{"total":91,"rows":[{"id":"2410.06940","title":"Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think","created":"2024-10-09","cited_by_count":703,"area":"diffusion"},{"id":"2510.12276","title":"Spatial Forcing: Implicit Spatial Representation Alignment for Vision-language-action Model","created":"2025-10-14","cited_by_count":134,"area":"vla"},{"id":"2402.18567","title":"Diffusion Language Models Are Versatile Protein Learners","created":"2024-02-28","cited_by_count":98,"area":"diffusion"},{"id":"2405.15177","title":"Diffusion Actor-Critic with Entropy Regulator","created":"2024-05-24","cited_by_count":94,"area":"diffusion"},{"id":"2512.10794","title":"What matters for Representation Alignment: Global Information or Spatial Structure?","created":"2025-12-11","cited_by_count":84,"area":null},{"id":"2403.00504","title":"Learning and Leveraging World Models in Visual Representation Learning","created":"2024-03-01","cited_by_count":72,"area":"wm"},{"id":"2505.02831","title":"No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves","created":"2025-05-05","cited_by_count":56,"area":"diffusion"},{"id":"2407.00783","title":"Diffusion Models and Representation Learning: A Survey","created":"2024-06-30","cited_by_count":54,"area":"diffusion"},{"id":"2409.02426","title":"Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions","created":"2024-09-04","cited_by_count":53,"area":"diffusion"},{"id":"2412.06016","title":"Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation","created":"2024-12-08","cited_by_count":44,"area":"diffusion"},{"id":"2604.20329","title":"Image Generators are Generalist Vision Learners","created":"2026-04-22","cited_by_count":43,"area":null},{"id":"2405.15986","title":"Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity","created":"2024-05-24","cited_by_count":42,"area":"diffusion"},{"id":"2403.00522","title":"VisionLLaMA: A Unified LLaMA Backbone for Vision Tasks","created":"2024-03-01","cited_by_count":28,"area":null},{"id":"2503.06132","title":"USP: Unified Self-Supervised Pretraining for Image Generation and Understanding","created":"2025-03-08","cited_by_count":24,"area":"diffusion"},{"id":"2410.10516","title":"UniGEM: A Unified Approach to Generation and Property Prediction for Molecules","created":"2024-10-14","cited_by_count":21,"area":null},{"id":"2502.12786","title":"Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo","created":"2025-02-18","cited_by_count":20,"area":"diffusion"},{"id":"2507.15856","title":"Latent Denoising Makes Good Tokenizers","created":"2025-07-21","cited_by_count":20,"area":null},{"id":"2510.24474","title":"Decoupled MeanFlow: Turning Flow Models into Flow Maps for Accelerated Sampling","created":"2025-10-28","cited_by_count":20,"area":null},{"id":"2502.05743","title":"Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling","created":"2025-02-09","cited_by_count":18,"area":"diffusion"},{"id":"2410.02369","title":"Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation","created":"2024-10-03","cited_by_count":16,"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":"2410.05711","title":"TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation","created":"2024-10-08","cited_by_count":14,"area":null},{"id":"2410.19635","title":"Frozen-DETR: Enhancing DETR with Image Understanding from Frozen Foundation Models","created":"2024-10-25","cited_by_count":13,"area":null},{"id":"2505.23426","title":"Enhanced DACER Algorithm with High Diffusion Efficiency","created":"2025-05-29","cited_by_count":13,"area":"diffusion"},{"id":"2507.08980","title":"Learning Diffusion Models with Flexible Representation Guidance","created":"2025-07-11","cited_by_count":13,"area":"diffusion"},{"id":"2408.15241","title":"GenRec: Unifying Video Generation and Recognition with Diffusion Models","created":"2024-08-27","cited_by_count":11,"area":"diffusion"},{"id":"2606.12217","title":"Making Foresight Actionable: Repurposing Representation Alignment in World Action Models","created":"2026-06-10","cited_by_count":11,"area":"wm"},{"id":"2412.09380","title":"Diffusion Model with Representation Alignment for Protein Inverse Folding","created":"2024-12-12","cited_by_count":10,"area":"diffusion"},{"id":"2404.02446","title":"Masked Completion via Structured Diffusion with White-Box Transformers","created":"2024-04-03","cited_by_count":9,"area":null},{"id":"2407.01960","title":"Zero-Shot Video Restoration and Enhancement Using Pre-Trained Image Diffusion Model","created":"2024-07-02","cited_by_count":8,"area":"diffusion"},{"id":"2411.01647","title":"Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generation","created":"2024-11-03","cited_by_count":8,"area":"diffusion"},{"id":"2509.21565","title":"No