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Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training

Towards Generalisable Video Moment Retrieval: Visual-Dynamic...

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

作者： Luo, Dezhao Huang, Jiabo Gong, Shaogang Jin, Hailin Liu, Yang Queen Mary Univ London London England Adobe Res San Francisco CA USA Peking Univ WICT Beijing Peoples R China

ISBN: (纸本)9798350301298

The correlation between the vision and text is essential for video moment retrieval (VMR), however, existing methods heavily rely on separate pre-training feature extractors for visual and textual understanding. Without sufficient temporal boundary annotations, it is non-trivial to learn universal video-text alignments. In this work, we explore multi-modal correlations derived from large-scale image-text data to facilitate generalisable VMR. To address the limitations of image-text pre-training models on capturing the video changes, we propose a generic method, referred to as Visual-Dynamic Injection (VDI), to empower the model's understanding of video moments. Whilst existing VMR methods are focusing on building temporal-aware video features, being aware of the text descriptions about the temporal changes is also critical but originally overlooked in pre-training by matching static images with sentences. Therefore, we extract visual context and spatial dynamic information from video frames and explicitly enforce their alignments with the phrases describing video changes (e.g. verb). By doing so, the potentially relevant visual and motion patterns in videos are encoded in the corresponding text embeddings (injected) so to enable more accurate video-text alignments. We conduct extensive experiments on two VMR benchmark datasets (Charades-STA and ActivityNet-Captions) and achieve state-of-the-art performances. Especially, VDI yields notable advantages when being tested on the out-of-distribution splits where the testing samples involve novel scenes and vocabulary.

关键词： and reasoning language vision

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Deep Equilibrium Optical Flow Estimation

Deep Equilibrium Optical Flow Estimation

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

作者： Bai, Shaojie Geng, Zhengyang Savani, Yash Kolter, J. Zico Carnegie Mellon Univ Pittsburgh PA 15213 USA Peking Univ Beijing Peoples R China Bosch Ctr AI Pittsburgh PA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Many recent state-of-the-art (SOTA) optical flow models use finite-step recurrent update operations to emulate traditional algorithms by encouraging iterative refinements toward a stable flow estimation. However, these RNNs impose large computation and memory overheads, and are not directly trained to model such "stable estimation". They can converge poorly and thereby suffer from performance degradation. To combat these drawbacks, we propose deep equilibrium (DEQ) flow estimators, an approach that directly solves for the flow as the infinite-level fixed point of an implicit layer (using any black-box solver) [3], and differentiates through this fixed point analytically (thus requiring O(1) training memory). This implicit-depth approach is not predicated on any specific model, and thus can be applied to a wide range of SOTA flow estimation model designs (e.g., RAFT [1] and GMA [2]). The use of these DEQ flow estimators allows us to compute the flow faster using, e.g., fixed-point reuse and inexact gradients, consumes 4 similar to 6x less training memory than the recurrent counterpart, and achieves better results with the same computation budget. In addition, we propose a novel, sparse fixed-point correction scheme to stabilize our DEQ flow estimators, which addresses a longstanding challenge for DEQ models in general. We test our approach in various realistic settings and show that it improves SOTA methods on Sintel and KITTI datasets with substantially better computational and memory efficiency.

关键词： Training Degradation computer vision Image motion analysis Computational modeling Memory management Estimation

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Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization 31

Towards Faster Training of Global Covariance Pooling Network...

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31st ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Peihua Xie, Jiangtao Wang, Qilong Gao, Zilin Dalian Univ Technol Dalian Peoples R China

ISBN: (纸本)9781538664209

Global covariance pooling in convolutional neural networks has achieved impressive improvement over the classical first-order pooling. Recent works have shown matrix square root normalization plays a central role in achieving state-of-the-art performance. However, existing methods depend heavily on eigendecomposition (EIG) or singular value decomposition (SVD), suffering from inefficient training due to limited support of EIG and SVD on GPU. Towards addressing this problem, we propose an iterative matrix square root normalization method for fast end-toend training of global covariance pooling networks. At the core of our method is a meta-layer designed with loop-embedded directed graph structure. The meta-layer consists of three consecutive nonlinear structured layers, which perform pre-normalization, coupled matrix iteration and post-compensation, respectively. Our method is much faster than EIG or SVD based ones, since it involves only matrix multiplications, suitable for parallel implementation on GPU. Moreover, the proposed network with ResNet architecture can converge in much less epochs, further accelerating network training. On large-scale ImageNet, we achieve competitive performance superior to existing counterparts. By finetuning our models pre-trained on ImageNet, we establish state-of-the-art results on three challenging finegrained benchmarks. The source code and network models will be available at http://***/iSQRT-COV.

关键词： Covariance matrices Graphics processing units Training Matrix decomposition Backpropagation Symmetric matrices computer architecture

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Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos

Spatial-Temporal Correlation and Topology Learning for Perso...

