: Few shot linear probes Here we take 10% of the validation set, and use it to train the probes. Zero-shot CLIP优于few-shot线性探头。 Zero-shot CLIP与在同一特征空间上训练的4-shot Linear Probe CLIP的平均性能相匹敌，并且与现有可用的16-shot线性分类器的最佳性能相匹敌。 When applying a ResNet model to other domains, a standard approach is to use a “linear probe”. the last-layer weights of the vision encoder (i. This problem has drawn tremendous attention for its projection to prevailing real-world applications, such as product categorization for newly added commodity categories on an E-commerce platform with scarce records or diagnoses for rare Zero-Shot性能为了分析CLIP Zero-Shot的性能，论文这里选择ResNet-50进行Linear Probe方式的监督学习作为基线模型，即将ResNet-50最后几层所学到的特征输入到一个分类器中，然后针对特定数据集进行微调（仅微调分类器的参数）。 Dec 11, 2022 · Few-shot node classification is tasked to provide accurate predictions for nodes from novel classes with only few representative labeled nodes. Zero-shot CLIP performs competitively against fully supervised Linear Probe on ResNet50 on a wide array of tasks (wins in 16/27 datasets). The standard linear probe (LP), initially evaluated as a few-shot adaptati utilizes the frozen vision features. for those asking: The last set of CIFAR-10 tests was 50,000 images × 64 patches ≈ 3. This problem has drawn tremendous attention for its projection to prevailing real-world applications, such as product categorization for newly added commodity categories on an E-commerce platform with scarce records or diagnoses for rare diseases on a Dec 8, 2022 · Zero-shot CLIP outperforms few-shot linear probes. The standard linear probe (LP), initially evaluated as a few-shot adaptation baseline in the CLIP paper [23], is a linear classifier that exclusively utilizes the frozen vision features. 7w次，点赞20次，收藏34次。线性探测（LinearProbing）是一种用于评估预训练模型性能的方法，通过替换模型的最后一层为线性层并保持其余部分不变。在此过程中，仅训练这个线性层，以测试模型的表征学习能力。该技术常用于自监督学习模型的评测，如何恺明的MAE模型。线性探测通常 LP++: A Surprisingly Strong Linear Probe for Few-Shot CLIP: Paper and Code. Apr 2, 2024 · 该方法的优点包括：黑盒操作，减少了优化超参数的验证搜索，比当前few-shot CLIP适应方法快几个数量级，并且在few-shot CLIP性能方面表现出了出乎意料的竞争力。 Evaluating few-shot linear probe performance on ImageNet. Hence, for a fair comparison, we re-evaluated Tip-Adapter-F* (Table 1) by (i) finding the ini-tial value of αinit ∈ [1, 10] on the small validation set de-ployed for all methods, and (ii) setting βinit = 1. We would regard this as few to many-shot learning. 7k次，点赞10次，收藏40次。本文详细介绍CLIP模型原理，包括对比学习目标、模型结构、训练数据集等，并通过zero-shot推理与linear probe分类任务验证模型性能。 We would like to show you a description here but the site won’t allow us. Jose Dolz's home pageSelected Awards Outstanding Reviewer, CVPR 2025 Best Paper Award: Towards foundation models and few-shot parameter-efficient fine-tuning for volumetric organ segmentation. We introduced LP++, a strong linear probe for few-shot CLIP adaptation. Oct 4, 2024 · Bibliographic details on LP++: A Surprisingly Strong Linear Probe for Few-Shot CLIP. The standard linear - probe (LP) baseline only uses frozen vision features and has lower performance compared to zero - shot predictions as it omits text encoder information. Dec 21, 2022 · View a PDF of the paper titled Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-shot In-Context Learners, by Hyunsoo Cho and 6 other authors Apr 2, 2024 · In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. 文章浏览阅读879次，点赞25次，收藏10次。“少样本线性探针”（Few-shot Linear Probe）是机器学习中一种评估预训练模型“特征迁移能力”的标准化方法，核心是用极少的标注数据（每个类别几个样本）训练一个简单的线性分类器，来测试预训练模型提取的特征是否足够通用。它不是一种“训练模型的 Note that there are two es-sential differences between the proposed CLIP-FSAR and the original linear-probe CLIP [43]: 1) The original linear-probe CLIP [43] performs a linear-probe evaluation for ac-tion recognition and needs to finetune on the test classes, which is completely different from our metric-based few-shot setup, where the training Few-shot CLIP Beyond its zero-shot capabilities, the CLIP model has also been explored for few-shot image clas-sification. In this work, we propose and examine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear classifier Aug 23, 2025 · Proposed Methods and Techniques In the context of molecular few-shot learning, two main approaches have been explored: the Linear Probe and the quadratic probe. Our findings reveal that ICL achieves the best performance, with commercial models like GPT Abstract Few-shot node classification is tasked to provide accurate predictions for nodes from novel classes with only few representative labeled nodes. 2021): (1) The original linear-probe CLIP (Radford et al. Apr 2, 2024 · In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. e. t. Few-shot CLIP Beyond its zero-shot capabilities, the CLIP model has also been explored for few-shot image clas-sification. Our results show that this method outperforms a linear probe with few-shot learning when using a small number of samples to tune the prompt configuration. The remarkable efficiency of Repre suggests its unique capability to leverage minimal data for maximum performance. Pitfalls of few-shot adapters due to the absence of a model selection strategy. Overall, LaBo demonstrates that inherently interpretable models can be widely applied at similar, or better, performance than black box approaches. That is where the ResNet learned features (from