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CREPE: Can vision-Language Foundation Models Reason Compositionally?

CREPE: Can Vision-Language Foundation Models Reason Composit...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ma, Zixian Hong, Jerry Gul, Mustafa Omer Ciandhi, Mona Geo, Irena krishna, Ranjay Stanford Univ Stanford CA 94305 USA Cornell Univ Ithaca NY USA Univ Penn Philadelphia PA USA Univ Washington Seattle WA USA

ISBN: (纸本)9798350301298

A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and language pretraining, we find that-across 7 architectures trained with 4 algorithms on massive datasets-they struggle at compositionality. To arrive at this conclusion, we introduce a new compositionality evaluation benchmark, CREPE, which measures two important aspects of compositionality identified by cognitive science literature: systematicity and productivity. To measure systematicity, CREPE consists of a test dataset containing over 370K image-text pairs and three different seen-unseen splits. The three splits are designed to test models trained on three popular training datasets: CC-12M, YFCC-15M, and LAION-400M. We also generate 325K, 316K, and 309K hard negative captions for a subset of the pairs. To test productivity, CREPE contains 17K image-text pairs with nine different complexities plus 278K hard negative captions with atomic, swapping and negation foils. The datasets are generated by repurposing the Visual Genome scene graphs and region descriptions and applying handcrafted templates and GPT-3. For systematicity, we find that model performance decreases consistently when novel compositions dominate the retrieval set, with Recall@1 dropping by up to 9%. For productivity, models' retrieval success decays as complexity increases, frequently nearing random chance at high complexity. These results hold regardless of model and training dataset size.

关键词： language reasoning vision

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Indescribable Multi-modal Spatial Evaluator

Indescribable Multi-modal Spatial Evaluator

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kong, Lingke Qi, X. Sharon Shen, Qijin Wang, Jiacheng Zhang, Jingyi Hu, Yanle Zhou, Qichao Manteia Tech Xiamen Peoples R China Univ Calif Los Angeles Los Angeles CA USA Fuzhou Univ Fuzhou Peoples R China Xiamen Univ Xiamen Peoples R China Mayo Clin Arizona Phoenix AZ 85054 USA

ISBN: (纸本)9798350301298

Multi-modal image registration spatially aligns two images with different distributions. One of its major challenges is that images acquired from different imaging machines have different imaging distributions, making it difficult to focus only on the spatial aspect of the images and ignore differences in distributions. In this study, we developed a self-supervised approach, Indescribable Multi-model Spatial Evaluator (IMSE), to address multi-modal image registration. IMSE creates an accurate multi-modal spatial evaluator to measure spatial differences between two images, and then optimizes registration by minimizing the error predicted of the evaluator. To optimize IMSE performance, we also proposed a new style enhancement method called Shuffle Remap which randomizes the image distribution into multiple segments, and then randomly disorders and remaps these segments, so that the distribution of the original image is changed. Shuffle Remap can help IMSE to predict the difference in spatial location from unseen target distributions. Our results show that IMSE outperformed the existing methods for registration using T1-T2 and CT-MRI datasets. IMSE also can be easily integrated into the traditional registration process, and can provide a convenient way to evaluate and visualize registration results. IMSE also has the potential to be used as a new paradigm for image-to-image translation. Our code is available at https://***/Kid-Liet/IMSE.

关键词： cell microscopy Medical and biological vision

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CaPriDe Learning: Confidential and Private Decentralized Learning based on Encryption-friendly Distillation Loss

CaPriDe Learning: Confidential and Private Decentralized Lea...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tastan, Nurbek Nandakumar, Karthik Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

