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CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning

CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual...

引用

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

作者： Cheng, Yiting Wei, Fangyun Bao, Jianmin Chen, Dong Zhang, Wenqiang Fudan Univ Sch Comp Sci Shanghai Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

This work focuses on sign language retrieval-a recently proposed task for sign language understanding. Sign language retrieval consists of two sub-tasks: text-to-sign-video (T2V) retrieval and sign-video-to-text (V2T) retrieval. Different from traditional video-text retrieval, sign language videos, not only contain visual signals but also carry abundant semantic meanings by themselves due to the fact that sign languages are also natural languages. Considering this character, we formulate sign language retrieval as a cross-lingual retrieval problem as well as a video-text retrieval task. Concretely, we take into account the linguistic properties of both sign languages and natural languages, and simultaneously identify the fine-grained cross-lingual (i.e., sign-to-word) mappings while contrasting the texts and the sign videos in a joint embedding space. This process is termed as cross-lingual contrastive learning. Another challenge is raised by the data scarcity issue-sign language datasets are orders of magnitude smaller in scale than that of speech recognition. We alleviate this issue by adopting a domain-agnostic sign encoder pre-trained on large-scale sign videos into the target domain via pseudo-labeling. Our framework, termed as domain-aware sign language retrieval via Cross-lingual Contrastive learning or CiCo for short, outperforms the pioneering method by large margins on various datasets, e.g., +22.4 T2V and +28.0 V2T R@1 improvements on How2Sign dataset, and +13.7 T2V and +17.1 V2T R@1 improvements on PHOENIX-2014T dataset. Code and models are available at: https://***/FangyunWei/SLRT.

关键词： vision applications and systems

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Learning Saliency From Fixations

Learning Saliency From Fixations

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Djilali, Yasser Abdelaziz Dahou McGuinness, Kevin O'Connor, Noel Dublin City Univ Dublin Ireland Technol Innovat Inst Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350318920;9798350318937

We present a novel approach for saliency prediction in images, leveraging parallel decoding in transformers to learn saliency solely from fixation maps. Models typically rely on continuous saliency maps, to overcome the difficulty of optimizing for the discrete fixation map. We attempt to replicate the experimental setup that generates saliency datasets. Our approach treats saliency prediction as a direct set prediction problem, via a global loss that enforces unique fixations prediction through bipartite matching and a transformer encoder-decoder architecture. By utilizing a fixed set of learned fixation queries, the cross-attention reasons over the image features to directly output the fixation points, distinguishing it from other modern saliency predictors. Our approach, named Saliency TRansformer (SalTR), achieves metric scores on par with state-of-the-art approaches on the Salicon and MIT300 benchmarks.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Network Expansion For Practical Training Acceleration

Network Expansion For Practical Training Acceleration

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

作者： Ding, Ning Tang, Yehui Han, Kai Xu, Chao Wang, Yunhe Peking Univ Sch Intelligence Sci & Technol Natl Key Lab Gen AI Beijing Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Recently, the sizes of deep neural networks and training datasets both increase drastically to pursue better performance in a practical sense. With the prevalence of transformer-based models in vision tasks, even more pressure is laid on the GPU platforms to train these heavy models, which consumes a large amount of time and computing resources as well. Therefore, it's crucial to accelerate the training process of deep neural networks. In this paper, we propose a general network expansion method to reduce the practical time cost of the model training process. Specifically, we utilize both width- and depth-level sparsity of dense models to accelerate the training of deep neural networks. Firstly, we pick a sparse sub-network from the original dense model by reducing the number of parameters as the starting point of training. Then the sparse architecture will gradually expand during the training procedure and finally grow into a dense one. We design different expanding strategies to grow CNNs and ViTs respectively, due to the great heterogeneity in between the two architectures. Our method can be easily integrated into popular deep learning frameworks, which saves considerable training time and hardware resources. Extensive experiments show that our acceleration method can significantly speed up the training process of modern vision models on general GPU devices with negligible performance drop (e.g. 1.42x faster for ResNet-101 and 1.34x faster for DeiT-base on ImageNet-1k). The code is available at https://***/huawei-noah / Efficient - Computing / tree / master / TrainingAcceleration / NetworkExpansion and https : / / gitee. com / mindspore / hub / blob / master / mshub _ res / assets / noah - cvlab / gpu / 1.8/networkexpansion_v1.0_***.

