{"list":"other","total":62,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2312.00752","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","created":"2023-12-01","cited_by_count":4718},{"id":"1711.00937","title":"Neural Discrete Representation Learning","created":"2017-11-02","cited_by_count":4712},{"id":"2111.00396","title":"Efficiently Modeling Long Sequences with Structured State Spaces","created":"2021-10-31","cited_by_count":2465},{"id":"2405.21060","title":"Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality","created":"2024-05-31","cited_by_count":1336},{"id":"2401.10166","title":"VMamba: Visual State Space Model","created":"2024-01-18","cited_by_count":969},{"id":"1802.01436","title":"Variational image compression with a scale hyperprior","created":"2018-02-01","cited_by_count":935},{"id":"2208.04933","title":"Simplified State Space Layers for Sequence Modeling","created":"2022-08-09","cited_by_count":788},{"id":"1611.01704","title":"End-to-end Optimized Image Compression","created":"2016-11-05","cited_by_count":696},{"id":"1809.02736","title":"Joint Autoregressive and Hierarchical Priors for Learned Image Compression","created":"2018-09-08","cited_by_count":664},{"id":"2001.01568","title":"Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules","created":"2020-01-06","cited_by_count":590},{"id":"2312.10035","title":"Point Transformer V3: Simpler, Faster, Stronger","created":"2023-12-15","cited_by_count":589},{"id":"1701.04128","title":"Understanding the Effective Receptive Field in Deep Convolutional Neural Networks","created":"2017-01-15","cited_by_count":553},{"id":"2212.14052","title":"Hungry Hungry Hippos: Towards Language Modeling with State Space Models","created":"2022-12-28","cited_by_count":499},{"id":"2003.05997","title":"Efficient Content-Based Sparse Attention with Routing Transformers","created":"2020-03-12","cited_by_count":471},{"id":"2402.15648","title":"MambaIR: A Simple Baseline for Image Restoration with State-Space Model","created":"2024-02-23","cited_by_count":352},{"id":"2403.06977","title":"VideoMamba: State Space Model for Efficient Video Understanding","created":"2024-03-11","cited_by_count":328},{"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":"2011.03029","title":"CompressAI: a PyTorch library and evaluation platform for end-to-end compression research","created":"2020-11-05","cited_by_count":276},{"id":"2007.08739","title":"Channel-wise Autoregressive Entropy Models for Learned Image Compression","created":"2020-07-17","cited_by_count":264},{"id":"2401.02436","title":"Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis","created":"2023-11-17","cited_by_count":239},{"id":"2303.14978","title":"Learned Image Compression with Mixed Transformer-CNN Architectures","created":"2023-03-27","cited_by_count":211},{"id":"2110.02037","title":"Autoregressive Diffusion Models","created":"2021-10-05","cited_by_count":210},{"id":"2103.15306","title":"Checkerboard Context Model for Efficient Learned Image Compression","created":"2021-03-29","cited_by_count":202},{"id":"2302.14402","title":"Neural Video Compression with Diverse Contexts","created":"2023-02-28","cited_by_count":165},{"id":"2203.08450","title":"The Devil Is in the Details: Window-based Attention for Image Compression","created":"2022-03-16","cited_by_count":147},{"id":"2207.05894","title":"Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression","created":"2022-07-13","cited_by_count":128},{"id":"2202.05492","title":"Entroformer: A Transformer-based Entropy Model for Learned Image Compression","created":"2022-02-11","cited_by_count":107},{"id":"2108.03690","title":"Enhanced Invertible Encoding for Learned Image Compression","created":"2021-08-08","cited_by_count":105},{"id":"2411.15269","title":"MambaIRv2: Attentive State Space Restoration","created":"2024-11-22","cited_by_count":94},{"id":"2211.07273","title":"MLIC: Multi-Reference Entropy Model for Learned Image Compression","created":"2022-11-14","cited_by_count":84},{"id":"2404.09498","title":"FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba","created":"2024-04-15","cited_by_count":81},{"id":"2206.07307","title":"VCT: A Video Compression Transformer","created":"2022-06-15","cited_by_count":74},{"id":"2401.08209","title":"Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary","created":"2024-01-16","cited_by_count":64},{"id":"2111.06707","title":"Transformer-based Image Compression","created":"2021-11-12","cited_by_count":62},{"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":"2404.09476","title":"FreqMamba: Viewing Mamba from a Frequency Perspective for Image Deraining","created":"2024-04-15","cited_by_count":57},{"id":"2312.02753","title":"C3: High-performance and low-complexity neural compression from a single image or video","created":"2023-12-05","cited_by_count":55},{"id":"2212.05458","title":"COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec","created":"2022-12-11","cited_by_count":51},{"id":"2405.16105","title":"MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space","created":"2024-05-25","cited_by_count":48},{"id":"2402.04139","title":"U-shaped Vision Mamba for Single Image Dehazing","created":"2024-02-06","cited_by_count":40},{"id":"2504.00496","title":"Learned Image Compression with Dictionary-based Entropy Model","created":"2025-04-01","cited_by_count":30},{"id":"2403.20106","title":"Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring","created":"2024-03-29","cited_by_count":25},{"id":"2502.05741","title":"Linear Attention Modeling for Learned Image Compression","created":"2025-02-09","cited_by_count":23},{"id":"2507.19125","title":"Learned Image Compression with Hierarchical Progressive Context Modeling","created":"2025-07-25","cited_by_count":21},{"id":"2503.12461","title":"MambaIC: State Space Models for High-Performance Learned Image Compression","created":"2025-03-16","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":"2502.16163","title":"Large Language Model for Lossless Image Compression with Visual Prompts","created":"2025-02-22","cited_by_count":16},{"id":"2308.02620","title":"Frequency Disentangled Features in Neural Image Compression","created":"2023-08-04","cited_by_count":14},{"id":"2307.12706","title":"Low-complexity Overfitted Neural Image Codec","created":"2023-07-24","cited_by_count":13},{"id":"2309.02529","title":"Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation","created":"2023-09-05","cited_by_count":12},{"id":"2501.13751","title":"On Disentangled Training for Nonlinear Transform in Learned Image Compression","created":"2025-01-23","cited_by_count":9},{"id":"2207.04589","title":"Learned Video Compression via Heterogeneous Deformable Compensation Network","created":"2022-07-11","cited_by_count":8},{"id":"2403.00628","title":"Region-Adaptive Transform with Segmentation Prior for Image Compression","created":"2024-03-01","cited_by_count":8},{"id":"2408.02600","title":"BioMamba: Domain-Adaptive Biomedical Language Models","created":"2024-08-05","cited_by_count":8},{"id":"2209.03353","title":"Learned Image Compression with Generalized Octave Convolution and Cross-Resolution Parameter Estimation","created":"2022-09-07","cited_by_count":6},{"id":"2401.11615","title":"Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding","created":"2024-01-21","cited_by_count":6},{"id":"2504.19119","title":"MLICv2: Enhanced Multi-Reference Entropy Modeling for Learned Image Compression","created":"2025-04-27","cited_by_count":6},{"id":"2502.04988","title":"CMamba: Learned Image Compression with State Space Models","created":"2025-02-07","cited_by_count":5},{"id":"2410.02981","title":"GABIC: Graph-based Attention Block for Image Compression","created":"2024-10-03","cited_by_count":4}],"next_cursor":null}