{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/diffusion/papers/19","list_of":"/method/diffusion","method":"Diffusion","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":19,"pages_in_order":139,"rows_per_page":100,"rows":[1801,1900],"of":13848,"counts":{"archive_papers_tagged":13848,"with_a_code_link":5365,"where_syntology_ran_a_sample":2249,"not_listed_spam_title":0,"listed":13848,"listed_where_code_ran":2249,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1969,"every_run_a_failure_of_syntologys_instrument":280,"listed_with_a_run_with_no_instrument_failure":1969,"listed_every_run_a_failure_of_syntologys_instrument":280,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/diffusion","prev":"/method/diffusion/papers/18","next":"/method/diffusion/papers/20","papers":[{"paper":null,"slug":"cinnamon-a-hybrid-approach-to-change-point","title":"CINNAMON: A hybrid approach to change point detection and parameter estimation in single-particle tracking data","date":"2025-03-18","arxiv_id":"2503.14253","n_code_links":0,"syntology":null},{"paper":null,"slug":"colson-controllable-learning-based-social","title":"COLSON: Controllable Learning-Based Social Navigation via Diffusion-Based Reinforcement Learning","date":"2025-03-18","arxiv_id":"2503.13934","n_code_links":0,"syntology":null},{"paper":null,"slug":"crce-coreference-retention-concept-erasure-in","title":"CRCE: Coreference-Retention Concept Erasure in Text-to-Image Diffusion Models","date":"2025-03-18","arxiv_id":"2503.14232","n_code_links":0,"syntology":null},{"paper":null,"slug":"ctsr-controllable-fidelity-realness-trade-off","title":"CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution","date":"2025-03-18","arxiv_id":"2503.14272","n_code_links":0,"syntology":null},{"paper":null,"slug":"defectfill-realistic-defect-generation-with","title":"DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection","date":"2025-03-18","arxiv_id":"2503.13985","n_code_links":0,"syntology":null},{"paper":"/paper/diffmoe-dynamic-token-selection-for-scalable","slug":"diffmoe-dynamic-token-selection-for-scalable","title":"DiffMoE: Dynamic Token Selection for Scalable Diffusion Transformers","date":"2025-03-18","arxiv_id":"2503.14487","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffusion-based-facial-aesthetics-enhancement","title":"Diffusion-based Facial Aesthetics Enhancement with 3D Structure Guidance","date":"2025-03-18","arxiv_id":"2503.14402","n_code_links":0,"syntology":null},{"paper":"/paper/diffvsgg-diffusion-driven-online-video-scene","slug":"diffvsgg-diffusion-driven-online-video-scene","title":"DIFFVSGG: Diffusion-Driven Online Video Scene Graph Generation","date":"2025-03-18","arxiv_id":"2503.13957","n_code_links":1,"syntology":null},{"paper":null,"slug":"empirical-risk-minimization-algorithm-for","title":"Empirical risk minimization algorithm for multiclass classification of S.D.E. paths","date":"2025-03-18","arxiv_id":"2503.14045","n_code_links":0,"syntology":null},{"paper":null,"slug":"free-lunch-color-texture-disentanglement-for","title":"Free-Lunch Color-Texture Disentanglement for Stylized Image Generation","date":"2025-03-18","arxiv_id":"2503.14275","n_code_links":0,"syntology":null},{"paper":"/paper/fusdreamer-label-efficient-remote-sensing","slug":"fusdreamer-label-efficient-remote-sensing","title":"FusDreamer: Label-efficient Remote Sensing World Model for Multimodal Data Classification","date":"2025-03-18","arxiv_id":"2503.13814","n_code_links":1,"syntology":null},{"paper":null,"slug":"gr00t-n1-an-open-foundation-model-for","title":"GR00T N1: An Open Foundation Model for Generalist Humanoid