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in2IN: Leveraging individual Information to Generate Human INteractions

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

引用

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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IMIL: Interactive Medical Image Learning Framework

IMIL: Interactive Medical Image Learning Framework

引用

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

作者： Adrit Rao Andrea Fisher Ken Chang John Christopher Panagides Katherine McNamara Joon-Young Lee Oliver Aalami Stanford University Palo Alto High School Adobe Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Data augmentations are widely used in training medical image deep learning models to increase the diversity and size of sparse datasets. However, commonly used augmentation techniques can result in loss of clinically relevant information from medical images, leading to incorrect predictions at inference time. We propose the Interactive Medical Image Learning (IMIL) framework, a novel approach for improving the training of medical image analysis algorithms that enables clinician-guided intermediate training data augmentations on misprediction outliers, focusing the algorithm on relevant visual information. To prevent the model from using irrelevant features during training, IMIL will ’blackout’ clinician-designated irrelevant regions and replace the original images with the augmented samples. This ensures that for originally mispredicted samples, the algorithm subsequently attends only to relevant regions and correctly correlates them with the respective diagnosis. We validate the efficacy of IMIL using radiology residents and compare its performance to state-of-the-art data augmentations. A 4.2% improvement in accuracy over ResNet-50 was observed when using IMIL on only 4% of the training set. Our study demonstrates the utility of clinician-guided interactive training to achieve meaningful data augmentations for medical image analysis algorithms.

关键词： Training computer vision Image analysis Accuracy Computational modeling Radiology Data augmentation

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Semantic Pre-supplement for Exposure Correction

Semantic Pre-supplement for Exposure Correction

引用

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

作者： Zhen Zou Wei Yu Jie Huang Feng Zhao University of Science and Technology of China He Fei China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Exposure correction tasks are dedicated to recovering the brightness and structural information of overexposed or underexposed images. The recovery difficulty of areas with different exposure levels is different, as severely exposed areas are more difficult to recover due to severe structural information loss than commonly exposed areas. However, existing methods focus on the simultaneous recovery of global brightness and structure, ignoring that the recovery difficulty varies between areas. To address this issue, we propose a novel exposure correction strategy named "Inpainting Assisted Exposure Correction"(IAEC), which pre-performs image structure repair on severely exposed areas to guide the exposure correction process. This method is based on the observation that the contextual semantic information contained in the image structure can effectively help the overall image recovery, and the lack of contextual semantic information in severely incorrectly exposed areas is very severe. The pre-performed structural repair by the inpainting model can well supplement the insufficient contextual semantic information caused by severe exposure. Therefore, we use an inpainting model to perform pre-structure repair on severely exposed areas to obtain supplementary contextual semantic information and then align the structure-repaired image with the improperly exposed input at the feature level. Extensive experiments demonstrate that our method gets superior results than the state-of-the-art methods and has the potential to be applied to other tasks with similar context loss problems.

关键词： Training computer vision conferences Semantics Brightness Maintenance engineering Cognition

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Adaptive Render-Video Streaming for Virtual Environments

Adaptive Render-Video Streaming for Virtual Environments

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

作者： Jia-Jie Lim Matthias S. Treder Aaron Chadha Yiannis Andreopoulos Sony Interactive Entertainment London UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In cloud-based gaming and virtual reality (G&VR), scene content is rendered in a cloud server and streamed as low-latency encoded video to the client device. In this context, distributed rendering aims to offload parts of the rendering to the client. An adaptive approach is proposed, which dynamically assigns assets to client-side vs. server-side rendering according to varying rendering time and bitrate targets. This is achieved by streaming perceptually-optimized scene control weights to the client, which are compressed with a composable autoencoder in conjunction with select video segments. This creates an adaptive render-video (REVI) streaming framework, which allows for substantial tradeoffs between client rendering time and the bitrate required to stream visually-lossless video from the server to the client. In order to estimate and control the client rendering time and the required bitrate of each subset of each scene, a random-forest based regressor is proposed, in conjunction with the use of AIMD (additive-increase/multiplicative-decrease) to ensure predetermined average bitrate or render-time targets are met. Experiments are presented based on typical sets of G&VR scenes rendered in Blender and HEVC low-latency encoding. A key result is that, when the client is providing for 50% of the rendering time needed to render the whole scene, up to 60% average bitrate saving is achieved versus streaming the entire scene to the client as video.

