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MAGVLT: Masked Generative vision-and-Language Transformer

MAGVLT: Masked Generative Vision-and-Language Transformer

引用

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

作者： Kim, Sungwoong Jo, Daejin Lee, Donghoon Kim, Jongmin Korea Univ Dept Artificial Intelligence Seoul South Korea Kakao Brain Seongnam South Korea

ISBN: (纸本)9798350301298

While generative modeling on multimodal image-text data has been actively developed with large-scale paired datasets, there have been limited attempts to generate both image and text data by a single model rather than a generation of one fixed modality conditioned on the other modality. In this paper, we explore a unified generative vision-and-language (VL) model that can produce both images and text sequences. Especially, we propose a generative VL transformer based on the non-autoregressive mask prediction, named MAGVLT, and compare it with an autoregressive generative VL transformer (ARGVLT). In comparison to ARGVLT, the proposed MAGVLT enables bidirectional context encoding, fast decoding by parallel token predictions in an iterative refinement, and extended editing capabilities such as image and text infilling. For rigorous training of our MAGVLT with image-text pairs from scratch, we combine the image-to-text, text-to-image, and joint image-and-text mask prediction tasks. Moreover, we devise two additional tasks based on the step-unrolled mask prediction and the selective prediction on the mixture of two image-text pairs. Experimental results on various downstream generation tasks of VL benchmarks show that our MAGVLT outperforms ARGVLT by a large margin even with significant inference speedup. Particularly, MAGVLT achieves competitive results on both zero-shot image-to-text and text-to-image generation tasks from MS-COCO by one moderatesized model (fewer than 500M parameters) even without the use of monomodal data and networks.

关键词： Multi-modal learning

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NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modeling

NIRVANA: Neural Implicit Representations of Videos with Adap...

引用

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

作者： Maiya, Shishira R. Girish, Sharath Ehrlich, Max Wang, Hanyu Lee, Kwot Sin Poirson, Patrick Wu, Pengxiang Wang, Chen Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA Snap Inc Santa Monica CA 90405 USA

ISBN: (纸本)9798350301298

Implicit Neural Representations (INR) have recently shown to be powerful tool for high-quality video compression. However, existing works are are limiting as they do not exploit the temporal redundancy in videos, leading to a long encoding time. Additionally, these methods have fixed architectures which do not scale to longer videos or higher resolutions. To address these issues, we propose NIRVANA, which treats videos as groups of frames and fits separate networks to each group performing patch-wise prediction. The video representation is modeled autoregressively, with networks fit on a current group initialized using weights from the previous group's model. To enhance efficiency, we quantize the parameters during training, requiring no post-hoc pruning or quantization. When compared with previous works on the benchmark UVG dataset, NIRVANA improves encoding quality from 37.36 to 37.70 (in terms of PSNR) and the encoding speed by 12x, while maintaining the same compression rate. In contrast to prior video INR works which struggle with larger resolution and longer videos, we show that our algorithm scales naturally due to its patch-wise and autoregressive design. Moreover, our method achieves variable bitrate compression by adapting to videos with varying inter-frame motion. NIRVANA also achieves 6x decoding speed scaling well with more GPUs, making it practical for various deployment scenarios.(1)

关键词： Efficient and scalable vision

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Panelformer: Sewing pattern Reconstruction from 2D Garment Images

Panelformer: Sewing Pattern Reconstruction from 2D Garment I...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Chen, Cheng-Hsiu Su, Jheng-Wei Hu, Min-Chun Yao, Chih-Yuan Chu, Hung-Kuo Natl Tsing Hua Univ Hsinchu Taiwan Natl Taiwan Univ Sci & Technol Taipei Taiwan