Robots","date":"2025-03-18","arxiv_id":"2503.14734","n_code_links":0,"syntology":null},{"paper":"/paper/leanvae-an-ultra-efficient-reconstruction-vae","slug":"leanvae-an-ultra-efficient-reconstruction-vae","title":"LeanVAE: An Ultra-Efficient Reconstruction VAE for Video Diffusion Models","date":"2025-03-18","arxiv_id":"2503.14325","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["westlake-repl/leanvae"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"less-is-more-improving-motion-diffusion","title":"Less is More: Improving Motion Diffusion Models with Sparse Keyframes","date":"2025-03-18","arxiv_id":"2503.13859","n_code_links":0,"syntology":null},{"paper":null,"slug":"lux-post-facto-learning-portrait-performance","title":"Lux Post Facto: Learning Portrait Performance Relighting with Conditional Video Diffusion and a Hybrid Dataset","date":"2025-03-18","arxiv_id":"2503.14485","n_code_links":0,"syntology":null},{"paper":null,"slug":"mag-multi-modal-aligned-autoregressive-co","title":"MAG: Multi-Modal Aligned Autoregressive Co-Speech Gesture Generation without Vector Quantization","date":"2025-03-18","arxiv_id":"2503.14040","n_code_links":0,"syntology":null},{"paper":null,"slug":"magiccomp-training-free-dual-phase-refinement","title":"MagicComp: Training-free Dual-Phase Refinement for Compositional Video Generation","date":"2025-03-18","arxiv_id":"2503.14428","n_code_links":0,"syntology":null},{"paper":null,"slug":"make-the-most-of-everything-further","title":"Make the Most of Everything: Further Considerations on Disrupting Diffusion-based Customization","date":"2025-03-18","arxiv_id":"2503.13945","n_code_links":0,"syntology":null},{"paper":null,"slug":"musicinfuser-making-video-diffusion-listen","title":"MusicInfuser: Making Video Diffusion Listen and Dance","date":"2025-03-18","arxiv_id":"2503.14505","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-weak-notions-of-no-arbitrage-in-a-1d","title":"On weak notions of no-arbitrage in a 1D general diffusion market with interest rates","date":"2025-03-18","arxiv_id":"2503.14078","n_code_links":0,"syntology":null},{"paper":null,"slug":"revealing-higher-order-neural-representations","title":"Revealing higher-order neural representations of uncertainty with the Noise Estimation through Reinforcement-based Diffusion (NERD) model","date":"2025-03-18","arxiv_id":"2503.14333","n_code_links":0,"syntology":null},{"paper":null,"slug":"rfmi-estimating-mutual-information-on","title":"RFMI: Estimating Mutual Information on Rectified Flow for Text-to-Image Alignment","date":"2025-03-18","arxiv_id":"2503.14358","n_code_links":0,"syntology":null},{"paper":"/paper/salad-skeleton-aware-latent-diffusion-for","slug":"salad-skeleton-aware-latent-diffusion-for","title":"SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and Editing","date":"2025-03-18","arxiv_id":"2503.13836","n_code_links":1,"syntology":null},{"paper":null,"slug":"sir-diff-sparse-image-sets-restoration-with","title":"SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model","date":"2025-03-18","arxiv_id":"2503.14463","n_code_links":0,"syntology":null},{"paper":null,"slug":"sketchfusion-learning-universal-sketch","title":"SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models","date":"2025-03-18","arxiv_id":"2503.14129","n_code_links":0,"syntology":null},{"paper":null,"slug":"stable-virtual-camera-generative-view","title":"Stable Virtual Camera: Generative View Synthesis with Diffusion Models","date":"2025-03-18","arxiv_id":"2503.14489","n_code_links":0,"syntology":null},{"paper":null,"slug":"stochastic-trajectory-prediction-under","title":"Stochastic Trajectory Prediction under Unstructured