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MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis

MoFusion: A Framework for Denoising-Diffusion-based Motion S...

引用

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

作者： Dabral, Rishabh Mughal, Muhammad Hamza Golyanik, Vladislav Theobalt, Christian Max Planck Inst Informat SIC Saarbrucken Germany Saarland Univ Saarbrucken Germany

ISBN: (纸本)9798350301298

Conventional methods for human motion synthesis have either been deterministic or have had to struggle with the trade-off between motion diversity vs motion quality. In response to these limitations, we introduce MoFusion, i.e., a new denoising-diffusion-based framework for high-quality conditional human motion synthesis that can synthesise long, temporally plausible, and semantically accurate motions based on a range of conditioning contexts (such as music and text). We also present ways to introduce well-known kinematic losses for motion plausibility within the motion-diffusion framework through our scheduled weighting strategy. The learned latent space can be used for several interactive motion-editing applications like in-betweening, seed-conditioning, and text-based editing, thus, providing crucial abilities for virtual-character animation and robotics. Through comprehensive quantitative evaluations and a perceptual user study, we demonstrate the effectiveness of MoFusion compared to the state of the art on established benchmarks in the literature. We urge the reader to watch our supplementary video at https://***/projects/MoFusion/.

关键词： language reasoning vision

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CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention

CUE-Net: Violence Detection Video Analytics with Spatial Cro...

引用

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

作者： Senadeera, Damith Chamalke Yang, Xiaoyun Kollias, Dimitrios Slabaugh, Gregory Queen Mary Univ London Sch Elect Engn & Comp Sci London England Queen Marys Digital Environm Res Inst DERI London England Remark AI UK Ltd London England

ISBN: (纸本)9798350365474

In this paper we introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance. As surveillance systems become more prevalent due to technological advances and decreasing costs, the challenge of efficiently monitoring vast amounts of video data has intensified. CUE-Net addresses this challenge by combining spatial Cropping with an enhanced version of the UniformerV2 architecture, integrating convolutional and self-attention mechanisms alongside a novel Modified Efficient Additive Attention mechanism (which reduces the quadratic time complexity of self-attention) to effectively and efficiently identify violent activities. This approach aims to overcome traditional challenges such as capturing distant or partially obscured subjects within video frames. By focusing on both local and global spatio-temporal features, CUE-Net achieves state-of-the-art performance on the RWF-2000 and RLVS datasets, surpassing existing methods. The source code is available at (1).

关键词： computer vision Cropping Deep Learning Efficient Additive Attention UniFormerV2 Video Analytics Violence Detection

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Iterative vision-and-Language Navigation

Iterative Vision-and-Language Navigation

引用

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

作者： Krantz, Jacob Banerjee, Shurjo Zhu, Wang Corso, Jason Anderson, Peter Lee, Stefan Thomason, Jesse Oregon State Univ Corvallis OR 97331 USA Univ Michigan Ann Arbor MI USA Univ Southern Calif Los Angeles CA USA Google Res Sunnyvale CA USA

ISBN: (纸本)9798350301298

We present Iterative vision-and-Language Navigation (IVLN), a paradigm for evaluating language-guided agents navigating in a persistent environment over time. Existing vision-and-Language Navigation (VLN) benchmarks erase the agent's memory at the beginning of every episode, testing the ability to perform cold-start navigation with no prior information. However, deployed robots occupy the same environment for long periods of time. The IVLN paradigm addresses this disparity by training and evaluating VLN agents that maintain memory across tours of scenes that consist of up to 100 ordered instruction-following Room-to-Room (R2R) episodes, each defined by an individual language instruction and a target path. We present discrete and continuous Iterative Room-to-Room (IR2R) benchmarks comprising about 400 tours each in 80 indoor scenes. We find that extending the implicit memory of high-performing transformer VLN agents is not sufficient for IVLN, but agents that build maps can benefit from environment persistence, motivating a renewed focus on map-building agents in VLN.

关键词： language reasoning vision

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Once for Both: Single Stage of Importance and Sparsity Search for vision Transformer Compression

Once for Both: Single Stage of Importance and Sparsity Searc...

