73 accepted papers
Text-attributed graph fraud detection (TAGFD) plays a critical role in preventing fraudulent activities on online social and e-commerce platforms. However, to evade detection, fraudsters continuously evolve their camouflaging strategies by deliberately mimicking textual responses of benign users, th
Next-scale prediction paradigm visual autoregressive (VAR) models have demonstrated significant potential for image super-resolution. However, their practical application is constrained by a rigid, size-specific design. This limitation stems from their reliance on memorizing fixed, absolute scaling
Assistive agents, especially those intended to sup- port Blind and Visually Impaired (BVI) users, require accessibility alignment as a first-class de- sign objective. Despite rapid progress in agen- tic AI, most current systems are designed and evaluated under assumptions that implicitly cen- ter si…
With the rapid development of generative models, such as generative adversarial networks and diffusion models, the task of face forgery detection has emerged, aiming to identify forged faces in real-world scenarios. A key challenge for current face forgery detection models is improving generalizatio
Extending traditional graph anomaly detection (GAD) from one-for-one to one-for-all paradigms, generalist GAD aims to learn a universal detector for identifying anomalies on unseen graphs without graph-specific retraining. Nevertheless, existing approaches primarily focus on aligning heterogeneous f…
The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated video detectors. However, most existing methods are limited to binary classification and lack the necessary explanations for human interpretation. In this
Vision-language models (VLMs) achieve strong multimodal performance, yet how computation is organized across populations of neurons remains poorly understood. In this work, we study VLMs through the lens of neural topology, representing each layer as a within-layer correlation graph derived from neu
Tabular anomaly detection (TAD) aims to identify samples that deviate from the majority in tabular data and is critical in many real-world applications. However, existing methods follow a ``one model for one dataset (OFO)'' paradigm, which relies on dataset-specific training and thus incurs high com…
Teleoperation is crucial for hazardous environment operations and serves as a key tool for collecting expert demonstrations in robot learning. However, existing methods face robotic hardware dependency and control frequency mismatches between teleoperation devices and robotic platforms. Our approach…
In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent, development, these two fields have evolved distinct architectural paradigms: the former predominantly relies on generative networks, while the lat
Robot scooping is a challenging and important task in robotic tool manipulation research due to the complex relationship between the robot, the tool, and target objects/environment. Taking into account different tools, different target objects and varying environments, the required scooping manipula
Monitoring real-time air quality is essential for safeguarding public health and fostering social progress. However, the widespread deployment of air quality monitoring stations is constrained by their significant costs. To address this limitation, we introduce AirRadar, a deep neural network design…
The emergence of visual autoregressive (AR) models has revolutionized image generation while presenting new challenges for synthetic image detection. Unlike previous GAN or diffusion-based methods, AR models generate images through discrete token prediction, exhibiting both marked improvements in im…
Skins wrapping around our bodies, leathers covering over the sofa, sheet metal coating the car – it suggests that objects are enclosed by a series of continuous surfaces, which provides us with informative geometry prior for objectness deduction. In this paper, we propose Gaussian-Det which leverage…
Image dehazing, particularly with learning-based methods, has gained significant attention due to its importance in real-world applications. However, relying solely on the RGB color space often fall short, frequently leaving residual haze. This arises from two main issues: the difficulty in obtainin…
Training reinforcement learning (RL) agents requires extensive trials and errors, which becomes prohibitively time-consuming in systems with costly reward evaluations. To address this challenge, we propose adaptive reward modeling (AdaReMo) which accelerates RL training by decomposing the complicate…
Robotic hand tool use has garnered significant attention from robotics researchers, because it enhances dexterity beyond the limitations imposed by manipulators with fixed tool configurations and human-involved manual tool changes. Despite extensive research, current methodologies predominantly focu
The advancements in Large Language Models (LLMs) have been hindered by their substantial sizes, which necessitates LLM compression methods for practical deployment. Singular Value Decomposition (SVD) offers a promising solution for LLM compression. However, state-of-the-art SVD-based LLM compression…
