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RMLVQA: A Margin Loss Approach For Visual Question Answering with Language Biases

RMLVQA: A Margin Loss Approach For Visual Question Answering...

引用

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

作者： Basu, Abhipsa Addepalli, Sravanti Babu, R. Venkatesh Indian Inst Sci Vis & AI Lab Bangalore India

ISBN: (纸本)9798350301298

Visual Question Answering models have been shown to suffer from language biases, where the model learns a correlation between the question and the answer, ignoring the image. While early works attempted to use question-only models or data augmentations to reduce this bias, we propose an adaptive margin loss approach having two components. The first component considers the frequency of answers within a question type in the training data, which addresses the concern of the class-imbalance causing the language biases. However, it does not take into account the answering difficulty of the samples, which impacts their learning. We address this through the second component, where instance-specific margins are learnt, allowing the model to distinguish between samples of varying complexity. We introduce a bias-injecting component to our model, and compute the instance-specific margins from the confidence of this component. We combine these with the estimated margins to consider both answer-frequency and task-complexity in the training loss. We show that, while the margin loss is effective for out-of-distribution (ood) data, the bias-injecting component is essential for generalising to in-distribution (id) data. Our proposed approach, Robust Margin Loss for Visual Question Answering (RMLVQA) (1) improves upon the existing state-of-the-art results when compared to augmentation-free methods on benchmark VQA datasets suffering from language biases, while maintaining competitive performance on id data, making our method the most robust one among all comparable methods.

关键词： language reasoning vision

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LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of vision & Language Models

LASP: Text-to-Text Optimization for Language-Aware Soft Prom...

引用

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

作者： Bulat, Adrian Tzimiropoulos, Georgios Samsung AI Cambridge Toronto ON Canada Tech Univ Iasi Iasi Romania Qucen Maty Univ London London England

ISBN: (纸本)9798350301298

Soft prompt learning has recently emerged as one of the methods of choice for adapting V&L models to a downstream task using a few training examples. However, current methods significantly overfit the training data, suffering from large accuracy degradation when tested on unseen classes from the same domain. To this end, in this paper, we make the following 4 contributions: (1) To alleviate base class overfitting, we propose a novel Language-Aware Soft Prompting (LASP) learning method by means of a text-to-text cross-entropy loss that maximizes the probability of the learned prompts to be correctly classified with respect to pre-defined hand-crafted textual prompts. (2) To increase the representation capacity of the prompts, we propose grouped LASP where each group of prompts is optimized with respect to a separate subset of textual prompts. (3) We identify a visual-language misalignment introduced by prompt learning and LASP, and more importantly, propose a re-calibration mechanism to address it. (4) We show that LASP is inherently amenable to including, during training, virtual classes, i.e. class names for which no visual samples are available, further increasing the robustness of the learned prompts. Through evaluations on 11 datasets, we show that our approach (a) significantly outperforms all prior works on soft prompting, and (b) matches and surpasses, for the first time, the accuracy on novel classes obtained by hand-crafted prompts and CLIP for 8 out of 11 test datasets. Code will be made available here.

关键词： continual low-shot meta or long-tail learning Transfer

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PD-Quant: Post-Training Quantization Based on Prediction Difference Metric

PD-Quant: Post-Training Quantization Based on Prediction Dif...

引用

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

作者： Liu, Jiawei Niu, Lin Yuan, Zhihang Yang, Dawei Wang, Xinggang Liu, Wenyu Huazhong Univ Sci & Technol Sch EIC Wuhan Peoples R China Houmo AI Nanjing Peoples R China

