Daily Papers
- Defect Detection
- Defect Segmentation
- Anomaly Detection
- 3D Anomaly Detection
- Multimodal Anomaly Detection
- Vector Quantization
Updated on 2026.07.20
Defect Detection
| Date | Title | Authors | Code | Comments | |
|---|---|---|---|---|---|
| 2026-7-16 | LPCAN: Lightweight Pyramid Cross-Attention Network for Rail Surface Defect Detection Using RGB-D Data | Jackie Alex et.al | paper | - | <summary>detail</summary>arXiv admin note: This paper has been withdrawn by arXiv due to unverifiable authorship and affiliation |
| 2026-7-13 | Rough Path Signature-Guided Geometry Augmentation for Few-Shot Industrial Surface Defect Detection | Jiaqi Kuang et.al | paper | - | - |
| 2026-7-6 | ICME 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing | Wei Sun et.al | paper | - | - |
| 2026-7-2 | Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation | Nadeem Nazer et.al | paper | - | <summary>detail</summary>Journal ref:European Conference on Computer Vision (ECCV 2026) |
| 2026-7-1 | Morphology-Aware Sample Assignment: Overcoming IoU Insensitivity for Surface Defect Detection | Pengfei Liu et.al | paper | - | - |
| 2026-6-23 | An Integrated Hardware-Software Design for Low-Data Spatial Defect Detection in Robotic Visual Inspection with Hybrid Optoelectronic Neural Networks | Chaoqing Tang et.al | paper | - | - |
| 2026-6-11 | Multi-Modal Agents for Power Distribution Defect Detection: An Evaluation of Foundation Models | Quan Quan et.al | paper | - | - |
| 2026-6-7 | Failure-Aware Refinement of Vision-Language Model for Lithography Defect Detection | Pangyun Jeong et.al | paper | - | - |
| 2026-6-3 | Attention-Guided Autoencoder Fusion for Insulator Defect Detection Using UAV Transmission-Line Imaging | Malak Allam et.al | paper | - | - |
| 2026-6-3 | Real-Time Industrial Defect Detection on Edge Hardware Using Fine-Tuned YOLOv8: A Systematic Benchmark on the NEU Surface Defect Database and MVTec AD with Automotive & Battery Manufacturing Extensions | Emmanuel Ezeji Somtochukwu et.al | paper | - | - |
| 2026-6-2 | Structure-Guided Mixed Masked Pretraining and Spatial Continuity Regularization for Printed Circuit Board Defect Detection | Peitong Wang et.al | paper | - | <summary>detail</summary>Preprint |
| 2026-6-2 | Contrastive Augmented Transformer with Domain-specific Enhancement for Robust Multi-scenario Metal Surface Defect Detection | Yiyao Liu et.al | paper | - | - |
| 2026-6-1 | Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation | Ni Li et.al | paper | - | - |
| 2026-5-30 | RefDiffNet: Learning to Expose Subtle PCB Defects Before Detection | Vinay Edula et.al | paper | - | - |
| 2026-5-28 | Unsupervised Defect Detection for Surgical Instruments | Joseph Huang et.al | paper | - | - |
| 2026-5-26 | Advancing Metallic Surface Defect Detection via Anomaly-Guided Pretraining on a Large Industrial Dataset | Chuni Liu et.al | paper | code | <summary>detail</summary>Accepted for publication in Pattern Recognition |
| 2026-5-26 | UniPCB: A Generation-Assisted Detection Framework for PCB Defect Inspection | Huan Zhang et.al | paper | - | - |
| 2026-5-25 | A Universal Cliff and a Design Fingerprint: Cross-Section Defect Detection Under LLM Orchestration | Hiroki Fukui et.al | paper | - | - |
| 2026-5-23 | From Full Boards to Tiny Defects: Scale-Aware Tile Inference with Topology-Aware Merging for High-Resolution PCB Defect Detection | Mohammad Alijanpour Shalmani et.al | paper | - | - |
| 2026-5-23 | FDDet: Achieving Data-Efficient Food Defect Detection Under Real-World Scenarios | Ruihao Xu et.al | paper | - | - |
| 2026-5-20 | BioDefect: The First Dataset for Defect Detection in Bioinformatics Software | Tianxiang Xu et.al | paper | - | - |
| 2026-5-19 | Interpretable Computer Vision for Defect Detection in X-ray Tomography of Aerospace SiC/SiC Composites | Antonio Peña Corredor et.al | paper | - | - |