Anthropomorphic robotic arms, mimicking the structure and function of human arms, show great potential for helping people in various tedious and repetitive household tasks. However, such arms mostly consist of multiple serial links controlled independently by actuators at joints with high reduction…
Tactile servoing is an effective approach to enabling robots to safely interact with unknown environments. One of the core problems in tactile servoing is to robustly converge the contact features to the desired ones via a dedicated controller. This paper proposes a Data-Driven Model Predictive Cont…
Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the majority within a graph, has garnered significant attention. However, current GAD methods necessitate training specific to each dataset, resulting in high training costs, substantial data requirements, and limi…
Spatio-temporal (ST) prediction plays a pivotal role in earth sciences, such as meteorological prediction, urban computing. Adequate high-quality data, coupled with deep models capable of inference, are both indispensable and prerequisite for achieving meaningful results. However, the sparsity of da…
Sampling-based planning algorithms such as RRT have been proved to be efficient in solving path planning problems for robotic systems. Various improvements to the RRT algorithm have been presented to improve the performance of the extension and convergence of the random trees, such as Informed RRT*.…
This paper proposes a new scoring calibration method named "Consistency-Aware Score Calibration", which introduces a Consistency Measure Factor (CMF) to measure the stability of audio voiceprints in similarity scores for speaker verification. The CMF is inspired by the limitations in segment scoring…
Drawing inspiration from human fine tactile and proprioceptive kinaesthetic sensing pathways, we propose a soft magnetic skin (m-skin) with multimodal sensing functions integrated into the anthropomorphic robotic finger. This paper mainly explores the magnetic tactile sensor's structural design, per
LiDAR point cloud semantic segmentation enables the robots to obtain fine-grained semantic information of the surrounding environment. Recently, many works project the point cloud onto the 2D image and adopt the 2D Convolutional Neural Networks (CNNs) or vision transformer for LiDAR point cloud sema…
Objects of daily life are designed to suit the human hand. Without major modifications to these objects and our environments, robots will need end-effectors with human hand-like configuration and dexterity to efficiently operate on them. Tight integration of tactile and proprioceptive sensors are al…
Neurophysiological measurements suggest that human information processing is evinced by neuronal activity. However, the quantitative relationship between the activity of a brain region and its information processing capacity remains unclear. In this paper, we introduce an information conservation la…
This paper proposes a novel efficient multi-phase trajectory generation algorithm for dynamic dexterous manipulation tasks, such as throwing, catching, dynamic regrasping, and dynamic handover, which can be decomposed into multiple manipulation primitives, including sticking, rolling, approaching, s…
Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the barriers to economic growth. Forecasting air quality has become an increasingly important endeavor with significant social impacts, especially in emerging countries. In this paper, we present a novel Trans…
Considering the ill-posed nature, contrastive regularization has been developed for single image dehazing, introducing the information from negative images as a lower bound. However, the contrastive samples are nonconsensual, as the negatives are usually represented distantly from the clear (i.e., p…
To fully explore the potential of robots for dexterous manipulation, this paper presents a whole dynamic grasping process to achieve fluent grasping of a target object by the robot end-effector. The process starts from the phase of approaching the object over the phases of colliding with the object…
Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data heterogeneity, yet incurs a dilemma in preserving privacy and promoting model performance. To alleviate the dilemma, we…
Real-world graphs generally have only one kind of tendency in their connections. These connections are either homophilic-prone or heterophily-prone. While graphs with homophily-prone edges tend to connect nodes with the same class (i.e., intra-class nodes), heterophily-prone edges tend to build rela…
Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively pred…
Cross-domain recommendation (CDR) aims to alleviate the data sparsity by transferring knowledge from an informative source domain to the target domain, which inevitably proposes stern challenges to data privacy and transferability during the transfer process. A small amount of recent CDR works have…
Centroidal dynamics, which describes the overall linear and angular motion of a robot, is often used in locomotion generation and control of legged robots. However, the equation of centroidal dynamics contains nonlinear terms mainly caused by the robot's angular motion and needs to be linearized for…
