{"list":"other","total":53,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2302.05543","title":"Adding Conditional Control to Text-to-Image Diffusion Models","created":"2023-02-10","cited_by_count":5503},{"id":"2201.12086","title":"BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation","created":"2022-01-28","cited_by_count":4322},{"id":"2012.09841","title":"Taming Transformers for High-Resolution Image Synthesis","created":"2020-12-17","cited_by_count":3253},{"id":"2108.05997","title":"MUSIQ: Multi-scale Image Quality Transformer","created":"2021-08-12","cited_by_count":1081},{"id":"2404.02905","title":"Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction","created":"2024-04-03","cited_by_count":1023},{"id":"2207.12396","title":"Exploring CLIP for Assessing the Look and Feel of Images","created":"2022-07-25","cited_by_count":991},{"id":"2004.07728","title":"Image Quality Assessment: Unifying Structure and Texture Similarity","created":"2020-04-16","cited_by_count":811},{"id":"2305.07015","title":"Exploiting Diffusion Prior for Real-World Image Super-Resolution","created":"2023-05-11","cited_by_count":489},{"id":"2312.00858","title":"DeepCache: Accelerating Diffusion Models for Free","created":"2023-12-01","cited_by_count":359},{"id":"2006.09965","title":"High-Fidelity Generative Image Compression","created":"2020-06-17","cited_by_count":338},{"id":"1812.00101","title":"DVC: An End-to-end Deep Video Compression Framework","created":"2018-11-30","cited_by_count":332},{"id":"2311.16518","title":"SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution","created":"2023-11-27","cited_by_count":315},{"id":"2306.03514","title":"Recognize Anything: A Strong Image Tagging Model","created":"2023-06-06","cited_by_count":309},{"id":"2203.10886","title":"ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding","created":"2022-03-21","cited_by_count":301},{"id":"2211.15736","title":"Post-training Quantization on Diffusion Models","created":"2022-11-28","cited_by_count":267},{"id":"1901.07821","title":"Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff","created":"2019-01-23","cited_by_count":211},{"id":"2305.10924","title":"Structural Pruning for Diffusion Models","created":"2023-05-18","cited_by_count":195},{"id":"2209.06950","title":"Lossy Image Compression with Conditional Diffusion Models","created":"2022-09-14","cited_by_count":170},{"id":"2310.10325","title":"Towards image compression with perfect realism at ultra-low bitrates","created":"2023-10-16","cited_by_count":103},{"id":"2301.11189","title":"Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models","created":"2023-01-26","cited_by_count":94},{"id":"2206.08889","title":"Lossy Compression with Gaussian Diffusion","created":"2022-06-17","cited_by_count":90},{"id":"2212.13824","title":"Multi-Realism Image Compression with a Conditional Generator","created":"2022-12-28","cited_by_count":80},{"id":"2307.01944","title":"Text + Sketch: Image Compression at Ultra Low Rates","created":"2023-07-04","cited_by_count":67},{"id":"2404.18820","title":"Towards Extreme Image Compression with Latent Feature Guidance and Diffusion Prior","created":"2024-04-29","cited_by_count":60},{"id":"2002.03370","title":"A Unified End-to-End Framework for Efficient Deep Image Compression","created":"2020-02-09","cited_by_count":58},{"id":"2307.15421","title":"MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression","created":"2023-07-28","cited_by_count":58},{"id":"2310.16387","title":"Frequency-Aware Transformer for Learned Image Compression","created":"2023-10-25","cited_by_count":58},{"id":"2302.02412","title":"Mixture of Diffusers for scene composition and high resolution image generation","created":"2023-02-05","cited_by_count":56},{"id":"2106.10311","title":"Universal Rate-Distortion-Perception Representations for Lossy Compression","created":"2021-06-18","cited_by_count":47},{"id":"2305.18723","title":"Towards Accurate Post-training Quantization for Diffusion Models","created":"2023-05-30","cited_by_count":47},{"id":"2301.05489","title":"A Residual Diffusion Model for High Perceptual Quality Codec Augmentation","created":"2023-01-13","cited_by_count":35},{"id":"2110.12805","title":"Algorithms for the Communication of Samples","created":"2021-10-25","cited_by_count":34},{"id":"2506.21977","title":"StableCodec: Taming One-Step Diffusion for Extreme Image Compression","created":"2025-06-27","cited_by_count":33},{"id":"2402.16749","title":"MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model","created":"2024-02-26","cited_by_count":30},{"id":"2502.01189","title":"Compressed Image Generation with Denoising Diffusion Codebook Models","created":"2025-02-03","cited_by_count":28},{"id":"2505.16091","title":"OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates","created":"2025-05-22","cited_by_count":26},{"id":"2501.09815","title":"Lossy Compression with Pretrained Diffusion Models","created":"2025-01-16","cited_by_count":25},{"id":"2410.02640","title":"RDEIC: Accelerating Diffusion-Based Extreme Image Compression with Relay Residual Diffusion","created":"2024-10-03","cited_by_count":23},{"id":"2512.20194","title":"Generative Latent Coding for Ultra-Low Bitrate Image Compression","created":"2025-12-23","cited_by_count":23},{"id":"2503.01428","title":"DLF: Extreme Image Compression with Dual-generative Latent Fusion","created":"2025-03-03","cited_by_count":21},{"id":"2505.08281","title":"Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion","created":"2025-05-13","cited_by_count":20},{"id":"2410.04847","title":"Causal Context Adjustment Loss for Learned Image Compression","created":"2024-10-07","cited_by_count":19},{"id":"2406.00758","title":"Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaptation","created":"2024-06-02","cited_by_count":16},{"id":"2404.13372","title":"HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression","created":"2024-04-20","cited_by_count":14},{"id":"2206.10082","title":"Optimally Controllable Perceptual Lossy Compression","created":"2022-06-21","cited_by_count":13},{"id":"2412.00505","title":"Good, Cheap, and Fast: Overfitted Image Compression with Wasserstein Distortion","created":"2024-11-30","cited_by_count":13},{"id":"2504.02579","title":"Bridging the Gap between Gaussian Diffusion Models and Universal Quantization for Image Compression","created":"2025-04-03","cited_by_count":8},{"id":"2412.11379","title":"Controllable Distortion-Perception Tradeoff Through Latent Diffusion for Neural Image Compression","created":"2024-12-16","cited_by_count":6},{"id":"2505.22438","title":"Synonymous Variational Inference for Perceptual Image Compression","created":"2025-05-28","cited_by_count":4}],"next_cursor":null}