Implicit neural representations store videos as neural networks and have performed well for
vision tasks such as video compression and denoising. With frame index and/or positional …

Recognizing and generating object-state compositions has been a challenging task,
especially when generalizing to unseen compositions. In this paper, we study the task of …

Video instance segmentation (VIS) is a challenging vision task that aims to detect segment
and track objects in videos. Conventional VIS methods rely on densely annotated object …

Implicit neural representations (INRs) have emerged as a promising approach for video
storage and processing showing remarkable versatility across various video tasks. However …

Recent advances in implicit neural representation (INR)-based video coding have
demonstrated its potential to compete with both conventional and other learning-based …

Neural representation for video (NeRV), which employs a neural network to parameterize
video signals, introduces a novel methodology in video representations. However, existing …

Abstract Implicit Neural Representations (INR) have recently shown to be powerful tool for
high-quality video compression. However, existing works are limiting as they do not explicitly …

Implicit neural representations (INRs) were recently proposed as a new video compression
paradigm with existing approaches performing on par with HEVC. However such methods …

Video instance segmentation requires classifying segmenting and tracking every object
across video frames. Unlike existing approaches that rely on masks boxes or category labels …

Abstract Learning-based Neural Video Codecs (NVCs) have emerged as a compelling
alternative to standard video codecs, demonstrating promising performance, and simple and …