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Learning from Unique Perspectives: User-aware Saliency Modeling

Learning from Unique Perspectives: User-aware Saliency Model...

引用

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

作者： Chen, Shi Valliappa, Nachiappan Shen, Shaolei Ye, Xinyu Kohlhoff, Kai He, Junfeng Univ Minnesota Minneapolis MN USA Google Res Mountain View CA USA Google Mountain View CA USA

ISBN: (纸本)9798350301298

Everyone is unique. Given the same visual stimuli, people's attention is driven by both salient visual cues and their own inherent preferences. Knowledge of visual preferences not only facilitates understanding of fine-grained attention patterns of diverse users, but also has the potential of benefiting the development of customized applications. Nevertheless, existing saliency models typically limit their scope to attention as it applies to the general population and ignore the variability between users' behaviors. In this paper, we identify the critical roles of visual preferences in attention modeling, and for the first time study the problem of user-aware saliency modeling. Our work aims to advance attention research from three distinct perspectives: (1) We present a new model with the flexibility to capture attention patterns of various combinations of users, so that we can adaptively predict personalized attention, user group attention, and general saliency at the same time with one single model;(2) To augment models with knowledge about the composition of attention from different users, we further propose a principled learning method to understand visual attention in a progressive manner;and (3) We carry out extensive analyses on publicly available saliency datasets to shed light on the roles of visual preferences. Experimental results on diverse stimuli, including naturalistic images and web pages, demonstrate the advantages of our method in capturing the distinct visual behaviors of different users and the general saliency of visual stimuli.

关键词： Low-level vision

来源：评论

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BEVHeight: A Robust Framework for vision-based Roadside 3D Object Detection

BEVHeight: A Robust Framework for Vision-based Roadside 3D O...

引用

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

作者： Yang, Lei Yu, Kaicheng Tang, Tao Li, Jun Yuan, Kun Wang, Li Zhang, Xinyu Chen, Peng Tsinghua Univ State Key Lab Automot Safety & Energy Beijing Peoples R China Alibaba Grp Autonomous Driving Lab Hangzhou Peoples R China Sun Yat Sen Univ Shenzhen Campus Guangzhou Peoples R China Peking Univ Ctr Machine Learning Res Beijing Peoples R China

ISBN: (纸本)9798350301298

While most recent autonomous driving system focuses on developing perception methods on ego-vehicle sensors, people tend to overlook an alternative approach to leverage intelligent roadside cameras to extend the perception ability beyond the visual range. We discover that the state-of-the-art vision-centric bird's eye view detection methods have inferior performances on roadside cameras. This is because these methods mainly focus on recovering the depth regarding the camera center, where the depth difference between the car and the ground quickly shrinks while the distance increases. In this paper, we propose a simple yet effective approach, dubbed BEVHeight, to address this issue. In essence, instead of predicting the pixel-wise depth, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods. On popular 3D detection benchmarks of roadside cameras, our method surpasses all previous vision-centric methods by a significant margin. The code is available at https://***/ADLab-AutoDrive/BEVHeight.

关键词： Autonomous driving

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Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

Mask DINO: Towards A Unified Transformer-based Framework for...

引用

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

作者： Li, Feng Zhang, Hao Xu, Huaizhe Liu, Shilong Zhang, Lei Ni, Lionel M. Shum, Heimg-Yeung Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tsinghua Univ Inst AI Dept CST BNIZist Ctr Beijing Peoples R China Int Digital Econ Acad IDEA Shenzhen Peoples R China Hong Kong Univ Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9798350301298

In this paper we present Mask DINO, a unified object detection and segmentation framework. Mask DINO extends DINO (DETR with Improved Denoising Anchor Boxes) by adding a mask prediction branch which supports all image segmentation tasks (instance, panoptic, and semantic). It makes use of the query embeddings from DINO to dot-product a high-resolution pixel embedding map to predict a set of binary masks. Some key components in DINO are extended for segmentation through a shared architecture and training process. Mask DINO is simple, efficient, and scalable, and it can benefit from joint large-scale detection and segmentation datasets. Our experiments show that Mask DINO significantly outperforms all existing specialized segmentation methods, both on a ResNet-50 backbone and a pre-trained model with SwinL backbone. Notably, Mask DINO establishes the best results to date on instance segmentation (54.5 AP on COCO), panoptic segmentation (59.4 PQ on COCO), and semantic segmentation (60.8 mIoU on ADE20K) among models under one billion parameters. Code is available at https://github. com/ IDEAResearch/MaskDINO.

