9 accepted papers
Rapid biodiversity loss underscore the urgency of effective monitoring, yet manual surveys remain resource-intensive. While on-device AI offers a scalable alternative, its performance in the wild is often challenged by environmental variability. Current methods rely heavily on cloud resource, which
Estimating the 3D poses of hands and objects from a single RGB image is a fundamental yet challenging problem, with broad applications in augmented reality and human-computer interaction. Existing methods largely rely on visual cues alone, often producing results that violate physical constraints su
Wild salmon are essential to the ecological, economic, and cultural sustainability of the North Pacific Rim. Yet climate variability, habitat loss, and data limitations in remote ecosystems that lack basic infrastructure support pose significant challenges to effective fisheries management. This pro
In Robot-assisted Minimally Invasive Surgery (RMIS), the estimation of the pose of surgical tools is crucial for applications such as surgical navigation, visual servoing, autonomous robotic task execution and augmented reality. A plethora of hardware-based and vision-based methods have been propose…
Large-scale pre-trained language models have shown outstanding performance in a variety of NLP tasks. However, they are also known to be significantly brittle against specifically crafted adversarial examples, leading to increasing interest in probing the adversarial robustness of NLP systems. We in…
This paper tackles the problem of estimating 3D body shape of clothed humans from single polarized 2D images, i.e. polarization images. Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applic…
We propose a method of gait recognition just from a single image for the first time, which enables latency-free gait recognition. To mitigate large intra-subject variations caused by a phase (gait pose) difference between a matching pair of input single images, we first reconstruct full gait cycles…
Existing gait recognition approaches typically focus on learning identity features that are invariant to covariates (e.g., the carrying status, clothing, walking speed, and viewing angle) and seldom involve learning features from the covariate aspect, which may lead to failure modes when variations…
In this paper, space-delay adaptive processing (SDAP) is proposed for motion mapping using MIMO RF signals. Typically, indoor RF localization methods require ultra-wideband signals to achieve the time-delay resolution required for separation of returns in complex multipath environments. Due to spect…