has significantly boosted few-shot classification (FSC) performance, few-shot object detection (FSOD) remains challenging for modern learning systems. Existin…
Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning
Jia-Fong Yeh, Hsin-Ying Lee, Bing-Chen Tsai +3
In recent years, few-shot learning problems have received a lot of attention. While methods in most previous works were trained and tested on datasets in one single domain, cross-d…