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FFF: Fragment-Guided Flexible Fitting for Building Complete Protein Structures

FFF: Fragment-Guided Flexible Fitting for Building Complete ...

引用

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

作者： Chen, Weijie Wang, Xinyan Wang, Yuhang DP Technol Ltd Camarillo CA 93012 USA Peking Univ Ctr Data Sci Beijing Peoples R China

ISBN: (纸本)9798350301298

Cryo-electron microscopy (cryo-EM) is a technique for reconstructing the 3-dimensional (3D) structure of biomolecules (especially large protein complexes and molecular assemblies). As the resolution increases to the near-atomic scale, building protein structures de novo from cryo-EM maps becomes possible. Recently, recognition-based de novo building methods have shown the potential to streamline this process. However, it cannot build a complete structure due to the low signal-to-noise ratio (SNR) problem. At the same time, AlphaFold has led to a great breakthrough in predicting protein structures. This has inspired us to combine fragment recognition and structure prediction methods to build a complete structure. In this paper, we propose a new method named FFF that bridges protein structure prediction and protein structure recognition with flexible fitting. First, a multi-level recognition network is used to capture various structural features from the input 3D cryo-EM map. Next, protein structural fragments are generated using pseudo peptide vectors and a protein sequence alignment method based on these extracted features. Finally, a complete structural model is constructed using the predicted protein fragments via flexible fitting. Based on our benchmark tests, FFF outperforms the baseline methods for building complete protein structures.

关键词： detection recognition: Categorization retrieval

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Emotic Masked Autoencoder on Dual-views with Attention Fusion for Facial Expression recognition

Emotic Masked Autoencoder on Dual-views with Attention Fusio...

引用

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

作者： Xuan-Bach Nguyen Hoang-Thien Nguyen Thanh-Huy Nguyen Nhu-Tai Do Quang Vinh Dinh Ho Chi Minh City Univ Technol Ho Chi Minh City Vietnam Posts & Telecommun Inst Technol Ho Chi Minh City Vietnam Ho Chi Minh City Univ Educ Ho Chi Minh City Vietnam Univ Econ Ho Chi Minh City UEH Vietnam Ho Chi Minh City Vietnam Vietnamese German Univ Binh Duong Vietnam

ISBN: (纸本)9798350365474

Facial Expression recognition (FER) is a critical task within computer vision with diverse applications across various domains. Addressing the challenge of limited FER datasets, which hampers the generalization capability of expression recognition models, is imperative for enhancing performance. Our paper presents an innovative approach integrating the MAE-Face self-supervised learning (SSL) method and multi-view Fusion Attention mechanism for expression classification, particularly showcased in the 6th Affective Behavior Analysis in-the-wild (ABAW) competition. By utilizing low-level feature information from the ipsilateral view (auxiliary view) before learning the highlevel feature that emphasizes the shift in the human facial expression, our work seeks to provide a straightforward yet innovative way to improve the examined view (main view). We also suggest easy-to-implement and no-training frameworks aimed at highlighting key facial features to determine if such features can serve as guides for the model, focusing on pivotal local elements. The efficacy of this method is validated by improvements in model performance on the Aff-wild2 dataset, as observed in both training and validation contexts.

关键词： Self-supervised learning

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Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning

Gradient-based Uncertainty Attribution for Explainable Bayes...

引用

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

作者： Wang, Hanjing Joshi, Dhiraj Wang, Shiqiang Ji, Qiang Rensselaer Polytech Inst Troy NY 12180 USA IBM Res Armonk NY USA

ISBN: (纸本)9798350301298

Predictions made by deep learning models are prone to data perturbations, adversarial attacks, and out-of-distribution inputs. To build a trusted AI system, it is therefore critical to accurately quantify the prediction uncertainties. While current efforts focus on improving uncertainty quantification accuracy and efficiency, there is a need to identify uncertainty sources and take actions to mitigate their effects on predictions. Therefore, we propose to develop explainable and actionable Bayesian deep learning methods to not only perform accurate uncertainty quantification but also explain the uncertainties, identify their sources, and propose strategies to mitigate the uncertainty impacts. Specifically, we introduce a gradient-based uncertainty attribution method to identify the most problematic regions of the input that contribute to the prediction uncertainty. Compared to existing methods, the proposed UA-Backprop has competitive accuracy, relaxed assumptions, and high efficiency. Moreover, we propose an uncertainty mitigation strategy that leverages the attribution results as attention to further improve the model performance. Both qualitative and quantitative evaluations are conducted to demonstrate the effectiveness of our proposed methods.

