compression.md

Compression

• (arXiv 2021.10) Accelerating Framework of Transformer by hardware Design and Model Compression Co-Optimization, [Paper]
• (arXiv 2021.11) Transformer-based Image Compression, [Paper]
• (arXiv 2021.12) Towards End-to-End Image Compression and Analysis with Transformers, [Paper], [Code]
• (arXiv 2021.12) CSformer: Bridging Convolution and Transformer for Compressive Sensing, [Paper]
• (arXiv 2022.01) Multi-Dimensional Model Compression of Vision Transformer, [Paper]
• (arXiv 2022.02) Entroformer: A Transformer-based Entropy Model for Learned Image Compression, [Paper], [Code]
• (arXiv 2022.03) Unified Visual Transformer Compression, [Paper], [Code]
• (arXiv 2022.03) Transformer Compressed Sensing via Global Image Tokens, [Paper], [supplementary]
• (arXiv 2022.03) Vision Transformer Compression with Structured Pruning and Low Rank Approximation, [Paper]
• (arXiv 2022.04) Searching Intrinsic Dimensions of Vision Transformers, [Paper]
• (arXiv 2022.04) Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging, [Paper]
• (arXiv 2022.06) VCT: A Video Compression Transformer, [Paper], [Code]
• (arXiv 2022.07) TransCL: Transformer Makes Strong and Flexible Compressive Learning, [Paper], [Code]
• (arXiv 2022.08) Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation, [Paper], [Code]
• (arXiv 2022.08) Unified Normalization for Accelerating and Stabilizing Transformers, [Paper], [Code]
• (arXiv 2022.09) Uformer-ICS: A Specialized U-Shaped Transformer for Image Compressive Sensing, [Paper]
• (arXiv 2022.09) Attacking Compressed Vision Transformers, [Paper]
• (arXiv 2023.01) GOHSP: A Unified Framework of Graph and Optimization-based Heterogeneous Structured Pruning for Vision Transformer, [Paper]
• (arXiv 2023.03) SeiT: Storage-Efficient Vision Training with Tokens Using 1% of Pixel Storage, [Paper], [Code]
• (arXiv 2023.03) Learned Image Compression with Mixed Transformer-CNN Architectures, [Paper], [Code]
• (arXiv 2023.04) Optimization-Inspired Cross-Attention Transformer for Compressive Sensing, [Paper], [Code]
• (arXiv 2023.05) ROI-based Deep Image Compression with Swin Transformers, [Paper]
• (arXiv 2023.05) Transformer-based Variable-rate Image Compression with Region-of-interest Control, [Paper]
• (arXiv 2023.06) Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in Learned Image Compression, [Paper]
• (arXiv 2023.07) AICT: An Adaptive Image Compression Transformer, [Paper]
• (arXiv 2023.07) JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer, [Paper]
• (arXiv 2023.09) Compressing Vision Transformers for Low-Resource Visual Learning, [Paper]
• (arXiv 2023.09) CAIT: Triple-Win Compression towards High Accuracy, Fast Inference, and Favorable Transferability For ViTs, [Paper]
• (arXiv 2023.10) USDC: Unified Static and Dynamic Compression for Visual Transformer, [Paper]
• (arXiv 2023.10) Frequency-Aware Transformer for Learned Image Compression, [Paper]
• (arXiv 2023.11) White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is, [Paper], [Code]
• (arXiv 2023.11) Corner-to-Center Long-range Context Model for Efficient Learned Image Compression, [Paper]
• (arXiv 2023.12) Input Compression with Positional Consistency for Efficient Training and Inference of Transformer Neural Networks, [Paper], [Code]
• (arXiv 2024.01) UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer, [Paper]
• (arXiv 2024.02) Memory-Efficient Vision Transformers: An Activation-Aware Mixed-Rank Compression Strategy, [Paper]
• (arXiv 2024.03) Unifying Generation and Compression: Ultra-low bitrate Image Coding Via Multi-stage Transformer, [Paper]
• (arXiv 2024.03) Content-aware Masked Image Modeling Transformer for Stereo Image Compression, [Paper]
• (arXiv 2024.03) Dense Vision Transformer Compression with Few Samples, [Paper]
• (arXiv 2024.06) ReduceFormer: Attention with Tensor Reduction by Summation, [Paper]
• (arXiv 2024.08) Bi-Level Spatial and Channel-aware Transformer for Learned Image Compression, [Paper]
• (arXiv 2024.12) Efficient Semantic Communication Through Transformer-Aided Compression, [Paper]
• (arXiv 2025.07) MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression, [Paper]
• (arXiv 2025.08) Context Guided Transformer Entropy Modeling for Video Compression, [Paper]
• (arXiv 2025.09) Communication Efficient Split Learning of ViTs with Attention-based Double Compression, [Paper]
• (arXiv 2025.10) Variable Rate Image Compression via N-Gram Context based Swin-transformer, [Paper]