| 2026-5-17 | Network Knowledge Prior Guided Learning for Data-Efficient Surface Defect Detection | Hang-Cheng Dong et.al | paper | - | - |
| 2026-5-13 | Hybrid Quantum-MambaVision: A Quantum-enhanced State Space Model for Calibrated Mixed-type Wafer Defect Detection | Satwik Sai Prakash Sahoo et.al | paper | - | - |
| 2026-5-9 | Micro-Defects Expose Macro-Fakes: Detecting AI-Generated Images via Local Distributional Shifts | Boxuan Zhang et.al | paper | code | - |
Defect Segmentation
| Date | Title | Authors | Code | Comments | |
|---|---|---|---|---|---|
| 2026-7-15 | XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation | Md Mahedi Hasan et.al | paper | code | - |
| 2026-7-2 | Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation | Nadeem Nazer et.al | paper | - | <summary>detail</summary>Journal ref:European Conference on Computer Vision (ECCV 2026) |
| 2026-6-2 | Cesarean Scar Defect Segmentation in Transvaginal Ultrasound Images: a Dataset and Benchmark | Yuan Tian et.al | paper | - | - |
| 2026-4-20 | DeltaSeg: Tiered Attention and Deep Delta Learning for Multi-Class Structural Defect Segmentation | Enrique Hernandez Noguera et.al | paper | - | - |
| 2026-4-13 | Boxes2Pixels: Learning Defect Segmentation from Noisy SAM Masks | Camile Lendering et.al | paper | code | <summary>detail</summary>Accepted for presentation at the AI4RWC Workshop at CVPR 2026 |
| 2026-3-15 | Multi-Period Texture Contrast Enhancement for Low-Contrast Wafer Defect Detection and Segmentation | Zihan Zhang et.al | paper | - | - |
| 2026-1-22 | A Segmentation-driven Editing Method for Bolt Defect Augmentation and Detection | Yangjie Xiao et.al | paper | code | - |
| 2025-11-24 | A Storage-Efficient Feature for 3D Concrete Defect Segmentation to Replace Normal Vector | Linxin Hua et.al | paper | - | - |
| 2025-11-8 | Point Cloud Segmentation of Integrated Circuits Package Substrates Surface Defects Using Causal Inference: Dataset Construction and Methodology | Bingyang Guo et.al | paper | - | - |
| 2025-11-6 | KARMA: Efficient Structural Defect Segmentation via Kolmogorov-Arnold Representation Learning | Md Meftahul Ferdaus et.al | paper | code | <summary>detail</summary>This work has been submitted to the IEEE for possible publication |
| 2025-10-15 | Sample-Centric Multi-Task Learning for Detection and Segmentation of Industrial Surface Defects | Hang-Cheng Dong et.al | paper | - | - |
| 2025-10-6 | Attention-Enhanced Prototypical Learning for Few-Shot Infrastructure Defect Segmentation | Christina Thrainer et.al | paper | - | - |
| 2025-10-1 | Defect Segmentation in OCT scans of ceramic parts for non-destructive inspection using deep learning | Andrés Laveda-Martínez et.al | paper | - | - |
| 2025-9-11 | Unsupervised Integrated-Circuit Defect Segmentation via Image-Intrinsic Normality | Botong Zhao et.al | paper | - | - |
| 2025-8-6 | MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning | Ylli Sadikaj et.al | paper | - | - |
| 2025-7-23 | Exploring Active Learning for Semiconductor Defect Segmentation | Lile Cai et.al | paper | - | <summary>detail</summary>accepted to ICIP 2022 |
| 2025-7-14 | Advancing Automatic Photovoltaic Defect Detection using Semi-Supervised Semantic Segmentation of Electroluminescence Images | Abhishek Jha et.al | paper | code | - |
| 2025-6-28 | Region-Aware CAM: High-Resolution Weakly-Supervised Defect Segmentation via Salient Region Perception | Hang-Cheng Dong et.al | paper | - | - |
| 2025-6-24 | Evolutionary computing-based image segmentation method to detect defects and features in Additive Friction Stir Deposition Process | Akshansh Mishra et.al | paper | - | - |
| 2025-6-17 | synth-dacl: Does Synthetic Defect Data Enhance Segmentation Accuracy and Robustness for Real-World Bridge Inspections? | Johannes Flotzinger et.al | paper | - | - |
| 2025-4-24 | Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guarantees | Cheng Shen et.al | paper | - | <summary>detail</summary>Under Review |
