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Found 13 papers, 5 papers with code
Can 3D Vision-Language Models Truly Understand Natural Language?
To test the language understandability of 3D-VL models, we first propose a language robustness task for systematically assessing 3D-VL models across various tasks, benchmarking their performance when presented with different language style variants.
Towards Adversarially Robust Dataset Distillation by Curvature Regularization
Extensive empirical experiments suggest that our method not only outperforms standard adversarial training on both accuracy and robustness with less computation overhead but is also capable of generating robust distilled datasets that can withstand various adversarial attacks.
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers
In this work, we provide a finetuning approach to enhance the robustness of vision transformers inspired by the concept of nullspace from linear algebra.
Beyond Finite Data: Towards Data-free Out-of-distribution Generalization via Extrapolation
Starting with the class of interest, we query the LLMs to extract relevant knowledge for these novel domains.
Gradient-Free Adaptive Global Pruning for Pre-trained Language Models
The transformative impact of large language models (LLMs) like LLaMA and GPT on natural language processing is countered by their prohibitive computational demands.
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
For instance, it has facilitated the identification of disease-predictive genes from gene expression data, significantly advancing healthcare.
Dataset Distillation via the Wasserstein Metric
Dataset Distillation (DD) emerges as a powerful strategy to encapsulate the expansive information of large datasets into significantly smaller, synthetic equivalents, thereby preserving model performance with reduced computational overhead.
Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization
SCMD leverages the capabilities of large vision-language models, specifically CLIP, to train a more efficient model, ensuring it acquires robust generalization capabilities across unseen domains.
Understanding Adversarial Transferability in Federated Learning
We investigate the robustness and security issues from a novel and practical setting: a group of malicious clients has impacted the model during training by disguising their identities and acting as benign clients, and only revealing their adversary position after the training to conduct transferable adversarial attacks with their data, which is usually a subset of the data that FL system is trained with.
Diverse Cotraining Makes Strong Semi-Supervised Segmentor
Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it.
Multi-metrics adaptively identifies backdoors in Federated learning
To evaluate the effectiveness of our approach, we conduct comprehensive experiments on different datasets under various attack settings, where our method achieves the best defensive performance.
Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection
In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD).
More than Encoder: Introducing Transformer Decoder to Upsample
The local and detailed feature from the shallower layer such as boundary and tissue texture is particularly more important in medical segmentation compared with natural image segmentation.