ISBN: (纸本)9798350301298

Post-training quantization (PTQ) is a neural network compression technique that converts a full-precision model into a quantized model using lower-precision data types. Although it can help reduce the size and computational cost of deep neural networks, it can also introduce quantization noise and reduce prediction accuracy, especially in extremely low-bit settings. How to determine the appropriate quantization parameters (e.g., scaling factors and rounding of weights) is the main problem facing now. Existing methods attempt to determine these parameters by minimize the distance between features before and after quantization, but such an approach only considers local information and may not result in the most optimal quantization parameters. We analyze this issue and propose PD-Quant, a method that addresses this limitation by considering global information. It determines the quantization parameters by using the information of differences between network prediction before and after quantization. In addition, PD-Quant can alleviate the overfitting problem in PTQ caused by the small number of calibration sets by adjusting the distribution of activations. Experiments show that PD-Quant leads to better quantization parameters and improves the prediction accuracy of quantized models, especially in low-bit settings. For example, PD-Quant pushes the accuracy of ResNet-18 up to 53.14% and RegNetX-600MF up to 40.67% in weight 2-bit activation 2-bit. The code is released at https://***/hustvl/PD-Quant.

关键词： Efficient and scalable vision

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Toward Stable, Interpretable, and Lightweight Hyperspectral Super-resolution

Toward Stable, Interpretable, and Lightweight Hyperspectral ...

引用

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

作者： Guo, Wen-Jin Xie, Weiying Jiang, Kai Li, Yunsong Lei, Jie Fang, Leyuan Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China

ISBN: (纸本)9798350301298

For real applications, existing HSI-SR methods are not only limited to unstable performance under unknown scenarios but also suffer from high computation consumption. In this paper, we develop a new coordination optimization framework for stable, interpretable, and lightweight HSI-SR. Specifically, we create a positive cycle between fusion and degradation estimation under a new probabilistic framework. The estimated degradation is applied to fusion as guidance for a degradation-aware HSI-SR. Under the framework, we establish an explicit degradation estimation method to tackle the indeterminacy and unstable performance caused by the black-box simulation in previous methods. Considering the interpretability in fusion, we integrate spectral mixing prior into the fusion process, which can be easily realized by a tiny autoencoder, leading to a dramatic release of the computation burden. Based on the spectral mixing prior, we then develop a partial fine-tune strategy to reduce the computation cost further. Comprehensive experiments demonstrate the superiority of our method against the state-of-the-arts under synthetic and real datasets. For instance, we achieve a 2.3 dB promotion on PSNR with 120x model size reduction and 4300x FLOPs reduction under the CAVE dataset. Code is available in https://***/WenjinGuo/DAEM.

关键词： Low-level vision

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WSRD: A Novel Benchmark for High Resolution Image Shadow Removal

WSRD: A Novel Benchmark for High Resolution Image Shadow Rem...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu University of Würzburg Computer Vision Laboratory Caidas & Ifi Germany

ISBN: (纸本)9798350302493

Shadow removal is an important computer vision task, whose aim is to successfully detect the shadow affected area appearing through light occlussion, followed by a photorealistic restoration of the affected image contents, textures, and details. After decades of research, a multitude of hand-crafted restoration techniques were proposed, following different observations on shadow formation models, with scenes altered in particular conditions. However, the increased popularity of deep learning based solutions enabled a significant step forward for the shadow removal solutions, both in terms of reconstruction fidelity and perceptual properties. However, the publicly available datasets remain focused around a particularly low complexity setup, with a low variety of light occluders and affected backgrounds, and with limited representation for more complex light interactions and complex shadow patterns. In this work, we propose WSRD, a novel benchmark for high resolution image shadow removal, characterized by a large variety in terms or represented objects, backgrounds and light occluders. We study more complex interactions, combining self shadows with externally casted shadows, to further extend the study of the phenomenon, its factors and effects. To prove WSRD as a relevant benchmark, we propose DNSR, a novel shadow removal method, comparing the results on WSRD with the performance level observed on other well-established benchmarks like ISTD and ISTD+. We validate our approach comparing with existing state-of-the-art (SOTA) methods, improving both in reconstruction fidelity and perceptual properties, setting a new SOTA for the field. © 2023 ieee.

关键词： Restoration

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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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ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing

ConZIC: Controllable Zero-shot Image Captioning by Sampling-...