Large volumes of data required to train accurate deep neural networks (DNNs) are seldom available with any single entity. Often, privacy concerns prevent entities from sharing data with each other or with a third-party learning service provider. While cross-silo federated learning (FL) allows collaborative learning of large DNNs without sharing the data itself, most existing cross-silo FL algorithms have an unacceptable utility-privacy trade-off. In this work, we propose a framework called Confidential and Private Decentralized (CaPriDe) learning, which optimally leverages the power of fully homomorphic encryption (FHE) to enable collaborative learning without compromising on the confidentiality and privacy of data. In CaPridDe learning, participating entities release their private data in an encrypted form allowing other participants to perform inference in the encrypted domain. The crux of CaPriDe learning is mutual knowledge distillation between multiple local models through a novel distillation loss, which is an approximation of the Kullback-Leibler (KL) divergence between the local predictions and encrypted inferences of other participants on the same data that can be computed in the encrypted domain. Extensive experiments on three datasets show that CaPriDe learning can improve the accuracy of local models without any central coordination, provide strong guarantees of data confidentiality and privacy, and has the ability to handle statistical heterogeneity. Constraints on the model architecture (arising from the need to be FHE-friendly), limited scalability, and computational complexity of encrypted domain inference are the main limitations of the proposed approach. The code can be found at https://***/tnurbek/capride-learning.

关键词： accountability ethics in vision fairness privacy Transparency

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Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data

Best of Both Worlds: Multimodal Contrastive Learning with Ta...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hager, Paul Menten, Martin J. Rueckert, Daniel Tech Univ Munich Munich Germany Klinikum Rechts Der Isar Munich Germany Imperial Coll London London England

ISBN: (纸本)9798350301298

Medical datasets and especially biobanks, often contain extensive tabular data with rich clinical information in addition to images. In practice, clinicians typically have less data, both in terms of diversity and scale, but still wish to deploy deep learning solutions. Combined with increasing medical dataset sizes and expensive annotation costs, the necessity for unsupervised methods that can pretrain multimodally and predict unimodally has risen. To address these needs, we propose the first self-supervised contrastive learning framework that takes advantage of images and tabular data to train unimodal encoders. Our solution combines SimCLR and SCARF, two leading contrastive learning strategies, and is simple and effective. In our experiments, we demonstrate the strength of our framework by predicting risks of myocardial infarction and coronary artery disease (CAD) using cardiac MR images and 120 clinical features from 40,000 UK Biobank subjects. Furthermore, we show the generalizability of our approach to natural images using the DVM car advertisement dataset. We take advantage of the high interpretability of tabular data and through attribution and ablation experiments find that morphometric tabular features, describing size and shape, have outsized importance during the contrastive learning process and improve the quality of the learned embeddings. Finally, we introduce a novel form of supervised contrastive learning, label as a feature (LaaF), by appending the ground truth label as a tabular feature during multimodal pretraining, outperforming all supervised contrastive baselines.

关键词： cell microscopy Medical and biological vision

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Rethinking Federated Learning with Domain Shift: A Prototype View

Rethinking Federated Learning with Domain Shift: A Prototype...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Huang, Wenke Ye, Mang Shi, Zekun Li, He Du, Bo Wuhan Univ Sch Comp Sci Hubei Key Lab Multimedia & Network Commun Engn Natl Engn Res Ctr Multimedia SoftwareInst Artifi Wuhan Peoples R China Hubei Luojia Lab Wuhan Peoples R China

ISBN: (纸本)9798350301298

Federated learning shows a bright promise as a privacy-preserving collaborative learning technique. However, prevalent solutions mainly focus on all private data sampled from the same domain. An important challenge is that when distributed data are derived from diverse domains. The private model presents degenerative performance on other domains (with domain shift). Therefore, we expect that the global model optimized after the federated learning process stably provides generalizability performance on multiple domains. In this paper, we propose Federated Prototypes Learning (FPL) for federated learning under domain shift. The core idea is to construct cluster prototypes and unbiased prototypes, providing fruitful domain knowledge and a fair convergent target. On the one hand, we pull the sample embedding closer to cluster prototypes belonging to the same semantics than cluster prototypes from distinct classes. On the other hand, we introduce consistency regularization to align the local instance with the respective unbiased prototype. Empirical results on Digits and Office Caltech tasks demonstrate the effectiveness of the proposed solution and the efficiency of crucial modules.

