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Efficient Computing

179 directly classified papers

Papers per year

Papers

Mixture of Nested Experts: Adaptive Processing of Visual Tokens NIPS 2024

Improving Robustness of 3D Point Cloud Recognition from a Fourier Perspective NIPS 2024

Real-time Core-Periphery Guided ViT with Smart Data Layout Selection on Mobile Devices NIPS 2024

LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition AAAI 2024

Bootstrapping SparseFormers from Vision Foundation Models CVPR 2024

SQAD: Automatic Smartphone Camera Quality Assessment and Benchmarking ICCV 2023

Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming ICML 2023

Quality-Aware Pre-Trained Models for Blind Image Quality Assessment CVPR 2023

Slide-Transformer: Hierarchical Vision Transformer With Local Self-Attention CVPR 2023

Efficient and Explicit Modelling of Image Hierarchies for Image Restoration CVPR 2023

SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer CVPR 2023

I-ViT: Integer-only Quantization for Efficient Vision Transformer Inference ICCV 2023

ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile Devices ICCV 2023

BiViT: Extremely Compressed Binary Vision Transformers ICCV 2023

Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks ICCV 2023

EfficientViT: Memory Efficient Vision Transformer With Cascaded Group Attention CVPR 2023

Efficient Beam Tree Recursion NIPS 2023

GOHSP: A Unified Framework of Graph and Optimization-Based Heterogeneous Structured Pruning for Vision Transformer AAAI 2023

Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient Vision Transformers CVPR 2023

RGB No More: Minimally-Decoded JPEG Vision Transformers CVPR 2023

RIFormer: Keep Your Vision Backbone Effective but Removing Token Mixer CVPR 2023

Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation CVPR 2023

NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers CVPR 2023

Global Vision Transformer Pruning With Hessian-Aware Saliency CVPR 2023

Your representations are in the network: composable and parallel adaptation for large scale models NIPS 2023