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Found 8 papers, 3 papers with code
Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control
We introduce Cosmos-Transfer, a conditional world generation model that can generate world simulations based on multiple spatial control inputs of various modalities such as segmentation, depth, and edge.
Multi-Dimensional Pruning: Joint Channel, Layer and Block Pruning with Latency Constraint
We develop a latency modeling technique that accurately captures model-wide latency variations during pruning, which is crucial for achieving an optimal latency-accuracy trade-offs at high pruning ratio.
FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
For 3D object detection, we instantiate this method as FocalFormer3D, a simple yet effective detector that excels at excavating difficult objects and improving prediction recall.
Ranked #8 on
3D Object Detection
on nuScenes
Adaptive Sharpness-Aware Pruning for Robust Sparse Networks
Robustness and compactness are two essential attributes of deep learning models that are deployed in the real world.
End-to-end 3D Tracking with Decoupled Queries
In this work, we present an end-to-end framework for camera-based 3D multi-object tracking, called DQTrack.
AdaViT: Adaptive Tokens for Efficient Vision Transformer
A-ViT achieves this by automatically reducing the number of tokens in vision transformers that are processed in the network as inference proceeds.
Ranked #34 on
Efficient ViTs
on ImageNet-1K (with DeiT-S)
DecomposeMe: Simplifying ConvNets for End-to-End Learning
Deep learning and convolutional neural networks (ConvNets) have been successfully applied to most relevant tasks in the computer vision community.
Motion Estimation via Robust Decomposition with Constrained Rank
To this end we propose the Robust Decomposition with Constrained Rank (RD-CR), a proximal gradient based method that enforces the rank constraints inherent to motion estimation.