关键词： Explainable computer vision

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ScaleDet: A Scalable Multi-Dataset Object Detector

ScaleDet: A Scalable Multi-Dataset Object Detector

引用

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

作者： Chen, Yanbei Wang, Manchen Mittal, Abhay Xu, Zhenlin Favaro, Paolo Tighe, Joseph Modolo, Davide AWS AI Labs Shanghai Peoples R China

ISBN: (纸本)9798350301298

Multi-dataset training provides a viable solution for exploiting heterogeneous large-scale datasets without extra annotation cost. In this work, we propose a scalable multi-dataset detector (ScaleDet) that can scale up its generalization across datasets when increasing the number of training datasets. Unlike existing multi-dataset learners that mostly rely on manual relabelling efforts or sophisticated optimizations to unify labels across datasets, we introduce a simple yet scalable formulation to derive a unified semantic label space for multi-dataset training. ScaleDet is trained by visual-textual alignment to learn the label assignment with label semantic similarities across datasets. Once trained, ScaleDet can generalize well on any given upstream and downstream datasets with seen and unseen classes. We conduct extensive experiments using LVIS, COCO, Objects365, OpenImages as upstream datasets, and 13 datasets from Object Detection in the Wild (ODinW) as downstream datasets. Our results show that ScaleDet achieves compelling strong model performance with an mAP of 50.7 on LVIS, 58.8 on COCO, 46.8 on Objects365, 76.2 on OpenImages, and 71.8 on ODinW, surpassing state-of-the-art detectors with the same backbone.

关键词： Efficient and scalable vision

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Regularization of polynomial networks for image recognition

Regularization of polynomial networks for image recognition

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

作者： Chrysos, Grigorios G. Wang, Bohan Deng, Jiankang Cevher, Volkan Ecole Polytech Fed Lausanne LIONS Lausanne Switzerland Huawei UKRD Cambridge England

ISBN: (纸本)9798350301298

Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising performance and improved interpretability but have yet to reach the performance of the powerful DNN baselines. In this work, we aim to close this performance gap. We introduce a class of PNs, which are able to reach the performance of ResNet across a range of six benchmarks. We demonstrate that strong regularization is critical and conduct an extensive study of the exact regularization schemes required to match performance. To further motivate the regularization schemes, we introduce D-PolyNets that achieve a higher-degree of expansion than previously proposed polynomial networks. D-PolyNets are more parameter-efficient while achieving a similar performance as other polynomial networks. We expect that our new models can lead to an understanding of the role of elementwise activation functions (which are no longer required for training PNs). The source code is available at https:// ***/grigorisg9gr/regularized_polynomials.

关键词： Deep learning architectures and techniques

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QAttn: Efficient GPU Kernels for mixed-precision vision Transformers

QAttn: Efficient GPU Kernels for mixed-precision Vision Tran...

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

作者： Kluska, Piotr Castello, Adrian Scheidegger, Florian Malossi, A. Cristiano I. Quintana-Orti, Enrique S. IBM Res Europe Ruschlikon Switzerland Univ Politecn Valencia Valencia Spain

ISBN: (纸本)9798350365474

vision Transformers have demonstrated outstanding performance in computer vision tasks. Nevertheless, this superior performance for large models comes at the expense of increasing memory usage for storing the parameters and intermediate activations. To accelerate model inference, in this work we develop and evaluate integer and mixed-precision kernels in Triton for the efficient execution of two fundamental building blocks of transformers -linear layer and attention- on graphics processing units (GPUs). On an NVIDIA A100 GPU, our kernel implementations of vision Transformers achieve a throughput speedup of up to 7x compared with reference kernels in PyTorch floating-point single precision (FP32). Additionally, the accuracy for the ViT Large model top-1 drops by less than one percent on the ImageNet1K classification task. We also observe up to 6x increased throughput by applying our kernels to the Segment Anything Model image encoder while keeping the mIOU close to the FP32 reference on the COCO2017 dataset for static and dynamic quantization. Furthermore, our kernels demonstrate improved speed to the TensorRT INT8 linear layer, and we improve the throughput of base FP16 (half precision) Triton attention on average by up to 19 +/- 4.01%. We have open-sourced the QAtnn framework, which is tightly integrated with the PyTorch quantization workflow https://***/IBM/qattn.

关键词： compression instance segmentation object classification quantization vision transformers

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Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action recognition from Egocentric RGB Videos

Hierarchical Temporal Transformer for 3D Hand Pose Estimatio...