Constraints","date":"2025-03-18","arxiv_id":"2503.14203","n_code_links":0,"syntology":null},{"paper":null,"slug":"superpc-a-single-diffusion-model-for-point","title":"SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization","date":"2025-03-18","arxiv_id":"2503.14558","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-power-of-context-how-multimodality","title":"The Power of Context: How Multimodality Improves Image Super-Resolution","date":"2025-03-18","arxiv_id":"2503.14503","n_code_links":0,"syntology":null},{"paper":null,"slug":"theoretical-foundation-of-flow-based-time","title":"Theoretical Foundation of Flow-Based Time Series Generation: Provable Approximation, Generalization, and Efficiency","date":"2025-03-18","arxiv_id":"2503.14076","n_code_links":0,"syntology":null},{"paper":null,"slug":"unified-analysis-of-decentralized-gradient","title":"Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Framework","date":"2025-03-18","arxiv_id":"2503.14353","n_code_links":0,"syntology":null},{"paper":null,"slug":"veggie-instructional-editing-and-reasoning","title":"VEGGIE: Instructional Editing and Reasoning Video Concepts with Grounded Generation","date":"2025-03-18","arxiv_id":"2503.14350","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comprehensive-survey-on-visual-concept","title":"A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models","date":"2025-03-17","arxiv_id":"2503.13576","n_code_links":0,"syntology":null},{"paper":null,"slug":"anatomically-and-metabolically-informed","title":"Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging","date":"2025-03-17","arxiv_id":"2503.13257","n_code_links":0,"syntology":null},{"paper":null,"slug":"ar-1-to-3-single-image-to-consistent-3d","title":"AR-1-to-3: Single Image to Consistent 3D Object Generation via Next-View Prediction","date":"2025-03-17","arxiv_id":"2503.12929","n_code_links":0,"syntology":null},{"paper":null,"slug":"asmr-adaptive-skeleton-mesh-rigging-and","title":"ASMR: Adaptive Skeleton-Mesh Rigging and Skinning via 2D Generative Prior","date":"2025-03-17","arxiv_id":"2503.13579","n_code_links":0,"syntology":null},{"paper":null,"slug":"blobctrl-a-unified-and-flexible-framework-for","title":"BlobCtrl: A Unified and Flexible Framework for Element-level Image Generation and Editing","date":"2025-03-17","arxiv_id":"2503.13434","n_code_links":0,"syntology":null},{"paper":"/paper/dtgbrepgen-a-novel-b-rep-generative-model","slug":"dtgbrepgen-a-novel-b-rep-generative-model","title":"DTGBrepGen: A Novel B-rep Generative Model through Decoupling Topology and Geometry","date":"2025-03-17","arxiv_id":"2503.13110","n_code_links":1,"syntology":{"ran":5,"of":8,"n_ran_checked":5,"n_instrument":0,"unverified":3,"pointer_only":8,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["jinli99/dtgbrepgen"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/five-a-fine-grained-video-editing-benchmark","slug":"five-a-fine-grained-video-editing-benchmark","title":"FiVE: A Fine-grained Video Editing Benchmark for Evaluating Emerging Diffusion and Rectified Flow Models","date":"2025-03-17","arxiv_id":"2503.13684","n_code_links":0,"syntology":null},{"paper":null,"slug":"flexworld-progressively-expanding-3d-scenes","title":"FlexWorld: Progressively Expanding 3D Scenes for Flexiable-View Synthesis","date":"2025-03-17","arxiv_id":"2503.13265","n_code_links":0,"syntology":null},{"paper":"/paper/fnse-sbgan-far-field-speech-enhancement-with","slug":"fnse-sbgan-far-field-speech-enhancement-with","title":"FNSE-SBGAN: Far-field Speech Enhancement with Schrodinger Bridge and Generative Adversarial Networks","date":"2025-03-17","arxiv_id":"2503.12936","n_code_links":1,"syntology":null},{"paper":null,"slug":"from-head-to-tail-towards-balanced","title":"From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration","date":"2025-03-17","arxiv_id":"2503.12821","n_code_links":0,"syntology":null},{"paper":"/paper/genstereo-towards-open-world-generation-of","slug":"genstereo-towards-open-world-generation-of","title":"GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised Matching","date":"2025-03-17","arxiv_id":"2503.12720","n_code_links":1,"syntology":null},{"paper":null,"slug":"let-synthetic-data-shine-domain-reassembly","title":"Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization","date":"2025-03-17","arxiv_id":"2503.13617","n_code_links":0,"syntology":null},{"paper":null,"slug":"magicdistillation-weak-to-strong-video","title":"MagicDistillation: Weak-to-Strong Video Distillation for Large-Scale Few-Step Synthesis","date":"2025-03-17","arxiv_id":"2503.13319","n_code_links":0,"syntology":null},{"paper":null,"slug":"mitigating-spectral-bias-in-neural-operators","title":"Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems","date":"2025-03-17","arxiv_id":"2503.13695","n_code_links":0,"syntology":null},{"paper":"/paper/next-scale-autoregressive-models-are-zero","slug":"next-scale-autoregressive-models-are-zero","title":"Next-Scale Autoregressive Models are Zero-Shot Single-Image Object View Synthesizers","date":"2025-03-17","arxiv_id":"2503.13588","n_code_links":1,"syntology":null},{"paper":"/paper/sampling-decisions","slug":"sampling-decisions","title":"Sampling Decisions","date":"2025-03-17","arxiv_id":"2503.14549","n_code_links":1,"syntology":null},{"paper":null,"slug":"sed-mvs-segmentation-driven-and-edge-aligned","title":"SED-MVS: Segmentation-Driven and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint","date":"2025-03-17","arxiv_id":"2503.13721","n_code_links":0,"syntology":null},{"paper":null,"slug":"seisrdt-latent-diffusion-model-based-on","title":"SeisRDT: Latent Diffusion Model Based On Representation Learning For Seismic Data Interpolation And Reconstruction","date":"2025-03-17","arxiv_id":"2503.21791","n_code_links":0,"syntology":null},{"paper":null,"slug":"textinvision-text-and-prompt-complexity","title":"TextInVision: Text and Prompt Complexity Driven Visual Text Generation Benchmark","date":"2025-03-17","arxiv_id":"2503.13730","n_code_links":0,"syntology":null},{"paper":"/paper/unlock-pose-diversity-accurate-and-efficient","slug":"unlock-pose-diversity-accurate-and-efficient","title":"Unlock Pose Diversity: Accurate and Efficient Implicit Keypoint-based Spatiotemporal Diffusion for Audio-driven Talking Portrait","date":"2025-03-17","arxiv_id":"2503.12963","n_code_links":1,"syntology":null},{"paper":null,"slug":"vastsd-learning-3d-vascular-tree-state-space","title":"VasTSD: Learning 3D Vascular Tree-state Space Diffusion Model for Angiography Synthesis","date":"2025-03-17","arxiv_id":"2503.12758","n_code_links":0,"syntology":null},{"paper":null,"slug":"business-entity-entropy","title":"Business Entity Entropy","date":"2025-03-16","arxiv_id":"2504.07106","n_code_links":0,"syntology":null},{"paper":null,"slug":"cncast-leveraging-3d-swin-transformer-and-dit","title":"CNCast: Leveraging 3D Swin Transformer and DiT for Enhanced Regional Weather Forecasting","date":"2025-03-16","arxiv_id":"2503.13546","n_code_links":0,"syntology":null},{"paper":null,"slug":"debiasing-diffusion-model-enhancing-fairness","title":"Debiasing Diffusion Model: Enhancing Fairness through Latent Representation Learning in Stable Diffusion Model","date":"2025-03-16","arxiv_id":"2503.12536","n_code_links":0,"syntology":null},{"paper":null,"slug":"eq-taa-equivariant-traffic-accident","title":"EQ-TAA: Equivariant Traffic Accident Anticipation via Diffusion-Based Accident Video Synthesis","date":"2025-03-16","arxiv_id":"2506.10002","n_code_links":0,"syntology":null},{"paper":"/paper/localized-concept-erasure-for-text-to-image","slug":"localized-concept-erasure-for-text-to-image","title":"Localized