20 accepted papers
Effectively modeling cross-variate dependencies is a central, yet challenging, task in multivariate time series forecasting. While attention-based methods have advanced the state-of-the-art by capturing global cross-variate dependencies, their quadratic complexity with respect to the number of varia…
Dataset diversity plays a pivotal role for the successful training of many machine learning models, particularly in the supervised fine-tuning (SFT) stage of large language model (LLM) development. Despite increasing recognition of its importance, systematic analyses of dataset diversity still remai
Video Large Language Models (VideoLLMs), which adopt large language models for video understanding, have been demonstrated for single-shot videos. However, they usually struggle in multi-shot videos with frequent shot changes, varying camera angles, etc., which makes VideoLLMs hardly answer question
Despite significant advancements in the research of aquatic-aerial robots, existing configurations struggle to efficiently perform underwater, surface, and aerial movement. In this paper, we propose a novel multimodal surfing aquatic-aerial vehicle, SurfAAV, which efficiently integrates underwater n…
This paper presents a complete quadrotor aerial docking system that utilizes both vision and magnetic field guidance to achieve high-precision docking. Visual guidance is implemented using a combination of a forward-facing camera and an upward-facing camera, which provide feedback on the local and r
This paper presents a novel, compact overactuated tricopter featuring a servo-driven twisting and tilting mechanism, preventing the adverse effects of internal force contradiction during flight. Each arm’s vectored thrust is provided by a single motor, with the twisting and tilting angles controlled
Text-to-video (T2V) generation has made tremendous progress in generating complicated scenes based on texts. However, human-object interaction (HOI) often cannot be precisely generated by current T2V models due to the lack of large-scale videos with accurate captions for HOI. To address this issue,…
Despite significant advancements in the research of aquatic-aerial robots, existing configurations struggle to efficiently perform underwater, surface, and aerial movement. In this paper, we propose a novel multimodal surfing aquaticaerial vehicle, SurfAAV, which efficiently integrates underwater na
Tactile sensing plays a critical role in enabling robots to interact safely with target objects in dynamic and unstructured environments. While various tactile sensors based on different sensing principles or different sensitive materials have been proposed, the development of flexible large-area ta…
Highly accurate and robust vectorized reconstruction of road structures is crucial for autonomous vehicles. Traditional LiDAR-based methods require multiple processes and are often expensive, time-consuming, labor-intensive, and cumbersome. In this paper, we propose a lightweight crowdsourced road s…
This letter presents a novel tilt-rotor octocopter that can generate tiltable thrust without the need for servo-driven mechanisms. The octocopter's eight rotors are divided into pairs and each pair is mounted on an arm, which is connected to the airframe through passive hinges. Each pair is also equ
This letter presents a novel control allocation compensator for a tilt-rotor octocopter using passive hinges, aimed at enhancing attitude control performance by accounting for the dynamic characteristics of the actuators. The introduction of a passive hinging mechanism results in a slower tilt angle
Vectorized reconstruction and topological cognition of road structures are crucial for autonomous vehicles to handle complex scenes. Traditional frameworks rely heavily on high-definition (HD) maps, which place significant demands on storage, computation, and manual labor. To overcome these limitati…
Remote center of motion (RCM) describes a robot with a rod-like end-effector operating through a hole in the interface separating the internal space from the external space. Considering that the control of RCM may be influenced by perturbations (noises) and that the end-effector is frequently replac…
Surveillance videos are important for public security. However current surveillance video tasks mainly focus on classifying and localizing anomalous events. Existing methods are limited to detecting and classifying the predefined events with unsatisfactory semantic understanding although they have o…
This paper presents an optimal time trajectory generation method for over-actuated multirotors. Different from underactuated multi-rotors that can only track a 4-D trajectory, over-actuated multi-rotors have the ability to track a 6-D trajectory. The proposed method can generate a 3-degree of freedo
Recent studies in deep learning have shown significant progress in named entity recognition (NER). However, most existing works assume clean data annotation, while real-world scenarios typically involve a large amount of noises from a variety of sources (e.g., pseudo, weak, or distant annotations).…
Shape morphing is at the core of future research, which shows promise for wide applications ranging from reconfigurable electronics to soft material robots. In this paper, we present a novel buckling-induced mechanism for shape morphing using dielectric elastomer actuators (DEAs), by bonding the pla…
Deep learning approaches to 3D shape segmentation are typically formulated as a multi-class labeling problem. These models are trained for a fixed set of labels, which greatly limits their flexibility and adaptivity. We opt for top-down recursive decomposition and develop the first deep learning mod…
It is often laborious and costly to manually annotate videos for training high-quality video recognition models, so there has been some work and interest in exploring alternative, cheap, and yet often noisy and indirect, training signals for learning the video representations. However, these signals…