36 accepted papers
Existing diffusion models have made significant progress in generating realistic images. However, their direct adaptation to remote sensing imagery often disregards intrinsic physical laws. This oversight frequently leads to spectral distortion and radiometric inconsistency, severely limiting the sc…
Residual reinforcement learning (RL) enables stable online refinement of expressive pretrained policies by freezing the base and learning only bounded corrections. However, value learning in residual RL poses unique challenges that remain poorly understood. In this work, we identify two key bottlene…
Inverse kinematics (IK) is at the core of manipulator control theory, with its solution accuracy directly impacting the robot's performance in executing tasks. This is particularly true for complex offset wrist manipulators (OWM), where existing evolutionary computation methods often face limitation
Global cooperation on climate change mitigation is essential to limit temperature increases while supporting long-term, equitable economic growth and sustainable development. Achieving such cooperation among diverse regions, each with different incentives, in a dynamic environment shaped by complex…
Loop closure is critical in Simultaneous Localization and Mapping (SLAM) systems to reduce accumulative drift and ensure global mapping consistency. However, conventional methods struggle in perceptually aliased environments, such as narrow pipes, due to vector quantization, feature sparsity, and re
Despite having fewer neurons than more complex life forms, insects are still capable of producing astonishing locomotive behaviors, such as traversing diverse environments and making rapid gait adaptations after extreme injury or autotomy. Biologists attribute this to a chain of segmental neuron clu
Auditory attention detection (AAD) aims to identify the direction of the attended speaker in multi-speaker environments from brain signals, such as Electroencephalography (EEG) signals. However, existing EEG-based AAD methods overlook the spatio-temporal dependencies of EEG signals, limiting their d
Model merging has emerged as an effective approach to combining multiple single-task models into a multitask model. This process typically involves computing a weighted average of the model parameters without additional training. Existing model-merging methods focus on improving average task accurac…
Auditory attention detection (AAD) aims to detect the target speaker in a multi-talker environment from brain signals, such as electroencephalography (EEG), which has made great progress. However, most AAD methods solely utilize attention mechanisms sequentially and overlook valuable multi-scale con
Effectively scaling up deep reinforcement learning models has proven notoriously difficult due to network pathologies during training, motivating various targeted interventions such as periodic reset and architectural advances such as layer normalization. Instead of pursuing more complex modificati…
While diffusion models have revolutionized text-to-image generation with their ability to synthesize realistic and diverse scenes, they continue to struggle with generating consistent and legible text within images. This shortcoming is commonly attributed to the locality bias inherent in diffusion-b
Signed Graph Neural Networks (SGNNs) have been shown to be effective in analyzing complex patterns in real-world situations where positive and negative links coexist. However, SGNN models suffer from poor explainability, which limit their adoptions in critical scenarios that require understanding th…
Scaling deep reinforcement learning networks is challenging and often results in degraded performance, yet the root causes of this failure mode remain poorly understood. Several recent works have proposed mechanisms to address this, but they are often complex and fail to highlight the causes underly…
We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured texts using pixel-level hints within images through complex reasoning. This task stems from the observation that text embedded in images intrinsically differs f…
Signed graphs can model friendly or antagonistic relations where edges are annotated with a positive or negative sign. The main downstream task in signed graph analysis is $\textit{link sign prediction}$. Signed Graph Neural Networks (SGNNs) have been widely used for signed graph representation lear…
This paper presents an Exploratory 3D Dance generation framework, E3D2, designed to address the exploration capability deficiency in existing music-conditioned 3D dance generation models. Current models often generate monotonous and simplistic dance sequences that misalign with human preferences bec…
Robots designed for in-pipe navigation, inspection, and repair require flexibility for intricate pipeline traversal and the strength to carry payloads. However, conventional wheeled in-pipe robots face challenges in simultaneously achieving both substantial flexibility and payload-carrying capacity.
In deep metric learning, the triplet loss has emerged as a popular method to learn many computer vision and natural language processing tasks such as facial recognition, object detection, and visual-semantic embeddings. One issue that plagues the triplet loss is network collapse, an undesirable phen…
Plasticity, the ability of a neural network to evolve with new data, is crucial for high-performance and sample-efficient visual reinforcement learning (VRL). Although methods like resetting and regularization can potentially mitigate plasticity loss, the influences of various components within the…
Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual reinforcement learning (RL) algorithms. Notably, employing simple observation transformations alone can yield outstanding performance without extra auxiliary representation tasks or pre-trained encoders. Howe…
With the development of advanced driver assistance systems~(ADAS) and autonomous vehicles, conducting experiments in various scenarios becomes an urgent need. Although having been capable of synthesizing photo-realistic street scenes, conventional image-to-image translation methods cannot produce co…
This paper presents a Video Inpainting algorithm that enables monocular-camera-laser-based pipeline inspection robots to capture both color and 3D information using only one video stream. Conventional monocular-camera-laser inspection methods are limited to capture either 2D color images or 3D point…
Increased usage of additive manufacturing (AM) in various industries has solidified its role as an advanced manufacturing technique. However, there is an inherent lack of reliability in AM processes, particularly common in extrusion or deposition-based methods due to the stochastic nature of ma-teri…
Robotic solutions for pipeline inspection promise enhancement of human labor by automating data acquisition for pipe condition assessments, which are vital for the early detection of pipe anomalies and the prevention of hazardous leakages and explosions. Through simultaneous localization and mapping…
The training process of federated learning is known to be vulnerable to adversarial attacks (e.g., backdoor attack). Previous works showed that differential privacy (DP) can be used to defend against backdoor attacks, yet at the cost of vastly losing model utility. To address this issue, we in this…
Tactile sensing typically involves active exploration of unknown surfaces and objects, making it especially effective at processing the characteristics of materials and textures. A key property extracted by human tactile perception in material classification is surface roughness, which relies on mea…
Cell rotation has great significance for cell manipulation, which is applied to intracytoplasmic sperm injection, preimplantation genetic screening and diagnosis, somatic cell nuclear transfer, etc. In this letter, an automatic cell rotation method is proposed based on real-time detection and tracki
Zebrafish (Danio Rerio) larvae have long been an important model organism for biomedicine and drug discovery. It is difficult to deliver the external materials into the circulatory system by conventional exposing administration, while vein microinjection is more efficient but more challenging. In th…
Three-dimensional reconstruction in confined spaces is important for the manufacturing of aircraft wings, inspection of narrow pipes, examination of turbine blades, etc. It is also challenging because confined spaces tend to lack a positioning infrastructure, and conventional sensors often cannot de…
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP).…
Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season…
Dielectric elastomer actuators (DEAs) have been widely employed as artificial muscles in soft robots. Due to material viscoelasticity and nonlinear electromechanical coupling, it is challenging to accurately model a viscoelastic DEA, especially when the actuator is of a complex or irregular configur
Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieve long-term localization under varying environmental conditions and changing viewpoints. SeqSLAM uses a brute-force sequentia…
We describe a surgical system that autonomously searches for tumors and dynamically displays a computer graphic model of them super-imposed on the organ (or in our case, phantom). Once localized, the phantom is tracked in real time and augmented with overlaid stiffness information in 3D. We believe…
Place recognition is one of the major challenges for the LiDAR-based effective localization and mapping task. Traditional methods are usually relying on geometry matching to achieve place recognition, where a global geometry map need to be restored. In this paper, we accomplish the place recognition…
This work presents the design and evaluation of a low-cost tri-axial force sensor, that has been developed to regain the sense of touch in minimally invasive surgeries (MIS). The force sensor uses an array of force sensitive resistors (FSR) with a mechanically pre-loaded structure to perform the for…