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Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal Models

Audio-Visual Generalized Zero-Shot Learning using Pre-Traine...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： David Kurzendörfer Otniel-Bogdan Mercea A. Sophia Koepke Zeynep Akata University of Tübingen Localyzer GmbH Tübingen AI Center Helmholtz Munich Technical University of Munich

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Audio-visual zero-shot learning methods commonly build on features extracted from pre-trained models, e.g. video or audio classification models. However, existing benchmarks predate the popularization of large multi-modal models, such as CLIP and CLAP. In this work, we explore such large pre-trained models to obtain features, i.e. CLIP for visual features, and CLAP for audio features. Furthermore, the CLIP and CLAP text encoders provide class label embeddings which are combined to boost the performance of the system. We propose a simple yet effective model that only relies on feed-forward neural networks, exploiting the strong generalization capabilities of the new audio, visual and textual features. Our framework achieves state-of-the-art performance on VGGSound-GZSL cls , UCF-GZSL cls , and ActivityNet-GZSL cls with our new features. Code and data available at: https://***/dkurzend/ClipClap-GZSL.

关键词： Visualization computer vision Codes Zero-shot learning conferences Computational modeling Benchmark testing

来源：评论

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Exploring the Impact of Dataset Bias on Dataset Distillation

Exploring the Impact of Dataset Bias on Dataset Distillation

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yao Lu Jianyang Gu Xuguang Chen Saeed Vahidian Qi Xuan Zhejiang University of Technology Zhejiang University Duke University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Dataset Distillation (DD) is a promising technique to synthesize a smaller dataset that preserves essential information from the original dataset. This synthetic dataset can serve as a substitute for the original large-scale one, and help alleviate the training workload. However, current DD methods typically operate under the assumption that the dataset is unbiased, overlooking potential bias issues within the dataset itself. To fill in this blank, we systematically investigate the influence of dataset bias on DD. Given that there are no suitable biased datasets for DD, we first construct two biased datasets, CMNIST-DD and CCIFAR10-DD, to establish a foundation for subsequent analysis. Then we utilize existing DD methods to generate synthetic datasets on CMNIST-DD and CCIFAR10-DD, and evaluate their performance following the standard process. Experiments demonstrate that biases present in the original dataset significantly impact the performance of the synthetic dataset in most cases, which highlights the necessity of identifying and mitigating biases in the original datasets during DD. Finally, we reformulate DD within the context of a biased dataset. Our code along with biased datasets are available at https://***/yaolu-zjut/Biased-DD.

关键词： Training computer vision Codes Prevention and mitigation conferences Mathematical models pattern recognition

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Exploring Text-to-Motion Generation with Human Preference

Exploring Text-to-Motion Generation with Human Preference

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jenny Sheng Matthieu Lin Andrew Zhao Kevin Pruvost Yu-Hui Wen Yangguang Li Gao Huang Yong-Jin Liu Tsinghua University Beijing Jiaotong University Shanghai AI Lab

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents an exploration of preference learning in text-to-motion generation. We find that current improvements in text-to-motion generation still rely on datasets requiring expert labelers with motion capture systems. Instead, learning from human preference data does not require motion capture systems; a labeler with no expertise simply compares two generated motions. This is particularly efficient because evaluating the model’s output is easier than gathering the motion that performs a desired task (e.g. backflip). To pioneer the exploration of this paradigm, we annotate 3,528 preference pairs generated by MotionGPT, marking the first effort to investigate various algorithms for learning from preference data. In particular, our exploration highlights important design choices when using preference data. Additionally, our experimental results show that preference learning has the potential to greatly improve current text-to-motion generative models. Our code and dataset are publicly available at https://***/THU-LYJ-Lab/InstructMotion to further facilitate research in this area.