关键词： Cloud computing Bit rate Lighting Virtual environments Streaming media Ray tracing Rendering (computer graphics)

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TlTScore: Towards Long-Tail Effects in Text-to-Visual Evaluation with Generative Foundation Models

TlTScore: Towards Long-Tail Effects in Text-to-Visual Evalua...

引用

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

作者： Pengliang Ji Junchen Liu Carnegie Mellon University UC Berkeley

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Evaluation of generative foundation models (GenFMs) for text-to-visual tasks has been enhanced by automatic alignment metrics such as CLIPScore, complementing human feedback. However, existing evaluation methods suffer from a severe long-tail effect, where the balance between token count and semantic validity in the initial step, hinders the accurate evaluation of advanced aspects such as composition. We analyze this drawback and attribute it to a lack of symbolic reasoning attention, while GenFMs demonstrate strong discriminative abilities in handling symbolism. To this end, we propose a pioneering paradigm for evaluating GenFMs’ text-to-visual (T2V) generation using neuro-symbolic thinking to mitigate the long-tail effect. By explicitly embedding Mixture-of-experts (MoE) Large vision Models (LVMs), we introduce symbolic-level understanding while maintaining the strong neuro-level reasoning capability. Through the fusion of semantic and compositional knowledge at the neuro-to-symbolic level, our approach outperforms state-of-the-art T2V evaluation methods, exhibiting stronger compositional reasoning ability on Winoground and better alignment with human judgment. We also demonstrate our impressive effectiveness on diverse tasks, including text-to-3D and text-to-video. To further advance the T2V evaluation of GenFMs, we propose a challenging benchmark that includes richer and more diverse compositional and semantic information compared to Winoground. Overall, our work opens a new direction for neuro-to-symbolic visio-linguistic evaluation of GenFMs and aims to drive further progress in the field.

关键词： Measurement computer vision Accuracy conferences Semantics Benchmark testing Cognition

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SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation

SegFormer3D: an Efficient Transformer for 3D Medical Image S...

引用

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

作者： Shehan Perera Pouyan Navard Alper Yilmaz Photogrammetric Computer Vision Lab The Ohio State University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The adoption of vision Transformers (ViTs) based architectures represents a significant advancement in 3D Medical Image (MI) segmentation, surpassing traditional Convolutional Neural Network (CNN) models by enhancing global contextual understanding. While this paradigm shift has significantly enhanced 3D segmentation performance, state-of-the-art architectures require extremely large and complex architectures with large scale computing resources for training and deployment. Furthermore, in the context of limited datasets, often encountered in medical imaging, larger models can present hurdles in both model generalization and convergence. In response to these challenges and to demonstrate that lightweight models are a valuable area of research in 3D medical imaging, we present SegFormer3D, a hierarchical Transformer that calculates attention across multiscale volumetric features. Additionally, SegFormer3D avoids complex decoders and uses an all-MLP decoder to aggregate local and global attention features to produce highly accurate segmentation masks. The proposed memory efficient Transformer preserves the performance characteristics of a significantly larger model in a compact design. SegFormer3D democratizes deep learning for 3D medical image segmentation by offering a model with 33× less parameters and a 13× reduction in GFLOPS compared to the current state-of-the-art (SOTA). We benchmark SegFormer3D against the current SOTA models on three widely used datasets Synapse, BRaTs, and ACDC, achieving competitive results. Code: https://***/OSUPCVLab/***

关键词： Training Image segmentation Solid modeling Three-dimensional displays Computational modeling computer architecture Transformers

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2D-3D Heterogeneous Face recognition Based on Deep Coupled Spectral Regression

2D-3D Heterogeneous Face Recognition Based on Deep Coupled S...

