Search Results for author:
Found 11 papers, 8 papers with code
VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness
Finetuning a pretrained vision model (PVM) is a common technique for learning downstream vision tasks.
Efficient Deweather Mixture-of-Experts with Uncertainty-aware Feature-wise Linear Modulation
In this work, we propose an efficient MoE architecture with weight sharing across the experts.
Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow
FlowEneDet achieves promising results on Cityscapes, Cityscapes-C, FishyScapes and SegmentMeIfYouCan benchmarks in IDM/OOD detection when applied to pretrained DeepLabV3+ and SegFormer semantic segmentation models.
Ranked #5 on
Anomaly Detection
on Fishyscapes L&F
MTTrans: Cross-Domain Object Detection with Mean-Teacher Transformer
To solve this problem, we propose an end-to-end cross-domain detection Transformer based on the mean teacher framework, MTTrans, which can fully exploit unlabeled target domain data in object detection training and transfer knowledge between domains via pseudo labels.
Contrastive Neural Processes for Self-Supervised Learning
In this paper, we propose a novel self-supervised learning framework that combines contrastive learning with neural processes.
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows
Our approach results in a computationally and memory-efficient model: CFLOW-AD is faster and smaller by a factor of 10x than prior state-of-the-art with the same input setting.
Ranked #13 on
Anomaly Detection
on VisA
(Detection AUROC metric)
AutoDO: Robust AutoAugment for Biased Data with Label Noise via Scalable Probabilistic Implicit Differentiation
To overcome these limitations, we reformulate AutoAugment as a generalized automated dataset optimization (AutoDO) task that minimizes the distribution shift between test data and distorted train dataset.
Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision
We theoretically derive an optimal acquisition function for AL in this setting.
Smart Home Appliances: Chat with Your Fridge
Current home appliances are capable to execute a limited number of voice commands such as turning devices on or off, adjusting music volume or light conditions.
Explanation-Based Attention for Semi-Supervised Deep Active Learning
We introduce an attention mechanism to improve feature extraction for deep active learning (AL) in the semi-supervised setting.
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions
We qualitatively and quantitatively show that the proposed explanation method can be used to find image features which cause failures in DNN object detection.