引用

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

作者： Wen, Yilin Pan, Hao Yang, Lei Pan, Jia Komura, Taku Wang, Wenping Univ Hong Kong Hong Kong Peoples R China Microsoft Res Asia Beijing Peoples R China TransGP Hong Kong Peoples R China Texas A&M Univ College Stn TX USA

ISBN: (纸本)9798350301298

Understanding dynamic hand motions and actions from egocentric RGB videos is a fundamental yet challenging task due to self-occlusion and ambiguity. To address occlusion and ambiguity, we develop a transformer-based framework to exploit temporal information for robust estimation. Noticing the different temporal granularity of and the semantic correlation between hand pose estimation and action recognition, we build a network hierarchy with two cascaded transformer encoders, where the first one exploits the short-term temporal cue for hand pose estimation, and the latter aggregates per-frame pose and object information over a longer time span to recognize the action. Our approach achieves competitive results on two first-person hand action benchmarks, namely FPHA and H2O. Extensive ablation studies verify our design choices.

关键词： body gesture Humans: Face movement pose

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Exploring the Zero-Shot Capabilities of vision-Language Models for Improving Gaze Following

Exploring the Zero-Shot Capabilities of Vision-Language Mode...

引用

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

作者： Gupta, Anshul Vuillecard, Pierre Farkhondeh, Arya Odobez, Jean-Marc Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9798350365474

Contextual cues related to a person's pose and interactions with objects and other people in the scene can provide valuable information for gaze following. While existing methods have focused on dedicated cue extraction methods, in this work we investigate the zero-shot capabilities of vision-Language Models (VLMs) for extracting a wide array of contextual cues to improve gaze following performance. We first evaluate various VLMs, prompting strategies, and in-context learning (ICL) techniques for zero-shot cue recognition performance. We then use these insights to extract contextual cues for gaze following, and investigate their impact when incorporated into a state of the art model for the task. Our analysis indicates that BLIP-2 is the overall top performing VLM and that ICL can improve performance. We also observe that VLMs are sensitive to the choice of the text prompt although ensembling over multiple text prompts can provide more robust performance. Additionally, we discover that using the entire image along with an ellipse drawn around the target person is the most effective strategy for visual prompting. For gaze following, incorporating the extracted cues results in better generalization performance, especially when considering a larger set of cues, highlighting the potential of this approach.

关键词： Gaze Following vision-Language Zero-Shot Evaluation

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VMCML: Video and Music Matching via Cross-Modality Lifting

VMCML: Video and Music Matching via Cross-Modality Lifting

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

作者： Lee, Yi-Shan Tseng, Wei-Cheng Wang, Fu-En Sun, Min Natl Tsing Hua Univ Hsinchu Taiwan Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9798350365474

We propose a content-based system for matching video and background music. The system aims to address the challenges in music recommendation for new users or new music give short-form videos. To this end, we propose a cross-modal framework VMCML (Video and Music Matching via Cross-Modality Lifting) that finds a shared embedding space between video and music representations. To ensure the embedding space can be effectively shared by both representations, we leverage CosFace loss based on margin-based cosine similarity loss. Furthermore, to confirm the music is not the original sound of the video and that more than one video is matched to the same music, we follow the rule and collect videos and music from a well-known multi-media platform. That is because there are limitations of previous datasets. We establish a large-scale dataset called MSV, which provide 390 individual music and the corresponding matched 150,000 videos. We conduct extensive experiments on Youtube-8M and our MSV datasets. Our quantitative and qualitative results demonstrate the effectiveness of our proposed framework and achieve state-of-the-art video and music matching performance.

关键词： computer music

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Learning Expressive Prompting With Residuals for vision Transformers

Learning Expressive Prompting With Residuals for Vision Tran...

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

作者： Das, Rajshekhar Dukler, Yonatan Ravichandran, Avinash Swarninathan, Ashwin Carnegie Mellon Univ Pittsburgh PA 15213 USA AWS AI Labs Shanghai Peoples R China

ISBN: (纸本)9798350301298

Prompt learning is an efficient approach to adapt transformers by inserting learnable set of parameters into the input and intermediate representations of a pre-trained model. In this work, we present Expressive Prompts with Residuals (EXPRES) which modifies the prompt learning paradigm specifically for effective adaptation of vision transformers (ViT). Our method constructs downstream representations via learnable "output" tokens (shallow prompts), that are akin to the learned class tokens of the ViT. Further for better steering of the downstream representation processed by the frozen transformer, we introduce residual learnable tokens that are added to the output of various computations. We apply EXPRES for image classification and few-shot semantic segmentation, and show our method is capable of achieving state of the art prompt tuning on 3/3 categories of the VTAB benchmark. In addition to strong performance, we observe that our approach is an order of magnitude more prompt efficient than existing visual prompting baselines. We analytically show the computational benefits of our approach over weight space adaptation techniques like finetuning. Lastly we systematically corroborate the architectural design of our method via a series of ablation experiments.

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

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