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iCLIP: Bridging Image Classification and Contrastive Language-Image Pre-training for Visual recognition

iCLIP: Bridging Image Classification and Contrastive Languag...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wei, Yixuan Cao, Yue Zhang, Zheng Peng, Houwen Yao, Zhuliang Xie, Zhenda Hue, Han Guo, Baining Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

This paper presents a method that effectively combines two prevalent visual recognition methods, i.e., image classification and contrastive language-image pre-training, dubbed iCLIP. Instead of naive multi-task learning that use two separate heads for each task, we fuse the two tasks in a deep fashion that adapts the image classification to share the same formula and the same model weights with the language-image pre-training. To further bridge these two tasks, we propose to enhance the category names in image classification tasks using external knowledge, such as their descriptions in dictionaries. Extensive experiments show that the proposed method combines the advantages of two tasks well: the strong discrimination ability in image classification tasks due to the clean category labels, and the good zero-shot ability in CLIP tasks ascribed to the richer semantics in the text descriptions. In particular, it reaches 82.9% top-1 accuracy on IN-1K, and meanwhile surpasses CLIP by 1.8%, with similar model size, on zero-shot recognition of Kornblith 12-dataset benchmark. The code and models are publicly available at https: //***/weiyx16/iCLIP.

关键词： language reasoning vision

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Gated Recurrent Unit-Based RNN for Remote Photoplethysmography Signal Segmentation

Gated Recurrent Unit-Based RNN for Remote Photoplethysmograp...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sabour, Rita Meziati Benezeth, Yannick Univ Bourgogne Franche Comte Dijon France Univ Bourgogne Franche Comte ImViA Lab Dijon France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Remote Photoplethysmography (rPPG) enables quantifying blood volume variations in the skin tissues from an input video recording, using a regular RGB camera. Obtained pulse signals often contain noisy portions due to motion, leading researchers to put aside a great number of rPPG signals in their studies. In this paper, an approach using a Gated Recurrent Unit-based neural network model in order to identify reliable portions in rPPG signals is proposed. This is done by classifying rPPG signal samples into reliable and unreliable samples. For this purpose, rPPG and electrocardiography signals (ECG) were collected from 11 participants, rPPG signal samples were labeled (ECG was used as ground truth), and data were augmented to reach a total number of 11000 1-minute-long rPPG signals. We developed a model composed of a unidimensional CNN and a Bidirectional GRU (1D-CNN+B-GRU) for this study, and obtained an accuracy rate of 85.88%.

关键词： computer network reliability Neural networks Electrocardiography Logic gates Photoplethysmography Skin pattern recognition

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PolyFormer: Referring Image Segmentation as Sequential Polygon Generation

PolyFormer: Referring Image Segmentation as Sequential Polyg...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Jiang Ding, Hui Cai, Zhaowei Zhang, Yuting Satzoda, Ravi Kumar Mahadevan, Vijay Manmatha, R. Johns Hopkins Univ Baltimore MD 21218 USA AWS AI Labs Pasadena CA USA

ISBN: (纸本)9798350301298

In this work, instead of directly predicting the pixel-level segmentation masks, the problem of referring image segmentation is formulated as sequential polygon generation, and the predicted polygons can be later converted into segmentation masks. This is enabled by a new sequence-to-sequence framework, Polygon Transformer (PolyFormer), which takes a sequence of image patches and text query tokens as input, and outputs a sequence of polygon vertices autoregressively. For more accurate geometric localization, we propose a regression-based decoder, which predicts the precise floating-point coordinates directly, without any coordinate quantization error. In the experiments, PolyFormer outperforms the prior art by a clear margin, e.g., 5.40% and 4.52% absolute improvements on the challenging RefCOCO+ and RefCOCOg datasets. It also shows strong generalization ability when evaluated on the referring video segmentation task without fine-tuning, e.g., achieving competitive 61.5% J&F on the Ref-DAVIS17 dataset.

