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Found 12 papers, 2 papers with code
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning
Federated Learning (FL) is a distributed training paradigm that enables clients scattered across the world to cooperatively learn a global model without divulging confidential data.
Text-driven Prompt Generation for Vision-Language Models in Federated Learning
Prompt learning for vision-language models, e. g., CoOp, has shown great success in adapting CLIP to different downstream tasks, making it a promising solution for federated learning due to computational reasons.
Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches
The success of this strategy relies heavily on the model's invariance to image pixel masking.
Defending Multimodal Fusion Models against Single-Source Adversaries
Beyond achieving high performance across many vision tasks, multimodal models are expected to be robust to single-source faults due to the availability of redundant information between modalities.
Certified robustness against adversarial patch attacks via randomized cropping
This paper proposes a certifiable defense against adversarial patch attacks on image classification.
Empirical robustification of pre-trained classifiers
Most pre-trained classifiers, though they may work extremely well on the domain they were trained upon, are not trained in a robust fashion, and therefore are sensitive to adversarial attacks.
Certified robustness against physically-realizable patch attack via randomized cropping
Our method improves upon the current state of the art in defending against patch attacks on CIFAR10 and ImageNet, both in terms of certified accuracy and inference time.
Accelerating Road Sign Ground Truth Construction with Knowledge Graph and Machine Learning
We show that our knowledge graph approach can reduce sign search space by 98. 9%.
Crossing You in Style: Cross-modal Style Transfer from Music to Visual Arts
Its major difference from the traditional image style transfer problem is that the style information is provided by music rather than images.
Noisy Batch Active Learning with Deterministic Annealing
We study the problem of training machine learning models incrementally with batches of samples annotated with noisy oracles.
Learning in Confusion: Batch Active Learning with Noisy Oracle
We study the problem of training machine learning models incrementally using active learning with access to imperfect or noisy oracles.
DeepBbox: Accelerating Precise Ground Truth Generation for Autonomous Driving Datasets
Autonomous driving requires various computer vision algorithms, such as object detection and tracking. Precisely-labeled datasets (i. e., objects are fully contained in bounding boxes with only a few extra pixels) are preferred for training such algorithms, so that the algorithms can detect exact locations of the objects.
