11 accepted papers
Machine unlearning, which selectively removes harmful knowledge from a pre-trained model without retraining from scratch, is crucial for addressing privacy, regulatory compliance, and ethical concerns in Large Language Models (LLMs). However, existing unlearning methods often struggle to thoroughly
The reliability of Large Language Models (LLMs) in high-stakes domains such as healthcare, law, and scientific discovery is often compromised by hallucinations. These failures typically stem from two sources: *data-driven hallucinations* and *reasoning-driven hallucinations*. However, existing detec…
Feature encoders play a key role in pixel-level crack segmentation by shaping the representation of fine textures and thin structures. Existing CNN-, Transformer-, and Mamba-based models each capture only part of the required spatial or structural information, leaving clear gaps in modeling complex
The safety of autonomous driving systems (ADS) depends on accurate perception across distance and driving conditions. The outputs of AI perception algorithms are stochastic, which has a major impact on decision making and safety outcomes, including time-to-collision estimation. However, current perc
The unlearning method aims at effectively removing harmful, sensitive, or outdated knowledge without costly retraining the model. However, existing methods suffer from two critical limitations: (1) collateral forgetting, where erasing target data inadvertently removes related but desirable knowledge
Network quantization is one of the most hardware friendly techniques to enable the deployment of convolutional neural networks (CNNs) on low-power mobile devices. Recent network quantization techniques quantize each weight kernel in a convolutional layer independently for higher inference accuracy,…
There has been recent interest in exploring generative goals for counterfactual reasoning, such as individualized treatment effect (ITE) estimation. However, existing solutions often fail to address issues that are unique to causal inference, such as covariate balancing and (infeasible) counterfactu…
Recently, the leading performance of human pose estimation is dominated by top-down methods. Being a fundamental component in training and inference, data processing has not been systematically considered in pose estimation community, to the best of our knowledge. In this paper, we focus on this pro…
In this paper, we address the problem of real-time image semantic segmentation for indoor scene. As for scene parsing, both accuracy and speed are equally important. However, most of existing methods mainly focus on improving accuracy rather than speed. How to find a balance between accuracy and spe…
In this paper, we propose a robust obstacle detection approach by leveraging Weighted Hough Transform (WHT) in combination with temporal information correlation from the stereo video sequences. First, to model the road surface or obstacles in the video, rather than using simple threshold from binari…
This paper describes a strategy to lift up a deformable 2D object resting on a horizontal plane. Inspired by human hand lifting behavior, the strategy plans grasping trajectories of two fingertips using modified Rapidly-exploring Random Trees (RRT). Compared to a straight squeeze, a planned finger m…