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 311–320 of 4891 papers

TitleStatusHype
Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTMCode1
Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context LearningCode1
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic ModelsCode1
Efficient and Effective Augmentation Strategy for Adversarial TrainingCode1
Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire DetectionCode1
Efficient and Information-Preserving Future Frame Prediction and BeyondCode1
Efficient Aggregated Kernel Tests using Incomplete U-statisticsCode1
Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer ApproachCode1
EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point CloudsCode1
Dynamic-VLM: Simple Dynamic Visual Token Compression for VideoLLMCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified