{"total":131,"rows":[{"id":"2405.01434","title":"StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation","created":"2024-05-02","cited_by_count":247,"area":"diffusion"},{"id":"2406.16864","title":"StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal","created":"2024-06-24","cited_by_count":154,"area":"diffusion"},{"id":"2311.17034","title":"Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence","created":"2023-11-28","cited_by_count":86,"area":null},{"id":"2312.10113","title":"Focus on Your Instruction: Fine-grained and Multi-instruction Image Editing by Attention Modulation","created":"2023-12-15","cited_by_count":62,"area":null},{"id":"2502.04320","title":"ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features","created":"2025-02-06","cited_by_count":52,"area":"diffusion"},{"id":"2405.10255","title":"When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models","created":"2024-05-16","cited_by_count":43,"area":"mllm"},{"id":"2312.03626","title":"TokenCompose: Text-to-Image Diffusion with Token-level Supervision","created":"2023-12-06","cited_by_count":40,"area":"diffusion"},{"id":"2311.18832","title":"Exploiting Diffusion Prior for Generalizable Dense Prediction","created":"2023-11-30","cited_by_count":38,"area":"diffusion"},{"id":"2310.07972","title":"Interpretable Diffusion via Information Decomposition","created":"2023-10-12","cited_by_count":37,"area":"diffusion"},{"id":"2408.12957","title":"Image Segmentation in Foundation Model Era: A Survey","created":"2024-08-23","cited_by_count":35,"area":"diffusion"},{"id":"2309.03179","title":"SLiMe: Segment Like Me","created":"2023-09-06","cited_by_count":34,"area":"mllm"},{"id":"2403.15382","title":"DragAPart: Learning a Part-Level Motion Prior for Articulated Objects","created":"2024-03-22","cited_by_count":32,"area":null},{"id":"2405.03150","title":"Video Diffusion Models: A Survey","created":"2024-05-06","cited_by_count":30,"area":"diffusion"},{"id":"2312.01985","title":"UniGS: Unified Representation for Image Generation and Segmentation","created":"2023-12-04","cited_by_count":29,"area":"diffusion"},{"id":"2403.14291","title":"Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models","created":"2024-03-21","cited_by_count":29,"area":"diffusion"},{"id":"2409.19365","title":"Conditional Image Synthesis with Diffusion Models: A Survey","created":"2024-09-28","cited_by_count":29,"area":"diffusion"},{"id":"2406.02842","title":"DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut","created":"2024-06-05","cited_by_count":26,"area":null},{"id":"2410.03558","title":"Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features","created":"2024-10-04","cited_by_count":25,"area":"diffusion"},{"id":"2509.18096","title":"Seg4Diff: Unveiling Open-Vocabulary Segmentation in Text-to-Image Diffusion Transformers","created":"2025-09-22","cited_by_count":24,"area":"diffusion"},{"id":"2312.04802","title":"MimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean Diffusion Model","created":"2023-12-08","cited_by_count":18,"area":"diffusion"},{"id":"2405.18025","title":"Where's Waldo: Diffusion Features for Personalized Segmentation and Retrieval","created":"2024-05-28","cited_by_count":18,"area":null},{"id":"2407.07077","title":"ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extraction","created":"2024-07-09","cited_by_count":16,"area":"diffusion"},{"id":"2503.07050","title":"TIDE : Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation","created":"2025-03-10","cited_by_count":16,"area":"diffusion"},{"id":"2312.00065","title":"Unsupervised Keypoints from Pretrained Diffusion Models","created":"2023-11-29","cited_by_count":15,"area":"diffusion"},{"id":"2405.19996","title":"DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild","created":"2024-05-30","cited_by_count":14,"area":"diffusion"},{"id":"2505.18582","title":"On Denoising Walking Videos for Gait Recognition","created":"2025-05-24","cited_by_count":14,"area":null},{"id":"2407.05352","title":"Exploring