关键词： Efficient and scalable vision

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Annealing-based Label-Transfer Learning for Open World Object Detection

Annealing-based Label-Transfer Learning for Open World Objec...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ma, Yuqing Li, Hainan Zhang, Zhange Guo, Jinyang Zhang, Shanghang Gong, Ruihao Liu, Xianglong Beihang Univ SKLSDE Lab Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China Peking Univ Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Hefei Comprehens Natl Sci Ctr Inst Data Space Hefei Peoples R China

ISBN: (纸本)9798350301298

Open world object detection (OWOD) has attracted extensive attention due to its practicability in the real world. Previous OWOD works manually designed unknown-discover strategies to select unknown proposals from the background, suffering from uncertainties without appropriate priors. In this paper, we claim the learning of object detection could be seen as an object-level feature-entanglement process, where unknown traits are propagated to the known proposals through convolutional operations and could be distilled to benefit unknown recognition without manual selection. Therefore, we propose a simple yet effective Annealing-based Label-Transfer framework, which sufficiently explores the known proposals to alleviate the uncertainties. Specifically, a Label-Transfer Learning paradigm is introduced to decouple the known and unknown features, while a Sawtooth Annealing Scheduling strategy is further employed to rebuild the decision boundaries of the known and unknown classes, thus promoting both known and unknown recognition. Moreover, previous OWOD works neglected the trade-off of known and unknown performance, and we thus introduce a metric called Equilibrium Index to comprehensively evaluate the effectiveness of the OWOD models. To the best of our knowledge, this is the first OWOD work without manual unknown selection. Extensive experiments conducted on the common-used benchmark validate that our model achieves superior detection performance (200% unknown mAP improvement with the even higher known detection performance) compared to other state-of-the-art methods. Our code is available at https://***/DIG-Beihang/***.

关键词： detection recognition: Categorization retrieval

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Global vision Transformer Pruning with Hessian-Aware Saliency

Global Vision Transformer Pruning with Hessian-Aware Salienc...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Huanrui Yin, Hongxu Shen, Maying Molchanov, Pavlo Li, Hai Kautz, Jan NVIDIA Santa Clara CA 95051 USA Univ Calif Berkeley Berkeley CA 94720 USA Duke Univ Durham NC 27706 USA

ISBN: (纸本)9798350301298

Transformers yield state-of-the-art results across many tasks. However, their heuristically designed architecture impose huge computational costs during inference. This work aims on challenging the common design philosophy of the vision Transformer (ViT) model with uniform dimension across all the stacked blocks in a model stage, where we redistribute the parameters both across transformer blocks and between different structures within the block via the first systematic attempt on global structural pruning. Dealing with diverse ViT structural components, we derive a novel Hessian-based structural pruning criteria comparable across all layers and structures, with latency-aware regularization for direct latency reduction. Performing iterative pruning on the DeiT-Base model leads to a new architecture family called NViT (Novel ViT), with a novel parameter redistribution that utilizes parameters more efficiently. On ImageNet-1K, NViT-Base achieves a 2.6x FLOPs reduction, 5.1x parameter reduction, and 1.9x run-time speedup over the DeiT-Base model in a near lossless manner. Smaller NViT variants achieve more than 1% accuracy gain at the same throughput of the DeiT Small/Tiny variants, as well as a lossless 3.3x parameter reduction over the SWIN-Small model. These results outperform prior art by a large margin. Further analysis is provided on the parameter redistribution insight of NViT, where we show the high prunability of ViT models, distinct sensitivity within ViT block, and unique parameter distribution trend across stacked ViT blocks. Our insights provide viability for a simple yet effective parameter redistribution rule towards more efficient ViTs for off-the-shelf performance boost.

关键词： Efficient and scalable vision

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Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression recognition

Multi-Task Multi-Modal Self-Supervised Learning for Facial E...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Halawa, Marah Blume, Florian Bideau, Pia Maier, Martin Rahman, Rasha Abdel Hellwich, Olaf Tech Univ Berlin Berlin Germany Univ Grenoble Alpes Grenoble INP CNRS INRIALJK Grenoble France Humboldt Univ Berlin Germany Res Cluster Excellence Sci Intelligence Berlin Germany