the last few layers) are fed into a linear classifier that is then fine-tuned for a specific dataset. In this work, we propose and examine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear classifier # Few-shot version too. Abstract In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often re-ported as a weak baseline. In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline A revisited zero-shot initialized Linear Probe (ZS-LP), tailored for CLIP-alike vision-language models. In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. Jun 22, 2024 · In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. In this work, we propose and exam-ine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear classifier Nov 2, 2022 · 作者对 zero-shot CLIP，few-shot CLIP 和之前 few-shot 的一些方法（预训练好冻结参数，然后做 linear probe，在下游任务数据集上进行训练）做了一些比较。 A novel view to mitigate the influence of noisy labels, CLIP-based Robust Few-shot learning (CRoF), which is a general plug-in module for CLIP-based models and outperforms fine-tuned and vanilla CLIP models on different noise types and noise ratios. Then, we maintain the implementation of the Sep 10, 2025 · 文章浏览阅读5. The results demonstrate accurate classification of extravasation severities with training using only 64 instances per class, achieving average F1 macro scores of 74. However, the existing literature predominantly focuses on transductive few-shot node classification, neglecting the widely studied inductive setting in the broader few-shot learning community. Jun 20, 2025 · Using the vanilla linear probe, Fig. The proposed text - driven linear probe (LP+text) integrates text knowledge through learnable class - wise multipliers In the evaluation with 11 diverse datasets, LaBo bottlenecks excel at few-shot classification: they are 11. However, transductive linear probing shows that fine-tuning a simple linear classification head after a pretrained graph neural networks can outperforms most of the sophisticated-designed graph meta learning algorithms. We introduce a CLass-Adaptive linear Probe (CLAP) objective, that constraints the learned prototypes to retain prior zero-shot knowledge adaptely based only on the few support shots, and uses an homogeneus learning configuration accross tasks. The other probes’ input all includes few-shot examples. Using the vanilla linear probe, Fig. A constraint formulation to retain prior knowledge of the robust zero-shot prototypes per class, CLass adaptive Linear Probing (CLAP). By eliciting these “safety heads” with few-shot linear probes and con-structing a detector based on their activations, malicious prompts can be identified and rejec standard linear-probe (LP) baseline. This has motivated intensive research building convoluted prompt learning or feature adaptation strategies. To save compute we train on all of the data simultaneously across shifts # In the paper we did this per-shift. Abstract This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS), with a focus on two significant is-sues in the state-of-the-art: foreground leakage and sparse point distribution. 3 (b) provides evidence that the performance gains from using few-shot validation sets are smaller than leveraging this data for training a validation-free approach that follows a fixed setting and does not require model selection. Models trained on LAION show similar transfer Oct 23, 2024 · The standard linear probe (LP), initially evaluated as a few-shot adaptation baseline in the CLIP paper [23], is a linear classifier that exclusively utilizes the frozen vision features. Meta learning has been the most popular solution for few-shot learning problem. , the Sep 30, 2024 · We also explore the zero-shot prediction potential of contrastive prompting using positive and negative landscape aesthetic concepts. Introducing HoloBench-v0. In [40], the authors evaluated linear probe, which performs a simple fine-tuning of the visual encoder’s final layer using a few-shot support set (i. This oversight limits our comprehensive Graph few-shot learning aims to predict well by training with very few labeled data. Apr 27, 2024 · We introduce a few-shot benchmark consisting of 7 different classification tasks native to the Polish language. , a few labeled sam-ples in the downstream task). 7% more accurate than black box linear probes at 1 shot and comparable with more data. Therefore Note that there are two essential differences between the proposed CLIP-FSAR and the original linear-probe CLIP (Radford et al. The former arises from non-uniform point sampling, allowing models to distinguish the density disparities between foreground and background for easier segmentation. It optimizes the following cross-entropy loss w. In this work, we propose and examine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear classifier CVPR 2024 paper: LP++: A Surprisingly Strong Linear Probe for Few-Shot CLIP Introduction LP++ is a simple generalization of the standard linear-probe classifier, which integrates text knowledge: We express the linear classifier weights as learnable functions of the text embeddings, with class-wise multipliers blending image and text features. About [CVPR 2024] Validation-free few-shot adaptation of CLIP, using a well-initialized Linear Probe (ZSLP) and class-adaptive constraints (CLAP). r. 2M visual tokens per Abstract In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often re-ported as a weak baseline. Sep 20, 2025 · 文章浏览阅读1. Oct 25, 2021 · Using a linear probe, CLIP beats other models in a few-shot context (up to 16 instances), and interestingly its 0-shot approach beats few shots up to 4. 