| 2025-4-11 | Weakly Supervised Panoptic Segmentation for Defect-Based Grading of Fresh Produce | Manuel Knott et.al | paper | code | <summary>detail</summary>Accepted as a paper to the 6th International Workshop on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture in conjunction with IEEE/CVF CVPR 2025 |
| 2025-2-11 | Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models | Tongkun Liu et.al | paper | code | - |
| 2025-1-23 | Effective Defect Detection Using Instance Segmentation for NDI | Ashiqur Rahman et.al | paper | code | - |
| 2025-1-17 | Multi-Modal Attention Networks for Enhanced Segmentation and Depth Estimation of Subsurface Defects in Pulse Thermography | Mohammed Salah et.al | paper | - | <summary>detail</summary>Pulse thermography |
Anomaly Detection
| Date | Title | Authors | Code | Comments | |
|---|---|---|---|---|---|
| 2026-7-17 | Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source | Alberto D. Cencillo et.al | paper | - | - |
| 2026-7-17 | Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes | Jaeyeong Lee et.al | paper | - | - |
| 2026-7-16 | Improving Network Anomaly Detection via Choquet-Integral-Based Feature Aggregation | Abreu Quevedo et.al | paper | - | <summary>detail</summary>This manuscript has been accepted for presentation at the IEEE International Symposium on Computers and Communications (ISCC 2026) |
| 2026-7-15 | SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection | Huong Ninh et.al | paper | - | - |
| 2026-7-15 | VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection | PengYu Chen et.al | paper | code | - |
| 2026-7-15 | RF Spectrogram Anomaly Detection with Quantum Kitchen Sinks: Architecture, Representation, and Hardware Validation | Abdallah Aaraba et.al | paper | - | <summary>detail</summary>Paper accepted to IEEE quantum week 2026 |
| 2026-7-15 | M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detection | Seyoung Jeong et.al | paper | - | - |
| 2026-7-15 | Explainable Artificial Intelligence for Anomaly Detection in Banking Transactions: An Internal Audit Perspective | Anupa Lodhi et.al | paper | - | - |
| 2026-7-14 | Statistical Non-linear Reconstruction Loss for Image Anomaly Detection | Nguyen Minh Tri et.al | paper | code | <summary>detail</summary>KES 2026 |
| 2026-7-14 | LARAD: Layout-Aware Road Anomaly Detection via Spatial-Logic Reasoning | Shiyi Mu et.al | paper | - | - |
| 2026-7-14 | Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices | Raheen Junaid Wani et.al | paper | - | - |
| 2026-7-14 | Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection | Martin Uray et.al | paper | - | <summary>detail</summary>This preprint has not undergone peer review or any post-submission improvements or corrections |
| 2026-7-13 | CFR-Net:Collaborative Feature Refnement Network for Medical Image Anomaly Detection | Zihan Nie et.al | paper | - | - |
| 2026-7-13 | TC-MAF: Train-Calibrated Bounded Multi-Evidence Fusion for Multimodal Industrial Anomaly Detection | Ming Deng et.al | paper | code | <summary>detail</summary>accepted by ACM MM 2026 |
| 2026-7-12 | Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection | Zongye Lyu et.al | paper | - | - |
| 2026-7-12 | Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection | Kaiqiang Li et.al | paper | code | <summary>detail</summary>CVPR 2026 |
| 2026-7-11 | Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection | Jian Ning et.al | paper | code | - |
| 2026-7-11 | Structured Evidence Selection for Weakly Supervised Video Anomaly Detection | Chenglizhao Chen et.al | paper | - | - |
| 2026-7-11 | Chain-Aware Encoding for Microservice Trace Anomaly Detection | Yiliu Xu et.al | paper | - | - |
| 2026-7-10 | Semantic Pareto-DQN: A Multi-Objective Reinforcement Learning Framework for Financial Anomaly Detection | Cláudio Lúcio do Val Lopes et.al | paper | - | <summary>detail</summary>BRACIS 2026 - 36th Brazilian Conference on Intelligent Systems |
