SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

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Showing 281–290 of 4891 papers

TitleStatusHype
EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point CloudsCode1
TriLiteNet: Lightweight Model for Multi-Task Visual PerceptionCode1
GS-I^3: Gaussian Splatting for Surface Reconstruction from Illumination-Inconsistent ImagesCode1
3D Gaussian Splatting against Moving Objects for High-Fidelity Street Scene ReconstructionCode1
MMS-LLaMA: Efficient LLM-based Audio-Visual Speech Recognition with Minimal Multimodal Speech TokensCode1
CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal ControlCode1
STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive ApplicationsCode1
SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language ModelsCode1
M^3amba: CLIP-driven Mamba Model for Multi-modal Remote Sensing ClassificationCode1
FastMap: Fast Queries Initialization Based Vectorized HD Map Reconstruction FrameworkCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified