6 accepted papers
Object detection methods have evolved from closed-set to open-set paradigms over the years. Current open-set object detectors, however, remain constrained by their exclusive reliance on positive indicators based on given prompts like text descriptions or visual exemplars. This positive-only paradigm
Event cameras are bio-inspired sensors that capture intensity changes asynchronously with distinct advantages, such as high temporal resolution. Existing methods for event-based object/action recognition predominantly sample and convert event representation at every fixed temporal interval (or frequ…
"Fusing an arbitrary number of modalities is vital for achieving robust multi-modal fusion of semantic segmentation yet remains less explored to date. Recent endeavors regard RGB modality as the center and the others as the auxiliary, yielding an asymmetric architecture with two branches. However, t…
Event cameras have recently been shown beneficial for practical vision tasks such as action recognition thanks to their high temporal resolution power efficiency and reduced privacy concerns. However current research is hindered by 1) the difficulty in processing events because of their prolonged du…
Event cameras harness advantages such as low latency, high temporal resolution, and high dynamic range (HDR), compared to standard cameras. Due to the distinct imaging paradigm shift, a dominant line of research focuses on event-to-video (E2V) reconstruction to bridge event-based and standard comput…
We present UniBind a flexible and efficient approach that learns a unified representation space for seven diverse modalities-- images text audio point cloud thermal video and event data. Existing works eg. ImageBind treat the image as the central modality and build an image-centered representation s…