38 accepted papers
Force feedback is essential for safe endovascular teleoperation, yet typically constrained by complex sensor integration. This article presents a compact active haptic interface system designed for robotic catheterization. Leveraging the intrinsic proprioception of a Permanent Magnet Synchronous Mot
We introduce FIXME, the first end-to-end and large-scale benchmark for evaluating Large Language Models (LLMs) in hardware design functional verification (FV). Comprising 747 tasks derived from real-world hardware designs, FIXME spans five core FV sub-sets: specification comprehension, reference mod
Large Language Models (LLMs) have shown strong capability in interpreting multimodal data but remain limited in their ability to natively handle time-series data. Addressing this limitation could enable the translation of longitudinal and wearable sensing data into actionable insights and patient-fa…
Recently, biped robot walking technology has been significantly developed; however, mainly in a bland walking scheme. To emulate human walking, robots need to step on the positions they see in unknown spaces accurately. In this paper, we present PolyMap, a perception-based locomotion planning framew…
Retrieval-augmented generation (RAG) has been extensively employed to mitigate hallucinations in large language models (LLMs). However, existing methods for multi-hop reasoning tasks often lack global planning, increasing the risk of falling into local reasoning impasses. Insufficient exploitation o
Zero-shot skeleton-based action recognition aims to recognize unseen actions by transferring knowledge from seen categories through semantic descriptions. Most existing methods typically align skeleton features with textual embeddings within a shared latent space. However, the absence of contextual
Visual AutoRegressive (VAR) modeling has garnered significant attention for its innovative next-scale prediction paradigm. However, mainstream VAR paradigms attend to all tokens across historical scales at each autoregressive step. As the next scale resolution grows, the computational complexity of
Embodied agents exhibit immense potential across a multitude of domains, making the assurance of their behavioral safety a fundamental prerequisite for their widespread deployment. However, existing research predominantly concentrates on the security of general large language models, lacking special
Human-robot collaborative manipulation with mobile, multiple manipulators is crucial for expanding robotic applications, requiring precise handling of coupled force-position constraints between partners. Current systems, however, exhibit end-effector oscillations and instability during dynamic inter
Recent advances in deep learning have led to increasingly complex models with deeper layers and more parameters, reducing interpretability and making their decisions harder to understand. While many methods explain black-box reasoning, most lack effective interventions or only operate at sample-leve…
Large Multimodal Models (LMMs) have recently demonstrated impressive performance on general video comprehension benchmarks. Nevertheless, for broader applications, the robustness of their temporal analysis capability needs to be thoroughly investigated yet predominantly ignored. Motivated by this, w…
Action recognition is a fundamental task in video understanding. Existing methods typically extract unified features to process all actions in one video, which makes it challenging to model the interactions between different objects in multi-action scenarios. To alleviate this issue, we explore dise…
Understanding the behaviors of robotic arms is essential for various robotic applications such as logistics management, precision agriculture, and automated manufacturing. However, the lack of large-scale and diverse datasets significantly hinders progress in video-based robotic arm action understan…
With the growing popularity of electric vehicles, the demand for robot-based unmanned automatic charging has become both urgent and challenging. Two key challenges need to be addressed: how to efficiently locate the charging port, and how to compliantly insert the connector into the port. In this pa
Unsupervised multi-view outlier detection has garnered increasing attention in recent years, yet existing methods face persistent challenges. Many approaches rely predominantly on first-order neighborhood information, overlooking the richer insights offered by higher-order structures, which can degr…
This paper introduces a novel method for matching visible light and infrared images, termed the Visible Light and Infrared Image Matching Network (VLIMNet). In the image encoding stage, we incorporate a generative architecture-based modality transformation network after the SuperPoint encoder, enabl…
Open Relation Extraction (OpenRE) is a task that involves discovering new relation types by referring to labeled instances. Existing methods mainly rely on large pre-trained models to obtain the relation representation of entity pairs, and then jointly train the supervised and unsupervised data usin…
Automatic anime sketch colorization aims to generate a color image from a sketch image, which is challenging due to limited structure and semantic understanding, leading to constrained style, and semantic color inconsistency. In this paper, we introduce a sketch to color diffusion model with semanti…
We present that visual grounding and image captioning, which perform as two mutually inverse processes, can be bridged together for collaborative training by careful designs. By consolidating this idea, we introduce CyCo, a cyclic-consistent learning framework to ameliorate the independent training…