Despite the rapid advance of unsupervised anomaly detection, existing methods require to train separate models for different objects. In this work, we present UniAD that accomplishes anomaly detection for multiple classes with a unified framework. Under such a challenging setting, popular reconstruc…
The wheel-bipedal robot has the advantages of both wheeled robots and legged robots, but as a cost, it is more challenging to perform flexible movements in various surroundings while keeping it balanced. The inaccurate dynamics of the robot makes the balance problem even more intractable. To solve t…
In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i.e. annotations of object centers). In order to remedy the information loss from box annotations to centers, our method, namely Back…
The mechanisms of locomotion in mammals have been extensively studied and inspire the related researches on designing the control architectures for the legged robots. Reinforcement learning (RL) is a promising approach allowing robots to automatically learn locomotion policies. However, careful rewa
In this paper, we introduce DA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> , the first large-scale dual-arm dexterity-aware dataset for the generation of optimal bimanual grasp
While recent automated data augmentation methods lead to state-of-the-art results, their design spaces and the derived data augmentation strategies still incorporate strong human priors. In this work, instead of fixing a set of hand-picked default augmentations alongside the searched data augmentati…
Optimizing the combustion efficiency of a thermal power generating unit (TPGU) is a highly challenging and critical task in the energy industry. We develop a new data-driven AI system, namely DeepThermal, to optimize the combustion control strategy for TPGUs. At its core, is a new model-based offlin…
A bathtub in a library, a sink in an office, a bed in a laundry room - the counter-intuition suggests that scene provides important prior knowledge for 3D object detection, which instructs to eliminate the ambiguous detection of similar objects. In this paper, we propose HyperDet3D to explore scene-…
This paper describes the multi-query multi-head attention (MQMHA) pooling and inter-topK penalty methods which were first proposed in our submitted system description for VoxCeleb speaker recognition challenge (VoxSRC) 2021. Most multi-head attention pooling mechanisms either attend to the whole fea…
Grasping of objects using multi-fingered robotic hands often fails due to small uncertainties in the hand motion control and the object's pose estimation. To tackle this problem, we propose a grasping adjustment strategy based on tactile seroving. Our technique employs feedback from a sensorized mul…
Using prompts to utilize language models to perform various downstream tasks, also known as prompt-based learning or prompt-learning, has lately gained significant success in comparison to the pre-train and fine-tune paradigm. Nonetheless, virtually most prompt-based methods are token-level such as…
Intercepting an object in flight through nonpre-hensile manipulation is a challenging problem, which is aimed at catching and stopping a flying object using little contacts without completely restraining its relative motion to the robot. This paper presents a two-stage optimal trajectory generation…
Dynamic models play a key role in robot motion generation and control and the identification of inertial parameters is a critical component for obtaining an accurate dynamic model of a robot. This paper presents a novel iterative primitive shape division method for the inertia parameter identificati…
Graph contrastive learning (GCL) alleviates the heavy reliance on label information for graph representation learning (GRL) via self-supervised learning schemes. The core idea is to learn by maximising mutual information for similar instances, which requires similarity computation between two node i…
This paper presents a unified controller for dual-arm robot dynamic multi-object nonprehensile transportation. The controller is composed of time-optimal path parameteri-zation (TOPP) and model predictive control (MPC) and aimed at efficiently and dynamically transporting objects using the dual-arm…
The performance of a camera network monitoring a set of targets depends crucially on the configuration of the cameras. In this paper, we investigate the reconfiguration strategy for the parameterized camera network model, with which the sensing qualities of the multiple targets can be optimized glob…
Robot hand is essential for a fully functional robot and designing a good robot hand is a sophisticated job that challenges the designer’s knowledge and experience. This paper presents a computational framework for automatic optimal robot hand design based on reinforcement learning (RL), which consi…
Quadruped locomotion is a challenging task for learning-based algorithms. It requires tedious manual tuning and is difficult to deploy in reality due to the reality gap. In this paper, we propose a quadruped robot learning system for agile locomotion which does not require any pre-training and works…