Phrase-Level Grounding with Text-to-Image Diffusion Model","created":"2024-07-07","cited_by_count":13,"area":"diffusion"},{"id":"2403.18791","title":"Object Pose Estimation via the Aggregation of Diffusion Features","created":"2024-03-27","cited_by_count":12,"area":"diffusion"},{"id":"2404.13274","title":"Augmented Object Intelligence with XR-Objects","created":"2024-04-20","cited_by_count":12,"area":null},{"id":"2403.11194","title":"MaskDiffusion: Exploiting Pre-trained Diffusion Models for Semantic Segmentation","created":"2024-03-17","cited_by_count":11,"area":"diffusion"},{"id":"2405.05852","title":"Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control","created":"2024-05-09","cited_by_count":11,"area":"diffusion"},{"id":"2506.05127","title":"PixCell: A generative foundation model for digital histopathology images","created":"2025-06-05","cited_by_count":11,"area":null},{"id":"2507.17657","title":"Attention (as Discrete-Time Markov) Chains","created":"2025-07-23","cited_by_count":11,"area":null},{"id":"2311.17893","title":"Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation","created":"2023-11-29","cited_by_count":10,"area":null},{"id":"2409.03209","title":"iSeg: An Iterative Refinement-based Framework for Training-free Segmentation","created":"2024-09-05","cited_by_count":10,"area":"diffusion"},{"id":"2410.18804","title":"Fast constrained sampling in pre-trained diffusion models","created":"2024-10-24","cited_by_count":10,"area":"diffusion"},{"id":"2503.07890","title":"Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?","created":"2025-03-10","cited_by_count":10,"area":"diffusion"},{"id":"2504.01955","title":"Scene-Centric Unsupervised Panoptic Segmentation","created":"2025-04-02","cited_by_count":10,"area":null},{"id":"2506.07883","title":"Diffusion Counterfactual Generation with Semantic Abduction","created":"2025-06-09","cited_by_count":10,"area":"diffusion"},{"id":"2410.08209","title":"Emergent Visual Grounding in Large Multimodal Models Without Grounding Supervision","created":"2024-10-10","cited_by_count":9,"area":"mllm"},{"id":"2411.10411","title":"Repurposing Stable Diffusion Attention for Training-Free Unsupervised Interactive Segmentation","created":"2024-11-15","cited_by_count":9,"area":"diffusion"},{"id":"2411.10745","title":"Bridging the Skeleton-Text Modality Gap: Diffusion-Powered Modality Alignment for Zero-shot Skeleton-based Action Recognition","created":"2024-11-16","cited_by_count":9,"area":"diffusion"},{"id":"2508.09949","title":"Stable Diffusion Models are Secretly Good at Visual In-Context Learning","created":"2025-08-13","cited_by_count":8,"area":"diffusion"},{"id":"2405.17965","title":"AttenCraft: Attention-guided Disentanglement of Multiple Concepts for Text-to-Image Customization","created":"2024-05-28","cited_by_count":6,"area":"diffusion"},{"id":"2408.16314","title":"ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual Grounding","created":"2024-08-29","cited_by_count":6,"area":null},{"id":"2412.06785","title":"Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation","created":"2024-12-09","cited_by_count":6,"area":null},{"id":"2501.11299","title":"MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching","created":"2025-01-20","cited_by_count":6,"area":null},{"id":"2506.02419","title":"Guiding Registration with Emergent Similarity from Pre-Trained Diffusion Models","created":"2025-06-03","cited_by_count":6,"area":"diffusion"},{"id":"2508.01651","title":"Diffusion Models are Open-World Affordance Learners: Leveraging Generative Priors for 3D Affordance Learning","created":"2025-08-03","cited_by_count":6,"area":"diffusion"},{"id":"2408.01694","title":"Bayesian Active Learning for Semantic Segmentation","created":"2024-08-03","cited_by_count":5,"area":null},{"id":"2501.09138","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","created":"2025-01-15","cited_by_count":5,"area":null},{"id":"2507.19808","title":"SeeDiff: Off-the-Shelf