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Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters

Continual Diffusion with STAMINA: STack-And-Mask INcremental...

引用

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

作者： James Seale Smith Yen-Chang Hsu Zsolt Kira Yilin Shen Hongxia Jin Samsung Research America Georgia Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent work has demonstrated a remarkable ability to customize text-to-image diffusion models to multiple, fine-grained concepts in a sequential (i.e., continual) manner while only providing a few example images for each concept. This setting is known as continual diffusion. Here, we ask the question: Can we scale these methods to longer concept sequences without forgetting? Although prior work mitigates the forgetting of previously learned concepts, we show that its capacity to learn new tasks reaches saturation over longer sequences. We address this challenge by introducing a novel method, STack-And-Mask INcremental Adapters (STAMINA), which is composed of low-ranked attention-masked adapters and customized MLP tokens. STAMINA is designed to enhance the robust fine-tuning properties of LoRA for sequential concept learning via learnable hard-attention masks parameterized with low rank MLPs, enabling precise, scalable learning via sparse adaptation. Notably, all introduced trainable parameters can be folded back into the model after training, inducing no additional inference parameter costs. We show that STAMINA outperforms the prior SOTA for the setting of text-to-image continual customization on a 50-concept benchmark composed of landmarks and human faces, with no stored replay data. Additionally, we extended our method to the setting of continual learning for image classification, demonstrating that our gains also translate to state-of-the-art performance in this standard benchmark.

关键词： Training Continuing education Costs Text to image Benchmark testing Diffusion models pattern recognition

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Multi-View Spatial-Temporal Learning for Understanding Unusual Behaviors in Untrimmed Naturalistic Driving Videos

Multi-View Spatial-Temporal Learning for Understanding Unusu...

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

作者： Huy-Hung Nguyen Chi Dai Tran Long Hoang Pham Duong Nguyen-Ngoc Tran Tai Huu-Phuong Tran Duong Khac Vu Quoc Pham-Nam Ho Ngoc Doan-Minh Huynh Hyung-Min Jeon Hyung-Joon Jeon Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The task of Naturalistic Driving Action recognition aims to detect and temporally localize distracting driving behavior in untrimmed videos. In this paper, we introduce our framework for Track 3 of the 8 th AI City Challenge in 2024. The approach is primarily based on large model fine-tuning and ensemble techniques to train a set of action recognition models on a small-scale dataset. Starting with raw videos, we segment them into individual action sequences based on their annotation. We then fine-tune four different action recognition models, with K-fold cross-validation applied to the segmented data. Following this, we execute a multi-view ensemble, selecting the most visible camera views for each action class to generate clip-level classification results for each video. Finally, a multi-step post-processing algorithm, which is designed for the AI City Challenge dataset’s specific features, is employed to perform temporal action localization and produce temporal segments for the actions. Our solution achieves a final mOS score of 0.7798 and attains the 5 th rank on the public leaderboard for the test set A2 of the challenge. The source code will be publicly available at https://***/SKKUAutoLab/AIC24-Track03.

关键词： Location awareness Source coding Urban areas X3D Prediction algorithms Classification algorithms pattern recognition

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A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning

A Method of Moments Embedding Constraint and its Application...

引用

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

作者： Michael Majurski Sumeet Menon Parniyan Farvardin David Chapman Information Technology Lab NIST University of Maryland University of Maryland University of Miami

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Discriminative deep learning models with a linear+softmax final layer have a problem: the latent space only predicts the conditional probabilities p(Y |X) but not the full joint distribution p(Y,X), which necessitates a generative approach. The conditional probability cannot detect outliers, causing outlier sensitivity in softmax networks. This exacerbates model over-confidence impacting many problems, such as hallucinations, confounding biases, and dependence on large datasets. To address this we introduce a novel embedding constraint based on the Method of Moments (MoM). We investigate the use of polynomial moments ranging from 1st through 4th order hyper-covariance matrices. Furthermore, we use this embedding constraint to train an Axis-Aligned Gaussian Mixture Model (AAGMM) final layer, which learns not only the conditional, but also the joint distribution of the latent space. We apply this method to the domain of semi-supervised image classification by extending FlexMatch with our technique. We find our MoM constraint with the AAGMM layer is able to match the reported FlexMatch accuracy, while also modeling the joint distribution, thereby reducing outlier sensitivity. We also present a preliminary outlier detection strategy based on Mahalanobis distance and discuss future improvements to this strategy. Code is available at: https://***/mmajurski/ssl-gmm

