14 accepted papers
Accurate relative pose estimation is critical for autonomous close-proximity satellite operations, such as on-orbit servicing and debris removal. However, this task remains highly challenging for unknown, non-cooperative targets due to the unavailability of geometric priors, scarce training data, an…
Enhancing the performance of semantic segmentation models with multi-spectral images (RGB-IR) is crucial, particularly for low-light and adverse environments. While multi-modal fusion techniques aim to learn cross-modality features for generating fused images or engage in knowledge distillation, the…
Physics-Informed Neural Networks (PINNs) offer a promising approach to simulating physical systems. Still, their application is limited by optimization challenges, mainly due to the lack of activation functions that generalize well across several physical systems. Existing activation functions often…
Recent advancements in motion planning for Autonomous Vehicles (AVs) show great promise in using expert driver behaviors in non-stationary driving environments. However, learning only through expert drivers needs more generalizability to recover from domain shifts and near-failure scenarios due to t…
While instance segmentation models excel at object detection in satellite imagery, their performance drops when applied to slant-angled aerial images due to occlusion and scale variation. This is mainly caused by a lack of training data for such diverse viewpoints and scales. To address this limitat…
Deep neural networks have shown exemplary performance on semantic scene understanding tasks on source domains but due to the absence of style diversity during training enhancing performance on unseen target domains using only single source domain data remains a challenging task. Generation of simula…
Enhancing the generalization capacity for semantic segmentation of aerial perception systems for safety-critical applications is vital, especially for environments with low-light and adverse conditions. Multi-spectral fusion techniques aim to maintain the merits of electro-optical (EO) and infrared…
The scarcity of annotated infrared (IR) image datasets limits deep learning networks from achieving performances comparable to those achieved with RGB data. To address this, we introduce a novel semi-supervised RGB-to-IR Image-to-Image Translation model (SSL-RGB2IR) that generates synthetic IR data…
Many fields, like document tagging, video labeling, and medical analysis, require associating the samples with multiple non-exclusive labels, driving the research in multi-label learning. Unlike several multi-label learning setups, practical applications are challenging because they need learning fr…
Deep learning models operating in the complex domain are used due to their rich representation capacity. However, most of these models are either restricted to the first quadrant of the complex plane or project the complex-valued data into the real domain, causing a loss of information. This paper p…
This paper considers the problem of cooperative localization of multiple robots under uncertainty, communicating over a partially connected, dynamic communication network and assisted by an agile landmark. Each robot owns an IMU and a relative pose sensing suite, which can get faulty due to system o…
This paper tackles the out-of-distribution (OOD) detection problem for natural language classifiers. While the previous OOD detection methods require large-scale in-distribution (ID) training data, we attack this problem from the few-shot perspective in an unsupervised manner where the training reli…
Zero-Shot Learning (ZSL) has shown great promise at the intersection of vision and language, and generative methods for ZSL are predominant owing to their efficiency. Moreover, textual description or attribute plays a critical role in transferring knowledge from the seen to unseen classes in ZSL. Su…
Recently, many researchers have attempted to improve deep learning-based object detection models, both in terms of accuracy and operational speeds. However, frequently, there is a trade-off between speed and accuracy of such models, which encumbers their use in practical applications such as autonom…