63 accepted papers
Multimodal Large Language Models (MLLMs) have recently emerged as a promising paradigm for vehicle-to-vehicle (V2V) cooperative autonomous driving, enabling language-based joint perception, prediction, and decision-making in safety-critical scenarios with severe occlusions. However, existing V2V–MLL…
Large language models (LLMs) face significant deployment challenges due to their massive computational demands. While pruning offers a promising compression solution, existing methods suffer from two critical limitations: (1) They neglect activation distribution shifts between calibration data and t
Neural operators (NOs) excel at learning mappings between function spaces, serving as efficient forward solution approximators for PDE-governed systems. However, as black-box solvers, they offer limited insight into the underlying physical mechanism, due to the lack of interpretable representations…
Federated fine-tuning of Large Language Models faces severe statistical heterogeneity. However, existing model-level defenses often overlook the root cause: intrinsic data distribution mismatches. In this work, we first establish Federated Self-Distillation (FedSD) as a fundamental and potent strate…
With the rapid integration of large language models (LLMs) into medical decision-support aids, ensuring reliability in reasoning steps—not just final answers—is increasingly critical. Two key safety dimensions are Chain-of-Thought (CoT) faithfulness, which assesses alignment of the model’s reasoning
Vision-and-Language Navigation (VLN) plays a critical role in tasks of embodied AI, particularly in unseen environments following natural language instructions. Recent advancements leverage large language models (LLMs) to improve the accuracy and generalizability of VLN systems by encoding image seq
While Large Language Models (LLMs) have achieved remarkable success across diverse tasks, they remain vulnerable to jailbreak attacks, which pose significant risks to their secure deployment. Current safetymechanisms primarily rely on output guardrails to filter harmful outputs, yet these defenses a…
Recent progress in large-scale robotic datasets and vision-language models (VLMs) has advanced research on vision-language-action (VLA) models. However, existing VLA models still face two fundamental challenges: (\textit{i}) producing precise low-level actions from high-dimensional observations, (\t…
Multimodal Large Language Models (MLLMs) have showcased impressive skills in tasks related to visual understanding and reasoning. Yet, their widespread application faces obstacles due to the high computational demands during both the training and inference phases, restricting their use to a limited…
Adversarial attacks poses a significant threat to the security of AI-based systems. To counteract these attacks, adversarial training (AT) and ensemble learning (EL) have emerged as widely adopted methods for enhancing model robustness. However, a counter-intuitive phenomenon arises where the simple…
The wide deployment of the generative pre-trained transformer (GPT) has raised privacy concerns for both clients and servers. While cryptographic primitives can be employed for secure GPT inference to protect the privacy of both parties, they introduce considerable performance overhead. To accelerat…
Humans possess a unified cognitive ability to perceive, comprehend, and interact with the physical world. Why can’t large language models replicate this holistic understanding? Through a systematic analysis of existing training paradigms in vision-language-action models (VLA), we identify two key ch
Learning visuomotor policy for multi-task robotic manipulation has been a long-standing challenge for the robotics community. The difficulty lies in the diversity of action space: typically, a goal can be accomplished in multiple ways, resulting in a multimodal action distribution for a single task.
Generative modeling-based visuomotor policies have been widely adopted in robotic manipulation, attributed to their ability to model multimodal action distributions. However, the high inference cost of multi-step sampling limits its applicability in real-time robotic systems. Existing approaches acc…
Teleoperation is essential for autonomous robot learning, especially in manipulation tasks that require human demonstrations or corrections. However, most existing systems only offer unilateral robot control and lack the ability to synchronize the robot’s status with the teleoperation hardware, prev
Improving data utilization, especially for imperfect data from task failures, is crucial for robotic manipulation due to the challenging, time-consuming, and expensive data collection process in the real world. Current imitation learning (IL) typically discards imperfect data, focusing solely on suc
Visual landmarks, with their unique textures, play a crucial role in vision-based environmental sensing applications (such as augmented reality-based advertising, indoor navigation). However, few studies have focused on the construction of visual landmark databases, which is usually time-consuming a…
Text embedding models are essential for various natural language processing tasks, enabling the effective encoding of semantic information into dense vector representations. These models are typically optimized using triplets of (query, positive, negative) data pairs for contrastive learning, where…
Attention mechanisms have emerged as transformative tools in core AI domains such as natural language processing and computer vision. Yet, their largely untapped potential for modeling intricate physical systems presents a compelling frontier. Learning such systems often entails discovering operator…
Developing robust and general-purpose manipulation policies is a key goal in robotics. To achieve effective generalization, it is essential to construct comprehensive datasets that encompass a large number of demonstration trajectories and diverse tasks. Unlike vision or language data, which can be…