引用

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

作者： Zeng, Zequn Zhang, Hao Lu, Ruiying Wang, Dongsheng Chen, Bo Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9798350301298

Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data. As a good start and the only existing outcome of zero-shot image captioning (IC), ZeroCap abandons supervised training and sequentially searches every word in the caption using the knowledge of large-scale pre-trained models. Though effective, its autoregressive generation and gradient-directed searching mechanism limit the diversity of captions and inference speed, respectively. Moreover, ZeroCap does not consider the controllability issue of zero-shot IC. To move forward, we propose a framework for Controllable Zero-shot IC, named ConZIC. The core of ConZIC is a novel sampling-based non-autoregressive language model named GibbsBERT, which can generate and continuously polish every word. Extensive quantitative and qualitative results demonstrate the superior performance of our proposed ConZIC for both zero-shot IC and controllable zero-shot IC. Especially, ConZIC achieves about 5x faster generation speed than ZeroCap, and about 1.5x higher diversity scores, with accurate generation given different control signals. Our code is available at https://***/joeyz0z/ConZIC.

关键词： language reasoning vision

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Event-Based Frame Interpolation with Ad-hoc Deblurring

Event-Based Frame Interpolation with Ad-hoc Deblurring

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

作者： Sun, Lei Sakaridis, Christos Liang, Jingyun Sun, Peng Cao, Jiezhang Zhang, Kai Jiang, Qi Wang, Kaiwei Van Gool, Luc Zhejiang Univ Hangzhou Peoples R China Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9798350301298

The performance of video frame interpolation is inherently correlated with the ability to handle motion in the input scene. Even though previous works recognize the utility of asynchronous event information for this task, they ignore the fact that motion may or may not result in blur in the input video to be interpolated, depending on the length of the exposure time of the frames and the speed of the motion, and assume either that the input video is sharp, restricting themselves to frame interpolation, or that it is blurry, including an explicit, separate deblurring stage before interpolation in their pipeline. We instead propose a general method for event-based frame interpolation that performs deblurring ad-hoc and thus works both on sharp and blurry input videos. Our model consists in a bidirectional recurrent network that naturally incorporates the temporal dimension of interpolation and fuses information from the input frames and the events adaptively based on their temporal proximity. In addition, we introduce a novel real-world high-resolution dataset with events and color videos named HighREV, which provides a challenging evaluation setting for the examined task. Extensive experiments on the standard GoPro benchmark and on our dataset show that our network consistently outperforms previous state-of-the-art methods on frame interpolation, single image deblurring and the joint task of interpolation and deblurring. Our code and dataset are available at https://***/AHupuJR/REFID.

关键词： Low-level vision

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Progressive Open Space Expansion for Open-Set Model Attribution

Progressive Open Space Expansion for Open-Set Model Attribut...

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

作者： Yang, Tianyun Wang, Danding Tang, Fan Zhao, Xinying Cao, Juan Tang, Sheng Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Zhejiang Lab Res Inst Intelligent Comp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Despite the remarkable progress in generative technology, the Janus-faced issues of intellectual property protection and malicious content supervision have arisen. Efforts have been paid to manage synthetic images by attributing them to a set of potential source models. However, the closed-set classification setting limits the application in real-world scenarios for handling contents generated by arbitrary models. In this study, we focus on a challenging task, namely Open-Set Model Attribution (OSMA), to simultaneously attribute images to known models and identify those from unknown ones. Compared to existing openset recognition (OSR) tasks focusing on semantic novelty, OSMA is more challenging as the distinction between images from known and unknown models may only lie in visually imperceptible traces. To this end, we propose a Progressive Open Space Expansion (POSE) solution, which simulates open-set samples that maintain the same semantics as closed-set samples but embedded with different imperceptible traces. Guided by a diversity constraint, the open space is simulated progressively by a set of lightweight augmentation models. We consider three real-world scenarios and construct an OSMA benchmark dataset, including unknown models trained with different random seeds, architectures, and datasets from known ones. Extensive experiments on the dataset demonstrate POSE is superior to both existing model attribution methods and off-the-shelf OSR methods. Github: https://***/ICTMCG/POSE

关键词： accountability ethics in vision fairness privacy Transparency

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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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