ISBN: (纸本)9798350318920;9798350318937

In this paper, we present a novel approach for reconstructing garment sewing patterns from 2D garment images. Our method addresses the challenge of handling occlusion in 2D images by leveraging the symmetric and correlated nature of garment panels. We introduce a transformer-based deep neural network called Panelformer that learns the parametric space of garment sewing patterns. The network comprises two components: the panel transformer and the stitch predictor. The panel transformer estimates the parametric panel shapes, including the occluded panels, by learning from the visible ones. The stitch predictor determines the stitching information among the predicted panels, enabling the reconstruction of the complete garment. To mitigate the overfitting problem caused by strong panel correlations, we propose two tailor-made data augmentation techniques: panel masking and garment mixing. These techniques generate a wider variety of panel combinations, enhancing the model's robustness and generalization capability. We evaluate the effectiveness of Panelformer using a synthetic dataset with diverse garment types. The experimental results demonstrate that our method outperforms competing baselines and achieves comparable performance to NeuralTailor, which operates on 3D point cloud data. This validates the efficacy of our approach in the context of garment sewing pattern reconstruction. By utilizing 2D images as input, our method expands the potential applications of garment modeling and offers easy accessibility to end users. Our code is available online(1).

关键词： 3D computer vision Algorithms Algorithms Image recognition and understanding

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Challenges in Video-Based Infant Action recognition: A Critical Examination of the State of the Art

Challenges in Video-Based Infant Action Recognition: A Criti...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Hatamimajoumerd, Elaheh Kakhaki, Pooria Daneshvar Huang, Xiaofei Luan, Lingfei Amraee, Somaieh Ostadabbas, Sarah Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Roux Inst Portland ME USA Univ Minnesota Minneapolis MN USA

ISBN: (纸本)9798350370287;9798350370713

Automated human action recognition, a burgeoning field within computer vision, boasts diverse applications spanning surveillance, security, human-computer interaction, tele-health, and sports analysis. Precise action recognition in infants serves a multitude of pivotal purposes, encompassing safety monitoring, developmental milestone tracking, early intervention for developmental delays, fostering parent-infant bonds, advancing computer-aided diagnostics, and contributing to the scientific comprehension of child development. This paper delves into the intricacies of infant action recognition, a domain that has remained relatively uncharted despite the accomplishments in adult action recognition. In this study, we introduce a ground-breaking dataset called "InfActPrimitive", encompassing five significant infant milestone action categories, and we incorporate specialized preprocessing for infant data. We conducted an extensive comparative analysis employing cutting-edge skeleton-based action recognition models using this dataset. Our findings reveal that, although the PoseC3D model achieves the highest accuracy at approximately 71%, the remaining models struggle to accurately capture the dynamics of infant actions. This highlights a substantial knowledge gap between infant and adult action recognition domains and the urgent need for data-efficient pipeline models

关键词： computer vision

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FCC: Feature Clusters Compression for Long-Tailed Visual recognition

FCC: Feature Clusters Compression for Long-Tailed Visual Rec...

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

作者： Li, Jian Meng, Ziyao Shi, Daqian Song, Rui Diao, Xiaolei Wang, Jingwen Xu, Hao Jilin Univ Changchun Peoples R China Univ Minho Braga Portugal Univ Toronto Toronto ON Canada

ISBN: (纸本)9798350301298

Deep Neural Networks (DNNs) are rather restrictive in long-tailed data, since they commonly exhibit an under-representation for minority classes. Various remedies have been proposed to tackle this problem from different perspectives, but they ignore the impact of the density of Backbone Features (BFs) on this issue. Through representation learning, DNNs can map BFs into dense clusters in feature space, while the features of minority classes often show sparse clusters. In practical applications, these features are discretely mapped or even cross the decision boundary resulting in misclassification. Inspired by this observation, we propose a simple and generic method, namely Feature Clusters Compression (FCC), to increase the density of BFs by compressing backbone feature clusters. The proposed FCC can be easily achieved by only multiplying original BFs by a scaling factor in training phase, which establishes a linear compression relationship between the original and multiplied features, and forces DNNs to map the former into denser clusters. In test phase, we directly feed original features without multiplying the factor to the classifier, such that BFs of test samples are mapped closer together and do not easily cross the decision boundary. Meanwhile, FCC can be friendly combined with existing long-tailed methods and further boost them. We apply FCC to numerous state-of-the-art methods and evaluate them on widely used longtailed benchmark datasets. Extensive experiments fully verify the effectiveness and generality of our method. Code is available at https://***/lijian16/FCC.