Self-evolution, the ability of agents to autonomously improve their reasoning and behavior, is essential for the embodied domain with long-horizon, real-world tasks. Despite current advancements in reinforcement fine-tuning (RFT) showing strong performance in enhancing reasoning in LLMs, its potenti…
Diffusion Policy is a powerful technique tool for learning end-to-end visuomotor robot control. It is expected that Diffusion Policy possesses scalability, a key attribute for deep neural networks, typically suggesting that increasing model size would lead to enhanced performance. However, our obser
Vision-Language-Action (VLA) models have shown remarkable potential in visuomotor control and instruction comprehension through end-to-end learning processes. However, current VLA models face significant challenges: they are slow during inference and require extensive pre-training on large amounts o
Camera-LiDAR fusion models significantly enhance perception performance in autonomous driving. The fusion mechanism leverages the strengths of each modality while minimizing their weaknesses. Moreover, in practice, camera-LiDAR fusion models utilize pre-trained backbones for efficient training. Howe…
The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM’s adversarial robustness. This paper proposes an efficient tool to audit the LLM’s adversarial robustness via a prompt-based adversarial attack (PromptAt…
Transformer-based Diffusion Probabilistic Models (DPMs) have shown more potential than CNN-based DPMs, yet their extensive computational requirements hinder widespread practical applications. To reduce the computation budget of transformer-based DPMs, this work proposes the Efficient Diffusion Trans…
Neural network compression techniques, such as knowledge distillation (KD) and network pruning, have received increasing attention. Recent work `Prune, then Distill' reveals that a pruned student-friendly teacher network can benefit the performance of KD. However, the conventional teacher-student pi…
Compared to identifying binary versions of the same function under different compilation options, existing Learning-Based Binary Code Similarity Detection (LB-BCSD) methods exhibit lower accuracy in recognizing functions with the same functionality but different implementations. To address this issu…
Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users. Unfortunately, the performance of most KBQA models tends to decline significantly in real-world scenar…
Neural operators (NOs) have emerged as effective tools for modeling complex physical systems in scientific machine learning. In NOs, a central characteristic is to learn the governing physical laws directly from data. In contrast to other machine learning applications, partial knowledge is often kno…
Deploying style transfer methods on resource-constrained devices is challenging, which limits their real-world applicability. To tackle this issue, we propose using pruning techniques to accelerate various visual style transfer methods. We argue that typical pruning methods may not be well-suited fo…
Despite recent popularity of attention-based neural architectures in core AI fields like natural language processing (NLP) and computer vision (CV), their potential in modeling complex physical systems remains under-explored. Learning problems in physical systems are often characterized as discoveri…
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for man
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for man…
Traffic prediction plays an important role in urban computing. However, many cities face data scarcity due to low levels of urban development. Although many approaches transfer knowledge from data-rich cities to data-scarce cities, the centralized training paradigm cannot uphold data privacy. For th…
Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. They have also shown the ability to perform chain-of-thought (CoT) reasoning to solve complex problems. Recent studies have explored CoT reasoning in complex multimodal…
Alignment between image and text has shown promising improvements on patch-level pre-trained document image models. However, investigating more effective or finer-grained alignment techniques during pre-training requires a large amount of computation cost and time. Thus, a question naturally arises:…
In this paper, we propose a novel representation for grasping using contacts between multi-finger robotic hands and objects to be manipulated. This representation significantly reduces the prediction dimensions and accelerates the learning process. We present an effective end-to-end network, CMG-Net…
Channel pruning has been widely studied as a prevailing method that effectively reduces both computational cost and memory footprint of the original network while keeping a comparable accuracy performance. Though great success has been achieved in channel pruning for 2D image-based convolutional net…
The traditional direct position determination (DPD) for multiple targets usually requires transmitting raw data to the fusion center (FC), which occupies large transmission bandwidth and hardware resource. To solve this problem, we adopt one-bit analog-to-digital converters (ADCs) for a distributed…
Fourier neural operators (FNOs) can learn highly nonlinear mappings between function spaces, and have recently become a popular tool for learning responses of complex physical systems. However, to achieve good accuracy and efficiency, FNOs rely on the Fast Fourier transform (FFT), which is restricte…
Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models and transformation-based contrastive models, we devise novel data-driven supervision for tabular data by introducing a ch…
