17 accepted papers
Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (RS) tasks, we observe widespread pseudo reasoning: models narrate the process of reasoning rather than genuinely reason toward the correct answer based on vis
In recent years, electroencephalography (EEG)-based visual decoding research has become a key direction for revealing brain processing mechanisms and realizing brain-computer interfaces. This emerging field has attracted extensive attention in the fields of brain science, cognitive neuroscience, and
A compact reconfigurable terrestrial-aerial robot that is capable of executing seamless transitions between three motion modes—rolling, crawling, and flying—via foldable mechanisms is presented in this letter. The rolling and crawling modes are facilitated by two passive gear-driven, foldable wheel-
Currently, leveraging large language models (LLMs) for autism intervention is a significant yet challenging task, particularly when directly employing LLMs as an intervention doctor. Researchers have mainly focused on using prompt engineering for role play as an intervention doctor and integrating a
Unmanned aerial vehicles equipped with modern vision algorithms are crucial for missions such as reconstruction and target acquisition. However, when deployed in the field, undulating terrain can cause significant fluctuations in image scale and degrade the performance of vision algorithms. Instead
Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios. Retrieval-Augmented Generation (RAG) introduces external knowledge but suffers from hallucinations and latency due to noisy retrievals. Continued pretraining internalizes domain knowledge but is
Real-world time series inherently exhibit significant non-stationarity, posing substantial challenges for forecasting. To address this issue, this paper proposes a novel prediction framework, TimeStacker, designed to overcome the limitations of existing models in capturing the characteristics of non…
Electroencephalography (EEG) is crucial for recording brain activity, with applications in medicine, neuroscience, and brain-computer interfaces (BCI). However, challenges such as low signal-to-noise ratio (SNR), high inter-subject variability, and channel mismatch complicate the extraction of…
Robot localization using subsurface features captured by Ground-Penetrating Radar (GPR) complements and improves robustness over existing common sensor modalities, as subsurface features are less sensitive to weather, season and surface scene changes. Here, we propose a novel subsurface feature-base…
Highly accurate sensor extrinsic calibration is critical for data fusion from multiple sensors, such as camera and Inertial Measurement Unit (IMU) sensor suit. A pre-calibrated extrinsics, however, may no longer be accurate due to external disturbances, e.g., vehicle vibration, which will lead to si
Point cloud scene flow estimation is of practical importance for dynamic scene navigation in autonomous driving. Since scene flow labels are hard to obtain, current methods train their models on synthetic data and transfer them to real scenes. However, large disparities between existing synthetic da…
"Few-shot action recognition aims to recognize few-labeled novel action classes and attracts growing attentions due to practical significance. Human skeletons provide explainable and data-efficient representation for this problem by explicitly modeling spatial-temporal relations among skeleton joint…
Detection of subsurface defects is important for maintaining runway structural health and reliability. A potential solution is to employ a robot equipped with a Ground Penetrating Radar (GPR) to perform subsurface scanning. To automate the inspection process, we develop a subsurface defect detection
We report system and algorithmic development for a sensing suite comprising multiple sensors for both surface and subsurface transportation infrastructure inspection focusing on multi-modal mapping for inspection. The sensing suite contains a camera, a ground penetrating radar (GPR), and a wheel enc…
Measuring the quality of roads and ensuring they are ready for autonomous driving is important for future transportation systems. Here we focus on developing metrics and algorithms to assess lane marking (LM)qualities from an egocentric view of an inspection vehicle equipped with a global positionin…
We propose a novel subsurface pipeline mapping method by fusing Ground Penetrating Radar (GPR) scans and camera images. To facilitate the simultaneous detection of multiple pipelines, we model the GPR sensing process and prove hyperbola response for general scanning with non-perpendicular angles. Fu…
We propose a Kullback-Leibler divergence (KLD) and deep neural net (DNN) based approach to cross-lingual TTS (CL-TTS) training. A speaker independent DNN (SI-DNN) ASR is used to equalize the speaker difference between a source speaker in L1 and a reference speaker in L2. Two speaker dependent GMM-HM…