6 accepted papers
Image quality assessment (IQA) is inherently multi-mage quality assessment (IQA) is inherently multi-dimensional, yet existing reward models are typically limited to a single task and become unstable when extended to multi-task settings. In particular, heterogeneous reward scales and variances acros…
Image compression methods are usually optimized isolatedly for human perception or machine analysis tasks. We reveal fundamental commonalities between these objectives: preserving accurate semantic information is paramount, as it directly dictates the integrity of critical information for intelligen…
Image compression techniques typically focus on compressing rectangular images for human consumption, however, resulting in transmitting redundant content for downstream applications. To overcome this limitation, some previous works propose to semantically structure the bitstream, which can meet spe…
"Image Coding for Machines (ICM) aims to compress images for AI tasks analysis rather than meeting human perception. Learning a kind of feature that is both general (for AI tasks) and compact (for compression) is pivotal for its success. In this paper, we attempt to develop an ICM framework by learn…
Wake word (WW) spotting is challenging in far-field not only because of the interference in signal transmission but also the complexity in acoustic environment. Traditional WW model training requires large amount of in-domain WW-specific data with substantial human annotations. This prevents the mod…
Robotic surgery and other minimally-invasive surgical techniques are an integral part of patient care, and readily yield large amounts of data. Surgical tool motion (kinematic data) contains information that is useful for assessment and education. Typically, assessment and education tools that rely