Concept Erasure for Text-to-Image Diffusion Models Using Training-Free Gated Low-Rank Adaptation","date":"2025-03-16","arxiv_id":"2503.12356","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["Hyun1A/GLoCE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/modality-composable-diffusion-policy-via","slug":"modality-composable-diffusion-policy-via","title":"Modality-Composable Diffusion Policy via Inference-Time Distribution-level Composition","date":"2025-03-16","arxiv_id":"2503.12466","n_code_links":1,"syntology":null},{"paper":"/paper/pathology-image-restoration-via-mixture-of","slug":"pathology-image-restoration-via-mixture-of","title":"Pathology Image Restoration via Mixture of Prompts","date":"2025-03-16","arxiv_id":"2503.12399","n_code_links":1,"syntology":null},{"paper":null,"slug":"personalize-anything-for-free-with-diffusion","title":"Personalize Anything for Free with Diffusion Transformer","date":"2025-03-16","arxiv_id":"2503.12590","n_code_links":0,"syntology":null},{"paper":null,"slug":"segment-any-quality-images-with-generative","title":"Segment Any-Quality Images with Generative Latent Space Enhancement","date":"2025-03-16","arxiv_id":"2503.12507","n_code_links":0,"syntology":null},{"paper":null,"slug":"sing-semantic-image-communications-using-null","title":"SING: Semantic Image Communications using Null-Space and INN-Guided Diffusion Models","date":"2025-03-16","arxiv_id":"2503.12484","n_code_links":0,"syntology":null},{"paper":null,"slug":"state-fourier-diffusion-language-model-sfdlm","title":"State Fourier Diffusion Language Model (SFDLM): A Scalable, Novel Iterative Approach to Language Modeling","date":"2025-03-16","arxiv_id":"2503.17382","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-driver-cognition-and-decision","title":"Understanding Driver Cognition and Decision-Making Behaviors in High-Risk Scenarios: A Drift Diffusion Perspective","date":"2025-03-16","arxiv_id":"2503.12637","n_code_links":0,"syntology":null},{"paper":"/paper/a-comprehensive-survey-on-knowledge-1","slug":"a-comprehensive-survey-on-knowledge-1","title":"A Comprehensive Survey on Knowledge Distillation","date":"2025-03-15","arxiv_id":"2503.12067","n_code_links":1,"syntology":null},{"paper":null,"slug":"att-adapter-a-robust-and-precise-domain","title":"Att-Adapter: A Robust and Precise Domain-Specific Multi-Attributes T2I Diffusion Adapter via Conditional Variational Autoencoder","date":"2025-03-15","arxiv_id":"2503.11937","n_code_links":0,"syntology":null},{"paper":"/paper/context-aware-multimodal-ai-reveals-hidden","slug":"context-aware-multimodal-ai-reveals-hidden","title":"Context-aware Multimodal AI Reveals Hidden Pathways in Five Centuries of Art Evolution","date":"2025-03-15","arxiv_id":"2503.13531","n_code_links":1,"syntology":null},{"paper":null,"slug":"cross-modal-diffusion-for-biomechanical","title":"Cross-Modal Diffusion for Biomechanical Dynamical Systems Through Local Manifold Alignment","date":"2025-03-15","arxiv_id":"2503.12214","n_code_links":0,"syntology":null},{"paper":"/paper/diffad-a-unified-diffusion-modeling-approach","slug":"diffad-a-unified-diffusion-modeling-approach","title":"DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving","date":"2025-03-15","arxiv_id":"2503.12170","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffgap-a-lightweight-diffusion-module-in","title":"DiffGAP: A Lightweight Diffusion Module in Contrastive Space for Bridging Cross-Model Gap","date":"2025-03-15","arxiv_id":"2503.12131","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffusion-dynamics-models-with-generative","title":"Diffusion Dynamics Models with Generative State