{"total":146,"rows":[{"id":"2404.12390","title":"BLINK: Multimodal Large Language Models Can See but Not Perceive","created":"2024-04-18","cited_by_count":550,"area":"mllm"},{"id":"2306.03881","title":"Emergent Correspondence from Image Diffusion","created":"2023-06-06","cited_by_count":446,"area":"diffusion"},{"id":"2112.05814","title":"Deep ViT Features as Dense Visual Descriptors","created":"2021-12-10","cited_by_count":330,"area":null},{"id":"2305.15347","title":"A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence","created":"2023-05-24","cited_by_count":301,"area":"diffusion"},{"id":"2304.06712","title":"What does CLIP know about a red circle? Visual prompt engineering for VLMs","created":"2023-04-13","cited_by_count":218,"area":"mllm"},{"id":"2305.14334","title":"Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence","created":"2023-05-23","cited_by_count":214,"area":"diffusion"},{"id":"2104.01538","title":"Hypercorrelation Squeeze for Few-Shot Segmentation","created":"2021-04-04","cited_by_count":193,"area":null},{"id":"2404.08636","title":"Probing the 3D Awareness of Visual Foundation Models","created":"2024-04-12","cited_by_count":177,"area":null},{"id":"2305.15581","title":"Unsupervised Semantic Correspondence Using Stable Diffusion","created":"2023-05-24","cited_by_count":120,"area":"diffusion"},{"id":"2207.10866","title":"Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation","created":"2022-07-22","cited_by_count":114,"area":null},{"id":"2311.17034","title":"Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence","created":"2023-11-28","cited_by_count":86,"area":null},{"id":"2412.01827","title":"RandAR: Decoder-only Autoregressive Visual Generation in Random Orders","created":"2024-12-02","cited_by_count":83,"area":"inference"},{"id":"2106.02520","title":"CATs: Cost Aggregation Transformers for Visual Correspondence","created":"2021-06-04","cited_by_count":77,"area":null},{"id":"2112.05143","title":"GAN-Supervised Dense Visual Alignment","created":"2021-12-09","cited_by_count":62,"area":null},{"id":"2105.06464","title":"DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision","created":"2021-05-13","cited_by_count":59,"area":null},{"id":"2407.00783","title":"Diffusion Models and Representation Learning: A Survey","created":"2024-06-30","cited_by_count":54,"area":"diffusion"},{"id":"2202.06817","title":"CATs++: Boosting Cost Aggregation with Convolutions and Transformers","created":"2022-02-14","cited_by_count":53,"area":null},{"id":"2306.09109","title":"NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations","created":"2023-06-15","cited_by_count":53,"area":null},{"id":"2305.11173","title":"Going Denser with Open-Vocabulary Part Segmentation","created":"2023-05-18","cited_by_count":51,"area":null},{"id":"2105.02343","title":"CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints","created":"2021-05-05","cited_by_count":49,"area":null},{"id":"2303.12786","title":"FeatureNeRF: Learning Generalizable NeRFs by Distilling Foundation Models","created":"2023-03-22","cited_by_count":47,"area":null},{"id":"2212.03862","title":"Teaching Matters: Investigating the Role of Supervision in Vision Transformers","created":"2022-12-07","cited_by_count":45,"area":null},{"id":"2412.03439","title":"CleanDIFT: Diffusion Features without Noise","created":"2024-12-04","cited_by_count":44,"area":"diffusion"},{"id":"2103.16831","title":"Convolutional Hough Matching Networks","created":"2021-03-31","cited_by_count":43,"area":null},{"id":"2104.03308","title":"Warp Consistency for Unsupervised Learning of Dense Correspondences","created":"2021-04-07","cited_by_count":42,"area":null},{"id":"2310.17569","title":"SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matching","created":"2023-10-26","cited_by_count":38,"area":"diffusion"},{"id":"2312.13216","title":"Improving Semantic Correspondence with Viewpoint-Guided Spherical Maps","created":"2023-12-20","cited_by_count":34,"area":null},{"id":"2108.00211","title":"Multi-scale Matching Networks for Semantic Correspondence","created":"2021-07-31","cited_by_count":32,"area":null},{"id":"2210.02689","title":"Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence","created":"2022-10-06","cited_by_count":32,"area":null},{"id":"2208.06974","title":"Learning Semantic Correspondence with Sparse Annotations","created":"2022-08-15","cited_by_count":31,"area":null},{"id":"2302.03956","title":"Neural Congealing: Aligning Images to a Joint Semantic Atlas","created":"2023-02-08","cited_by_count":31,"area":null},{"id":"2203.04279","title":"Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences","created":"2022-03-08","cited_by_count":30,"area":null},{"id":"2212.04085","title":"Graph Matching with Bi-level Noisy Correspondence","created":"2022-12-08","cited_by_count":30,"area":null},{"id":"2205.11634","title":"TransforMatcher: Match-to-Match