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Found 9 papers, 3 papers with code
NeRFCodec: Neural Feature Compression Meets Neural Radiance Fields for Memory-Efficient Scene Representation
The emergence of Neural Radiance Fields (NeRF) has greatly impacted 3D scene modeling and novel-view synthesis.
ECNet: Effective Controllable Text-to-Image Diffusion Models
Secondly, to overcome the issue of limited conditional supervision, we introduce Diffusion Consistency Loss (DCL), which applies supervision on the denoised latent code at any given time step.
DORec: Decomposed Object Reconstruction Utilizing 2D Self-Supervised Features
Decomposing a target object from a complex background while reconstructing is challenging.
SteerNeRF: Accelerating NeRF Rendering via Smooth Viewpoint Trajectory
Neural Radiance Fields (NeRF) have demonstrated superior novel view synthesis performance but are slow at rendering.
Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition
To solve this problem, we present a multi-scale spatial graph convolution (MS-GC) module and a multi-scale temporal graph convolution (MT-GC) module to enrich the receptive field of the model in spatial and temporal dimensions.
MIA-Former: Efficient and Robust Vision Transformers via Multi-grained Input-Adaptation
ViTs are often too computationally expensive to be fitted onto real-world resource-constrained devices, due to (1) their quadratically increased complexity with the number of input tokens and (2) their overparameterized self-attention heads and model depth.
ShiftAddNet: A Hardware-Inspired Deep Network
Multiplication (e. g., convolution) is arguably a cornerstone of modern deep neural networks (DNNs).
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets
Rather than learning a shared global model in classic federated learning, each client learns a personalized model via LotteryFL; the communication cost can be significantly reduced due to the compact size of lottery networks.
MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
Our experiments show that different adversarial strengths, i. e., perturbation levels of adversarial examples, have different working zones to resist the attack.
