2022-06-03 更新
CyCLIP: Cyclic Contrastive Language-Image Pretraining
Authors:Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A. Rossi, Vishwa Vinay, Aditya Grover
Recent advances in contrastive representation learning over paired image-text data have led to models such as CLIP that achieve state-of-the-art performance for zero-shot classification and distributional robustness. Such models typically require joint reasoning in the image and text representation spaces for downstream inference tasks. Contrary to prior beliefs, we demonstrate that the image and text representations learned via a standard contrastive objective are not interchangeable and can lead to inconsistent downstream predictions. To mitigate this issue, we formalize consistency and propose CyCLIP, a framework for contrastive representation learning that explicitly optimizes for the learned representations to be geometrically consistent in the image and text space. In particular, we show that consistent representations can be learned by explicitly symmetrizing (a) the similarity between the two mismatched image-text pairs (cross-modal consistency); and (b) the similarity between the image-image pair and the text-text pair (in-modal consistency). Empirically, we show that the improved consistency in CyCLIP translates to significant gains over CLIP, with gains ranging from 10%-24% for zero-shot classification accuracy on standard benchmarks (CIFAR-10, CIFAR-100, ImageNet1K) and 10%-27% for robustness to various natural distribution shifts. The code is available at https://github.com/goel-shashank/CyCLIP.
PDF 19 pages, 11 tables, 6 figures
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A Generalized Supervised Contrastive Learning Framework
Authors:Jaewon Kim, Jooyoung Chang, Sang Min Park
Based on recent remarkable achievements of contrastive learning in self-supervised representation learning, supervised contrastive learning (SupCon) has successfully extended the batch contrastive approaches to the supervised context and outperformed cross-entropy on various datasets on ResNet. In this work, we present GenSCL: a generalized supervised contrastive learning framework that seamlessly adapts modern image-based regularizations (such as Mixup-Cutmix) and knowledge distillation (KD) to SupCon by our generalized supervised contrastive loss. Generalized supervised contrastive loss is a further extension of supervised contrastive loss measuring cross-entropy between the similarity of labels and that of latent features. Then a model can learn to what extent contrastives should be pulled closer to an anchor in the latent space. By explicitly and fully leveraging label information, GenSCL breaks the boundary between conventional positives and negatives, and any kind of pre-trained teacher classifier can be utilized. ResNet-50 trained in GenSCL with Mixup-Cutmix and KD achieves state-of-the-art accuracies of 97.6% and 84.7% on CIFAR10 and CIFAR100 without external data, which significantly improves the results reported in the original SupCon (1.6% and 8.2%, respectively). Pytorch implementation is available at https://t.ly/yuUO.
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Dog nose print matching with dual global descriptor based on Contrastive Learning
Authors:Bin Li, Zhongan Wang, Nan Wu, Shuai Shi, Qijun Ma
Recent studies in biometric-based identification tasks have shown that deep learning methods can achieve better performance. These methods generally extract the global features as descriptor to represent the original image. Nonetheless, it does not perform well for biometric identification under fine-grained tasks. The main reason is that the single image descriptor contains insufficient information to represent image. In this paper, we present a dual global descriptor model, which combines multiple global descriptors to exploit multi level image features. Moreover, we utilize a contrastive loss to enlarge the distance between image representations of confusing classes. The proposed framework achieves the top2 on the CVPR2022 Biometrics Workshop Pet Biometric Challenge. The source code and trained models are publicly available at: https://github.com/flyingsheepbin/pet-biometrics
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Strongly Augmented Contrastive Clustering
Authors:Xiaozhi Deng, Dong Huang, Ding-Hua Chen, Chang-Dong Wang, Jian-Huang Lai
Deep clustering has attracted increasing attention in recent years due to its capability of joint representation learning and clustering via deep neural networks. In its latest developments, the contrastive learning has emerged as an effective technique to substantially enhance the deep clustering performance. However, the existing contrastive learning based deep clustering algorithms mostly focus on some carefully-designed augmentations (often with limited transformations to preserve the structure), referred to as weak augmentations, but cannot go beyond the weak augmentations to explore the more opportunities in stronger augmentations (with more aggressive transformations or even severe distortions). In this paper, we present an end-to-end deep clustering approach termed strongly augmented contrastive clustering (SACC), which extends the conventional two-augmentation-view paradigm to multiple views and jointly leverages strong and weak augmentations for strengthened deep clustering. Particularly, we utilize a backbone network with triply-shared weights, where a strongly augmented view and two weakly augmented views are incorporated. Based on the representations produced by the backbone, the weak-weak view pair and the strong-weak view pairs are simultaneously exploited for the instance-level contrastive learning (via an instance projector) and the cluster-level contrastive learning (via a cluster projector), which, together with the backbone, can be jointly optimized in a purely unsupervised manner. Experimental results on five challenging image datasets have shown the superior performance of the proposed SACC approach over the state-of-the-art.
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FaIRCoP: Facial Image Retrieval using Contrastive Personalization
Authors:Devansh Gupta, Aditya Saini, Drishti Bhasin, Sarthak Bhagat, Shagun Uppal, Rishi Raj Jain, Ponnurangam Kumaraguru, Rajiv Ratn Shah
Retrieving facial images from attributes plays a vital role in various systems such as face recognition and suspect identification. Compared to other image retrieval tasks, facial image retrieval is more challenging due to the high subjectivity involved in describing a person’s facial features. Existing methods do so by comparing specific characteristics from the user’s mental image against the suggested images via high-level supervision such as using natural language. In contrast, we propose a method that uses a relatively simpler form of binary supervision by utilizing the user’s feedback to label images as either similar or dissimilar to the target image. Such supervision enables us to exploit the contrastive learning paradigm for encapsulating each user’s personalized notion of similarity. For this, we propose a novel loss function optimized online via user feedback. We validate the efficacy of our proposed approach using a carefully designed testbed to simulate user feedback and a large-scale user study. Our experiments demonstrate that our method iteratively improves personalization, leading to faster convergence and enhanced recommendation relevance, thereby, improving user satisfaction. Our proposed framework is also equipped with a user-friendly web interface with a real-time experience for facial image retrieval.
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Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation
Authors:Ange Lou, Kareem Tawfik, Xing Yao, Ziteng Liu, Jack Noble
Segmentation of images is a popular topic in medical AI. This is mainly due to the difficulty to obtain a significant number of pixel-level annotated data to train a neural network. To address this issue, we proposed a semi-supervised segmentation network based on contrastive learning. In contrast to the previous state-of-the-art, we introduce Min-Max Similarity (MMS), a contrastive learning form of dual-view training by employing classifiers and projectors to build all-negative, and positive and negative feature pairs respectively to formulate the learning problem as solving min-max similarity problem. The all-negative pairs are used to supervise the networks learning from different views and make sure to capture general features, and the consistency of unlabeled predictions is measured by pixel-wise contrastive loss between positive and negative pairs. To quantitative and qualitative evaluate our proposed method, we test it on two public endoscopy surgical tool segmentation datasets and one cochlear implant surgery dataset which we manually annotate the cochlear implant in surgical videos. The segmentation performance (dice coefficients) indicates that our proposed method outperforms state-of-the-art semi-supervised and fully supervised segmentation algorithms consistently. And our semi-supervised segmentation algorithm can successfully recognize unknown surgical tools and provide good predictions. Also, our MMS could achieve about 40 frames per second (fps) and suitable to deal with the real-time video segmentation.
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