关键词： detection recognition: Categorization retrieval

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DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics

DYNAFED: Tackling Client Data Heterogeneity with Global Dyna...

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

作者： Pi, Renjie Zhang, Weizhong Xie, Yueqi Gao, Jiahui Wang, Xiaoyu Kim, Sunghun Chen, Qifeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China Fudan Univ Shanghai Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected local optimum, which causes the client models drift further away from each other and degrades the aggregated global model's performance. A natural solution is to gather all client data onto the server, such that the server has a global view of the entire data distribution. Unfortunately, this reduces to regular training, which compromises clients' privacy and conflicts with the purpose of FL. In this paper, we put forth an idea to collect and leverage global knowledge on the server without hindering data privacy. We unearth such knowledge from the dynamics of the global model's trajectory. Specifically, we first reserve a short trajectory of global model snapshots on the server. Then, we synthesize a small pseudo dataset such that the model trained on it mimics the dynamics of the reserved global model trajectory. Afterward, the synthesized data is used to help aggregate the deflected clients into the global model. We name our method DYNAFED, which enjoys the following advantages: 1) we do not rely on any external on-server dataset, which requires no additional cost for data collection;2) the pseudo data can be synthesized in early communication rounds, which enables DYNAFED to take effect early for boosting the convergence and stabilizing training;3) the pseudo data only needs to be synthesized once and can be directly utilized on the server to help aggregation in subsequent rounds. Experiments across extensive benchmarks are conducted to showcase the effectiveness of DYNAFED. We also provide insights and understanding of the underlying mechanism of our method.

关键词： accountability ethics in vision fairness privacy Transparency

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Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction

Open-World Multi-Task Control Through Goal-Aware Representat...

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

作者： Cai, Shaofei Wang, Zihao Ma, Xiaojian Liu, Anji Liang, Yitao Peking Univ Inst Artificial Intelligence Beijing Peoples R China Peking Univ Sch Intelligence Sci & Technol Beijing Peoples R China Univ Calif Los Angeles Dept Comp Sci Los Angeles CA USA Beijing Inst Gen Artificial Intelligence BIGAI Beijing Peoples R China

ISBN: (纸本)9798350301298

We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment for developing human-level multi-task agents. We first identify two main challenges of learning such policies: 1) the indistinguishability of tasks from the state distribution, due to the vast scene diversity, and 2) the non-stationary nature of environment dynamics caused by partial observability. To tackle the first challenge, we propose Goal-Sensitive Backbone (GSB) for the policy to encourage the emergence of goal-relevant visual state representations. To tackle the second challenge, the policy is further fueled by an adaptive horizon prediction module that helps alleviate the learning uncertainty brought by the non-stationary dynamics. Experiments on 20 Minecraft tasks show that our method significantly outperforms the best baseline so far;in many of them, we double the performance. Our ablation and exploratory studies then explain how our approach beat the counterparts and also unveil the surprising bonus of zero-shot generalization to new scenes (biomes). We hope our agent could help shed some light on learning goal-conditioned, multi-task agents in challenging, open-ended environments like Minecraft. The code is released at https://***/CraftJarvis/MC-Controller.

关键词： Embodied vision: Active agents simulation

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Adaptive Data-Free Quantization

Adaptive Data-Free Quantization

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

作者： Qian, Biao Wang, Yang Hong, Richang Wang, Meng Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Sch Comp Sci & Informat Engn Hefei Peoples R China

ISBN: (纸本)9798350301298

Data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i.e., informative or not to the learning process of Q, resulting into the overflow of generalization error. Building on this, several critical questions how to measure the sample adaptability to Q under varied bit-width scenarios? whether the largest adaptability is the best? how to generate the samples with adaptive adaptability to improve Q's generalization? To answer the above questions, in this paper, we propose an Adaptive Data-Free Quantization (AdaDFQ) method, which revisits DFQ from a zero-sum game perspective upon the sample adaptability between two players - a generator and a quantized network. Following this viewpoint, we further define the disagreement and agreement samples to form two boundaries, where the margin between two boundaries is optimized to adaptively regulate the adaptability of generated samples to Q, so as to address the over-and-under fitting issues. Our AdaDFQ reveals: 1) the largest adaptability is NOT the best for sample generation to benefit Q's generalization;2) the knowledge of the generated sample should not be informative to Q only, but also related to the category and distribution information of the training data for P. The theoretical and empirical analysis validate the advantages of AdaDFQ over the state-of-the-arts. Our code is available at https://***/hfutqian/AdaDFQ.