| 2026-7-10 | AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders | Georgios Anyfantis et.al | paper | - | - |
| 2026-7-10 | Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms | Peipei Zhu et.al | paper | - | - |
| 2026-7-9 | Federated Low-Rank Koopman Learning for Multivariate Time-Series Anomaly Detection in IoT Systems | Tung-Anh Nguyen et.al | paper | - | - |
| 2026-7-9 | CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistency | Xin Wang et.al | paper | - | <summary>detail</summary>KDD 2026 (Research Track) |
| 2026-7-9 | Self-Adaptive Anomaly Detection with Reinforcement Learning and Human Feedback in Connected Vehicles | Matthias Weiß et.al | paper | - | <summary>detail</summary>the 30th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2026) |
3D Anomaly Detection
| Date | Title | Authors | Code | Comments | |
|---|---|---|---|---|---|
| 2026-7-15 | M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detection | Seyoung Jeong et.al | paper | - | - |
| 2026-7-12 | Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection | Kaiqiang Li et.al | paper | code | <summary>detail</summary>CVPR 2026 |
| 2026-7-11 | Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection | Jian Ning et.al | paper | code | - |
| 2026-7-6 | DDStereo: Efficient Dual Decoder Transformers for Stereo 3D Road Anomaly Detection | Shiyi Mu et.al | paper | code | <summary>detail</summary>Accepted by ECCV2026 |
| 2026-6-27 | Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection | Ali Balapour et.al | paper | - | - |
| 2026-6-24 | Point Cloud Diffusion with Global and Local Reconstruction for Instance-Level 3D Anomaly Detection | Linchun Wu et.al | paper | - | - |
| 2026-6-23 | CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection | Ke Xu et.al | paper | code | <summary>detail</summary>ICML 2026 |
| 2026-6-17 | Toward Training-Free Zero-Shot Anomaly Detection in 3D Medical Images: A Batch-Based Approach Using 2D Foundation Models | Tai Le-Gia et.al | paper | - | <summary>detail</summary>ACM Class:I |
| 2026-6-5 | Automated 3D Kinematic Monitoring for Circadian Activity and Anomaly Detection in Juvenile Fish | Chih-Wei Huang et.al | paper | - | - |
| 2026-6-2 | VT-3DAD: Cross-Category 3D Anomaly Detection via Visual-Text Normal Space Alignment | Zi Wang et.al | paper | - | - |
| 2026-5-25 | GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning | Zehao Deng et.al | paper | code | <summary>detail</summary>Accepted by CVPR 2026 |
| 2026-5-7 | Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection | Letian Bai et.al | paper | - | - |
| 2026-5-6 | Two Steps Are All You Need: Efficient 3D Point Cloud Anomaly Detection with Consistency Models | Pranav A et.al | paper | - | <summary>detail</summary>CVPR 2026 |
| 2026-5-6 | Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection | Haibo Xiao et.al | paper | code | - |
| 2026-4-29 | Breaking the Rigid Prior: Towards Articulated 3D Anomaly Detection | Jinye Gan et.al | paper | - | - |
| 2026-4-6 | Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection | Yihan Sun et.al | paper | code | - |
| 2026-4-5 | Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection | Xueyang Kang et.al | paper | - | - |
| 2026-4-2 | Modulate-and-Map: Crossmodal Feature Mapping with Cross-View Modulation for 3D Anomaly Detection | Alex Costanzino et.al | paper | - | <summary>detail</summary>CVPR Findings 2026 |
| 2026-4-1 | Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects | Hanzhe Liang et.al | paper | code | <summary>detail</summary>Resources: https://github |
| 2026-3-26 | A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detection | SuYeon Kim et.al | paper | - | <summary>detail</summary>Accepted by CVPR 2026 |