关键词： Efficient and scalable vision

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CAMOT: Camera Angle-aware Multi-Object Tracking

CAMOT: Camera Angle-aware Multi-Object Tracking

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Limanta, Felix Uto, Kuniaki Shinoda, Koichi Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

This paper proposes CAMOT, a simple camera angle estimator for multi-object tracking to tackle two problems: 1) occlusion and 2) inaccurate distance estimation in the depth direction. Under the assumption that multiple objects are located on a flat plane in each video frame, CAMOT estimates the camera angle using object detection. In addition, it gives the depth of each object, enabling pseudo-3D MOT. We evaluated its performance by adding it to various 2D MOT methods on the MOT17 and MOT20 datasets and confirmed its effectiveness. Applying CAMOT to ByteTrack, we obtained 63.8% HOTA, 80.6% MOTA, and 78.5% IDF1 in MOT17, which are state-of-the-art results. Its computational cost is significantly lower than the existing deep-learning-based depth estimators for tracking.

关键词： Algorithms Video recognition and understanding

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CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching

CORA: Adapting CLIP for Open-Vocabulary Detection with Regio...

引用

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

作者： Wu, Xiaoshi Zhu, Feng Zhao, Rui Li, Hongsheng Chinese Univ Hong Kong Multimedia Lab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China Ctr Perceptual & Interact Intelligence CPII Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects from novel categories beyond the base categories on which the detector is trained. Recent OVD methods rely on large-scale visual-language pre-trained models, such as CLIP, for recognizing novel objects. We identify the two core obstacles that need to be tackled when incorporating these models into detector training: (1) the distribution mismatch that happens when applying a VL-model trained on whole images to region recognition tasks;(2) the difficulty of localizing objects of unseen classes. To overcome these obstacles, we propose CORA, a DETR-style framework that adapts CLIP for Open-vocabulary detection by Region prompting and Anchor pre-matching. Region prompting mitigates the whole-to-region distribution gap by prompting the region features of the CLIP-based region classifier. Anchor pre-matching helps learning generalizable object localization by a class-aware matching mechanism. We evaluate CORA on the COCO OVD benchmark, where we achieve 41.7 AP50 on novel classes, which outperforms the previous SOTA by 2.4 AP50 even without resorting to extra training data. When extra training data is available, we train CORA(+) on both ground-truth base-category annotations and additional pseudo bounding box labels computed by CORA. CORA(+) achieves 43.1 AP50 on the COCO OVD benchmark and 28.1 box APr on the LVIS OVD benchmark. The code is available at https://***/tgxs002/CORA.

关键词： detection recognition: Categorization retrieval

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Quantifying Extrinsic Curvature in Neural Manifolds

Quantifying Extrinsic Curvature in Neural Manifolds

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

作者： Acosta, Francisco Sanborn, Sophia Duc, Khanh Dao Madhav, Manu Miolane, Nina UC Santa Barbara Phys Santa Barbara CA 93106 USA UC Santa Barbara Elect & Comp Engn Santa Barbara CA USA UC Santa Barbara Math Santa Barbara CA USA UC Santa Barbara Santa Barbara CA USA

ISBN: (纸本)9798350302493

The neural manifold hypothesis postulates that the activity of a neural population forms a low-dimensional manifold whose structure reflects that of the encoded task variables. In this work, we combine topological deep generative models and extrinsic Riemannian geometry to introduce a novel approach for studying the structure of neural manifolds. This approach (i) computes an explicit parameterization of the manifolds and (ii) estimates their local extrinsic curvature-hence quantifying their shape within the neural state space. Importantly, we prove that our methodology is invariant with respect to transformations that do not bear meaningful neuroscience information, such as permutation of the order in which neurons are recorded. We show empirically that we correctly estimate the geometry of synthetic manifolds generated from smooth deformations of circles, spheres, and tori, using realistic noise levels. We additionally validate our methodology on simulated and real neural data, and show that we recover geometric structure known to exist in hippocampal place cells. We expect this approach to open new avenues of inquiry into geometric neural correlates of perception and behavior.

关键词： computer vision

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Distilling Cross-Temporal Contexts for Continuous Sign Language recognition

Distilling Cross-Temporal Contexts for Continuous Sign Langu...

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

作者： Guo, Leming Xue, Wanli Guo, Qing Liu, Bo Zhang, Kaihua Yuan, Tiantian Chen, Shengyong Tianjin Univ Technol Sch Comp Sci & Engn Tianjin Peoples R China ASTAR Ctr Frontier AI Res Singapore Singapore Walmart Global Tech Sunnyvale CA USA Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Peoples R China Tianjin Univ Technol Tech Coll Deaf Tianjin Peoples R China

ISBN: (纸本)9798350301298

Continuous sign language recognition (CSLR) aims to recognize glosses in a sign language video. State-of-the-art methods typically have two modules, a spatial perception module and a temporal aggregation module, which are jointly learned end-to-end. Existing results in [9,20,25,36] have indicated that, as the frontal component of the overall model, the spatial perception module used for spatial feature extraction tends to be insufficiently trained. In this paper, we first conduct empirical studies and show that a shallow temporal aggregation module allows more thorough training of the spatial perception module. However, a shallow temporal aggregation module cannot well capture both local and global temporal context information in sign language. To address this dilemma, we propose a cross-temporal context aggregation (CTCA) model. Specifically, we build a dual-path network that contains two branches for perceptions of local temporal context and global temporal context. We further design a cross-context knowledge distillation learning objective to aggregate the two types of context and the linguistic prior. The knowledge distillation enables the resultant one-branch temporal aggregation module to perceive local-global temporal and semantic context. This shallow temporal perception module structure facilitates spatial perception module learning. Extensive experiments on challenging CSLR benchmarks demonstrate that our method outperforms all state-of-the-art methods.