Estimation for Cloth Manipulation","date":"2025-03-15","arxiv_id":"2503.11999","n_code_links":0,"syntology":null},{"paper":"/paper/qdm-quadtree-based-region-adaptive-sparse","slug":"qdm-quadtree-based-region-adaptive-sparse","title":"QDM: Quadtree-Based Region-Adaptive Sparse Diffusion Models for Efficient Image Super-Resolution","date":"2025-03-15","arxiv_id":"2503.12015","n_code_links":1,"syntology":null},{"paper":"/paper/reflect-dit-inference-time-scaling-for-text-1","slug":"reflect-dit-inference-time-scaling-for-text-1","title":"Reflect-DiT: Inference-Time Scaling for Text-to-Image Diffusion Transformers via In-Context Reflection","date":"2025-03-15","arxiv_id":"2503.12271","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"pointer_only":3,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; the one sample that ran constructed an object rather than computing a result","official":null}},{"paper":"/paper/seal-semantic-aware-image-watermarking-1","slug":"seal-semantic-aware-image-watermarking-1","title":"SEAL: Semantic Aware Image Watermarking","date":"2025-03-15","arxiv_id":"2503.12172","n_code_links":1,"syntology":null},{"paper":null,"slug":"tailor-an-integrated-text-driven-cg-ready","title":"Tailor: An Integrated Text-Driven CG-Ready Human and Garment Generation System","date":"2025-03-15","arxiv_id":"2503.12052","n_code_links":0,"syntology":null},{"paper":null,"slug":"wifi-diffusion-achieving-fine-grained-wifi","title":"WiFi-Diffusion: Achieving Fine-Grained WiFi Radio Map Estimation With Ultra-Low Sampling Rate by Diffusion Models","date":"2025-03-15","arxiv_id":"2503.12004","n_code_links":0,"syntology":null},{"paper":"/paper/winning-the-midst-challenge-new-membership","slug":"winning-the-midst-challenge-new-membership","title":"Winning the MIDST Challenge: New Membership Inference Attacks on Diffusion Models for Tabular Data Synthesis","date":"2025-03-15","arxiv_id":"2503.12008","n_code_links":1,"syntology":null},{"paper":null,"slug":"your-text-encoder-can-be-an-object-level","title":"Your Text Encoder Can Be An Object-Level Watermarking Controller","date":"2025-03-15","arxiv_id":"2503.11945","n_code_links":0,"syntology":null},{"paper":null,"slug":"acmo-attribute-controllable-motion-generation","title":"ACMo: Attribute Controllable Motion Generation","date":"2025-03-14","arxiv_id":"2503.11038","n_code_links":0,"syntology":null},{"paper":null,"slug":"advanced-deep-learning-methods-for-protein","title":"Advanced Deep Learning Methods for Protein Structure Prediction and Design","date":"2025-03-14","arxiv_id":"2503.13522","n_code_links":0,"syntology":null},{"paper":null,"slug":"advancing-3d-gaussian-splatting-editing-with","title":"Advancing 3D Gaussian Splatting Editing with Complementary and Consensus Information","date":"2025-03-14","arxiv_id":"2503.11601","n_code_links":0,"syntology":null},{"paper":"/paper/bevdiffloc-end-to-end-lidar-global","slug":"bevdiffloc-end-to-end-lidar-global","title":"BEVDiffLoc: End-to-End LiDAR Global Localization in BEV View based on Diffusion Model","date":"2025-03-14","arxiv_id":"2503.11372","n_code_links":1,"syntology":null},{"paper":null,"slug":"cross-modal-learning-for-music-to-music-video","title":"Cross-Modal Learning for Music-to-Music-Video Description Generation","date":"2025-03-14","arxiv_id":"2503.11190","n_code_links":0,"syntology":null},{"paper":null,"slug":"cyclepose-leveraging-cycle-consistency-for","title":"CyclePose -- Leveraging Cycle-Consistency for Annotation-Free Nuclei Segmentation in Fluorescence Microscopy","date":"2025-03-14","arxiv_id":"2503.11266","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffuse-cloc-guided-diffusion-for-physics","title":"Diffuse-CLoC: Guided Diffusion for Physics-based