关键词： and reasoning language vision

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CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language

CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Div...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sanghi, Aditya Fu, Rao Liu, Vivian Willis, Karl D. D. Shayani, Hooman Khasahmadi, Amir H. Sridhar, Srinath Ritchie, Daniel Autodesk Res San Francisco CA 94105 USA Brown Univ Providence RI USA Columbia Univ New York NY USA

ISBN: (纸本)9798350301298

Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We introduce CLIP-Sculptor, a method to address these constraints by producing high-fidelity and diverse 3D shapes without the need for (text, shape) pairs during training. CLIP-Sculptor achieves this in a multi-resolution approach that first generates in a low-dimensional latent space and then upscales to a higher resolution for improved shape fidelity. For improved shape diversity, we use a discrete latent space which is modeled using a transformer conditioned on CLIP's image-text embedding space. We also present a novel variant of classifier-free guidance, which improves the accuracy-diversity trade-off. Finally, we perform extensive experiments demonstrating that CLIP-Sculptor outperforms state-of-the-art baselines.

关键词： vision + graphics

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Learned Compression of High Dimensional Image Datasets

Learned Compression of High Dimensional Image Datasets

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cole, Elizabeth Meng, Qingxi Pauly, John Vasanawala, Shreyas Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Radiol Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In many applications, such as burst photography and magnetic resonance imaging (MRI), multiple images are acquired to reduce the noise of the eventual reconstructed image. However, this leads to very high dimensional datasets which have redundant information across the various acquired images. In MRI, multiple images are acquired via multiple RF coil arrays in the scanner. Afterwards, coil compression is performed to convert the original set of coil images into a smaller set of virtual coil images to enable smaller datasets and faster computation time. However, traditional iterative coil compression methods are lossy and time-consuming. In this work, we propose a novel neural network-based coil compression method in pursuit of higher reconstruction accuracy and faster coil compression. Our learned compression method achieves up to 1.5x lower NRMSE and up to 10 times runtime speed compared to traditional methods on a benchmark test dataset.

关键词： Radio frequency Photography Image coding Runtime Magnetic resonance imaging conferences pattern recognition

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Weakly Supervised Video Emotion Detection and Prediction via Cross-Modal Temporal Erasing Network

Weakly Supervised Video Emotion Detection and Prediction via...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Zhicheng Wang, Lijuan Yang, Jufeng Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China

ISBN: (纸本)9798350301298

Automatically predicting the emotions of user-generated videos (UGVs) receives increasing interest recently. However, existing methods mainly focus on a few key visual frames, which may limit their capacity to encode the context that depicts the intended emotions. To tackle that, in this paper, we propose a cross-modal temporal erasing network that locates not only keyframes but also context and audio-related information in a weakly-supervised manner. In specific, we first leverage the intra- and inter-modal relationship among different segments to accurately select keyframes. Then, we iteratively erase keyframes to encourage the model to concentrate on the contexts that include complementary information. Extensive experiments on three challenging video emotion benchmarks demonstrate that our method performs favorably against state-of-the-art approaches. The code is released on https://***/nku-zhichengzhang/WECL.

关键词： vision applications and systems

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Light Source Separation and Intrinsic Image Decomposition under AC Illumination

Light Source Separation and Intrinsic Image Decomposition un...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yoshida, Yusaku Kawahara, Ryo Okabe, Takahiro Kyushu Inst Technol Dept Artificial Intelligence 680-4 Kawazu Iizuka Fukuoka 8208502 Japan

ISBN: (纸本)9798350301298

Artificial light sources are often powered by an electric grid, and then their intensities rapidly oscillate in response to the grid's alternating current (AC). Interestingly, the flickers of scene radiance values due to AC illumination are useful for extracting rich information on a scene of interest. In this paper, we show that the flickers due to AC illumination is useful for intrinsic image decomposition (IID). Our proposed method conducts the light source separation (LSS) followed by the IID under AC illumination. In particular, we reveal the ambiguity in the blind LSS via matrix factorization and the ambiguity in the IID assuming the diffuse reflection model, and then show why and how those ambiguities can be resolved via a physics-based approach. We experimentally confirmed that our method can recover the colors of the light sources, the diffuse reflectance values, and the diffuse and specular intensities (shadings) under each of the light sources, and that the IID under AC illumination is effective for application to auto white balancing.