Attention for Semantic Correspondence","created":"2022-05-23","cited_by_count":29,"area":null},{"id":"2205.15213","title":"Backpropagation through Combinatorial Algorithms: Identity with Projection Works","created":"2022-05-30","cited_by_count":29,"area":"lr"},{"id":"2303.16201","title":"ASIC: Aligning Sparse in-the-wild Image Collections","created":"2023-03-28","cited_by_count":27,"area":null},{"id":"2403.14625","title":"LiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT Descriptors","created":"2024-03-21","cited_by_count":27,"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":"2505.18584","title":"Unleashing Diffusion Transformers for Visual Correspondence by Modulating Massive Activations","created":"2025-05-24","cited_by_count":23,"area":"diffusion"},{"id":"2507.14137","title":"Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning","created":"2025-07-18","cited_by_count":21,"area":null},{"id":"2311.14671","title":"SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation","created":"2023-11-24","cited_by_count":19,"area":null},{"id":"2408.16357","title":"Law of Vision Representation in MLLMs","created":"2024-08-29","cited_by_count":18,"area":"mllm"},{"id":"2512.21218","title":"Latent Implicit Visual Reasoning","created":"2025-12-24","cited_by_count":18,"area":"lr"},{"id":"2412.03512","title":"Distillation of Diffusion Features for Semantic Correspondence","created":"2024-12-04","cited_by_count":17,"area":null},{"id":"2112.11685","title":"Cost Aggregation Is All You Need for Few-Shot Segmentation","created":"2021-12-22","cited_by_count":16,"area":null},{"id":"2203.16038","title":"Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels","created":"2022-03-30","cited_by_count":16,"area":null},{"id":"2408.11054","title":"Near, far: Patch-ordering enhances vision foundation models' scene understanding","created":"2024-08-20","cited_by_count":15,"area":null},{"id":"2510.04819","title":"Visual Representations inside the Language Model","created":"2025-10-06","cited_by_count":15,"area":"mllm"},{"id":"2403.11120","title":"Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence","created":"2024-03-17","cited_by_count":14,"area":null},{"id":"2410.01534","title":"Toward a Holistic Evaluation of Robustness in CLIP Models","created":"2024-10-02","cited_by_count":13,"area":"mllm"},{"id":"2506.05312","title":"Do It Yourself: Learning Semantic Correspondence from Pseudo-Labels","created":"2025-06-05","cited_by_count":13,"area":null},{"id":"2604.02486","title":"VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors","created":"2026-04-02","cited_by_count":13,"area":"mllm"},{"id":"2109.05221","title":"Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence","created":"2021-09-11","cited_by_count":12,"area":null},{"id":"2109.10967","title":"Learning Contrastive Representation for Semantic Correspondence","created":"2021-09-22","cited_by_count":12,"area":null},{"id":"2501.14945","title":"MATCHA:Towards Matching Anything","created":"2025-01-24","cited_by_count":12,"area":null},{"id":"2603.28480","title":"INSID3: Training-Free In-Context Segmentation with DINOv3","created":"2026-03-30","cited_by_count":12,"area":null},{"id":"2106.02206","title":"Stochastic Iterative Graph Matching","created":"2021-06-04","cited_by_count":10,"area":null},{"id":"2207.05054","title":"Demystifying Unsupervised Semantic Correspondence Estimation","created":"2022-07-11","cited_by_count":10,"area":null},{"id":"2403.12038","title":"Zero-Shot Image Feature Consensus with Deep Functional Maps","created":"2024-03-18","cited_by_count":10,"area":null},{"id":"2407.09648","title":"3x2: 3D Object Part Segmentation by 2D Semantic Correspondences","created":"2024-07-12","cited_by_count":10,"area":null},{"id":"2603.22387","title":"Efficient Universal Perception Encoder","created":"2026-03-23","cited_by_count":10,"area":null},{"id":"2206.07259","title":"Self-Supervised Learning of Image Scale and Orientation","created":"2022-06-15","cited_by_count":9,"area":null},{"id":"2504.21749","title":"Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature Space","created":"2025-04-30","cited_by_count":9,"area":null},{"id":"2109.00240","title":"Joint Graph Learning and Matching for Semantic Feature Correspondence","created":"2021-09-01","cited_by_count":8,"area":null},{"id":"2212.09735","title":"Correspondence Distillation from NeRF-based GAN","created":"2022-12-19","cited_by_count":8,"area":null},{"id":"2503.21766","title":"Stable-SCore: A Stable Registration-based Framework for 3D Shape Correspondence","created":"2025-03-27","cited_by_count":7,"area":null},{"id":"2503.22462","title":"SemAlign3D: Semantic Correspondence between RGB-Images through Aligning 3D Object-Class Representations","created":"2025-03-28","cited_by_count":7,"area":null},{"id":"2512.11508","title":"On Geometric Understanding and Learned Priors in Feed-forward 3D Reconstruction