关键词： computer vision Codes conferences Computational modeling Motion capture pattern recognition

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DGBD: Depth Guided Branched Diffusion for Comprehensive Controllability in Multi-View Generation

DGBD: Depth Guided Branched Diffusion for Comprehensive Cont...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hovhannes Margaryan Daniil Hayrapetyan Wenyan Cong Zhangyang Wang Humphrey Shi Picsart AI Research (PAIR) UT Austin SHI Labs @ Georgia Tech Oregon & UIUC

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents an innovative approach to multi-view generation that can be comprehensively controlled over both perspectives (viewpoints) and non-perspective attributes (such as depth maps). Our controllable dual-branch pipeline, named Depth Guided Branched Diffusion (DGBD), leverages depth maps and perspective information to generate images from alternative viewpoints while preserving shape and size fidelity. In the first DGBD branch, we fine-tune a pre-trained diffusion model on multi-view data, introducing a regularized batch-aware self-attention mechanism for multi-view consistency and generalization. Direct control over perspective is then achieved through cross-attention conditioned on camera position. Meanwhile, the second DGBD branch introduces non-perspective control using depth maps. Qualitative and quantitative experiments validate the effectiveness of our approach, surpassing or matching the performance of state-of-the-art novel view and multi-view synthesis methods.

关键词： Geometry computer vision Shape conferences Pipelines Text to image Cameras

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Gaze Scanpath Transformer: Predicting Visual Search Target by Spatiotemporal Semantic Modeling of Gaze Scanpath

Gaze Scanpath Transformer: Predicting Visual Search Target b...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Takumi Nishiyasu Yoichi Sato The University of Tokyo Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce a new method, called the Gaze Scanpath Transformer, for predicting a search target category during a visual search task. Previous methods for estimating visual search targets focus solely on the image features at gaze fixation positions. As a result, the previous methods are unable to take into account the spatiotemporal information of gaze scanpaths and lack consideration of the semantic interrelationships between objects on gaze fixations. In contrast, our method can estimate a visual search target based on the spatiotemporal information of a gaze scanpath and interrelationships among image semantic features at gaze fixation positions. This is achieved by embedding the position and the duration of each fixation, and the order of fixations into the image semantic features, and by using the model’s attention mechanism to facilitate information exchange and emphasis on image semantic features at gaze fixation positions. Evaluation using the COCO-Search18 dataset demonstrates that our proposed method achieves significant performance improvements over other state-ofthe-art baseline models for search target prediction.

关键词： Visualization computer vision conferences Computational modeling Semantics Predictive models Transformers

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Efficient Light Field Image Super-Resolution via Progressive Disentangling

Efficient Light Field Image Super-Resolution via Progressive...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Gaosheng Liu Huanjing Yue Jingyu Yang School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The performance of light field (LF) image super-resolution (SR) has been significantly improved with the development of deep learning techniques. In recent state-of-the-art methods, increasingly deeper and wider networks with a massive number of layers are employed to improve SR performance. However, these approaches often incur heavy computational costs, hindering efficient inference and practical applications. In this paper, we address the problem by introducing an efficient network for LF image SR. Specifically, we propose an efficient progressive disentangling block (PDistgB), where the intermediate LF feature is progressively channel-wise split and selectively domain-specific disentangled. The PDistgB can well incorporate the LF structure prior while requiring fewer computational costs compared with existing disentangling strategies. In addition, we apply Transformer on the angular domain to incorporate angular correlations from all views for further improving the SR accuracy. Experimental results on public datasets demonstrate that our method achieves state-of-the-art performance with high efficiency. Codes and models are available at https://***/GaoshengLiu/PDistgNet.

关键词： Deep learning computer vision Correlation conferences Computational modeling Superresolution Transformers

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One-Shot GAN: Learning to Generate Samples from Single Images and Videos

One-Shot GAN: Learning to Generate Samples from Single Image...

引用

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

作者： Sushko, Vadim Gall, Juergen Khoreva, Anna Bosch Ctr Artificial Intelligence Stuttgart Germany Univ Bonn Bonn Germany

ISBN: (纸本)9781665448994

Training GANs in low-data regimes remains a challenge, as overfitting often leads to memorization or training divergence. In this work, we introduce One-Shot GAN that can learn to generate samples from a training set as little as one image or one video. We propose a two-branch discriminator, with content and layout branches designed to judge the internal content separately from the scene layout realism. This allows synthesis of visually plausible, novel compositions of a scene, with varying content and layout, while preserving the context of the original sample. Compared to previous single-image GAN models, One-Shot GAN achieves higher diversity and quality of synthesis. It is also not restricted to the single image setting, successfully learning in the introduced setting of a single video.