Alignment Needed for Generation: Learning Linearly Separable Representations in Diffusion Models","created":"2025-09-25","cited_by_count":8,"area":"diffusion"},{"id":"2512.25034","title":"Generative Classifiers Avoid Shortcut Solutions","created":"2025-12-31","cited_by_count":8,"area":null},{"id":"2410.02467","title":"SIDE: Surrogate Conditional Data Extraction from Diffusion Models","created":"2024-10-03","cited_by_count":7,"area":"diffusion"},{"id":"2504.10188","title":"Efficient Generative Model Training via Embedded Representation Warmup","created":"2025-04-14","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":"2406.00777","title":"Diffusion Features to Bridge Domain Gap for Semantic Segmentation","created":"2024-06-02","cited_by_count":5,"area":"diffusion"},{"id":"2410.14672","title":"BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities","created":"2024-10-18","cited_by_count":5,"area":null},{"id":"2502.06552","title":"Diffusion Models for Computational Neuroimaging: A Survey","created":"2025-02-10","cited_by_count":5,"area":"diffusion"},{"id":"2503.00733","title":"UniWav: Towards Unified Pre-training for Speech Representation Learning and Generation","created":"2025-03-02","cited_by_count":5,"area":null},{"id":"2503.01411","title":"Learning Actionable World Models for Industrial Process Control","created":"2025-03-03","cited_by_count":4,"area":"wm"},{"id":"2507.07104","title":"Vision-Language-Vision Auto-Encoder: Scalable Knowledge Distillation from Diffusion Models","created":"2025-07-09","cited_by_count":4,"area":"mllm"},{"id":"2511.22549","title":"Diff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Prior","created":"2025-11-27","cited_by_count":4,"area":null},{"id":"2504.19353","title":"Flow Along the K-Amplitude for Generative Modeling","created":"2025-04-27","cited_by_count":3,"area":null},{"id":"2505.10999","title":"Conditioning Residuals for Diffusion Models via Representation Feedback","created":"2025-05-16","cited_by_count":3,"area":"diffusion"},{"id":"2507.07106","title":"Towards Multimodal Understanding via Stable Diffusion as a Task-Aware Feature Extractor","created":"2025-07-09","cited_by_count":3,"area":"mllm"},{"id":"2509.20952","title":"Flow Matching in the Low-Noise Regime: Pathologies and a Contrastive Remedy","created":"2025-09-25","cited_by_count":3,"area":"diffusion"},{"id":"2512.19729","title":"High-Performance Self-Supervised Learning by Joint Training of Flow Matching","created":"2025-12-17","cited_by_count":3,"area":"diffusion"},{"id":"2403.11077","title":"Zippo: Zipping Color and Transparency Distributions into a Single Diffusion Model","created":"2024-03-17","cited_by_count":2,"area":"diffusion"},{"id":"2406.17688","title":"Unified Auto-Encoding with Masked Diffusion","created":"2024-06-25","cited_by_count":2,"area":"diffusion"},{"id":"2502.06314","title":"From Pixels to Components: Eigenvector Masking for Visual Representation Learning","created":"2025-02-10","cited_by_count":2,"area":null},{"id":"2510.22926","title":"Simple Denoising Diffusion Language Models","created":"2025-10-27","cited_by_count":2,"area":"diffusion"},{"id":"2406.07536","title":"Towards Fundamentally Scalable Model Selection: Asymptotically Fast Update and Selection","created":"2024-06-11","cited_by_count":1,"area":null},{"id":"2407.00905","title":"Learning Robust 3D Representation from CLIP via Dual Denoising","created":"2024-07-01","cited_by_count":1,"area":"mllm"},{"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":"2505.04956","title":"Graffe: Graph Representation Learning via Diffusion Probabilistic Models","created":"2025-05-08","cited_by_count":1,"area":"diffusion"},{"id":"2506.08457","title":"Audio Generation Through Score-Based Generative Modeling: Design Principles and Implementation","created":"2025-06-10","cited_by_count":1,"area":"diffusion"},{"id":"2508.20705","title":"EEGDM: Learning EEG Representation with Latent Diffusion Model","created":"2025-08-28","cited_by_count":1,"area":"diffusion"},{"id":"2601.13416","title":"Diffusion Representations for Fine-Grained Image Classification: A Marine Plankton Case Study","created":"2026-01-19","cited_by_count":1,"area":"diffusion"},{"id":"2602.01905","title":"Learning Sparse Visual Representations via Spatial-Semantic Factorization","created":"2026-02-02","cited_by_count":1,"area":null},{"id":"2602.08749","title":"Shifting the Breaking Point of Flow Matching for Multi-Instance Editing","created":"2026-02-09","cited_by_count":1,"area":"diffusion"},{"id":"2602.17901","title":"MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis","created":"2026-02-19","cited_by_count":1,"area":"diffusion"},{"id":"2606.00583","title":"Improving