关键词： Deep learning Sensitivity Semisupervised learning Predictive models Polynomials Distance measurement pattern recognition

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ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery

ConceptHash: Interpretable Fine-Grained Hashing via Concept ...

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

作者： Kam Woh Ng Xiatian Zhu Yi-Zhe Song Tao Xiang CVSSP University of Surrey iFlyTek-Surrey Joint Research Centre on Artificial Intelligence Surrey Institute for People-Centred Artificial Intelligence

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing fine-grained hashing methods typically lack code interpretability as they compute hash code bits holistically using both global and local features. To address this limitation, we propose ConceptHash, a novel method that achieves sub-code level interpretability. In ConceptHash, each sub-code corresponds to a human-understandable concept, such as an object part, and these concepts are automatically discovered without human annotations. Specifically, we leverage a vision Transformer architecture and introduce concept tokens as visual prompts, along with image patch tokens as model inputs. Each concept is then mapped to a specific sub-code at the model output, providing natural sub-code interpretability. To capture subtle visual differences among highly similar sub-categories (e.g., bird species), we incorporate language guidance to ensure that the learned hash codes are distinguishable within fine-grained object classes while maintaining semantic alignment. This approach allows us to develop hash codes that exhibit similarity within families of species while remaining distinct from species in other families. Extensive experiments on four fine-grained image retrieval benchmarks demonstrate that ConceptHash outperforms previous methods by a significant margin, offering unique sub-code interpretability as an additional benefit. Code at: https://***/kamwoh/concepthash.

关键词： Visualization computer vision Codes Semantics Image retrieval Manuals Inspection

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Video Interaction recognition using an Attention Augmented Relational Network and Skeleton Data

Video Interaction Recognition using an Attention Augmented R...

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

作者： Farzaneh Askari Cyril Yared Rohit Ramaprasad Devin Garg Anjun Hu James J. Clark University of McGill Montreal QC Canada University of California San Diego San Diego CA United States University of Oxford Oxford United Kingdom

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recognizing interactions in multi-person videos, known as Video Interaction recognition (VIR), is crucial for understanding video content. Often the human skeleton pose (skeleton, for short) is a popular feature for VIR as the main feature, given its success for the task in hand. While many studies have made progress using complex architectures like Graph Neural Networks (GNN) and Transformers to capture interactions in videos, studies such as [33] that apply simple, easy to train, and adaptive architectures such as Relation reasoning Network (RN) [37], yield competitive results. Inspired by this trend, we propose the Attention Augmented Relational Network (AARN), a straightforward yet effective model that uses skeleton data to recognize interactions in videos. AARN outperforms other RN-based models and remains competitive against larger, more intricate models. We evaluate our approach on a challenging real-world Hockey Penalty Dataset (HPD), where the videos depict complex interactions between players in a non-laboratory recording setup, in addition to popular benchmark datasets demonstrating strong performance. Lastly, we show the impact of skeleton quality on the classification accuracy and the struggle of off-the-shelf pose estimators to extract precise skeleton from the challenging HPD dataset.

关键词： Adaptation models computer architecture Transformers Skeleton Graph neural networks Data models Recording

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in2IN: Leveraging individual Information to Generate Human INteractions

in2IN: Leveraging individual Information to Generate Human I...

