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Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning

Progressive Semantic-Visual Mutual Adaption for Generalized ...

引用

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

作者： Liu, Man Li, Feng Zhang, Chunjie Wei, Yunchao Bai, Huihui Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China Hefei Univ Technol Hefei Peoples R China

ISBN: (纸本)9798350301298

Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic information. Prior works mainly localize regions corresponding to the sharing attributes. When various visual appearances correspond to the same attribute, the sharing attributes inevitably introduce semantic ambiguity, hampering the exploration of accurate semantic-visual interactions. In this paper, we deploy the dual semantic-visual transformer module (DSVTM) to progressively model the correspondences between attribute prototypes and visual features, constituting a progressive semantic-visual mutual adaption (PSVMA) network for semantic disambiguation and knowledge transferability improvement. Specifically, DSVTM devises an instance-motivated semantic encoder that learns instance-centric prototypes to adapt to different images, enabling the recast of the unmatched semantic-visual pair into the matched one. Then, a semantic-motivated instance decoder strengthens accurate cross-domain interactions between the matched pair for semantic-related instance adaption, encouraging the generation of unambiguous visual representations. Moreover, to mitigate the bias towards seen classes in GZSL, a debiasing loss is proposed to pursue response consistency between seen and unseen predictions. The PSVMA consistently yields superior performances against other state-of-the-art methods. Code will be available at: https://***/ManLiuCoder/PSVMA.

关键词： detection recognition: Categorization retrieval

来源：评论

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Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning

Video-Text as Game Players: Hierarchical Banzhaf Interaction...

引用

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

作者： Jin, Peng Huang, Jinfa Xiong, Pengfei Tian, Shangxuan Liu, Chang Ji, Xiangyang Yuan, Li Chen, Jie Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Peking Univ Shenzhen Grad Sch AI Sci AI4S Preferred Program Shenzhen Peoples R China Shopee Shenzhen Peoples R China Tsinghua Univ Dept Automat & BNRist Beijing Peoples R China

ISBN: (纸本)9798350301298

Contrastive learning-based video-language representation learning approaches, e.g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs. To clarify this coarse-grained global interaction and move a step further, we have to encounter challenging shell-breaking interactions for fine-grained cross-modal learning. In this paper, we creatively model video-text as game players with multivariate cooperative game theory to wisely handle the uncertainty during fine-grained semantic interaction with diverse granularity, flexible combination, and vague intensity. Concretely, we propose Hierarchical Banzhaf Interaction (HBI) to value possible correspondence between video frames and text words for sensitive and explainable cross-modal contrast. To efficiently realize the cooperative game of multiple video frames and multiple text words, the proposed method clusters the original video frames (text words) and computes the Banzhaf Interaction between the merged tokens. By stacking token merge modules, we achieve cooperative games at different semantic levels. Extensive experiments on commonly used text-video retrieval and video-question answering benchmarks with superior performances justify the efficacy of our HBI. More encouragingly, it can also serve as a visualization tool to promote the understanding of cross-modal interaction, which have a far-reaching impact on the community. Project page is available at https://***/HBI/.

关键词： and reasoning language vision

来源：评论

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Burstormer: Burst Image Restoration and Enhancement Transformer

Burstormer: Burst Image Restoration and Enhancement Transfor...

引用

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

作者： Dudhane, Akshay Zamir, Syed Waqas Khan, Salman Khan, Fahad Shahbaz Yang, Ming-Hsuan Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Incept Inst AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra Australia Linkoping Univ Linkoping Sweden Univ Calif Merced Merced CA USA Yonsei Univ Seoul South Korea Google Res Mountain View CA USA

ISBN: (纸本)9798350301298

On a shutter press, modern handheld cameras capture multiple images in rapid succession and merge them to generate a single image. However, individual frames in a burst are misaligned due to inevitable motions and contain multiple degradations. The challenge is to properly align the successive image shots and merge their complimentary information to achieve high-quality outputs. Towards this direction, we propose Burstormer: a novel transformer-based architecture for burst image restoration and enhancement. In comparison to existing works, our approach exploits multi-scale local and non-local features to achieve improved alignment and feature fusion. Our key idea is to enable inter-frame communication in the burst neighborhoodsf or information aggregation and progressive fusion while modeling the burst-wide context. However, the input burst frames need to be properly aligned before fusing their information. Therefore, we propose an enhanced deformable alignment module for aligning burst features with regards to the reference frame. Unlike existing methods, the proposed alignment module not only aligns burst features but also exchanges feature information and maintains focused communication with the reference frame through the proposed reference-based feature enrichment mechanism, which facilitates handling complex motions. Aft er multi-level alignment and enrichment, we re-emphasize on inter-frame communication within burst using a cyclic burst sampling module. Finally, the inter-frame information is aggregated using the proposed burst feature fusion module followed by progressive upsampling. Our Burstormer outperforms state-of-the-art methods on burst super-resolution, burst denoising and burst low-light enhancement. Our codes and pre-trained models are available at https://***/akshaydudhane16/Burstormer.