Seeded Mask Generation from Diffusion Models","created":"2025-07-26","cited_by_count":5,"area":"diffusion"},{"id":"2309.01369","title":"Exploring Limits of Diffusion-Synthetic Training with Weakly Supervised Semantic Segmentation","created":"2023-09-04","cited_by_count":4,"area":"diffusion"},{"id":"2412.10804","title":"Medical Manifestation-Aware De-Identification","created":"2024-12-14","cited_by_count":4,"area":null},{"id":"2412.20838","title":"Dual-Space Augmented Intrinsic-LoRA for Wind Turbine Segmentation","created":"2024-12-30","cited_by_count":4,"area":null},{"id":"2503.07982","title":"TRACE: Your Diffusion Model is Secretly an Instance Edge Detector","created":"2025-03-11","cited_by_count":4,"area":"diffusion"},{"id":"2505.17994","title":"Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation","created":"2025-05-23","cited_by_count":4,"area":null},{"id":"2508.00443","title":"SDMatte: Grafting Diffusion Models for Interactive Matting","created":"2025-08-01","cited_by_count":4,"area":"diffusion"},{"id":"2604.24575","title":"Diffusion Model as a Generalist Segmentation Learner","created":"2026-04-27","cited_by_count":4,"area":"diffusion"},{"id":"2406.11384","title":"Understanding Multi-Granularity for Open-Vocabulary Part Segmentation","created":"2024-06-17","cited_by_count":3,"area":null},{"id":"2412.04678","title":"Unsupervised Segmentation by Diffusing, Walking and Cutting","created":"2024-12-06","cited_by_count":3,"area":"diffusion"},{"id":"2412.14432","title":"IntroStyle: Training-Free Introspective Style Attribution using Diffusion Features","created":"2024-12-19","cited_by_count":3,"area":null},{"id":"2412.18149","title":"Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction","created":"2024-12-24","cited_by_count":3,"area":"diffusion"},{"id":"2506.18463","title":"DIP: Unsupervised Dense In-Context Post-training of Visual Representations","created":"2025-06-23","cited_by_count":3,"area":null},{"id":"2507.09102","title":"Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning","created":"2025-07-12","cited_by_count":3,"area":"diffusion"},{"id":"2510.10577","title":"Injecting Frame-Event Complementary Fusion into Diffusion for Optical Flow in Challenging Scenes","created":"2025-10-12","cited_by_count":3,"area":"diffusion"},{"id":"2510.23633","title":"Noise is All You Need: Solving Linear Inverse Problems by Noise Combination Sampling with Diffusion Models","created":"2025-10-24","cited_by_count":3,"area":"diffusion"},{"id":"2406.08773","title":"DenoiseRep: Denoising Model for Representation Learning","created":"2024-06-13","cited_by_count":2,"area":null},{"id":"2406.10197","title":"Composing Parts for Expressive Object Generation","created":"2024-06-14","cited_by_count":2,"area":"diffusion"},{"id":"2410.11473","title":"InvSeg: Test-Time Prompt Inversion for Semantic Segmentation","created":"2024-10-15","cited_by_count":2,"area":"diffusion"},{"id":"2503.10660","title":"Text-to-3D Generation using Jensen-Shannon Score Distillation","created":"2025-03-08","cited_by_count":2,"area":"diffusion"},{"id":"2504.02391","title":"Marine Saliency Segmenter: Object-Focused Conditional Diffusion with Region-Level Semantic Knowledge Distillation","created":"2025-04-03","cited_by_count":2,"area":"diffusion"},{"id":"2504.17253","title":"DIVE: Inverting Conditional Diffusion Models for Discriminative Tasks","created":"2025-04-24","cited_by_count":2,"area":"diffusion"},{"id":"2505.15263","title":"gen2seg: Generative Models Enable Generalizable Instance Segmentation","created":"2025-05-21","cited_by_count":2,"area":null},{"id":"2507.23313","title":"The Cow of Rembrandt - Analyzing Artistic Prompt Interpretation in Text-to-Image Models","created":"2025-07-31","cited_by_count":2,"area":"diffusion"},{"id":"2508.20020","title":"GS: Generative Segmentation via Label Diffusion","created":"2025-08-27","cited_by_count":2,"area":"diffusion"},{"id":"2509.06165","title":"UNO: Unifying One-stage Video Scene Graph Generation