research
∙
06/13/2023
AVIS: Autonomous Visual Information Seeking with Large Language Models
In this paper, we propose an autonomous information seeking visual quest...
share
research
∙
06/12/2023
Retrieval-Enhanced Contrastive Vision-Text Models
Contrastive image-text models such as CLIP form the building blocks of m...
research
∙
04/11/2023
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data
Retrieval augmented models are becoming increasingly popular for compute...
research
∙
12/10/2022
REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory
In this paper, we propose an end-to-end Retrieval-Augmented Visual Langu...
research
∙
10/10/2022
A Memory Transformer Network for Incremental Learning
We study class-incremental learning, a training setup in which new class...
research
∙
09/08/2022
im2nerf: Image to Neural Radiance Field in the Wild
We propose im2nerf, a learning framework that predicts a continuous neur...
research
∙
05/09/2022
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation
We present Panoptic Neural Fields (PNF), an object-aware neural scene re...
research
∙
04/21/2022
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
Deep learning has recently achieved significant progress in trajectory f...
research
∙
12/15/2020
Object-Centric Neural Scene Rendering
We present a method for composing photorealistic scenes from captured im...
research
∙
09/24/2020
Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection
A common dilemma in 3D object detection for autonomous driving is that h...
research
∙
07/26/2020
Virtual Multi-view Fusion for 3D Semantic Segmentation
Semantic segmentation of 3D meshes is an important problem for 3D scene ...
research
∙
07/24/2020
An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds
Detecting objects in 3D LiDAR data is a core technology for autonomous d...
research
∙
07/20/2020
Pillar-based Object Detection for Autonomous Driving
We present a simple and flexible object detection framework optimized fo...
research
∙
04/02/2020
DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes
We propose DOPS, a fast single-stage 3D object detection method for LIDA...
research
∙
03/30/2020
3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation
We present 3D-MPA, a method for instance segmentation on 3D point clouds...
research
∙
06/16/2019
Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction
We propose 4 insights that help to significantly improve the performance...
research
∙
06/25/2018
Tracking Emerges by Colorizing Videos
We use large amounts of unlabeled video to learn models for visual track...
research
∙
01/03/2018
Instance Embedding Transfer to Unsupervised Video Object Segmentation
We propose a method for unsupervised video object segmentation by transf...
research
∙
07/18/2017
The Devil is in the Decoder
Many machine vision applications require predictions for every pixel of ...
research
∙
03/30/2017
Semantic Instance Segmentation via Deep Metric Learning
We propose a new method for semantic instance segmentation, by first com...
research
∙
11/30/2016
Speed/accuracy trade-offs for modern convolutional object detectors
The goal of this paper is to serve as a guide for selecting a detection ...
research
∙
06/23/2014
VideoSET: Video Summary Evaluation through Text
In this paper we present VideoSET, a method for Video Summary Evaluation...
research
∙
12/15/2013
An introduction to synchronous self-learning Pareto strategy
In last decades optimization and control of complex systems that possess...
research
∙
12/14/2013