{"total":60,"rows":[{"id":"2403.04692","title":"PixArt-\\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation","created":"2024-03-07","cited_by_count":329,"area":"diffusion"},{"id":"2310.00227","title":"Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement","created":"2023-09-30","cited_by_count":77,"area":null},{"id":"2308.01045","title":"Dynamic Token Pruning in Plain Vision Transformers for Semantic Segmentation","created":"2023-08-02","cited_by_count":62,"area":"inference"},{"id":"2408.14690","title":"Training-Free Activation Sparsity in Large Language Models","created":"2024-08-26","cited_by_count":55,"area":"inference"},{"id":"2306.07050","title":"Revisiting Token Pruning for Object Detection and Instance Segmentation","created":"2023-06-12","cited_by_count":50,"area":"inference"},{"id":"2404.01882","title":"Scene Adaptive Sparse Transformer for Event-based Object Detection","created":"2024-04-02","cited_by_count":39,"area":null},{"id":"2404.06918","title":"HRVDA: High-Resolution Visual Document Assistant","created":"2024-04-10","cited_by_count":37,"area":"mllm"},{"id":"2407.11087","title":"Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV","created":"2024-07-14","cited_by_count":36,"area":null},{"id":"2503.19903","title":"Scaling Vision Pre-Training to 4K Resolution","created":"2025-03-25","cited_by_count":28,"area":null},{"id":"2310.04361","title":"Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion","created":"2023-10-06","cited_by_count":16,"area":"moe"},{"id":"2510.17777","title":"SparseVILA: Decoupling Visual Sparsity for Efficient VLM Inference","created":"2025-10-20","cited_by_count":15,"area":"mllm"},{"id":"2407.17261","title":"Embedding-Free Transformer with Inference Spatial Reduction for Efficient Semantic Segmentation","created":"2024-07-24","cited_by_count":14,"area":null},{"id":"2410.09873","title":"Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy","created":"2024-10-13","cited_by_count":14,"area":"diffusion"},{"id":"2409.11923","title":"Agglomerative Token Clustering","created":"2024-09-18","cited_by_count":13,"area":null},{"id":"2510.18091","title":"Accelerating Vision Transformers with Adaptive Patch Sizes","created":"2025-10-20","cited_by_count":13,"area":null},{"id":"2310.00632","title":"Win-Win: Training High-Resolution Vision Transformers from Two Windows","created":"2023-10-01","cited_by_count":12,"area":null},{"id":"2405.14700","title":"Sparse-Tuning: Adapting Vision Transformers with Efficient Fine-tuning and Inference","created":"2024-05-23","cited_by_count":12,"area":null},{"id":"2412.06263","title":"iLLaVA: An Image is Worth Fewer Than 1/3 Input Tokens in Large Multimodal Models","created":"2024-12-09","cited_by_count":11,"area":"mllm"},{"id":"2403.07965","title":"Conditional computation in neural networks: principles and research trends","created":"2024-03-12","cited_by_count":10,"area":"moe"},{"id":"2405.16486","title":"Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation","created":"2024-05-26","cited_by_count":10,"area":"moe"},{"id":"2511.14751","title":"Co-Me: Confidence-Guided Token Merging for Visual Geometric Transformers","created":"2025-11-18","cited_by_count":10,"area":"inference"},{"id":"2511.21317","title":"HTTM: Head-wise Temporal Token Merging for Faster VGGT","created":"2025-11-26","cited_by_count":9,"area":null},{"id":"2505.21036","title":"RainFusion: Adaptive Video Generation Acceleration via Multi-Dimensional Visual Redundancy","created":"2025-05-27","cited_by_count":7,"area":"diffusion"},{"id":"2509.00419","title":"LightVLM: Acceleraing Large Multimodal Models with Pyramid Token Merging and KV Cache Compression","created":"2025-08-30","cited_by_count":7,"area":"mllm"},{"id":"2507.01299","title":"La RoSA: Enhancing LLM Efficiency via Layerwise Rotated Sparse Activation","created":"2025-07-02","cited_by_count":6,"area":null},{"id":"2407.12067","title":"MaskVD: Region Masking for Efficient Video Object Detection","created":"2024-07-16","cited_by_count":5,"area":null},{"id":"2410.07278","title":"PAR: Prompt-Aware Token Reduction Method for Efficient Large Multimodal Models","created":"2024-10-09","cited_by_count":5,"area":"mllm"},{"id":"2501.07451","title":"A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion","created":"2025-01-13","cited_by_count":5,"area":null},{"id":"2512.18747","title":"IPCV: Information-Preserving Compression for MLLM Visual Encoders","created":"2025-12-21","cited_by_count":5,"area":"mllm"},{"id":"2309.05224","title":"SparseSwin: Swin Transformer with Sparse Transformer Block","created":"2023-09-11","cited_by_count":4,"area":null},{"id":"2403.19882","title":"Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights","created":"2024-03-28","cited_by_count":4,"area":null},{"id":"2406.19391","title":"Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across