Search Results for author:
Found 21 papers, 16 papers with code
Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting
Building upon our MVControl architecture, we employ a unique hybrid diffusion guidance method to direct the optimization process.
Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
We categorize Mamba into four roles for modeling videos, deriving a Video Mamba Suite composed of 14 models/modules, and evaluating them on 12 video understanding tasks.
Ranked #1 on
Temporal Action Localization
on FineAction
Improving Group Connectivity for Generalization of Federated Deep Learning
Bridging the gap between LMC and FL, in this paper, we leverage fixed anchor models to empirically and theoretically study the transitivity property of connectivity from two models (LMC) to a group of models (model fusion in FL).
Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusion
In deep learning, stochastic gradient descent often yields functionally similar yet widely scattered solutions in the weight space even under the same initialization, causing barriers in the Linear Mode Connectivity (LMC) landscape.
Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications
The advancements in speed and efficiency of DCNv4, combined with its robust performance across diverse vision tasks, show its potential as a foundational building block for future vision models.
DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving
In this work, we delve into the potential of large language models (LLMs) in autonomous driving (AD).
Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?
We initially observed that the nuScenes dataset, characterized by relatively simple driving scenarios, leads to an under-utilization of perception information in end-to-end models incorporating ego status, such as the ego vehicle's velocity.
ET3D: Efficient Text-to-3D Generation via Multi-View Distillation
The main insight is that we exploit the images generated by a large pre-trained text-to-image diffusion model, to supervise the training of a text conditioned 3D generative adversarial network.
MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation
Our approach enables the generation of controllable multi-view images and view-consistent 3D content.
Swift Parameter-free Attention Network for Efficient Super-Resolution
To address this issue, we propose the Swift Parameter-free Attention Network (SPAN), a highly efficient SISR model that balances parameter count, inference speed, and image quality.
Ranked #37 on
Image Super-Resolution
on Set14 - 4x upscaling
Leveraging Vision-Centric Multi-Modal Expertise for 3D Object Detection
Current research is primarily dedicated to advancing the accuracy of camera-only 3D object detectors (apprentice) through the knowledge transferred from LiDAR- or multi-modal-based counterparts (expert).
Ranked #6 on
3D Object Detection
on nuScenes Camera Only
FB-BEV: BEV Representation from Forward-Backward View Transformations
Currently, the two most prominent VTM paradigms are forward projection and backward projection.
FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation
This technical report summarizes the winning solution for the 3D Occupancy Prediction Challenge, which is held in conjunction with the CVPR 2023 Workshop on End-to-End Autonomous Driving and CVPR 23 Workshop on Vision-Centric Autonomous Driving Workshop.
Ranked #1 on
Prediction Of Occupancy Grid Maps
on Occ3D-nuScenes
PointGame: Geometrically and Adaptively Masked Auto-Encoder on Point Clouds
Self-supervised learning is attracting large attention in point cloud understanding.
RemoteTouch: Enhancing Immersive 3D Video Communication with Hand Touch
A key challenge in emulating the remote hand touch is the realistic rendering of the participant's hand and arm as the hand touches the screen.
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state.
Ranked #1 on
Instance Segmentation
on COCO test-dev
(AP50 metric, using extra
training data)
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.
Federated Learning with Label Distribution Skew via Logits Calibration
Extensive experiments on federated datasets and real-world datasets demonstrate that FedLC leads to a more accurate global model and much improved performance.
BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers
In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV queries.
Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers
Specifically, we supervise the attention modules in the mask decoder in a layer-wise manner.
Ranked #4 on
Panoptic Segmentation
on COCO test-dev
An Introduction of mini-AlphaStar
StarCraft II (SC2) is a real-time strategy game in which players produce and control multiple units to fight against opponent's units.