via Object-Centric Visual Representation Learning","created":"2025-09-07","cited_by_count":2,"area":null},{"id":"2512.10959","title":"StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space","created":"2025-12-11","cited_by_count":2,"area":null},{"id":"2605.13974","title":"Few Channels Draw The Whole Picture: Revealing Massive Activations in Diffusion Transformers","created":"2026-05-13","cited_by_count":2,"area":"diffusion"},{"id":"2412.08116","title":"Diffusion-based Data Augmentation and Knowledge Distillation with Generated Soft Labels Solving Data Scarcity Problems of SAR Oil Spill Segmentation","created":"2024-12-11","cited_by_count":1,"area":"diffusion"},{"id":"2503.16254","title":"M2N2V2: Multi-Modal Unsupervised and Training-free Interactive Segmentation","created":"2025-03-20","cited_by_count":1,"area":null},{"id":"2503.19902","title":"ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models","created":"2025-03-25","cited_by_count":1,"area":"diffusion"},{"id":"2504.07008","title":"Latent Diffusion U-Net Representations Contain Positional Embeddings and Anomalies","created":"2025-04-09","cited_by_count":1,"area":"diffusion"},{"id":"2504.17628","title":"Beyond Labels: Zero-Shot Diabetic Foot Ulcer Wound Segmentation with Self-attention Diffusion Models and the Potential for Text-Guided Customization","created":"2025-04-24","cited_by_count":1,"area":"diffusion"},{"id":"2505.09385","title":"FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization","created":"2025-05-14","cited_by_count":1,"area":null},{"id":"2506.11496","title":"Taming Stable Diffusion for Computed Tomography Blind Super-Resolution","created":"2025-06-13","cited_by_count":1,"area":"diffusion"},{"id":"2506.23323","title":"FA-Seg: A Fast and Accurate Diffusion-Based Method for Open-Vocabulary Segmentation","created":"2025-06-29","cited_by_count":1,"area":"diffusion"},{"id":"2507.17248","title":"Reality Proxy: Fluid Interactions with Real-World Objects in MR via Abstract Representations","created":"2025-07-23","cited_by_count":1,"area":null},{"id":"2508.08498","title":"CObL: Toward Zero-Shot Ordinal Layering without User Prompting","created":"2025-08-11","cited_by_count":1,"area":null},{"id":"2510.01841","title":"Leveraging Prior Knowledge of Diffusion Model for Person Search","created":"2025-10-02","cited_by_count":1,"area":"diffusion"},{"id":"2512.10954","title":"Group Diffusion: Enhancing Image Generation by Unlocking Cross-Sample Collaboration","created":"2025-12-11","cited_by_count":1,"area":"diffusion"},{"id":"2601.04065","title":"Unsupervised Modular Adaptive Region Growing and RegionMix Classification for Wind Turbine Segmentation","created":"2026-01-07","cited_by_count":1,"area":null},{"id":"2601.13816","title":"Discriminant Learning-based Colorspace for Blade Segmentation","created":"2026-01-20","cited_by_count":1,"area":null},{"id":"2601.13852","title":"Probabilistic Deep Discriminant Analysis for Wind Blade Segmentation","created":"2026-01-20","cited_by_count":1,"area":null},{"id":"2605.21190","title":"Semantic Granularity Navigation in Image Editing","created":"2026-05-20","cited_by_count":1,"area":null},{"id":"2606.04925","title":"Scene-Centric Unsupervised Video Panoptic Segmentation","created":"2026-06-03","cited_by_count":1,"area":null},{"id":"2606.15590","title":"Unlocking Diffusion Hierarchies: Adaptive Timestep Selection for Zero-Shot Segmentation","created":"2026-06-14","cited_by_count":1,"area":"diffusion"},{"id":"2607.00766","title":"Decoupled Guidance: Disentangling Subject and Context Pathways in Text-to-Image Personalization","created":"2026-07-01","cited_by_count":1,"area":"diffusion"},{"id":"2312.01682","title":"ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learning","created":"2023-12-04","cited_by_count":0,"area":"diffusion"},{"id":"2312.02420","title":"Repurposing SAM for User-Defined Semantics Aware Segmentation","created":"2023-12-05","cited_by_count":0,"area":null}],"next_cursor":"100"}