关键词： Low-level vision

来源：评论

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Learning unbiased classifiers from biased data with meta-learning

Learning unbiased classifiers from biased data with meta-lea...

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

作者： Ragonesi, Ruggero Morerio, Pietro Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis PAVIS Genoa Italy Univ Verona Dept Comp Sci Verona Italy

ISBN: (纸本)9798350302493

It is well known that large deep architectures are powerful models when adequately trained, but may exhibit undesirable behavior leading to confident incorrect predictions, even when evaluated on slightly different test examples. Test data characterized by distribution shifts (from training data distribution), outliers, and adversarial samples are among the types of data affected by this problem. This situation worsens whenever data are biased, meaning that predictions are mostly based on spurious correlations present in the data. Unfortunately, since such correlations occur in the most of data, a model is prevented from correctly generalizing the considered classes. In this work, we tackle this problem from a meta-learning perspective. Considering the dataset as composed of unknown biased and unbiased samples, we first identify these two subsets by a pseudo-labeling algorithm, even if coarsely. Subsequently, we apply a bi-level optimization algorithm in which, in the inner loop, we look for the best parameters guiding the training of the two subsets, while in the outer loop, we train the final model taking benefit from augmented data generated using Mixup. Properly tuning the contributions of biased and unbiased data, together with the regularization introduced by the mixed data has proved to be an effective training strategy to learn unbiased models, showing superior generalization capabilities. Experimental results on synthetically and realistically biased datasets surpass state-of-the-art performance, as compared to existing methods.

关键词： computer vision

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Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective

Solving Oscillation Problem in Post-Training Quantization Th...

引用

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

作者： Ma, Yuexiao Li, Huixia Zheng, Xiawu Xiao, Xuefeng Wang, Rui Wen, Shilei Pan, Xin Chao, Fei Ji, Rongrong Xiamen Univ Minist Educ China Key Lab Multimedia Trusted Percept & Efficient Co Sch Informat Xiamen 361005 Peoples R China ByteDance Inc Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Xiamen Univ Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Post-training quantization (PTQ) is widely regarded as one of the most efficient compression methods practically, benefitting from its data privacy and low computation costs. We argue that an overlooked problem of oscillation is in the PTQ methods. In this paper, we take the initiative to explore and present a theoretical proof to explain why such a problem is essential in PTQ. And then, we try to solve this problem by introducing a principled and generalized framework theoretically. In particular, we first formulate the oscillation in PTQ and prove the problem is caused by the difference in module capacity. To this end, we define the module capacity (ModCap) under data-dependent and data-free scenarios, where the differentials between adjacent modules are used to measure the degree of oscillation. The problem is then solved by selecting top-k differentials, in which the corresponding modules are jointly optimized and quantized. Extensive experiments demonstrate that our method successfully reduces the performance drop and is generalized to different neural networks and PTQ methods. For example, with 2/4 bit ResNet-50 quantization, our method surpasses the previous state-of-the-art method by 1.9%. It becomes more significant on small model quantization, e.g. surpasses BRECQ method by 6.61% on MobileNetV2 x0.5.

关键词： Efficient and scalable vision

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MIVC: Multiple Instance Visual Component for Visual-Language Models

MIVC: Multiple Instance Visual Component for Visual-Language...

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

作者： Wu, Wenyi Li, Qi Zhong, Wenliang Huang, Junzhou Amazon 424 5th Ave New York NY 10018 USA Amazon 410 Terry Ave N Seattle WA USA Univ Texas Arlington 500 UTA Blvd Arlington TX USA

ISBN: (纸本)9798350318920;9798350318937

vision-language models have been widely explored across a wide range of tasks and achieve satisfactory performance. However, it's under-explored how to consolidate entity understanding through a varying number of images and to align it with the pre-trained language models for generative tasks. In this paper, we propose MIVC, a general multiple instance visual component to bridge the gap between various image inputs with off-the-shelf vision-language models by aggregating visual representations in a permutation-invariant fashion through a neural network. We show that MIVC could be plugged into the visual-language models to improve the model performance consistently on visual question answering, classification and captioning tasks on a public available e-commerce dataset with multiple images per product. Furthermore, we show that the component provides insight into the contribution of each image to the downstream tasks.

关键词： Algorithms Applications Commercial / retail Image recognition and understanding

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AShapeFormer : Semantics-Guided Object-Level Active Shape Encoding for 3D Object Detection via Transformers

AShapeFormer : Semantics-Guided Object-Level Active Shape En...

