Search Results for author:
Found 9 papers, 7 papers with code
InteractiveVideo: User-Centric Controllable Video Generation with Synergistic Multimodal Instructions
We introduce $\textit{InteractiveVideo}$, a user-centric framework for video generation.
Multimodal Pathway: Improve Transformers with Irrelevant Data from Other Modalities
We propose to improve transformers of a specific modality with irrelevant data from other modalities, e. g., improve an ImageNet model with audio or point cloud datasets.
Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors
Recently, researchers have attempted to improve the genuineness of 3D objects by directly training on 3D datasets, albeit at the cost of low-quality texture generation due to the limited texture diversity in 3D datasets.
OneLLM: One Framework to Align All Modalities with Language
In detail, we first train an image projection module to connect a vision encoder with LLM.
Ranked #79 on
Visual Question Answering
on MM-Vet
Online Vectorized HD Map Construction using Geometry
In our work, we propose GeMap ($\textbf{Ge}$ometry $\textbf{Map}$), which end-to-end learns Euclidean shapes and relations of map instances beyond basic perception.
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition
1) We propose four architectural guidelines for designing large-kernel ConvNets, the core of which is to exploit the essential characteristics of large kernels that distinguish them from small kernels - they can see wide without going deep.
Ranked #1 on
Object Detection
on COCO 2017
(mAP metric)
Towards Unified and Effective Domain Generalization
We propose $\textbf{UniDG}$, a novel and $\textbf{Uni}$fied framework for $\textbf{D}$omain $\textbf{G}$eneralization that is capable of significantly enhancing the out-of-distribution generalization performance of foundation models regardless of their architectures.
Ranked #1 on
Domain Generalization
on TerraIncognita
Deep Reinforcement Learning for Artificial Upwelling Energy Management
The potential of artificial upwelling (AU) as a means of lifting nutrient-rich bottom water to the surface, stimulating seaweed growth, and consequently enhancing ocean carbon sequestration, has been gaining increasing attention in recent years.
Meta-Transformer: A Unified Framework for Multimodal Learning
Multimodal learning aims to build models that can process and relate information from multiple modalities.