引用

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

作者： Ye, Hancheng Yu, Chong Ye, Peng Xia, Renqiu Tang, Yansong Lu, Jiwen Chen, Tao Zhang, Bo Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Fudan Univ Acad Engn & Technol Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Recent vision Transformer Compression (VTC) works mainly follow a two-stage scheme, where the importance score of each model unit is first evaluated or preset in each submodule, followed by the sparsity score evaluation according to the target sparsity constraint. Such a separate evaluation process induces the gap between importance and sparsity score distributions, thus causing high search costs for VTC. In this work, for the first time, we investigate how to integrate the evaluations of importance and sparsity scores into a single stage, searching the optimal subnets in an efficient manner. Specifically, we present OFB, a cost-efficient approach that simultaneously evaluates both importance and sparsity scores, termed Once for Both (OFB), for VTC. First, a bi-mask scheme is developed by entangling the importance score and the differentiable sparsity score to jointly determine the pruning potential (prunability) of each unit. Such a bi-mask search strategy is further used together with a proposed adaptive one-hot loss to realize the progressive-andefficient search for the most important subnet. Finally, Progressive Masked Image Modeling (PMIM) is proposed to regularize the feature space to be more representative during the search process, which may be degraded by the dimension reduction. Extensive experiments demonstrate that OFB can achieve superior compression performance over state-of-the-art searching-based and pruning-based methods under various vision Transformer architectures, meanwhile promoting search efficiency significantly, e.g., costing one GPU search day for the compression of DeiT-S on ImageNet-1K.

关键词： Image compression

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Revisiting Self-Similarity: Structural Embedding for Image Retrieval

Revisiting Self-Similarity: Structural Embedding for Image R...

引用

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

作者： Lee, Seongwon Lee, Suhyeon Seong, Hongje Kim, Euntai Yonsci Univ Sch Elect & Elect Engn Seoul South Korea

ISBN: (纸本)9798350301298

Despite advances in global image representation, existing image retrieval approaches rarely consider geometric structure during the global retrieval stage. In this work, we revisit the conventional self-similarity descriptor from a convolutional perspective, to encode both the visual and structural cues of the image to global image representation. Our proposed network, named Structural Embedding Network (SENet), captures the internal structure of the images and gradually compresses them into dense self-similarity descriptors while learning diverse structures from various images. These self-similarity descriptors and original image features are fused and then pooled into global embedding, so that global embedding can represent both geometric and visual cues of the image. Along with this novel structural embedding, our proposed network sets new state-of-the-art performances on several image retrieval benchmarks, convincing its robustness to look-alike distractors. The code and models are available: https://***/sungonce/SENet.

关键词： detection recognition: Categorization retrieval

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Drone-HAT: Hybrid Attention Transformer for Complex Action recognition in Drone Surveillance Videos

Drone-HAT: Hybrid Attention Transformer for Complex Action R...

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

作者： Khan, Mustaqeem Ahmad, Jamil El Saddik, Abdulmotaleb Gueaieb, Wail De Masi, Giulia Karray, Fakhri MBZUAI Abu Dhabi U Arab Emirates Univ Ottawa Ottawa ON Canada Technol Innovat Inst Abu Dhabi U Arab Emirates Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9798350365474

Ultra-high-resolution aerial videos are becoming increasingly popular for enhancing surveillance capabilities in sparsely populated areas. However, analyzing human activities automatically, such as "who is doing what?" in these videos, is desirable to realize their surveillance potential. In contrast, atomic visual action detection has successfully recognized such activities in movie data. However, adapting it to ultra-high resolution aerial videos is challenging because the target persons appear relatively tiny from overhead views and are sparsely located. Additionally, existing atomic visual action detection methods are based on single-label actions. However, people can perform multiple actions simultaneously, so a multi-label approach would be more appropriate. To address these problems, we propose a multi-label action detection/recognition framework using a hybrid attention vision transformer (HAT) to recognize recurrent actions more efficiently. Additionally, a multi-scale, multi-granularity module inside the action recognition transformer block extracts relevant features without redundancy. Using the Okutama Dataset, we demonstrated that our method performs better than existing state-of-the-art methodologies for interpreting aerial videos for human activity.

关键词： Aerial Surveillance Hybrid Attention Transformer Multi-granularity and Multi-scale Fusion Multi-label Action recognition

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ABCD : Arbitrary Bitwise Coefficient for De-quantization

ABCD : Arbitrary Bitwise Coefficient for De-quantization

引用

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

作者： Han, Woo Kyoung Lee, Byeonghun Park, Sang Hyun Jin, Kyong Hwan Daegu Gyeongbuk Inst Sci & Technol DGIST Daegu South Korea

ISBN: (纸本)9798350301298

Modern displays and contents support more than 8bits image and video. However, bit-starving situations such as compression codecs make low bit-depth (LBD) images (<8bits), occurring banding and blurry artifacts. Previous bit depth expansion (BDE) methods still produce unsatisfactory high bit-depth (HBD) images. To this end, we propose an implicit neural function with a bit query to recover de-quantized images from arbitrarily quantized inputs. We develop a phasor estimator to exploit the information of the nearest pixels. Our method shows superior performance against prior BDE methods on natural and animation images. We also demonstrate our model on YouTube UGC datasets for de-banding. Our source code is available at https://***/WooKyoungHan/ABCD