关键词： Efficient and scalable vision

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Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior

Uncertainty-Aware Unsupervised Image Deblurring with Deep Re...

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

作者： Tang, Xiaole Zhao, Xile Liu, Jun Wang, Jianli Miao, Yuchun Zeng, Tieyong Univ Elect Sci & Technol China Chengdu Peoples R China Northeast Normal Univ Changchun Peoples R China Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Non-blind deblurring methods achieve decent performance under the accurate blur kernel assumption. Since the kernel uncertainty (i.e. kernel error) is inevitable in practice, semi-blind deblurring is suggested to handle it by introducing the prior of the kernel (or induced) error. However, how to design a suitable prior for the kernel (or induced) error remains challenging. Hand-crafted prior, incorporating domain knowledge, generally performs well but may lead to poor performance when kernel (or induced) error is complex. Data-driven prior, which excessively depends on the diversity and abundance of training data, is vulnerable to out-of-distribution blurs and images. To address this challenge, we suggest a dataset-free deep residual prior for the kernel induced error (termed as residual) expressed by a customized untrained deep neural network, which allows us to flexibly adapt to different blurs and images in real scenarios. By organically integrating the respective strengths of deep priors and hand-crafted priors, we propose an unsupervised semi-blind deblurring model which recovers the clear image from the blurry image and inaccurate blur kernel. To tackle the formulated model, an efficient alternating minimization algorithm is developed. Extensive experiments demonstrate the favorable performance of the proposed method as compared to model-driven and data-driven methods in terms of image quality and the robustness to different types of kernel error.

关键词： Explainable computer vision

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Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention at vision Transformer Inference

Castling-ViT: Compressing Self-Attention via Switching Towar...

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

作者： You, Haoran Xiong, Yunyang Dai, Xiaoliang Wu, Bichen Zhang, Peizhao Fan, Haoqi Vajda, Peter Lin, Yingyan (Celine) Georgia Inst Technol Atlanta GA 30332 USA Meta Res Menlo Pk CA USA

ISBN: (纸本)9798350301298

vision Transformers (ViTs) have shown impressive performance but still require a high computation cost as compared to convolutional neural networks (CNNs), one reason is that ViTs' attention measures global similarities and thus has a quadratic complexity with the number of input tokens. Existing efficient ViTs adopt local attention or linear attention, which sacrifice ViTs' capabilities of capturing either global or local context. In this work, we ask an important research question: Can ViTs learn both global and local context while being more efficient during inference? To this end, we propose a framework called Castling-ViT, which trains ViTs using both linear-angular attention and masked softmax-based quadratic attention, but then switches to having only linear-angular attention during inference. Our Castling-ViT leverages angular kernels to measure the similarities between queries and keys via spectral angles. And we further simplify it with two techniques: (1) a novel linear-angular attention mechanism: we decompose the angular kernels into linear terms and high-order residuals, and only keep the linear terms;and (2) we adopt two parameterized modules to approximate high-order residuals: a depthwise convolution and an auxiliary masked softmax attention to help learn global and local information, where the masks for softmax attention are regularized to gradually become zeros and thus incur no overhead during inference. Extensive experiments validate the effectiveness of our Castling-ViT, e.g., achieving up to a 1.8% higher accuracy or 40% MACs reduction on classification and 1.2 higher mAP on detection under comparable FLOPs, as compared to ViTs with vanilla softmax-based attentions. Project page is available at here.

关键词： Deep learning architectures and techniques

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