1st International Workshop on Foundation Models for General Medical AI Outstanding Reviewer, ICCV 2023 Outstanding Reviewer, NeurIPS 2022 Outstanding Reviewer, CVPR 2022 Outstanding Reviewer, CVPR 2021 Sep 16, 2023 · Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting. import numpy as np. The few - shot image classification involves a foundation model pre - trained on image - text pairs. The cross-shift model selection matri-ces (i, j) depict the relative improvement w. Enhancing in-context learning with foundation models via few-shot linear probe calibration Download PDF In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. In this work, we propose and exam-ine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear Our methodology applies linear probe techniques to feature vectors obtained from CLIP using few-shot instances. In this work, we propose and exam-ine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear Through our experiments, we found that the initial value of α plays a substantial role in the final performance of the Tip-Adapter-F model. We evaluate i) models trained on various LAION subsets and ii) the original CLIP models. In this work, we propose and examine from convex-optimization perspectives a May 22, 2024 · Typically, Linear Probe is regarded as a less capable few-shot framework as compared to those few-shot prompt learners such as MaPLe and CoOp. In this work, we propose and exam-ine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear classifier We propose a novel approach that meets the requirements of real-world scenarios. Zero-shot CLIP roughly matches the performance of the best performing 16-shot classifier in the evaluation suite, which uses the features of a BiT-M ResNet-152×2 trained on ImageNet-21K. 3(b) provides evidence that the performance gains from using few-shot val-idation sets are smaller than leveraging this data for training a validation-free approach that follows a fixed setting and does not require model selection. 2 — The First Benchmark Suite Designed for Holonomic AI. erceptions towards malicious prompts. This has motivated intensive research building convoluted prompt learning or feature … Jun 14, 2023 · Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting. a zero-shot initialized Linear Probing when using the optimal hyperparameters for the dataset i (rows), for adapting in another task j (columns), for each SoTA method (first three plots) and our approach (last plot). 2021) per-forms a linear-probe evaluation for action recognition and needs to finetune on the test classes, which is completely different from our metric-based Abstract In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often re-ported as a weak baseline. The latter results from Jul 15, 2021 · Hello, could you please explain how you determined the L2 regularization strength for the few-shot linear probe? Did you use a hyperparameter sweep on the validation sets, and if so, how did you do that given that there isn't a lot of available training data in few-shot learning? Linear Probe CLIP To run linear probe baselines, make sure that your current working directory is lpclip/. Nov 4, 2022 · Linear-probe CLIP trains an additional linear classifier after the weight-frozen CLIP on the few-shot training set. ‘Linear Probe W/O Few-shot’ refers to, when getting hidden states f from Li in G, the input only includes the code without few-shot examples. We conducted an empirical comparison with 0 and 16 shots between fine-tuning, linear probing, SetFit, and in-context learning (ICL) using various pre-trained commercial and open-source models. Apr 2, 2024 · This paper proposes a training-free adaption method for CLIP to conduct few-shot classification, termed as Tip-Adapter, which not only inherits the training- free advantage of zero-shot CLIP but also performs comparably to those training-required approaches. A specific modeling of the classifier weights, blending visual prototypes and text embeddings via learnable multipliers, along with convex-optimization ingredients, often overlooked in deep learning practices, led to the surprising results. Feb 23, 2023 · By training on these embeddings, we can train a simple linear probe on only a few datapoints (tens to hundreds of examples) and still get good performance. Figure 1. Apr 2, 2024 · This work proposes and exam-ine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear classifier weights are learnable functions of the text embedding, with class-wise multipliers blending image and text knowledge. 08% for GroundingDINO-CLIP. CoOp adopts learnable prompts for training, and we select its best-performing variant for comparison, that is, placing the class token at the end of the 16-token prompts without class-specific contexts.

Few shot linear probes. It optimizes the following cross-entropy loss w.