引用

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

作者： Li, Zechuan Yu, Hongshan Yang, Zhengeng Chen, Tongjia Akhtar, Naveed Hunan Univ Changsha Peoples R China Natl Univ Def Technol Changsha Peoples R China Hunan Univ Quanzhou Inst Ind Design & Machine Intelligence I Changsha Peoples R China Univ Western Australia Perth WA Australia

ISBN: (纸本)9798350301298

3D object detection techniques commonly follow a pipeline that aggregates predicted object central point features to compute candidate points. However, these candidate points contain only positional information, largely ignoring the object-level shape information. This eventually leads to sub-optimal 3D object detection. In this work, we propose AShapeFormer, a semantics-guided object-level shape encoding module for 3D object detection. This is a plug-n-play module that leverages multi-head attention to encode object shape information. We also propose shape tokens and object-scene positional encoding to ensure that the shape information is fully exploited. Moreover, we introduce a semantic guidance sub-module to sample more foreground points and suppress the influence of background points for a better object shape perception. We demonstrate a straightforward enhancement of multiple existing methods with our AShapeFormer. Through extensive experiments on the popular SUN RGB-D and ScanNetV2 dataset, we show that our enhanced models are able to outperform the baselines by a considerable absolute margin of up to 8.1%. Code will be available at https://***/ZechuanLi/AShapeFormer

关键词： detection recognition: Categorization retrieval

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CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection

CAT: LoCalization and IdentificAtion Cascade Detection Trans...

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

作者： Ma, Shuailei Wang, Yuefeng Wei, Ying Fan, Jiaqi Li, Thomas H. Liu, Hongli Lv, Fanbing Northeast Univ Shenyang Peoples R China Peking Univ Informat Technol R&D Innovat Ctr Shenzhen Peoples R China Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Changsha Hisense Intelligent Syst Res Inst Co Ltd Changsha Peoples R China

ISBN: (纸本)9798350301298

Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects. The existing works which employ standard detection framework and fixed pseudo-labelling mechanism (PLM) have the following problems: (i) The inclusion of detecting unknown objects substantially reduces the model's ability to detect known ones. (ii) The PLM does not adequately utilize the priori knowledge of inputs. (iii) The fixed selection manner of PLM cannot guarantee that the model is trained in the right direction. We observe that humans subconsciously prefer to focus on all foreground objects and then identify each one in detail, rather than localize and identify a single object simultaneously, for alleviating the confusion. This motivates us to propose a novel solution called CAT: LoCalization and IdentificAtion Cascade Detection Transformer which decouples the detection process via the shared decoder in the cascade decoding way. In the meanwhile, we propose the self-adaptive pseudo-labelling mechanism which combines the model-driven with input-driven PLM and self-adaptively generates robust pseudo-labels for unknown objects, significantly improving the ability of CAT to retrieve unknown objects. Experiments on two benchmarks, i.e., MS-COCO and PASCAL VOC, show that our model outperforms the state-of-the-art methods. The code is publicly available at https://***/xiaomabufei/CAT.

关键词： detection recognition: Categorization retrieval

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Lightweight Portrait Matting via Regional Attention and Refinement

Lightweight Portrait Matting via Regional Attention and Refi...

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

作者： Zhong, Yatao Zharkov, Ilya Microsoft Redmond WA 98052 USA

ISBN: (纸本)9798350318920;9798350318937

We present a lightweight model for high resolution portrait matting. The model does not use any auxiliary inputs such as trimaps or background captures and achieves real time performance for HD videos and near real time for 4K. Our model is built upon a two-stage framework with a low resolution network for coarse alpha estimation followed by a refinement network for local region improvement. However, a naive implementation of the two-stage model suffers from poor matting quality if not utilizing any auxiliary inputs. We address the performance gap by leveraging the vision transformer (ViT) as the backbone of the low resolution network, motivated by the observation that the tokenization step of ViT can reduce spatial resolution while retain as much pixel information as possible. To inform local regions of the context, we propose a novel cross region attention (CRA) module in the refinement network to propagate the contextual information across the neighboring regions. We demonstrate that our method achieves superior results and outperforms other baselines on three benchmark datasets while only uses 1/20 of the FLOPS compared to the existing state-of-the-art model.

关键词： Algorithms Algorithms Computational photography image and video synthesis Image recognition and understanding

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An Iterative Quantum Approach for Transformation Estimation from Point Sets

An Iterative Quantum Approach for Transformation Estimation ...

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

作者： Meli, Natacha Kuete Mannel, Florian Lellmann, Jan Univ Lubeck Inst Math & Image Comp Lubeck Germany

ISBN: (纸本)9781665469463

We propose an iterative method for estimating rigid transformations from point sets using adiabatic quantum computation. Compared to existing quantum approaches, our method relies on an adaptive scheme to solve the problem to high precision, and does not suffer from inconsistent rotation matrices. Experimentally, our method performs robustly on several 2D and 3D datasets even with high outlier ratio.

关键词： computer vision Three-dimensional displays Annealing Estimation Quantum annealing pattern recognition Iterative methods

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