12 accepted papers
In this paper, we present a new approach to improve the neural rendering fidelity of in-the-wild unmanned aerial vehicle (UAV)-based scenes. Our formulation is designed for dynamic scenes, consisting of small moving objects or human actions in particular. We propose an extension of K-Planes Neural R
We present a novel unsupervised obstacle segmentation architecture that follows a novel Relation Distillation (RD) paradigm. Our architecture design was inspired by a self-supervised teacher-student approach that relies on the Semantic Distillation originally devised for representation learning. Whi…
Tremendous variations coupled with large degrees of freedom in UAV-based imaging conditions lead to a significant lack of data in adequately learning UAV-based perception models. Using various synthetic renderers in conjunction with perception models is prevalent to create synthetic data to augment…
To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles. As a viable alternative to laborious and costly data c…
"We present a Momentum Re-identification (MoReID) framework that can leverage a very large number of negative samples in training for general re-identification task. The design of this framework is inspired by Momentum Contrast (MoCo), which uses a dictionary to store current and past batches to bui…
Recently, self-supervised learning has attracted attention due to its remarkable ability to acquire meaningful representations for classification tasks without using semantic labels. This paper introduces a self-supervised learning framework suitable for hyperspectral images that are inherently chal…
We present a novel event recognition approach called Spatially-preserved Doubly-injected Object Detection CNN (S-DOD-CNN), which incorporates the spatially preserved object detection information in both a direct and an indirect way. Indirect injection is carried out by simply sharing the weights bet…
Recognizing an event in an image can be enhanced by detecting relevant objects in two ways: 1) indirectly utilizing object detection information within the unified architecture or 2) directly making use of the object detection output results. We introduce a novel approach, referred to as Doubly-inje…
Learning an event classifier is challenging when the scenes are semantically different but visually similar. However, as humans, we typically handle such tasks painlessly by adding our background semantic knowledge. Motivated by this observation, we aim to provide an empirical study about how additi…
A novel template matching algorithm that can incorporate the concept of deformable parts, is presented in this paper. Unlike the deformable part model (DPM) employed in object recognition, the proposed template-matching approach called Deformable Template Matching (DTM) does not require a training s…
The different strategies for feature extraction and synthesis employed by humans and computers are often complementary, hence combining the two into an integrated object recognition system may considerably improve performance over either used in isolation. Rapid Serial Visual Presentation (RSVP) is…
Human-autonomy sensor fusion is an emerging technology with a wide range of applications, including object detection/recognition, surveillance, collaborative control, and prosthetics. For object detection, humans and computer-vision-based systems employ different strategies to locate targets, likely…