| 2026-3-4 | Cross-Modal Mapping and Dual-Branch Reconstruction for 2D-3D Multimodal Industrial Anomaly Detection | Radia Daci et.al | paper | code | - |
| 2026-2-16 | Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models | Tai Le-Gia et.al | paper | - | <summary>detail</summary>Accepted for MIDL 2026 |
| 2026-2-11 | DMP-3DAD: Cross-Category 3D Anomaly Detection via Realistic Depth Map Projection with Few Normal Samples | Zi Wang et.al | paper | - | - |
| 2025-12-15 | 3D Human-Human Interaction Anomaly Detection | Shun Maeda et.al | paper | - | - |
| 2025-12-14 | A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features | Hanzhe Liang et.al | paper | - | <summary>detail</summary>Preprint |
Multimodal Anomaly Detection
| Date | Title | Authors | Code | Comments | |
|---|---|---|---|---|---|
| 2026-7-13 | TC-MAF: Train-Calibrated Bounded Multi-Evidence Fusion for Multimodal Industrial Anomaly Detection | Ming Deng et.al | paper | code | <summary>detail</summary>accepted by ACM MM 2026 |
| 2026-7-7 | Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tuners | Xinyue Liu et.al | paper | code | <summary>detail</summary>Accepted by TMM |
| 2026-7-7 | Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems | Alexander Apartsin et.al | paper | - | - |
| 2026-7-4 | Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection | Runzhi Deng et.al | paper | - | <summary>detail</summary>Accepted by ECCV 2026 |
| 2026-7-2 | CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection | Wen Dong et.al | paper | code | - |
| 2026-5-31 | AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection | Junru Zhang et.al | paper | - | <summary>detail</summary>ICML 2026 |
| 2026-5-18 | Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild | Shanle Yao et.al | paper | - | - |
| 2026-5-18 | UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction | Robson W. S. Pessoa et.al | paper | - | - |
| 2026-5-15 | Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection | Heqiang Wang et.al | paper | - | - |
| 2026-5-7 | EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models | Xiaomeng Peng et.al | paper | - | - |
| 2026-4-24 | Text-Guided Multimodal Unified Industrial Anomaly Detection | Zewen Li et.al | paper | - | - |
| 2026-4-23 | Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach | Thomas Debelle et.al | paper | - | - |
| 2026-4-20 | ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection | Qiuhui Chen et.al | paper | - | - |
| 2026-4-14 | Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference | Kevin Wilkinghoff et.al | paper | - | - |
| 2026-4-13 | MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models | Xincheng Yao et.al | paper | - | <summary>detail</summary>Accepted by CVPR2026 |
| 2026-4-10 | Multimodal Anomaly Detection for Human-Robot Interaction | Guilherme Ribeiro et.al | paper | - | - |
| 2026-4-7 | Reasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models | Sakshi Agarwal et.al | paper | - | <summary>detail</summary>under review at conference |
| 2026-4-7 | SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection | Letian Bai et.al | paper | - | - |
| 2026-4-1 | VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection | Huilin Deng et.al | paper | - | - |
| 2026-3-29 | Bidirectional Multimodal Prompt Learning with Scale-Aware Training for Few-Shot Multi-Class Anomaly Detection | Yujin Lee et.al | paper | - | <summary>detail</summary>accepted to CVPR 2026 |
| 2026-3-23 | Multimodal Industrial Anomaly Detection via Geometric Prior | Min Li et.al | paper | - | <summary>detail</summary>Accepted for publication in IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) |
| 2026-3-23 | Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction | Shiyan Hu et.al | paper | code | <summary>detail</summary>ICLR 2026 |
| 2026-3-23 | Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection | Mingle Zhou et.al | paper | - | - |
