211 citations · 327 across the 10 of their papers we have counts for
15 papers · 1 filter
A Memory Transformer Network for Incremental Learning
Ahmet Iscen, Thomas Bird, Mathilde Caron +2
We study class-incremental learning, a training setup in which new classes of data are observed over time for the model to learn from. Despite the straightforward problem formulati…
im2nerf: Image to Neural Radiance Field in the Wild
Lu Mi, Abhijit Kundu, David Ross +3
We propose im2nerf, a learning framework that predicts a continuous neural object representation given a single input image in the wild, supervised by only segmentation output from…
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation
Abhijit Kundu, Kyle Genova, Xiaoqi Yin +6
We present Panoptic Neural Fields (PNF), an object-aware neural scene representation that decomposes a scene into a set of objects (things) and background (stuff). Each object is r…
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
Chenfeng Xu, Tian Li, Chen Tang +5
Deep learning has recently achieved significant progress in trajectory forecasting. However, the scarcity of trajectory data inhibits the data-hungry deep-learning models from lear…
Object-Centric Neural Scene Rendering
Michelle Guo, Alireza Fathi, Jiajun Wu +1
We present a method for composing photorealistic scenes from captured images of objects. Our work builds upon neural radiance fields (NeRFs), which implicitly model the volumetric…
Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection
Yue Wang, Alireza Fathi, Jiajun Wu +2
A common dilemma in 3D object detection for autonomous driving is that high-quality, dense point clouds are only available during training, but not testing. We use knowledge distil…