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

作者： Pablo Ruiz-Ponce German Barquero Cristina Palmero Sergio Escalera José García-Rodríguez Universidad de Alicante San Vicente del Raspeig Spain Universitat de Barcelona Barcelona Spain Computer Vision Center Cerdanyola del Vallès Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating human-human motion interactions conditioned on textual descriptions is a very useful application in many areas such as robotics, gaming, animation, and the metaverse. Alongside this utility also comes a great diffi-culty in modeling the highly dimensional inter-personal dynamics. In addition, properly capturing the intra-personal diversity of interactions has a lot of challenges. Current methods generate interactions with limited diversity of intra-person dynamics due to the limitations of the available datasets and conditioning strategies. For this, we introduce in2IN, a novel diffusion model for human-human motion generation which is conditioned not only on the textual description of the overall interaction but also on the individual descriptions of the actions performed by each person involved in the interaction. To train this model, we use a large language model to extend the InterHuman dataset with individual descriptions. As a result, in2IN achieves state-of-the- art performance in the InterHuman dataset. Furthermore, in order to increase the intra-personal diversity on the existing interaction datasets, we propose DualMDM, a model composition technique that combines the motions generated with in2IN and the motions generated by a single-person motion prior pre-trained on HumanML3D. As a result, DualMDM generates motions with higher individual diversity and improves control over the intra-person dynamics while maintaining inter-personal coherence.

关键词： Legged locomotion computer vision Metaverse Large language models conferences Dynamics Diversity reception

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Fourier Prior-Based Two-Stage Architecture for Image Restoration

Fourier Prior-Based Two-Stage Architecture for Image Restora...

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

作者： Hemkant Nehete Amit Monga Partha Kaushik Brajesh Kumar Kaushik Indian Institute of Technology Roorkee India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This work presents a novel two stage architecture designed to enhance degraded images affected by environmental factors such as haze, blur, fog, and rain. Despite the dominance of deep Convolutional Neural Networks (CNNs) and Transformers in single image restoration tasks, existing methods neglect the intrinsic priors for physical properties of degradation. To enhance the generalization ability of image restoration models, we propose Fourier prior based on a key observation that substituting the Fourier amplitude of degraded images with that of clean images effectively mitigates degradation. Therefore, amplitude contains degradation information, while the phase retains background structures. Consequently, a two-stage model is proposed, that consists of Amplitude Refinement Unit (ARU) and the Phase Refinement Unit (PRU), that separately restore both amplitude and phase information, respectively. ARU and PRU leverage a CNN-Transformer-based architecture to extract local and global features, overcoming computational constraints posed by large image sizes in Transformers. Additionally, a multi-scale approach in ARU refines amplitude features at coarse and fine levels, improving restoration efficiency. Experimental results across multiple image restoration tasks, like image deraining, dehazing, and low-light enhancement, indicate that the proposed architecture improved the performance in terms of PSNR, SSIM, and computational efficiency compared to state-of-the-art Transformer approaches.

关键词： Degradation Rain Computational modeling Frequency-domain analysis computer architecture Transformers Feature extraction

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NTIRE 2024 Challenge on HR Depth from Images of Specular and Transparent Surfaces

NTIRE 2024 Challenge on HR Depth from Images of Specular and...

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

作者： Pierluigi Zama Ramirez Fabio Tosi Luigi Di Stefano Radu Timofte Alex Costanzino Matteo Poggi Samuele Salti Stefano Mattoccia Yangyang Zhang Cailin Wu Zhuangda He Shuangshuang Yin Jiaxu Dong Yangchenxu Liu Hao Jiang Jun Shi Yong A Yixiang Jin Dingzhe Li Bingxin Ke Anton Obukhov Tinafu Wang Nando Metzger Shengyu Huang Konrad Schindler Yachuan Huang Jiaqi Li Junrui Zhang Yiran Wang Zihao Huang Tianqi Liu Zhiguo Cao Pengzhi Li Jui-Lin Wang Wenjie Zhu Hui Geng Yuxin Zhang Long Lan Kele Xu Tao Sun Qisheng Xu Sourav Saini Aashray Gupta Sahaj K. Mistry Aryan Shukla Vinit Jakhetiya Sunil Jaiswal Yuejin Sun Zhuofan Zheng Yi Ning Jen-Hao Cheng Hou-I Liu Hsiang-Wei Huang Cheng-Yen Yang Zhongyu Jiang Yi-Hao Peng Aishi Huang Jenq-Neng Hwang

