s the one with the highest similarity. The zero-shot top-1 accuracy for ImageNet [4] using CLIP variants (CLIP ViT-L) matches the performance of the orig nal ResNet model trained from … From arxiv.org
A COMPREHENSIVE STUDY ON ROBUSTNESS OF IMAGE CLASSIFICATION MODELS ...
For image classification models in the evaluation, we consider 55 models on ImageNet covering two mainstream types of network architectures: CNNs and Transformers, and four training … From ar5iv.labs.arxiv.org
CLEANCLIP: MITIGATING DATA POISONING ATTACKS IN MULTIMODAL …
Remarkably, these models achieve impressive zero-shot classification performance on ImageNet [14] and demonstrate robustness to natural distribution shift datasets like ImageNet-V2 [46], … From openaccess.thecvf.com
MODULE 4: AI ROBUSTNESS- BENCHMARKING ADVERSARIAL ROBUSTNESS FOR IMAGE ...
Jun 4, 2022 Such datasets are significantly more likely in the real world implying that hedging for adversarial robustness does not necessarily prepare a model for real-world datasets. … From ucladeepvision.github.io
BENCHMARKING ADVERSARIAL ROBUSTNESS ON IMAGE CLASSIFICATION
Datasets: We use the CIFAR-10 [27] and ImageNet [46] datasets to perform adversarial robustness evaluation in this paper. We use the test set containing 10, 000 images of CIFAR … From openaccess.thecvf.com
In this paper, we establish a comprehensive and rigorous benchmark called ARES-Bench1to evaluate model robustness on the image classi ca- tion task. Our benchmark evaluates both … From arxiv.org
GITHUB - LAION-AI/CLIP_BENCHMARK: CLIP-LIKE MODEL EVALUATION
Here is an example for CIFAR-10 zero-shot classification using OpenCLIP's pre-trained model on LAION-400m: clip_benchmark eval --dataset=cifar10 --task=zeroshot_classification - … From github.com
PRE-TRAINED MODEL GUIDED FINE-TUNING FOR ZERO-SHOT …
From Fig. 1b, we can easily observe that the model fine-tuned on clean TinyImageNet, compared with the original CLIP, only shows improved accuracy when tested on TinyIma-geNet itself, … From openaccess.thecvf.com
A CLIP model (ViT-B/32) is able to match the Resnet50 model performance on Imagenet without ever seeing any training examples, in a zero-shot fashion. This model has a non-convolutional … From artofrobust.github.io
We show a summary of results on zero-shot classification and vision-language tasks for original and fine-tuned ViT-L/14 CLIP models. CLIP-only means that we evaluate the respective CLIP … From github.com
ADVERSARIAL DOMAIN ADAPTATION WITH CLIP FOR FEW-SHOT IMAGE ...
Nov 30, 2024 Few-shot learning focuses on training efficient models with limited amounts of training data. Its mainstream approaches have evolved from single-modal to multi-modal … From link.springer.com
EFFICIENTLY ROBUSTIFY PRE-TRAINED MODELS - CVF OPEN ACCESS
Fig. 2 shows the analysis of various CLIP model archi-tectures on the ImageNet-R, ObjectNet and ObjectNet-C datasets under both Linear Probe and zero-shot settings along with the unimodal … From openaccess.thecvf.com
ABSTRACT ly robust zero-shot image classifier. We ground our work on CLIP, a vision-language pre-trained encoder model that can perform zero-shot classification by matching an image with … From arxiv.org
A COMPREHENSIVE STUDY ON ROBUSTNESS OF IMAGE CLASSIFICATION MODELS ...
Aug 9, 2024 For image classification models in the evaluation, we consider 61 models on ImageNet covering two mainstream types of network architectures: CNNs and Transformers, … From link.springer.com
IMPROVING ZERO-SHOT GENERALIZATION AND ROBUSTNESS OF MULTI …
1. Introduction Vision-language multi-modal models trained on large-scale data have achieved significant success in numerous domains and have demonstrated excellent zero-shot gener … From openaccess.thecvf.com
A COMPREHENSIVE STUDY ON ROBUSTNESS OF IMAGE CLASSIFICATION MODELS ...
Feb 28, 2023 In our benchmark, we evaluate the robustness of 55 typical deep learning models on ImageNet with diverse architectures (e.g., CNNs, Transformers) and learning algorithms … From arxiv.org
The EfficientNet-B7 trained with AdvProp reports the strongest results on these datasets—it obtains 52.9% mCE on ImageNet-C, 44.7% top-1 accuracy on ImageNet-A and 26.6% top-1 … From openaccess.thecvf.com
GITHUB - ZHANGMINGKUN1/CLIPURE: IMPLEMENTATION CODE FOR THE …
Mar 7, 2013 We conducted extensive experiments on CIFAR-10, ImageNet, and 13 datasets that previous CLIP-based defense methods used for evaluating zero-shot classification robustness. From github.com
ENHANCING IMAGE CLASSIFICATION ROBUSTNESS THROUGH …
Unlike conventional methods that attack the model directly, our approach sources adversarial pertur-bations from higher-level tasks and integrates them into the training of new tasks. This … From openaccess.thecvf.com
Apr 18, 2025 4. Evaluation Results Summary The CLIP repository doesn't directly include the evaluation results in code form, but the paper demonstrates that: CLIP achieves competitive … From deepwiki.com
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