16 accepted papers
Estimating the generalization gap and developing optimization methods that improve generalization are crucial for deep learning models, for both theoretical understanding and practical applications. Leveraging unlabeled data for these purposes offers significant advantages in real-world scenarios. T…
Mobile robot navigation in dynamic human environments requires policies that balance adaptability to diverse behaviors with compliance to safety constraints. We hypothesize that integrating data-driven rewards with rule-based objectives enables navigation policies to achieve a more effective balance…
Post-training quantization (PTQ) has emerged as a prevailing technique for deploying large language models (LLMs) efficiently in terms of both memory and computation, across edge devices and server platforms. Existing PTQ methods primarily aim to reduce precision in weights and activations by mitiga…
LiDAR semantic segmentation must remain robust under various sensor and environmental corruptions to be reliable in safety-critical applications. Existing test-time adaptation methods, including approaches based on pseudo-labels and normalization statistics, have shown promising results but can stil
Playful deception, a common feature in human social interactions, remains underexplored in Human-Robot Interaction (HRI). Inspired by the Turkish Ice Cream (TIC) vendor routine, we investigate how bounded, culturally familiar forms of deception influence user trust, enjoyment, engagement, and willin…
Robust 3D object detection across diverse weather con- ditions is crucial for safe autonomous driving, and RADAR is increasingly leveraged for its resilience in adverse weather. Recent advancements have explored 4D RADAR and LiDAR-RADAR fusion to enhance 3D perception capabilities, specifically targ…
This paper presents a novel learning-based approach to dynamic robot-to-human handover, addressing the challenges of delivering objects to a moving receiver. We hypothesize that dynamic handover, where the robot adjusts to the receiver's movements, results in more efficient and comfortable interacti
Weakly Supervised Semantic Segmentation (WSSS) relies on Class Activation Maps (CAMs) to extract spatial information from image-level labels. With the success of Vision Transformer (ViT) the migration of ViT is actively conducted in WSSS. This work proposes a novel WSSS framework with Class Token In…
"LiDAR-based 3D object detection models show remarkable performance, however their effectiveness diminishes in adverse weather. On the other hand, 4D radar exhibits strengths in adverse weather but faces limitations in standalone use. While fusing LiDAR and 4D radar seems to be the most intuitive ap…
"LiDAR semantic segmentation is important for understanding the surrounding environment in autonomous driving. Existing methods assume closed-set situations with the same training and testing label space. However, in the real world, unknown classes not encountered during training may appear during t…
This letter presents the design and development of an innovative interactive robotic system to enhance audience engagement using character-like personas. Built upon the foundations of persona-driven dialog agents, this work extends the agent's application to the physical realm, employing robots to p
Detecting objects in 3D under various (normal and adverse) weather conditions is essential for safe autonomous driving systems. Recent approaches have focused on employing weather-insensitive 4D radar sensors and leveraging them with other modalities such as LiDAR. However they fuse multi-modal info…
Recognizing objects from sparse and noisy events becomes extremely difficult when paired images and category labels do not exist. In this paper, we study label-free event-based object recognition where category labels and paired images are not available. To this end, we propose a joint formulation o…
Existing image stitching approaches based on global or local homography estimation are not free from the parallax problem and suffer from undesired artifacts. In this paper, instead of relying on the homography-based warp, we propose a novel deep image stitching framework exploiting the pixel-wise w…
With the success of the 3D deep learning models, various perception technologies for autonomous driving have been developed in the LiDAR domain. While these models perform well in the trained source domain, they struggle in unseen domains with a domain gap. In this paper, we propose a single domain…
Weakly supervised semantic segmentation (WSSS) using image-level classification labels usually utilizes the Class Activation Maps (CAMs) to localize objects of interest in images. While pointing out that CAMs only highlight the most discriminative regions of the classes of interest, adversarial eras…