In this paper, we propose an autonomous information seeking visual quest...
Contrastive image-text models such as CLIP form the building blocks of m...
Retrieval augmented models are becoming increasingly popular for compute...
In this paper, we propose an end-to-end Retrieval-Augmented Visual Langu...
We study class-incremental learning, a training setup in which new class...
We propose im2nerf, a learning framework that predicts a continuous neur...
We present Panoptic Neural Fields (PNF), an object-aware neural scene
re...
Deep learning has recently achieved significant progress in trajectory
f...
We present a method for composing photorealistic scenes from captured im...
A common dilemma in 3D object detection for autonomous driving is that
h...
Semantic segmentation of 3D meshes is an important problem for 3D scene
...
Detecting objects in 3D LiDAR data is a core technology for autonomous
d...
We present a simple and flexible object detection framework optimized fo...
We propose DOPS, a fast single-stage 3D object detection method for LIDA...
We present 3D-MPA, a method for instance segmentation on 3D point clouds...
We propose 4 insights that help to significantly improve the performance...
We use large amounts of unlabeled video to learn models for visual track...
We propose a method for unsupervised video object segmentation by
transf...
Many machine vision applications require predictions for every pixel of ...
We propose a new method for semantic instance segmentation, by first
com...
The goal of this paper is to serve as a guide for selecting a detection
...
In this paper we present VideoSET, a method for Video Summary Evaluation...
In last decades optimization and control of complex systems that possess...
Bio inspiration is a branch of artificial simulation science that shows
...