Recently, unified information extraction has garnered widespread attention from the NLP community, which aims to use a unified paradigm to perform various information extraction tasks. However, prevalent unified IE approaches inevitably encounter challenges such as noise interference, abstract label…
Although neural networks excel in video action recognition tasks their "black-box" nature makes it challenging to understand the rationale behind their decisions. Recent approaches used inherently interpretable models to analyze video actions in a manner akin to human reasoning. However it has been…
Despite the remarkable progress of image captioning, existing captioners typically lack the controllable capability to generate desired image captions, e.g., describing the image in a rough or detailed manner, in a factual or emotional view, etc. In this paper, we show that a unified model is qualif…
Recent years have witnessed the rapid progress of image captioning. However, the demands for large memory storage and heavy computational burden prevent these captioning models from being deployed on mobile devices. The main obstacles lie in the heavyweight visual feature extractors (i.e., object de…
In ophthalmic surgery, particularly in procedures involving the posterior segment, clinicians face significant challenges in maintaining precise control of hand-held instruments without damaging the fundus tissue. Typical targets of this type of surgery are the internal limiting membrane (ILM) and t
In retinal surgery clinicians access the internal volume of the eyeball through small scale trocar ports, typically 0.65 mm in diameter, to treat vitreoretinal disorders like idiopathic epiretinal membrane and age-related macular holes. The treatment of these conditions involves the removal of thin
It is essential for the robot manipulator to adapt to unexpected events and dynamic environments while executing the physical contact-rich tasks. Although a range of methods have been investigated to enhance the adaptability and generalization capability of robot manipulation, it is still difficult
Time delay, especially varying time delay, is always an important factor affecting the stability to the human-in-the-loop system. Previous research usually focuses on the performance of the internal signal transmission part, but rarely considers the whole system with human and environmental factors
Up-to-date High-Definition (HD) maps are essential for self-driving cars. To achieve constantly updated HD maps, we present a deep neural network (DNN), Diff-Net, to detect changes in them. Compared to traditional methods based on object detectors, the essential design in our work is a parallel feat…
In this paper, we focus on the self-supervised learning of visual correspondence using unlabeled videos in the wild. Our method simultaneously considers intra- and inter-video representation associations for reliable correspondence estimation. The intra-video learning transforms the image contents a…
Semi-supervised video object segmentation is a task of segmenting the target object in a video sequence given only a mask annotation in the first frame. The limited information available makes it an extremely challenging task. Most previous best-performing methods adopt matching-based transductive r…
In video object tracking, there exist rich temporal contexts among successive frames, which have been largely overlooked in existing trackers. In this work, we bridge the individual video frames and explore the temporal contexts across them via a transformer architecture for robust object tracking.…
Multi-robot systems are widely used in environmental exploration and modeling, especially in hazardous environments. However, different types of robots are limited by different mobility, battery life, sensor type, etc. Heterogeneous robot systems are able to utilize various types of robots and provi…
The success of deep neural networks relies on significant architecture engineering. Recently neural architecture search (NAS) has emerged as a promise to greatly reduce manual effort in network design by automatically searching for optimal architectures, although typically such algorithms need an ex…
Existing inpainting methods have achieved promising performance for recovering regular or small image defects. However, filling in large continuous holes remains difficult due to the lack of constraints for the hole center. In this paper, we devise a Recurrent Feature Reasoning (RFR) network which i…
We propose an unsupervised visual tracking method in this paper. Different from existing approaches using extensive annotated data for supervised learning, our CNN model is trained on large-scale unlabeled videos in an unsupervised manner. Our motivation is that a robust tracker should be effective…
In recent years, many tracking algorithms achieve impressive performance via fusing multiple types of features, however, most of them fail to fully explore the context among the adopted multiple features and the strength of them. In this paper, we propose an efficient multi-cue analysis framework fo…
In this paper, a comprehensive friction model for collaborative industrial robot joints is proposed which takes into account the velocity, temperature and load torque effects. The model indicates that the velocity and temperature have a strong influence on viscous friction nonlinearly, whereas load…
This paper presents an improved force-free control method based on current, which can be applied to industrial articulated robotic arms with large mass and large friction torque for direct teaching. Three kinds of torques that influence direct teaching are analyzed, and thus a calibration method and…