Large language models (LLMs), such as GPT-3 and ChatGPT, have demonstrated remarkable results in various natural language processing (NLP) tasks with in-context learning, which involves inference based on a few demonstration examples. Despite their successes in NLP tasks, no investigation has been c…
Neural operators, which emerge as implicit solution operators of hidden governing equations, have recently become popular tools for learning responses of complex real-world physical systems. Nevertheless, the majority of neural operator applications has thus far been data-driven, which neglects the…
Despite the prominent success of general object detection, the performance and efficiency of Small Object Detection (SOD) are still unsatisfactory. Unlike existing works that struggle to balance the trade-off between inference speed and SOD performance, in this paper, we propose a novel Scale-aware…
This letter deals with the control-oriented modeling of a multilayered dielectric elastomer actuator based tube. The actuator is clamped at both sides and performs a radial displacement. The hyperelastic deformation and the viscoelastic performance, together with the electro-mechanical coupling are
The IMPRESSIONS section of a radiology report about an imaging study is a summary of the radiologist’s reasoning and conclusions, and it also aids the referring physician in confirming or excluding certain diagnoses. A cascade of tasks are required to automatically generate an abstractive summary of…
Knowledge distillation (KD) can effectively compress neural networks by training a smaller network (student) to simulate the behavior of a larger one (teacher). A counter-intuitive observation is that a more expansive teacher does not make a better student, but the reasons for this phenomenon remain…
Deep reinforcement learning (DRL) demonstrates great potential in mapless navigation domain. However, such a navigation model is normally restricted to a fixed configuration of the range sensor because its input format is fixed. In this letter, we propose a DRL model that can address range data obta
In deep model compression, the recent finding "Lottery Ticket Hypothesis" (LTH) pointed out that there could exist a winning ticket (i.e., a properly pruned sub-network together with original weight initialization) that can achieve competitive performance than the original dense network. However, it…
Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory-Economic Sparse Training (MEST) framework targeting for accurate and fast execution on edge devices. The proposed MEST…
Electrospinning has exhibited excellent benefits to treat the trauma for tissue engineering due to its produced micro/nano fibrous structure. It can effectively adhere to the tissue surface for long-term continuous therapy. This paper develops a robotic electrospinning platform for endoluminal thera…
There have been long-standing controversies and inconsistencies over the experiment setup and criteria for identifying the "winning ticket" in literature. To reconcile such, we revisit the definition of lottery ticket hypothesis, with comprehensive and more rigorous conditions. Under our new definit…
Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structu…
We develop a novel self-supervised learning method named Shape Self-Correction for point cloud analysis. Our method is motivated by the principle that a good shape representation should be able to find distorted parts of a shape and correct them. To learn strong shape representations in an unsupervi…
Accurate force sensing is important for endoluminal intervention in terms of both safety and lesion targeting. This paper develops an FBG-based force sensor for robotic bronchoscopy by configuring three FBG sensors at the lateral side of a conical substrate. It allows a large and eccentric inner lum…
In this paper, we study physical adversarial attacks on object detectors in the wild. Previous works mostly craft instance-dependent perturbations only for rigid or planar objects. To this end, we propose to learn an adversarial pattern to effectively attack all instances belonging to the same objec…
We propose an attention-injective deformable convolutional network called ADCrowdNet for crowd understanding that can address the accuracy degradation problem of highly congested noisy scenes. ADCrowdNet contains two concatenated networks. An attention-aware network called Attention Map Generator (A…
Endobronchial intervention is increasingly used as a minimally invasive means of lung intervention. Vision-based localization approaches are often sensitive to image artifacts in bronchoscopic videos. In this letter, a robust navigation system based on a context-aware depth recovery approach for mon
In this paper, we propose a deep reinforcement learning (DRL)-based rate adaptation algorithm for adaptive 360-degree video streaming, which is able to maximize the quality of experience of viewers by adapting the transmitted video quality to the time-varying network conditions. Specifically, to red…
Bronchoscopic intervention, as a minimally invasive method for the diagnosis and treatment of lung diseases, has attracted more and more attention in recent years. However, existing endobronchial instruments lack the steerability accessing the peripheral airways with difficult bifurcations. This pap
Bronchoscopic intervention, as a minimally invasive method for the diagnosis and treatment of lung diseases, has attracted more and more attention in recent years. However, existing endobronchial instruments lack the steerability accessing the peripheral airways with difficult bifurcations. This pap…
Bronchoscopic interventions are widely performed for the diagnosis and treatment of lung diseases. However, for most endobronchial devices, the lack of a bendable tip restricts their access ability to get into distal bronchi with complex bifurcations. This paper presents the design of a new wire-dri