关键词： Physics-based vision and shape-from-X

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Enriched Robust Multi-View Kernel Subspace Clustering

Enriched Robust Multi-View Kernel Subspace Clustering

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Mengyuan Liu, Kai Clemson Univ Clemson SC 29631 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Subspace clustering is to find underlying low-dimensional subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. Most existing methods suffer from two critical issues. First, they usually adopt a two-stage framework and isolate the processes of affinity learning, multi-view information fusion and clustering. Second, they assume the data lies in a linear subspace which may fail in practice as most real-world datasets may have non-linearity structures. To address the above issues, in this paper we propose a novel Enriched Robust Multi-View Kernel Subspace Clustering framework where the consensus affinity matrix is learned from both multi-view data and spectral clustering. Due to the objective and constraints which is difficult to optimize, we propose an iterative optimization method which is easy to implement and can yield closed solution in each step. Extensive experiments have validated the superiority of our method over state-of-the-art clustering methods.

关键词： computer vision Clustering methods conferences Optimization methods Benchmark testing Iterative methods Kernel

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Architectural Backdoors in Neural Networks

Architectural Backdoors in Neural Networks

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Bober-Irizar, Mikel Shumailov, Ilia Zhao, Yiren Mullins, Robert Papernot, Nicolas Univ Cambridge Cambridge England Vector Inst Toronto ON Canada Univ Oxford Oxford England Imperial Coll London London England Univ Toronto Toronto ON Canada

ISBN: (纸本)9798350301298

Machine learning is vulnerable to adversarial manipulation. Previous literature demonstrated that at the training stage attackers can manipulate data [14] and data sampling procedures [29] to control model behaviour. A common attack goal is to plant backdoors i.e. force the victim model to learn to recognise a trigger known only by the adversary. In this paper, we introduce a new class of backdoor attacks that hide inside model architectures i.e. in the inductive bias of the functions used to train. These backdoors are simple to implement, for instance by publishing open-source code for a backdoored model architecture that others will reuse unknowingly. We demonstrate that model architectural backdoors represent a real threat and, unlike other approaches, can survive a complete re-training from scratch. We formalise the main construction principles behind architectural backdoors, such as a connection between the input and the output, and describe some possible protections against them. We evaluate our attacks on computer vision benchmarks of different scales and demonstrate the underlying vulnerability is pervasive in a variety of common training settings.

关键词： accountability ethics in vision fairness privacy Transparency

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CorrGAN: Input Transformation Technique Against Natural Corruptions

CorrGAN: Input Transformation Technique Against Natural Corr...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Haque, Mirazul Budnik, Christof J. Yang, Wei UT Dallas Richardson TX 75080 USA Siemens Corp Technol Princeton NJ USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial perturbations and corruptions. Specifically, natural corruptions like fog, blur, contrast etc can affect the prediction of DNN in an autonomous vehicle. In real time, these corruptions are needed to be detected and also the corrupted inputs are needed to be de-noised to be predicted correctly. In this work, we propose CorrGAN approach, which can generate benign input when a corrupted input is provided. In this framework, we train Generative Adversarial Network (GAN) with novel intermediate output-based loss function. The GAN can denoise the corrupted input and generate benign input. Through experimentation, we show that up to 75.2% of the corrupted misclassified inputs can be classified correctly by DNN using CorrGAN.

关键词： Deep learning computer vision Perturbation methods conferences Neural networks computer architecture Generative adversarial networks

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