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Found 11 papers, 6 papers with code
ManipDreamer: Boosting Robotic Manipulation World Model with Action Tree and Visual Guidance
Based on the action tree and visual guidance, ManipDreamer significantly boosts the instruction-following ability and visual quality.
MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders
Visual encoders are fundamental components in vision-language models (VLMs), each showcasing unique strengths derived from various pre-trained visual foundation models.
EMD: Explicit Motion Modeling for High-Quality Street Gaussian Splatting
To address this, we propose Explicit Motion Decomposition (EMD), which models the motions of dynamic objects by introducing learnable motion embeddings to the Gaussians, enhancing the decomposition in street scenes.
Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibration
First, a depth estimation (DE) scheme leverages relative depth information to realize the effective feature lifting from 2D to 3D spaces.
SupFusion: Supervised LiDAR-Camera Fusion for 3D Object Detection
In this paper, we propose a novel training strategy called SupFusion, which provides an auxiliary feature level supervision for effective LiDAR-Camera fusion and significantly boosts detection performance.
Lift3D: Synthesize 3D Training Data by Lifting 2D GAN to 3D Generative Radiance Field
This work explores the use of 3D generative models to synthesize training data for 3D vision tasks.
RepVGG: Making VGG-style ConvNets Great Again
We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology.
Ranked #49 on
Semantic Segmentation
on Cityscapes val
Activate or Not: Learning Customized Activation
We present a simple, effective, and general activation function we term ACON which learns to activate the neurons or not.
Funnel Activation for Visual Recognition
We present a conceptually simple but effective funnel activation for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by adding a negligible overhead of spatial condition.
WeightNet: Revisiting the Design Space of Weight Networks
WeightNet is easy and memory-conserving to train, on the kernel space instead of the feature space.
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Datasets, Transforms and Models specific to Computer Vision
Ranked #948 on
Image Classification
on ImageNet