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

作者： Liu, Jiawei Zha, Zheng-Jun Wu, Wei Zheng, Kecheng Sun, Qibin Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781665445092

Video-based person re-identification aims to match pedestrians from video sequences across non-overlapping camera views. The key factor for video person re- identification is to effectively exploit both spatial and temporal clues from video sequences. In this work, we propose a novel Spatial-Temporal Correlation and Topology Learning framework (CTL) to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. Specifically, CTL utilizes a CNN backbone and a key points estimator to extract semantic local features from human body at multiple granularities as graph nodes. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body. Moreover, a 3D graph convolution and a cross-scale graph convolution are designed, which facilitate direct cross-spacetime and cross-scale information propagation for capturing hierarchical spatial-temporal dependencies and structural information. By jointly performing the two convolutions, CTL effectively mines comprehensive clues that are complementary with appearance information to enhance representational capacity. Extensive experiments on two video benchmarks have demonstrated the effectiveness of the proposed method and the state-of-the-art performance.

关键词： computer vision Correlation Three-dimensional displays Convolution Biological system modeling Video sequences Semantics

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An Efficient Training Approach for Very Large Scale Face recognition

An Efficient Training Approach for Very Large Scale Face Rec...

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

作者： Wang, Kai Wang, Shuo Zhang, Panpan Zhou, Zhipeng Zhu, Zheng Wang, Xiaobo Peng, Xiaojiang Sun, Baigui Li, Hao You, Yang Natl Univ Singapore Singapore Singapore Alibaba Grp Hangzhou Peoples R China Tsinghua Univ Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Shenzhen Technol Univ Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Face recognition has achieved significant progress in deep learning era due to the ultra-large-scale and well-labeled datasets. However, training on the outsize datasets is time-consuming and takes up a lot of hardware resource. Therefore, designing an efficient training approach is indispensable. The heavy computational and memory costs mainly result from the million-level dimensionality of the fully connected (FC) layer. To this end, we propose a novel training approach, termed Faster Face Classification ((FC)-C-2), to alleviate time and cost without sacrificing the performance. This method adopts Dynamic Class Pool (DCP) for storing and updating the identities' features dynamically, which could be regarded as a substitute for the FC layer. DCP is efficiently time-saving and cost-saving, as its smaller size with the independence from the whole face identities together. We further validate the proposed (FC)-C-2 method across several face benchmarks and private datasets, and display comparable results, meanwhile the speed is faster than state-of-the-art FC-based methods in terms of recognition accuracy and hardware costs. Moreover, our method is further improved by a well-designed dual data loader including indentity-based and instance-based loaders, which makes it more efficient for updating DCP parameters.

关键词： Training Deep learning Technological innovation Ethics computer vision Costs Face recognition

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Exploring Geometric Consistency for Monocular 3D Object Detection

Exploring Geometric Consistency for Monocular 3D Object Dete...

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

作者： Lian, Qing Ye, Botao Xu, Ruijia Yao, Weilong Zhang, Tong Hong Kong Univ Sci & Technol Hong Kong Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Autowise AI Hayward CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper investigates the geometric consistency for monocular 3D object detection, which suffers from the ill-posed depth estimation. We first conduct a thorough analysis to reveal how existing methods fail to consistently localize objects when different geometric shifts occur. In particular, we design a series of geometric manipulations to diagnose existing detectors and then illustrate their vulnerability to consistently associate the depth with object apparent sizes and positions. To alleviate this issue, we propose four geometry-aware data augmentation approaches to enhance the geometric consistency of the detectors. We first modify some commonly used data augmentation methods for 2D images so that they can maintain geometric consistency in 3D spaces. We demonstrate such modifications are important. In addition, we propose a 3D-specific image perturbation method that employs the camera movement. During the augmentation process, the camera system with the corresponding image is manipulated, while the geometric visual cues for depth recovery are preserved. We show that by using the geometric consistency constraints, the proposed augmentation techniques lead to improvements on the KITTI and nuScenes monocular 3D detection benchmarks with state-of-the-art results. In addition, we demonstrate that the augmentation methods are well suited for semi-supervised training and cross-dataset generalization.

关键词： Training Visualization computer vision Three-dimensional displays Perturbation methods Estimation Object detection

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Discovering Objects that Can Move

Discovering Objects that Can Move

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

作者： Bao, Zhipeng Tokmakov, Pavel Jabri, Allan Wang, Yu-Xiong Gaidon, Adrien Hebert, Martial CMU Pittsburgh PA 15213 USA Toyota Res Inst Tokyo Japan Univ Calif Berkeley Berkeley CA USA UIUC Champaign IL USA TRI Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper studies the problem of object discovery - separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. However, by relying on appearance alone, these methods fail to separate objects from the background in cluttered scenes. This is a fundamental limitation since the definition of an object is inherently ambiguous and context-dependent. To resolve this ambiguity, we choose to focus on dynamic objects - entities that can move independently in the world We then scale the recent auto-encoder based frameworks for unsupervised object discovery from toy synthetic images to complex real-world scenes. 'lb this end, we simplify their architecture, and augment the resulting model with a weak learning signal from general motion segmentation algorithms. Our experiments demonstrate that, despite only capturing a small subset of the objects that move, this signal is enough to generalize to segment both moving and static instances of dynamic objects. We show that our model scales to a newly collected, photo-realistic synthetic dataset with street driving scenarios. Additionally, we leverage ground truth segmentation and flow annotations in this dataset for thorough ablation and evaluation. Finally, our experiments on the real-world KITTI benchmark demonstrate that the proposed approach outperforms both heuristic- and learning-based methods by capitalizing on motion cues.