关键词： Low-level vision

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X-Pruner: eXplainable Pruning for vision Transformers

X-Pruner: eXplainable Pruning for Vision Transformers

引用

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

作者： Yu, Lu Xiang, Wei James Cook Univ Townsville Australia La Trobe Univ Melbourne Australia

ISBN: (纸本)9798350301298

Recently vision transformer models have become prominent models for a range of tasks. These models, however, usually suffer from intensive computational costs and heavy memory requirements, making them impractical for deployment on edge platforms. Recent studies have proposed to prune transformers in an unexplainable manner, which overlook the relationship between internal units of the model and the target class, thereby leading to inferior performance. To alleviate this problem, we propose a novel explainable pruning framework dubbed X-Pruner, which is designed by considering the explainability of the pruning criterion. Specifically, to measure each prunable unit's contribution to predicting each target class, a novel explainability-aware mask is proposed and learned in an end-to-end manner. Then, to preserve the most informative units and learn the layer-wise pruning rate, we adaptively search the layer-wise threshold that differentiates between unpruned and pruned units based on their explainability-aware mask values. To verify and evaluate our method, we apply the X-Pruner on representative transformer models including the DeiT and Swin Transformer. Comprehensive simulation results demonstrate that the proposed X-Pruner outperforms the state-of-the-art black-box methods with significantly reduced computational costs and slight performance degradation. Code is available at https: //***/vickyyu90/XPruner.

关键词： Explainable computer vision

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SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection

SAFDNet: A Simple and Effective Network for Fully Sparse 3D ...

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

作者： Zhang, Gang Chen, Junnan Gao, Guohuan Li, Jianmin Liu, Si Hu, Xiaolin Tsinghua Univ Inst AI Dept Comp Sci & Technol BNRist Beijing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Beijing Inst Technol Beijing Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Tsinghua Lab Brain & Intelligence THBI Beijing Peoples R China Chinese Inst Brain Res CIBR Beijing 100010 Peoples R China

ISBN: (纸本)9798350353006

LiDAR-based 3D object detection plays an essential role in autonomous driving. Existing high-performing 3D object detectors usually build dense feature maps in the backbone network and prediction head. However, the computational costs introduced by the dense feature maps grow quadratically as the perception range increases, making these models hard to scale up to long-range detection. Some recent works have attempted to construct fully sparse detectors to solve this issue;nevertheless, the resulting models either rely on a complex multi-stage pipeline or exhibit inferior performance. In this work, we propose a fully sparse adaptive feature diffusion network (SAFDNet) for LiDAR-based 3D object detection. In SAFDNet, an adaptive feature diffusion strategy is designed to address the center feature missing problem. We conducted extensive experiments on Waymo Open, nuScenes, and Argoverse2 datasets. SAFDNet performed slightly better than the previous SOTA on the first two datasets but much better on the last dataset, which features long-range detection, verifying the efficacy of SAFDNet in scenarios where long-range detection is required. Notably, on Argoverse2, SAFDNet surpassed the previous best hybrid detector HEDNet by 2.6% mAP while being 2.1x faster, and yielded 2.1% mAP gains over the previous best sparse detector FSDv2 while being 1.3x faster. The code will be available at https://***/zhanggang001/HEDNet.

关键词： 3D Object Detection computer vision Feature Diffusion Fully Sparse Detector

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Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate

Ranking Regularization for Critical Rare Classes: Minimizing...

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

作者： Mohammadi, Kiarash Zhao, He Zhai, Mengyao Tung, Frederick Borealis AI Toronto ON Canada Univ Montreal Mila Montreal PQ Canada

ISBN: (纸本)9798350301298

In many real-world settings, the critical class is rare and a missed detection carries a disproportionately high cost. For example, tumors are rare and a false negative diagnosis could have severe consequences on treatment outcomes;fraudulent banking transactions are rare and an undetected occurrence could result in significant losses or legal penalties. In such contexts, systems are often operated at a high true positive rate, which may require tolerating high false positives. In this paper, we present a novel approach to address the challenge of minimizing false positives for systems that need to operate at a high true positive rate. We propose a ranking-based regularization (RankReg) approach that is easy to implement, and show empirically that it not only effectively reduces false positives, but also complements conventional imbalanced learning losses. With this novel technique in hand, we conduct a series of experiments on three broadly explored datasets (CIFAR-10&100 and Melanoma) and show that our approach lifts the previous state-of-the-art performance by notable margins.

关键词： detection recognition: Categorization retrieval

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