14 accepted papers
High-fidelity generative models are increasingly needed in privacy-sensitive scenarios, where access to data is severely restricted due to regulatory and copyright constraints. This scarcity hampers model development--ironically, in settings where generative models are most needed to compensate for
The effective utilization of data through Deep Neural Networks (DNNs) has profoundly influenced various aspects of society. The growing demand for high-quality, particularly personalized, data has spurred research efforts to prevent data leakage and protect privacy in recent years. Early privacy-pre…
Recent advances in Large Language Models (LLMs) have enabled them to process increasingly longer sequences, ranging from 2K to 2M tokens and even beyond. However, simply extending the input sequence length does not necessarily lead to effective long-context understanding. In this study, we integrate
Completely automated public Turing test to tell humans apart (CAPTCHA) is an effective mechanism to protect websites and online applications from malicious bots programs. Image-based CAPTCHA is one of the most widely used schemes. However, deep learning techniques have significantly weakened the sec…
We propose a multimodal 3D scene editing framework MMEditor to create or modify objects within an extant 3D Gaussian Splatting (3DGS) according to text and image prompts. MMEditor employs a multimodal image editing module to iteratively optimize 3D Gaussians in editing regions for delicate and multi…
LLMs have immense potential for generating plans, transforming an initial world state into a desired goal state. A large body of research has explored the use of LLMs for various planning tasks, from web navigation to travel planning and database querying. However, many of these systems are tailored…
Previous physical adversarial attacks have shown that carefully crafted perturbations can deceive face recognition systems, revealing critical security vulnerabilities. However, these attacks often struggle to impersonate multiple targets and frequently fail to bypass liveness detection. For example…
In this paper, we present a novel registration method based on plane constraints for large-scale LiDAR point clouds, effectively decoupling rotation estimation and translation estimation. For rotation estimation, we propose an outlier removal method that combines coarse filtering with rotation-invar…
In this letter, we propose a guaranteed outlier removal method based on computational geometry consistency checks, named PNGOR, effectively leveraging planar normals from 3D scenes to estimate rotation. The challenge of estimating rotation can be conceptualized as a maximum consensus problem and we
The success of self-supervised contrastive learning hinges on identifying positive data pairs, such that when they are pushed together in embedding space, the space encodes useful information for subsequent downstream tasks. Constructing positive pairs is non-trivial as the pairing must be similar e…
Physical adversarial attacks can deceive deep neural networks (DNNs), leading to erroneous predictions in real-world scenarios. To uncover potential security risks, attacking the safety-critical task of person detection has garnered significant attention. However, we observe that existing attack met…
Adversarial attacks on thermal infrared imaging expose the risk of related applications. Estimating the security of these systems is essential for safely deploying them in the real world. In many cases, realizing the attacks in the physical space requires elaborate special perturbations. These solut…
Many robot tasks, such as grasping and inspection, may require complete 3D models with enough surface details. Fully autonomous environment exploration and 3D reconstruction of unknown objects are challenging for a robot when little or no knowledge about an object is known as a priori. Previous work
The view planning (VP) problem in robotic active vision enables a robot system to automatically perform object reconstruction tasks. Lacking prior knowledge, next-best-view (NBV) methods are typically used to plan a view sequence, with the goal of covering as many object surface areas as possible in