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Found 6 papers, 4 papers with code
MMAD: The First-Ever Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
In the field of industrial inspection, Multimodal Large Language Models (MLLMs) have a high potential to renew the paradigms in practical applications due to their robust language capabilities and generalization abilities.
CAR: Controllable Autoregressive Modeling for Visual Generation
To the best of our knowledge, we are the first to propose a control framework for pre-trained autoregressive visual generation models.
Decision Boundary-aware Knowledge Consolidation Generates Better Instance-Incremental Learner
Two issues have to be tackled in the new IIL setting: 1) the notorious catastrophic forgetting because of no access to old data, and 2) broadening the existing decision boundary to new observations because of concept drift.
Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution
Although some Unsupervised Degradation Prediction (UDP) methods are proposed to bypass this problem, the \textit{inconsistency} between degradation embedding and SR feature is still challenging.
Thunder: Thumbnail based Fast Lightweight Image Denoising Network
To achieve promising results on removing noise from real-world images, most of existing denoising networks are formulated with complex network structure, making them impractical for deployment.
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
A detailed description of all models developed in the challenge is provided in this paper.