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement (NTIRE) workshop at CVPR 2024. This challenge aims to advance the research on depth estimation, specifically to address two of the main open issues in the field: high-resolution and non-Lambertian surfaces. The challenge proposes two tracks on stereo and single-image depth estimation, attracting about 120 registered participants. In the final testing stage, 2 and 8 participating teams submitted their models and fact sheets for the two tracks.

关键词： computer vision conferences Computational modeling Estimation Market research pattern recognition Image restoration

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Semi-Stereo: A Universal Stereo Matching Framework for Imperfect Data via Semi-supervised Learning

Semi-Stereo: A Universal Stereo Matching Framework for Imper...

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

作者： Xin Yue Zongqing Lu Xiangru Lin Wenjia Ren Zhijing Shao Haonan Hu Yu Zhang Qingmin Liao Tsinghua University Prometheus Vision Technology Co. Ltd.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Data matters in deep-learning-based binocular stereo matching. Obtaining a perfect dataset for stereo matching is hard and thus imperfect data is common in existing benchmark datasets, such as KITTI, ETH3D and Middlebury. The imperfectness typically has two forms: sparse-labeled data or even unlabeled data. Current stereo matching networks ignore the supervision from these imperfect data itself, even the semi-supervised networks often suffer from confirmation bias in the predictions. Besides, current methods lack a unified solution to utilize the supervision signal from those imperfect data. To mitigate this research gap, we propose Semi-Stereo, the first unified stereo matching framework empowered by the teacher-student paradigm where the teacher and the student networks are trained in a mutual-beneficial manner. To explore the rich knowledge in imperfect data, we propose a consistency regularization module with weak-strong augmentation strategies. Further, in order for the teacher to provide more reliable pseudo labels, we design a confidence module, powered by left-right consistency (LRC) check and disparity distribution entropy (DDE). Extensive experiments demonstrate Semi-Stereo produces accurate and consistent predictions in untrained semantic regions and improves the performance of baseline networks in multiple tasks, including domain adaptation and domain generalization.

关键词： Measurement computer vision conferences computer network reliability Semantics Semisupervised learning Benchmark testing

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Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis

Enhancing Traffic Safety with Parallel Dense Video Captionin...

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

作者： Maged Shoman Dongdong Wang Armstrong Aboah Mohamed Abdel-Aty Department of Civil Environmental and Construction Engineering Smart and Safe Transportation (SST) Lab University of Central Florida United States Department of Civil Construction and Environmental Engineering North Dakota State University United States Department of Civil Environmental and Construction Engineering Joint Appointment Department of Computer Science Smart and Safe Transportation (SST) Lab University of Central Florida United States

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces our solution for Track 2 in AI City Challenge 2024. The task aims to solve traffic safety description and analysis with the dataset of Woven Traffic Safety (WTS), a real-world Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding. Our solution mainly focuses on the following points: 1) To solve dense video captioning, we leverage the framework of dense video captioning with parallel decoding (PDVC) to model visual-language sequences and generate dense caption by chapters for video. 2) Our work leverages CLIP to extract visual features to more efficiently perform cross-modality training between visual and textual representations. 3) We conduct domain-specific model adaptation to mitigate domain shift problem that poses recognition challenge in video understanding. 4) Moreover, we leverage BDD-5K captioned videos to conduct knowledge transfer for better understanding WTS videos and more accurate captioning. Our solution has yielded on the test set, achieving 6th place in the competition. The open-source code will be available at https://***/UCF-SST-Lab/AICity2024cvprw

关键词： Training Visualization Adaptation models Urban areas Feature extraction Weaving Tokenization

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