ISBN: (纸本)9798350365474

Human communication is multi-modal;e.g., face-to-face interaction involves auditory signals (speech) and visual signals (face movements and hand gestures). Hence, it is essential to exploit multiple modalities when designing machine learning-based facial expression recognition systems. In addition, given the ever-growing quantities of video data that capture human facial expressions, such systems should utilize raw unlabeled videos without requiring expensive annotations. Therefore, in this work, we employ a multitask multi-modal self-supervised learning method for facial expression recognition from in-the-wild video data. Our model combines three self-supervised objective functions: First, a multi-modal contrastive loss, that pulls diverse data modalities of the same video together in the representation space. Second, a multi-modal clustering loss that preserves the semantic structure of input data in the representation space. Finally, a multi-modal data reconstruction loss. We conduct a comprehensive study on this multimodal multi-task self-supervised learning method on three facial expression recognition benchmarks. To that end, we examine the performance of learning through different combinations of self-supervised tasks on the facial expression recognition downstream task. Our model ConCluGen outperforms several multi-modal self-supervised and fully supervised baselines on the CMU-MOSEI dataset. Our results generally show that multi-modal self-supervision tasks offer large performance gains for challenging tasks such as facial expression recognition, while also reducing the amount of manual annotations required. We release our pre-trained models as well as source code publicly (1) .

关键词： computer vision facial expression recognition multi-modal representation learning self-supervised

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BAEFormer: Bi-directional and Early Interaction Transformers for Bird's Eye View Semantic Segmentation

BAEFormer: Bi-directional and Early Interaction Transformers...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Pan, Cong He, Yonghao Peng, Junran Zhang, Qian Sui, Wei Zhang, Zhaoxiang Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Future Technol Beijing Peoples R China Horizon Robot Beijing Peoples R China Huawei Inc Shenzhen Guangdong Peoples R China HKISI CAS Ctr Artificial Intelligence & Robot Beijing Peoples R China

ISBN: (纸本)9798350301298

Bird's Eye View (BEV) semantic segmentation is a critical task in autonomous driving. However, existing Transformer-based methods confront difficulties in transforming Perspective View (PV) to BEV due to their unidirectional and posterior interaction mechanisms. To address this issue, we propose a novel Bi-directional and Early Interaction Transformers framework named BAEFormer, consisting of (i) an early-interaction PV-BEV pipeline and (ii) a bi-directional cross-attention mechanism. Moreover, we find that the image feature maps' resolution in the cross-attention module has a limited effect on the final performance. Under this critical observation, we propose to enlarge the size of input images and downsample the multiview image features for cross-interaction, further improving the accuracy while keeping the amount of computation controllable. Our proposed method for BEV semantic segmentation achieves state-of-the-art performance in real-time inference speed on the nuScenes dataset, i.e., 38.9 mIoU at 45 FPS on a single A100 GPU.

关键词： 3D from multi-view and sensors

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ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection

ZBS: Zero-shot Background Subtraction via Instance-level Bac...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： An, Yongqi Zhao, Xu Yu, Tao Guo, Haiyun Zhao, Chaoyang Tang, Ming Wang, Jinqiao Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350301298

Background subtraction (BGS) aims to extract all moving objects in the video frames to obtain binary foreground segmentation masks. Deep learning has been widely used in this field. Compared with supervised-based BGS methods, unsupervised methods have better generalization. However, previous unsupervised deep learning BGS algorithms perform poorly in sophisticated scenarios such as shadows or night lights, and they cannot detect objects outside the pre-defined categories. In this work, we propose an unsupervised BGS algorithm based on zero-shot object detection called Zero-shot Background Subtraction (ZBS). The proposed method fully utilizes the advantages of zero-shot object detection to build the open-vocabulary instance-level background model. Based on it, the foreground can be effectively extracted by comparing the detection results of new frames with the background model. ZBS performs well for sophisticated scenarios, and it has rich and extensible categories. Furthermore, our method can easily generalize to other tasks, such as abandoned object detection in unseen environments. We experimentally show that ZBS surpasses state-of-the-art unsupervised BGS methods by 4.70% F-Measure on the CDnet 2014 dataset. The code is released at https://***/CASIA-IVA-Lab/ZBS.

关键词： and tracking motion Video: Low-level analysis

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