Character Look-ahead Control","date":"2025-03-14","arxiv_id":"2503.11801","n_code_links":0,"syntology":null},{"paper":"/paper/drivegen-generalized-and-robust-3d-detection","slug":"drivegen-generalized-and-robust-3d-detection","title":"DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion Generation","date":"2025-03-14","arxiv_id":"2503.11122","n_code_links":1,"syntology":null},{"paper":null,"slug":"emodiffusion-enhancing-emotional-3d-facial","title":"EmoDiffusion: Enhancing Emotional 3D Facial Animation with Latent Diffusion Models","date":"2025-03-14","arxiv_id":"2503.11028","n_code_links":0,"syntology":null},{"paper":null,"slug":"flow-to-the-mode-mode-seeking-diffusion","title":"Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization","date":"2025-03-14","arxiv_id":"2503.11056","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-denoising-score-matching-to-langevin","title":"From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting","date":"2025-03-14","arxiv_id":"2503.11615","n_code_links":0,"syntology":null},{"paper":"/paper/harnessing-frequency-spectrum-insights-for","slug":"harnessing-frequency-spectrum-insights-for","title":"Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion Models","date":"2025-03-14","arxiv_id":"2503.11071","n_code_links":1,"syntology":null},{"paper":null,"slug":"industrial-grade-sensor-simulation-via","title":"Industrial-Grade Sensor Simulation via Gaussian Splatting: A Modular Framework for Scalable Editing and Full-Stack Validation","date":"2025-03-14","arxiv_id":"2503.11731","n_code_links":0,"syntology":null},{"paper":null,"slug":"inversebench-benchmarking-plug-and-play","title":"InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences","date":"2025-03-14","arxiv_id":"2503.11043","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-diffusion-knowledge-for-generative","title":"Leveraging Diffusion Knowledge for Generative Image Compression with Fractal Frequency-Aware Band Learning","date":"2025-03-14","arxiv_id":"2503.11321","n_code_links":0,"syntology":null},{"paper":"/paper/lusd-localized-update-score-distillation-for","slug":"lusd-localized-update-score-distillation-for","title":"LUSD: Localized Update Score Distillation for Text-Guided Image Editing","date":"2025-03-14","arxiv_id":"2503.11054","n_code_links":1,"syntology":null},{"paper":null,"slug":"mtv-inpaint-multi-task-long-video-inpainting","title":"MTV-Inpaint: Multi-Task Long Video Inpainting","date":"2025-03-14","arxiv_id":"2503.11412","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-stage-generative-upscaler","title":"Multi-Stage Generative Upscaler: Reconstructing Football Broadcast Images via Diffusion Models","date":"2025-03-14","arxiv_id":"2503.11181","n_code_links":0,"syntology":null},{"paper":"/paper/neurons-emulating-the-human-visual-cortex","slug":"neurons-emulating-the-human-visual-cortex","title":"Neurons: Emulating the Human Visual Cortex Improves Fidelity and Interpretability in fMRI-to-Video Reconstruction","date":"2025-03-14","arxiv_id":"2503.11167","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["xmed-lab/neurons"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"noise-synthesis-for-low-light-image-denoising","title":"Noise Synthesis for Low-Light Image Denoising with Diffusion Models","date":"2025-03-14","arxiv_id":"2503.11262","n_code_links":0,"syntology":null},{"paper":null,"slug":"pathology-image-compression-with-pre-trained","title":"Pathology Image Compression with Pre-trained Autoencoders","date":"2025-03-14","arxiv_id":"2503.11591","n_code_links":0,"syntology":null}],"record_sha256":"6a435073c05989055f4513b050926ef1a1a1d5e7b2fde4ddc2472a73e51bebbe","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}