Heads","created":"2024-06-27","cited_by_count":3,"area":null},{"id":"2407.19014","title":"Sparse Refinement for Efficient High-Resolution Semantic Segmentation","created":"2024-07-26","cited_by_count":3,"area":null},{"id":"2507.19131","title":"MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective","created":"2025-07-25","cited_by_count":3,"area":null},{"id":"2407.02098","title":"DM3D: Distortion-Minimized Weight Pruning for Lossless 3D Object Detection","created":"2024-07-02","cited_by_count":2,"area":null},{"id":"2403.12543","title":"HCPM: Hierarchical Candidates Pruning for Efficient Detector-Free Matching","created":"2024-03-19","cited_by_count":1,"area":null},{"id":"2410.20926","title":"Long Sequence Modeling with Attention Tensorization: From Sequence to Tensor Learning","created":"2024-10-28","cited_by_count":1,"area":null},{"id":"2410.23608","title":"Context-Aware Token Selection and Packing for Enhanced Vision Transformer","created":"2024-10-31","cited_by_count":1,"area":null},{"id":"2501.13018","title":"Multi-Objective Hyperparameter Selection via Hypothesis Testing on Reliability Graphs","created":"2025-01-22","cited_by_count":1,"area":null},{"id":"2503.24057","title":"AMMSM: Adaptive Motion Magnification and Sparse Mamba for Micro-Expression Recognition","created":"2025-03-31","cited_by_count":1,"area":"ssm"},{"id":"2509.03872","title":"Focus Through Motion: RGB-Event Collaborative Token Sparsification for Efficient Object Detection","created":"2025-09-04","cited_by_count":1,"area":null},{"id":"2607.03784","title":"Rethinking Depth Pruning for Vision Transformers: A Heterogeneity-Aware Perspective","created":"2026-07-04","cited_by_count":1,"area":null},{"id":"2307.14988","title":"Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs","created":"2023-07-27","cited_by_count":0,"area":null},{"id":"2503.21262","title":"vGamba: Attentive State Space Bottleneck for efficient Long-range Dependencies in Visual Recognition","created":"2025-03-27","cited_by_count":0,"area":null},{"id":"2504.05774","title":"TMT: Cross-domain Semantic Segmentation with Region-adaptive Transferability Estimation","created":"2025-04-08","cited_by_count":0,"area":null},{"id":"2507.01654","title":"SPoT: Subpixel Placement of Tokens in Vision Transformers","created":"2025-07-02","cited_by_count":0,"area":null},{"id":"2509.22481","title":"PSTTS: A Plug-and-Play Token Selector for Efficient Event-based Spatio-temporal Representation Learning","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2511.20722","title":"DinoLizer: Separating VAE and Diffusion Artifacts in Generative Inpainting Localization","created":"2025-11-25","cited_by_count":0,"area":null},{"id":"2512.22760","title":"Neighbor-Aware Token Reduction via Hilbert Curve for Vision Transformers","created":"2025-12-28","cited_by_count":0,"area":"inference"},{"id":"2601.15506","title":"ViT Registers and Fractal ViT","created":"2026-01-21","cited_by_count":0,"area":null},{"id":"2602.21081","title":"Scaling Vision Transformers: Evaluating DeepSpeed for Image-Centric Workloads","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2605.08371","title":"PrePARE: Pre-AA Token Pruning for Frozen Multi-View Geometry Transformers","created":"2026-05-08","cited_by_count":0,"area":"inference"},{"id":"2605.17743","title":"MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation","created":"2026-05-18","cited_by_count":0,"area":"distill"},{"id":"2605.23868","title":"Vision Transformers Need Better Token Interaction","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2606.31015","title":"Dual Sparse Aggregation Transformer for Multispectral Object Detection","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2607.27591","title":"Prox: Training-Free FFN Activation Sparsity via Approximate Intermediate-Channel Salience in LLMs","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2608.03681","title":"Keep the Needle, Prune the Haystack: Defect-Preserving Token Pruning for Efficient Zero-Shot Anomaly Detection","created":"2026-08-04","cited_by_count":0,"area":"inference"},{"id":"2608.07616","title":"HSMLA: Hierarchical Softmax Multi-scale Linear Attention for Efficient Vision Transformers","created":"2026-08-07","cited_by_count":0,"area":null},{"id":"2608.07982","title":"AdaDINO: Pair-Aware In-Backbone Adaptation of Frozen DINO for Efficient Remote Sensing Change Detection","created":"2026-08-08","cited_by_count":0,"area":null},{"id":"2608.22526","title":"RS$^3$-Prune: Read-Sparse, Store-Sparse Token Pruning for Video Object Segmentation","created":"2026-08-23","cited_by_count":0,"area":"inference"}],"next_cursor":null}