关键词： body gesture Humans: Face movement pose

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An Erudite Fine-Grained Visual Classification Model

An Erudite Fine-Grained Visual Classification Model

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

作者： Chang, Dongliang Tong, Yujun Du, Ruoyi Hospedales, Timothy Song, Yi-Zhe Ma, Zhanyu Beijing Univ Posts & Telecommunicat Beijing Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland Univ Surrey SketchX CVSSP Surrey England

ISBN: (纸本)9798350301298

Current fine-grained visual classification (FGVC) models are isolated. In practice, we first need to identify the coarse-grained label of an object, then select the corresponding FGVC model for recognition. This hinders the application of FGVC algorithms in real-life scenarios. In this paper, we propose an erudite FGVC model jointly trained by several different datasets(1), which can efficiently and accurately predict an object's fine-grained label across the combined label space. We found through a pilot study that positive and negative transfers co-occur when different datasets are mixed for training, i.e., the knowledge from other datasets is not always useful. Therefore, we first propose a feature disentanglement module and a feature re-fusion module to reduce negative transfer and boost positive transfer between different datasets. In detail, we reduce negative transfer by decoupling the deep features through many dataset-specific feature extractors. Subsequently, these are channel-wise re-fused to facilitate positive transfer. Finally, we propose a meta-learning based dataset-agnostic spatial attention layer to take full advantage of the multi-dataset training data, given that localisation is dataset-agnostic between different datasets. Experimental results across 11 different mixed-datasets built on four different FGVC datasets demonstrate the effectiveness of the proposed method. Furthermore, the proposed method can be easily combined with existing FGVC methods to obtain state-of-the-art results. Our code is available at https:// ***/PRIS-CV/An-Erudite-FGVC-Model.

关键词： detection recognition: Categorization retrieval

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OTAS: Unsupervised Boundary Detection for Object-Centric Temporal Action Segmentation

OTAS: Unsupervised Boundary Detection for Object-Centric Tem...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Li, Yuerong Xue, Zhengrong Xu, Huazhe Zhejiang Univ Hangzhou Peoples R China Tsinghua Univ Beijing Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Temporal action segmentation is typically achieved by discovering the dramatic variances in global visual descriptors. In this paper, we explore the merits of local features by proposing the unsupervised framework of Object-centric Temporal Action Segmentation (OTAS). Broadly speaking, OTAS consists of self-supervised global and local feature extraction modules as well as a boundary selection module that fuses the features and detects salient boundaries for action segmentation. As a second contribution, we discuss the pros and cons of existing frame-level and boundary-level evaluation metrics. Through extensive experiments, we find OTAS is superior to the previous state-of-the-art method by 41% on average in terms of our recommended F1 score. Surprisingly, OTAS even outperforms the ground-truth human annotations in the user study. Moreover, OTAS is efficient enough to allow real-time inference.

关键词： Algorithms Video recognition and understanding

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CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion

CDDFuse: Correlation-Driven Dual-Branch Feature Decompositio...

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

作者： Zhao, Zixiang Bai, Haowen Zhang, Jiangshe Zhang, Yulun Xu, Shuang Lin, Zudi Timofte, Radu Van Gool, Luc Xi An Jiao Tong Univ Xian Peoples R China Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Northwestern Polytech Univ Shenzhen Inst Res & Dev Shenzhen Peoples R China Northwestern Polytech Univ Xian Peoples R China Harvard Univ Cambridge England Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9798350301298

Multi-modality (MM) image fusion aims to render fused images that maintain the merits of different modalities, e.g., functional highlight and detailed textures. To tackle the challenge in modeling cross-modality features and decomposing desirable modality-specific and modality-shared features, we propose a novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network. Firstly, CDDFuse uses Restormer blocks to extract cross-modality shallow features. We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle low-frequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information. A correlation-driven loss is further proposed to make the low-frequency features correlated while the high-frequency features un-correlated based on the embedded information. Then, the LT-based global fusion and INN-based local fusion layers output the fused image. Extensive experiments demonstrate that our CDDFuse achieves promising results in multiple fusion tasks, including infrared-visible image fusion and medical image fusion. We also show that CDDFuse can boost the performance in downstream infrared-visible semantic segmentation and object detection in a unified benchmark. The code is available at https://***/Zhaozixiang1228/MMIF-CDDFuse.

关键词： Low-level vision

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