Safety and efficiency are two crucial factors for human-robot collaboration. It is challenging to ensure human safety while not sacrificing the task efficiency. In this letter, we present a reinforcement learning (RL) based method with a hazard estimator to balance these two factors. Our method has
This paper presents a balance control technique for a novel wheel-legged robot. We first derive a dynamic model of the robot and then apply a linear feedback controller based on output regulation and linear quadratic regulator (LQR) methods to maintain the standing of the robot on the ground without…
This letter studies the adaptive optimal control problem for a wheel-legged robot in the absence of an accurate dynamic model. A crucial strategy is to exploit recent advances in reinforcement learning (RL) and adaptive dynamic programming (ADP) to derive a learning-based solution to adaptive optima
Living objects are hard to grasp because they can actively dodge and struggle by writhing or deforming while or even prior to being contacted and modeling or predicting their responses to grasping is extremely difficult. This letter presents an algorithm based on reinforcement learning (RL) to attac
Purchase prediction is an essential task in both online and offline retail industry, especially during major shopping festivals, when strong promotion boosts consumption dramatically. It is important for merchants to forecast such surge of sales and have better preparation. This is a challenging pro…
In this paper, a learning-based whole-body loco-motion controller is proposed, which enables quadruped robots to perform running in the style of real animals. We use a low-level controller based on multi-rigid body dynamics to calculate desired torques for each joint, while the high-level neural net…
At present, there are many challenges for robotic systems to grasp in a dynamic and unstructured environment. The Robotic Grasping and Manipulation Competition (RGMC) aims to encourage researchers to focus on these challenges. The solution proposed in this letter was used to compete in the 2019 and
Having the merits of chemical inertness and immunity to electromagnetic interference, light weight, small size, and softness, optical waveguides have attracted much attention in making tactile sensors recently. This paper presents a new design of waveguide using two layers of cores, one of which has…
To leverage deep learning for image aesthetics assessment, one critical but unsolved issue is how to seamlessly incorporate the information of image aspect ratios to learn more robust models. In this paper, an adaptive fractional dilated convolution (AFDC), which is aspect-ratio-embedded, compositio…
We address the problem of partial index tracking, replicating a benchmark index using a small number of assets. Accurate tracking with a sparse portfolio is extensively studied as a classic finance problem. However in practice, a tracking portfolio must also be diverse in order to minimise risk-a re…
Existing Neural Architecture Search (NAS) methods either encode neural architectures using discrete encodings that do not scale well, or adopt supervised learning-based methods to jointly learn architecture representations and optimize architecture search on such representations which incurs search…
Efficient exploration is a major challenge in Reinforcement Learning (RL) and has been studied extensively. However, for a new task existing methods explore either by taking actions that maximize task agnostic objectives (such as information gain) or applying a simple dithering strategy (such as noi…
In this paper, the gain scheduling technique is applied to design a balance controller for an autonomous bicycle with an inertia wheel. Previously, two different balance controllers are needed depending on whether the bicycle is stationary or dynamic. The switch between the two different controllers…
In this paper, we propose a Rotation-robust Intersection over Union ($ extit{RIoU}$) for 3D object detection, which aims to jointly learn the overlap of rotated bounding boxes. In most existing 3D object detection methods, the norm-based loss is adopted to individually regress the parameters of boun…
There are substantial instruction videos on the Internet, which enables us to acquire knowledge for completing various tasks. However, most existing datasets for instruction video analysis have the limitations in diversity and scale, which makes them far from many real-world applications where more…
The symmetry for the corners of a box, the continuity for the surfaces of a monitor, the linkage between the torso and other body parts --- it suggests that 3D objects may have common and underlying inner relations between local structures, and it is a fundamental ability for intelligent species to…
This paper solves the problem of computing the best grasp in a discrete point set based on a popular grasp quality measure, namely the largest origin-centered ball contained in the grasp wrench set. So far, the solution to this problem is very limited. Noticing that the quality measure for a grasp i…
Having human-like motions will make humanoid robots more predictable and safer for the people around them. An effective way to realize this would be to use human motions as reference. Due to different kinematic and dynamic properties between humans and humanoid robots, however, a human motion could…