25 accepted papers
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key ingredient for unlocking complex reasoning capabilities in large language models. Recent work ProRL \citep{liu2025prorl} has shown promise in scaling RL by increasing the number of training steps. However, performance plateau…
Reinforcement Learning with Verifiable Rewards (RLVR) has become a cornerstone for unlocking complex reasoning in Large Language Models (LLMs). Yet, scaling up RL is bottlenecked by limited existing verifiable data, where improvements increasingly saturate over prolonged training. To overcome this, …
Stochastic programming is often challenged by epistemic uncertainty, where critical probability distributions are poorly characterized or unknown due to a lack of data. To address this, we pioneer a novel framework for stochastic programming that minimizes an upper confidence bound (UCB) on the expe…
Reinforcement learning (RL) for LLM reasoning has rapidly emerged as a prominent research area, marked by a significant surge in related studies on both algorithmic innovations and practical applications. Despite this progress, several critical challenges remain, including the absence of standardize…
Task-generic promptable image segmentation aims to achieve segmentation of diverse samples under a single task description by utilizing only one task-generic prompt. Current methods leverage the generalization capabilities of Vision-Language Models (VLMs) to infer instance-specific prompts from thes
Visual-textual correlations in the attention maps derived from text-to-image diffusion models are proven beneficial to dense visual prediction tasks, e.g., semantic segmentation. However, a significant challenge arises due to the input distributional discrepancy between the context-rich sentences us…
Recent advances in reasoning-centric language models have highlighted reinforcement learning (RL) as a promising method for aligning models with verifiable rewards. However, it remains contentious whether RL truly expands a model’s reasoning capabilities or merely amplifies high-reward outputs alrea…
Video Temporal Grounding (TG) aims to temporally locate video segments matching a natural language description (a query) in a long video. While Vision-Language Models (VLMs) are effective at holistic semantic matching, they often struggle with fine-grained temporal localisation. Recently, Group Rela…
Transorbital Neuro Surgery (TNS) offers a novel treatment towards the lesion inside skull pursuing minimal invasiveness. Most conventional TNS tools are rigid and straight, limiting the dexterity and accessibility in passing a small port. Bendable and steerable surgical tools provides an alternative…
Magnetically-actuated continuum robots (MCRs) have the potential to be miniaturized to submillimeter sizes. However, their limited deformation modes hinder their ability to navigate through narrow and tortuous lumens. In this letter, we introduce a novel 2-degrees of freedom (DoF) MCR with diverse d
Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgic…
Person Re-identification (ReID) has been advanced remarkably over the last 10 years. However, the i.i.d. (independent and identically distributed) assumption is somewhat non-applicable to ReID considering its objective to identify images of the same pedestrian across cameras at different locations.…
Patient modelling has a wide range of applications in medicine and healthcare, such as clinical teaching, surgery navigation and automatic robotized scanning. While patients are typically covered or occluded in medical scenes, directly regressing human meshes from single RGB images is challenging. T
Promptable segmentation typically requires instance-specific manual prompts to guide the segmentation of each desired object. To minimize such a need, task-generic promptable segmentation has been introduced, which employs a single task-generic prompt to segment various images of different objects i…
Robot-assisted minimally invasive surgery (MIS) using flexible endoscopy has emerged as a groundbreaking technology for improving traditional surgical approaches. However, a major challenge in advancing this technology is the lack of shape sensing, which leads to inaccurate navigation and control of
Force feedback is of importance in Minimally Invasive Surgery (MIS) as it reduces surgical risks and enhances surgical safety. However, equipping force sensing to the tip of surgical instruments presents challenges due to their diminutive dimensions and often curved shapes. To address this issue, a
Camouflaged object detection (COD) approaches heavily rely on pixel-level annotated datasets. Weakly-supervised COD (WSCOD) approaches use sparse annotations like scribbles or points to reduce annotation efforts, but this can lead to decreased accuracy. The Segment Anything Model (SAM) shows remar…
X-ray to CT registration is an essential technique to provide on-site guidance for clinicians and medical robots by aligning preoperative information with intraoperative images. Current methods focus on local registration with small capture ranges and necessitate a manual initial alignment before pr…
Intraoperative contact sensing has the potential to reduce the risk of surgical errors and enhance manipulation capabilities for medical robots, particularly in contact force control. Current intrinsic force sensing (IFS) methods are limited in application to medical instruments with arbitrary shape
Deep learning based face swapping technologies have opened new frontiers for entertainment industries while pose novel threats to identity security. Applying face swapping to real-world products, as well as defending against its misuse, rely on the capacity to generate high quality face swapped imag…
"Domain adaptation (DA) aims to transfer knowledge learned from a labeled source domain to an unlabeled or a less labeled but related target domain. Ideally, the source and target distributions should be aligned to each other equally to achieve unbiased knowledge transfer. However, due to the signif…
To realize dexterous robotic manipulation and enhance the human-machine interaction, nowadays increasing efforts have been made towards multi-dimensional force sensing. However, there are still bottlenecks in integrating these sensors into robots because of the limitation on conformability to comple
In minimally invasive surgery, miniaturisation and in situ adjustable stiffness of robotic manipulators are desired features. Previous research proposed a simple and effective tendon-driven curve-joint manipulator design using a variable neutral-line mechanism, which highly satisfies both criteria.…
Emulating a human-like somatosensory system in instruments such as robotic hands and surgical grippers has the potential to revolutionize these domains. Using a combination of different sensing modalities is problematic due to the limited space and incompatibility of these sensing principles. Theref
Domain adaptation (DA) has been a fundamental building block for Transfer Learning (TL) which assumes that source and target domain share the same label space. A more general and realistic setting is that the label space of target domain is a subset of the source domain, as termed by Partial domain…