Models","created":"2025-12-12","cited_by_count":7,"area":null},{"id":"2512.17875","title":"Visually Prompted Benchmarks Are Surprisingly Fragile","created":"2025-12-19","cited_by_count":7,"area":null},{"id":"2209.08742","title":"Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence","created":"2022-09-19","cited_by_count":6,"area":null},{"id":"2305.02385","title":"SimSC: A Simple Framework for Semantic Correspondence with Temperature Learning","created":"2023-05-03","cited_by_count":6,"area":null},{"id":"2311.04336","title":"Efficient Semantic Matching with Hypercolumn Correlation","created":"2023-11-07","cited_by_count":6,"area":null},{"id":"2403.06457","title":"Ensemble Quadratic Assignment Network for Graph Matching","created":"2024-03-11","cited_by_count":6,"area":null},{"id":"2407.11850","title":"SpaceJAM: a Lightweight and Regularization-free Method for Fast Joint Alignment of Images","created":"2024-07-16","cited_by_count":6,"area":null},{"id":"2506.08220","title":"Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence","created":"2025-06-09","cited_by_count":6,"area":null},{"id":"2507.14367","title":"Hallucination Score: Towards Mitigating Hallucinations in Generative Image Super-Resolution","created":"2025-07-18","cited_by_count":6,"area":null},{"id":"2508.00746","title":"GECO: Geometrically Consistent Embedding with Lightspeed Inference","created":"2025-08-01","cited_by_count":6,"area":null},{"id":"2509.21989","title":"Mind-the-Glitch: Visual Correspondence for Detecting Inconsistencies in Subject-Driven Generation","created":"2025-09-26","cited_by_count":6,"area":"diffusion"},{"id":"2210.09082","title":"A Solver-Free Framework for Scalable Learning in Neural ILP Architectures","created":"2022-10-17","cited_by_count":5,"area":null},{"id":"2503.15897","title":"Learning 3D Scene Analogies with Neural Contextual Scene Maps","created":"2025-03-20","cited_by_count":4,"area":null},{"id":"2108.00394","title":"Deep graph matching meets mixed-integer linear programming: Relax at your own risk ?","created":"2021-08-01","cited_by_count":3,"area":null},{"id":"2505.18060","title":"Semantic Correspondence: Unified Benchmarking and a Strong Baseline","created":"2025-05-23","cited_by_count":3,"area":null},{"id":"2509.21263","title":"Towards Geometry-Grounded Dense Semantic Matching with VGGT Priors","created":"2025-09-25","cited_by_count":3,"area":null},{"id":"2511.18787","title":"Understanding Task Transfer in Vision-Language Models","created":"2025-11-24","cited_by_count":3,"area":"mllm"},{"id":"2604.18267","title":"MARCO: Navigating the Unseen Space of Semantic Correspondence","created":"2026-04-20","cited_by_count":3,"area":null},{"id":"2111.10817","title":"Understanding Pixel-level 2D Image Semantics with 3D Keypoint Knowledge Engine","created":"2021-11-21","cited_by_count":2,"area":null},{"id":"2307.08930","title":"Unsupervised Deep Graph Matching Based on Cycle Consistency","created":"2023-07-18","cited_by_count":2,"area":null},{"id":"2311.18540","title":"Match me if you can: Semi-Supervised Semantic Correspondence Learning with Unpaired Images","created":"2023-11-30","cited_by_count":2,"area":null},{"id":"2312.07338","title":"Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification","created":"2023-12-12","cited_by_count":2,"area":null},{"id":"2404.02678","title":"Independently Keypoint Learning for Small Object Semantic Correspondence","created":"2024-04-03","cited_by_count":2,"area":null},{"id":"2410.06719","title":"Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques","created":"2024-10-09","cited_by_count":2,"area":"diffusion"},{"id":"2410.16718","title":"Learning Partial Graph Matching via Optimal Partial Transport","created":"2024-10-22","cited_by_count":2,"area":null},{"id":"2509.03893","title":"Weakly-Supervised Learning of Dense Functional Correspondences","created":"2025-09-04","cited_by_count":2,"area":"mllm"},{"id":"2601.13886","title":"Revisiting Multi-Task Visual Representation Learning","created":"2026-01-20","cited_by_count":2,"area":"mllm"},{"id":"2201.01603","title":"Deep Probabilistic Graph Matching","created":"2022-01-05","cited_by_count":1,"area":null},{"id":"2404.02125","title":"3D Congealing: 3D-Aware Image Alignment in the Wild","created":"2024-04-02","cited_by_count":1,"area":null},{"id":"2503.17715","title":"Normalized Matching Transformer","created":"2025-03-22","cited_by_count":1,"area":null},{"id":"2509.24318","title":"Similarity-Aware Selective State-Space Modeling for Semantic Correspondence","created":"2025-09-29","cited_by_count":1,"area":"ssm"},{"id":"2510.22842","title":"FastJAM: a Fast Joint Alignment Model for Images","created":"2025-10-26","cited_by_count":1,"area":null},{"id":"2602.09934","title":"VersaViT: Enhancing MLLM Vision Backbones via Task-Guided Optimization","created":"2026-02-10","cited_by_count":1,"area":"mllm"}],"next_cursor":"100"}