引用

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

作者： Yangtao Zheng Di Huang Weixin Li Shupeng Wang Yunhong Wang IRIP Lab School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

As one of the major branches in Face recognition (FR), 2D-3D Heterogeneous FR (HFR), where face comparison is achieved across the texture and shape modalities, has become more important. This paper proposes a novel deep learning based end-to-end approach, namely Deep Coupled Spectral Regression (DCSR), for such an issue. It jointly makes use of both the advantages of CNN based deep features and CSR based common subspace. Specifically, from 2D texture and 3D depth face maps, DCSR extracts more powerful features by a deep network with the cross-modality triplet loss, which show much better uniqueness and robustness than the hand-crafted ones. Further, DCSR learns the shared space between different modalities with the constraints of sample labels, and is thereby more discriminative than the widely used unsupervised methods. More importantly, the two steps above are integrated through a couple layer to explicitly optimize the weights of deep features and projection directions rather than a simple combination. Experiments are carried out on the FRGC v2.0 database, and the results reported clearly demonstrate the competency of our proposed method. Its generalization ability is also validated by additional experiments conducted on the CASIA NIR-VIS 2.0 database.

关键词： Face Three-dimensional displays Two dimensional displays Feature extraction Shape Databases Lighting

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SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations

SciFlow: Empowering Lightweight Optical Flow Models with Sel...

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

作者： Jamie Menjay Lin Jisoo Jeong Hong Cai Risheek Garrepalli Kai Wang Fatih Porikli Qualcomm Technologies Inc. Qualcomm AI Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Optical flow estimation is crucial to a variety of vision tasks. Despite substantial recent advancements, achieving real-time on-device optical flow estimation remains a complex challenge. First, an optical flow model must be sufficiently lightweight to meet computation and memory constraints to ensure real-time performance on devices. Second, the necessity for real-time on-device operation imposes constraints that weaken the model’s capacity to adequately handle ambiguities in flow estimation, thereby intensifying the difficulty of preserving flow *** paper introduces two synergistic techniques, Self-Cleaning Iteration (SCI) and Regression Focal Loss (RFL), designed to enhance the capabilities of optical flow models, with a focus on addressing optical flow regression ambiguities. These techniques prove particularly effective in mitigating error propagation, a prevalent issue in optical flow models that employ iterative refinement. Notably, these techniques add negligible to zero overhead in model parameters and inference latency, thereby preserving real-time on-device *** effectiveness of our proposed SCI and RFL techniques, collectively referred to as SciFlow for brevity, is demonstrated across two distinct lightweight optical flow model architectures in our experiments. Remarkably, SciFlow enables substantial reduction in error metrics (EPE and Fl-all) over the baseline models by up to 6.3% and 10.5% for in-domain scenarios and by up to 6.2% and 13.5% for cross-domain scenarios on the Sintel and KITTI 2015 datasets, respectively.

关键词： Optical losses Training Performance evaluation computer vision Image motion analysis Computational modeling Estimation

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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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Unknown Sample Discovery for Source Free Open Set Domain Adaptation

Unknown Sample Discovery for Source Free Open Set Domain Ada...

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

作者： Chowdhury Sadman Jahan Andreas Savakis Center for Imaging Science Rochester Institute of Technology Rochester NY USA Department of Computer Engineering Rochester Institute of Technology Rochester NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Open Set Domain Adaptation (OSDA) aims to adapt a model trained on a source domain to a target domain that undergoes distribution shift and contains samples from novel classes outside the source domain. Test-time or source-free OSDA (SF-OSDA) techniques eliminate the need to access source domain samples, but current SF-OSDA methods utilize only the known classes in the target domain for adaptation, and require access to the entire target domain even during inference after adaptation, to distinguish between known and unknown samples. In this paper, we introduce Unknown Sample Discovery (USD) as an SF-OSDA method that utilizes a temporally ensembled teacher model to conduct known-unknown target sample separation and adapts the student model to the target domain over all classes using co-training and temporal consistency between the teacher and the student. USD promotes Jensen-Shannon distance (JSD) as an effective measure for known-unknown sample separation. Our teacher-student framework significantly reduces error accumulation resulting from imperfect known-unknown sample separation, while curriculum guidance helps to reliably learn the distinction between target known and target unknown subspaces. USD appends the target model with an unknown class node, thus readily classifying a target sample into any of the known or unknown classes in subsequent post-adaptation inference stages. Empirical results show that USD is superior to existing SF-OSDA methods and is competitive with current OSDA models that require both source and target domains during adaptation.

关键词： Adaptation models computer vision conferences Computational modeling Data models pattern recognition Reliability

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