关键词： Representation learning Learning systems computer vision Tracking Shape Motion segmentation Dynamics

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The Flag Median and FlagIRLS

The Flag Median and FlagIRLS

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

作者： Mankovich, Nathan King, Emily J. Peterson, Chris Kirby, Michael Colorado State Univ Ft Collins CO 80523 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Finding prototypes (e.g., mean and median) for a dataset is central to a number of common machine learning algorithms. Subspaces have been shown to provide useful, robust representations for datasets of images, videos and more. Since subspaces correspond to points on a Grassmann manifold, one is led to consider the idea of a subspace prototype for a Grassmann-valued dataset. While a number of different subspace prototypes have been described, the calculation of some of these prototypes has proven to be computationally expensive while other prototypes are affected by outliers and produce highly imperfect clustering on noisy data. This work proposes a new subspace prototype, the flag median, and introduces the FlagIRLS algorithm for its calculation. We provide evidence that the flag median is robust to outliers and can be used effectively in algorithms like Linde-Buzo-Grey (LBG) to produce improved clusterings on Grassmannians. Numerical experiments include a synthetic dataset, the MNIST handwritten digits dataset, the Mind's Eye video dataset and the UCF YouTube action dataset. The flag median is compared the other leading algorithms for computing prototypes on the Grassmannian, namely, the l(2)-median and to the flag mean. We find that using FlagIRLS to compute the flag median converges in 4 iterations on a synthetic dataset. We also see that Grassmannian LBG with a codebook size of 20 and using the flag median produces at least a 10% improvement in cluster purity over Grassmannian LBG using the flag mean or l(2) -median on the Mind's Eye dataset.

关键词： Manifolds computer vision Machine learning algorithms Video on demand Pose estimation Prototypes Clustering algorithms

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Progressive Semantic-Guided vision Transformer for Zero-Shot Learning

Progressive Semantic-Guided Vision Transformer for Zero-Shot...

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

作者： Chen, Shiming Hou, Wenjin Khan, Salman Khan, Fahad Shahbaz Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Huazhong Univ Sci & Technol Wuhan Peoples R China Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones, supported by semantic information (e.g., attributes). However, existing ZSL methods simply extract visual features using a pre-trained network backbone (i.e., CNN or ViT), which fail to learn matched visual-semantic correspondences for representing semantic-related visual features as lacking of the guidance of semantic information, resulting in undesirable visual-semantic interactions. To tackle this issue, we propose a progressive semantic-guided vision transformer for zero-shot learning (dubbed ZSLViT). ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly, and ii) discard the semantic-unrelated visual information. Specifically, we first introduce semantic-embedded token learning to improve the visual-semantic correspondences via semantic enhancement and discover the semantic-related visual tokens explicitly with semantic-guided token attention. Then, we fuse low semantic-visual correspondence visual tokens to discard the semantic-unrelated visual information for visual enhancement. These two operations are integrated into various encoders to progressively learn semantic-related visual representations for accurate visual-semantic interactions in ZSL. The extensive experiments show that our ZSLViT achieves significant performance gains on three popular benchmark datasets, i.e., CUB, SUN, and AWA2.

关键词： zero-shot learning

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GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet

GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet

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

作者： You, Shan Huang, Tao Yang, Mingmin Wang, Fei Qian, Chen Zhang, Changshui SenseTime Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Huazhong Univ Sci & Technol Sch CST Dian Grp Wuhan Peoples R China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

Training a supernet matters for one-shot neural architecture search (NAS) methods since it serves as a basic performance estimator for different architectures (paths). Current methods mainly hold the assumption that a supernet should give a reasonable ranking over all paths. They thus treat all paths equally, and spare much effort to train paths. However, it is harsh for a single supernet to evaluate accurately on such a huge-scale search space (e.g., 7(21)). In this paper, instead of covering all paths, we ease the burden of supernet by encouraging it to focus more on evaluation of those potentially-good ones, which are identified using a surrogate portion of validation data. Concretely, during training, we propose a multi-path sampling strategy with rejection, and greedily filter the weak paths. The training efficiency is thus boosted since the training space has been greedily shrunk from all paths to those potentially-good ones. Moreover;we further adopt an exploration and exploitation policy by introducing an empirical candidate path pool. Our proposed method GreedyNAS is easy-to-follow, and experimental results on ImageNet dataset indicate that it can achieve better Top-1 accuracy under same search space and FLOPs or latency level, but with only similar to 60% of supernet training cost. By searching on a larger space, our GreedyNAS can also obtain new state-of-the-art architectures.

关键词： Training computer architecture Task analysis Graphics processing units Hardware computer vision Estimation

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