activity
20222025
most citedMobileCodec: Neural Inter-frame Video Compression on Mobile Devices

4 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2025

The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results

Qiuyu Chen, Xin Jin, Yue Song +45

This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop a…

cs.CV2025

Controllable 3D Placement of Objects with Scene-Aware Diffusion Models

Mohamed Omran, Dimitris Kalatzis, Jens Petersen +2

Image editing approaches have become more powerful and flexible with the advent of powerful text-conditioned generative models. However, placing objects in an environment with a pr…

cs.CV2025

Scene-Aware Location Modeling for Data Augmentation in Automotive Object Detection

Jens Petersen, Davide Abati, Amirhossein Habibian +1

Generative image models are increasingly being used for training data augmentation in vision tasks. In the context of automotive object detection, methods usually focus on producin…

cs.CV2025

Gaussian Splatting is an Effective Data Generator for 3D Object Detection

Farhad G. Zanjani, Davide Abati, Auke Wiggers +4

We investigate data augmentation for 3D object detection in autonomous driving. We utilize recent advancements in 3D reconstruction based on Gaussian Splatting for 3D object placem…

eess.IV2023

MobileNVC: Real-time 1080p Neural Video Compression on a Mobile Device

Ties van Rozendaal, Tushar Singhal, Hoang Le +10

Neural video codecs have recently become competitive with standard codecs such as HEVC in the low-delay setting. However, most neural codecs are large floating-point networks that…

cs.CV20224 cited

MobileCodec: Neural Inter-frame Video Compression on Mobile Devices

Hoang Le, Liang Zhang, Amir Said +6

Realizing the potential of neural video codecs on mobile devices is a big technological challenge due to the computational complexity of deep networks and the power-constrained mob…