关键词： Training Visualization computer vision conferences Computational modeling Layout Tools

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Adversarial Identity Injection for Semantic Face Image Synthesis

Adversarial Identity Injection for Semantic Face Image Synth...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Giuseppe Tarollo Tomaso Fontanini Claudio Ferrari Guido Borghi Andrea Prati Department of Engineering and Architecture University of Parma Parma Italy Department of Computer Science and Engineering University of Bologna Cesena Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Nowadays, deep learning models have reached incredible performance in the task of image generation. Plenty of literature works address the task of face generation and editing, with human and automatic systems that struggle to distinguish what’s real from generated. Whereas most systems reached excellent visual generation quality, they still face difficulties in preserving the identity of the starting input subject. Among all the explored techniques, Semantic Image Synthesis (SIS) methods, whose goal is to generate an image conditioned on a semantic segmentation mask, are the most promising, even though preserving the perceived identity of the input subject is not their main concern. Therefore, in this paper, we investigate the problem of identity preservation in face image generation and present an SIS architecture that exploits a cross-attention mechanism to merge identity, style, and semantic features to generate faces whose identities are as similar as possible to the input ones. Experimental results reveal that the proposed method is not only suitable for preserving the identity but is also effective in the face recognition adversarial attack, i.e. hiding a second identity in the generated faces.

关键词： Deep learning Visualization computer vision Image synthesis Face recognition Semantic segmentation conferences

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Demographic Bias Effects on Face Image Synthesis

Demographic Bias Effects on Face Image Synthesis

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Roberto Leyva Victor Sanchez Gregory Epiphaniou Carsten Maple WMG University of Warwick UK Computer Science University of Warwick UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face image synthesis has shown remarkable progress in recent years. However, the effect that the demographics of the data used to train synthesizers has on the generation of new face images remains an open question. This paper investigates the effects of the training set demographics in the face image synthesis task. To this end, we propose a strategy that allows synthesizing face images for specific groups of people with a high visual quality. The strategy uses an unsupervised learning approach to discover groups of people in the training set based on Bayesian inference via a probabilistic mixture model. If labels are available to define the groups, our strategy can also exploit such information in lieu of unsupervised learning. Once the groups are defined, our strategy trains a Generative Adversarial Network on each group to generate new face images with specific characteristics. Our results show remarkable performance in terms of image quality compared to several state-of-the-art baselines. More importantly, our strategy allows synthesizing face images with reduced demographic biases.

关键词： Training Image quality Visualization Image synthesis Face recognition Synthesizers Mixture models

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Is Our Continual Learner Reliable? Investigating Its Decision Attribution Stability through SHAP Value Consistency

Is Our Continual Learner Reliable? Investigating Its Decisio...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yusong Cai Shimou Ling Liang Zhang Lili Pan Hongliang Li University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this work, we identify continual learning (CL) methods’ inherent differences in sequential decision attribution. In the sequential learning process, inconsistent decision attribution may undermine the interpretability of a continual learner. However, existing CL evaluation metrics, as well as current interpretability methods, cannot measure the decision attribution stability of a continual learner. To bridge the gap, we introduce Shapley value, a well-known decision attribution theory, and define SHAP value consistency (SHAPC) to measure the consistency of a continual learner’s decision attribution. Furthermore, we define the mean and the variance of SHAPC values, namely SHAPC-Mean and SHAPC-Var, to jointly evaluate the decision attribution stability of continual learners over sequential tasks. On Split CIFAR-10, Split CIFAR-100, and Split TinyImageNet, we compare the decision attribution stability of different CL methods using the proposed metrics, providing a new perspective for evaluating their reliability.

关键词： Measurement Continuing education computer vision Current measurement conferences Decision making Reliability theory

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