| 2026-3-4 | Cross-Modal Mapping and Dual-Branch Reconstruction for 2D-3D Multimodal Industrial Anomaly Detection | Radia Daci et.al | paper | code | - |
| 2026-3-3 | Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspective | Kaifang Long et.al | paper | - | - |
Vector Quantization
| Date | Title | Authors | Code | Comments | |
|---|---|---|---|---|---|
| 2026-7-17 | Distributional Matching for Vector Quantization: A Unified Theoretical and Empirical Framework | Xianghong Fang et.al | paper | - | - |
| 2026-7-15 | NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache | Donghyun Son et.al | paper | code | - |
| 2026-7-14 | AVQ-Attention: Adaptive Vector-Quantized Attention | Winfried van den dool et.al | paper | - | <summary>detail</summary>ECCV 2026 |
| 2026-7-7 | Leech Lattice Vector Quantization for Efficient LLM Compression | Tycho F. A. van der Ouderaa et.al | paper | - | - |
| 2026-7-2 | Sparse-Aware Vector Quantization for Bandwidth-Efficient Collaborative 3D Semantic Occupancy Prediction | Feng Li et.al | paper | - | <summary>detail</summary>Accepted by ECCV26 |
| 2026-6-30 | UniSVQ: 2-bit Unified Scalar-Vector Quantization | Haoyu Wang et.al | paper | code | <summary>detail</summary>Accepted by ICML 2026 |
| 2026-6-30 | LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization | Haoyu Wang et.al | paper | code | <summary>detail</summary>Accepted by ICML 2026 |
| 2026-6-21 | LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression | Xi Zhang et.al | paper | code | <summary>detail</summary>Accepted by CVPR 2023 |
| 2026-6-19 | VQActFlow: Vector-Quantized Action Mode Steering for Multi-Task Robot Manipulation | Zhigen Zhao et.al | paper | - | - |
| 2026-6-19 | Fast-TurboQuant: A Multiplier-Free Online Vector Quantization Approach | Pedro M. R. Pereira et.al | paper | - | - |
| 2026-6-15 | Price of metric universality in vector quantization is at most 0.11 bit | Alina Harbuzova et.al | paper | - | <summary>detail</summary>41 page |
| 2026-6-12 | VQ4SNN: Vector Quantization for Memory-Efficient FPGA Spiking Neural Networks | Dimitrios Sekertzis et.al | paper | - | - |
| 2026-6-9 | ViP-VL: Vietnamese Self-supervised Speech Pretraining Model with Vector-Quantization Learning | Khanh Le et.al | paper | - | <summary>detail</summary>INTERSPEECH 2026 |
| 2026-6-9 | Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery | Xuemin Yu et.al | paper | - | - |
| 2026-6-9 | NSVQ: Mitigating Codebook Collapse by Stabilizing Encoder Drift in Vector Quantization | Hao Lu et.al | paper | - | - |
| 2026-6-5 | ASH: Asymmetric Scalar Hashing With Learned Dimensionality Reduction for High-Fidelity Vector Quantization | Mariano Tepper et.al | paper | - | - |
| 2026-6-3 | RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression | Shima Rafiei et.al | paper | - | - |
| 2026-6-1 | Channel-wise Vector Quantization | Wei Song et.al | paper | - | - |
| 2026-6-1 | Massive Spikes in LLMs are Bias Vectors: Mechanistic Uncovering and Spike-Free Quantization | Yung-Chin Chen et.al | paper | - | - |
| 2026-5-31 | Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning | Zian Zhai et.al | paper | - | <summary>detail</summary>ICML2026 |
| 2026-5-27 | ArcVQ-VAE: A Spherical Vector Quantization Framework with ArcCosine Additive Margin | Jaeyung Kim et.al | paper | code | <summary>detail</summary>To appear in Proceedings of the 43rd International Conference on Machine Learning (ICML 2026) |
| 2026-5-26 | DiVeQ: Differentiable Vector Quantization Using the Reparameterization Trick | Mohammad Hassan Vali et.al | paper | - | - |
| 2026-5-26 | Training-Free Vector Quantization via Gaussian VAEs | Tongda Xu et.al | paper | code | - |
| 2026-5-24 | BandVQ: Band-Wise Vector-Quantized EEG Foundation Model | Jamiyan Sukhbaatar et.al | paper | - | - |
| 2026-5-22 | EVA: Accelerating LLM Decoding via an Efficient Vector Quantization Architecture | Bowen Duan et.al | paper | code | - |