Visual Representation Alignment Generation with GRPO","created":"2026-05-30","cited_by_count":1,"area":"diffusion"},{"id":"2606.09718","title":"Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles","created":"2026-06-08","cited_by_count":1,"area":"diffusion"},{"id":"2608.01298","title":"UDT: Reconciling U-Nets and Diffusion Transformers with Data-Adaptive Token Reduction","created":"2026-08-02","cited_by_count":1,"area":"diffusion"},{"id":"2608.03082","title":"DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers","created":"2026-08-04","cited_by_count":1,"area":"diffusion"},{"id":"2412.18735","title":"Automatic Self-supervised Learning for Social Recommendations","created":"2024-12-25","cited_by_count":0,"area":null},{"id":"2503.15060","title":"Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis","created":"2025-03-19","cited_by_count":0,"area":null},{"id":"2505.05732","title":"Automated Learning of Semantic Embedding Representations for Diffusion Models","created":"2025-05-09","cited_by_count":0,"area":"diffusion"},{"id":"2506.09955","title":"Canonical Latent Representations in Conditional Diffusion Models","created":"2025-06-11","cited_by_count":0,"area":"diffusion"},{"id":"2507.15216","title":"Improving Joint Embedding Predictive Architecture with Diffusion Noise","created":"2025-07-21","cited_by_count":0,"area":"diffusion"},{"id":"2508.02829","title":"Elucidating the Role of Feature Normalization in IJEPA","created":"2025-08-04","cited_by_count":0,"area":"distill"},{"id":"2510.13160","title":"DP-TTA: Test-time Adaptation for Transient Electromagnetic Signal Denoising via Dictionary-driven Prior Regularization","created":"2025-10-15","cited_by_count":0,"area":null},{"id":"2510.19623","title":"Learning and Simulating Building Evacuation Patterns for Enhanced Safety Design Using Generative Models","created":"2025-10-22","cited_by_count":0,"area":null},{"id":"2601.07273","title":"GenDet: Painting Colored Bounding Boxes on Images via Diffusion Model for Object Detection","created":"2026-01-12","cited_by_count":0,"area":"diffusion"},{"id":"2602.03564","title":"CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting","created":"2026-02-03","cited_by_count":0,"area":"diffusion"},{"id":"2602.16918","title":"Xray-Visual Models: Scaling Vision models on Industry Scale Data","created":"2026-02-18","cited_by_count":0,"area":null},{"id":"2602.19931","title":"Expanding the Role of Diffusion Models for Robust Classifier Training","created":"2026-02-23","cited_by_count":0,"area":"diffusion"},{"id":"2603.19122","title":"Revisiting Autoregressive Models for Generative Image Classification","created":"2026-03-19","cited_by_count":0,"area":"diffusion"},{"id":"2604.21502","title":"VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection","created":"2026-04-23","cited_by_count":0,"area":null},{"id":"2605.18714","title":"Semantic Generative Tuning for Unified Multimodal Models","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2605.19371","title":"Multi-Scale Generative Modeling with Heat Dissipation Flow Matching","created":"2026-05-19","cited_by_count":0,"area":"diffusion"},{"id":"2605.25012","title":"Learning from Semantic Dictionaries: Discriminative Codebook Contrastive Learning for Unified Visual Representation and Generation","created":"2026-05-24","cited_by_count":0,"area":null},{"id":"2606.30147","title":"T2LDM++: A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation","created":"2026-06-29","cited_by_count":0,"area":"wm"},{"id":"2609.08253","title":"Revisiting Spectral Representations in Generative Diffusion Models","created":"2026-09-08","cited_by_count":0,"area":"diffusion"},{"id":"2609.20267","title":"CleanVideo: Adaptive Concept Erasure for Text-to-Video Diffusion Models","created":"2026-07-29","cited_by_count":0,"area":"diffusion"},{"id":"2609.33217","title":"RepFlow: Reciprocal Supervision Improves Generation and Representation in Flow Models","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.33895","title":"Residual-Stream Burden Shapes Representation Learning in Diffusion Transformers","created":"2026-09-27","cited_by_count":0,"area":"diffusion"},{"id":"2609.34732","title":"What Visual Generators Need from Teachers: Rethinking Representation Alignment","created":"2026-09-28","cited_by_count":0,"area":"diffusion"},{"id":"2610.05711","title":"From Pixels, Without Pre-training: Joint Generative and Self-Supervised Representation Learning in One Model","created":"2026-10-05","cited_by_count":0,"area":null},{"id":"2610.14517","title":"We Have Been Interpreting Diffusion Transformer Backbone Behaviors Wrong","created":"2026-10-10","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}