关键词： vision applications and systems

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Pixels, Regions, and Objects: Multiple Enhancement for Salient Object Detection

Pixels, Regions, and Objects: Multiple Enhancement for Salie...

引用

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

作者： Wang, Yi Wang, Ruili Fan, Xin Wang, Tianzhu He, Xiangjian Dalian Univ Technol DUT RU Int Sch Informat Sci & Engn Dalian Peoples R China Massey Univ Sch Math & Computat Sci Palmerston North New Zealand Univ Nottingham Ningbo China Sch Comp Sci Ningbo Peoples R China

ISBN: (纸本)9798350301298

Salient object detection (SOD) aims to mimic the human visual system (HVS) and cognition mechanisms to identify and segment salient objects. However, due to the complexity of these mechanisms, current methods are not perfect. Accuracy and robustness need to be further improved, particularly in complex scenes with multiple objects and background clutter. To address this issue, we propose a novel approach called Multiple Enhancement Network (MENet) that adopts the boundary sensibility, content integrity, iterative refinement, and frequency decomposition mechanisms of HVS. A multi-level hybrid loss is firstly designed to guide the network to learn pixel-level, region-level, and object-level features. A flexible multiscale feature enhancement module (ME-Module) is then designed to gradually aggregate and refine global or detailed features by changing the size order of the input feature sequence. An iterative training strategy is used to enhance boundary features and adaptive features in the dual-branch decoder of MENet. Comprehensive evaluations on six challenging benchmark datasets show that MENet achieves state-of-the-art results. Both the codes and results are publicly available at https://***/yiwangtz/MENet.

关键词： detection recognition: Categorization retrieval

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Open-set Fine-grained Retrieval via Prompting vision-Language Evaluator

Open-set Fine-grained Retrieval via Prompting Vision-Languag...

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

作者： Wang, Shijie Chang, Jianlong Li, Haojie Wang, Zhihui Ouyang, Wanli Tian, Qi Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China Huawei Cloud & AI Shenzhen Peoples R China Shandong Univ Sci & Technol Coll Comp & Engn Qingdao Peoples R China Univ Sydney Sense Time Comp Vis Res Grp Sydney NSW Australia

ISBN: (纸本)9798350301298

Open-set fine-grained retrieval is an emerging challenge that requires an extra capability to retrieve unknown subcategories during evaluation. However, current works focus on close-set visual concepts, where all the subcategories are pre-defined, and make it hard to capture discriminative knowledge from unknown subcategories, consequently failing to handle unknown subcategories in open-world scenarios. In this work, we propose a novel Prompting vision-Language Evaluator (PLEor) framework based on the recently introduced contrastive language-image pretraining (CLIP) model, for open-set fine-grained retrieval. PLEor could leverage pre-trained CLIP model to infer the discrepancies encompassing both pre-defined and unknown subcategories, called category-specific discrepancies, and transfer them to the backbone network trained in the close-set scenarios. To make pre-trained CLIP model sensitive to category-specific discrepancies, we design a dual prompt scheme to learn a vision prompt specifying the category-specific discrepancies, and turn random vectors with category names in a text prompt into category-specific discrepancy descriptions. Moreover, a vision-language evaluator is proposed to semantically align the vision and text prompts based on CLIP model, and reinforce each other. In addition, we propose an open-set knowledge transfer to transfer the category-specific discrepancies into the backbone network using knowledge distillation mechanism. Quantitative and qualitative experiments show that our PLEor achieves promising performance on open-set fine-grained datasets.

关键词： vision applications and systems

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OvarNet: Towards Open-vocabulary Object Attribute recognition

OvarNet: Towards Open-vocabulary Object Attribute Recognitio...

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

作者： Chen, Keyan Jiang, Xiaolong Hu, Yao Tang, Xu Gao, Yan Chen, Jianqi Xie, Weidi Beihang Univ Beijing Peoples R China Xiaohongshu Inc Shanghai Peoples R China Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

In this paper, we consider the problem of simultaneously detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at the training stage, resembling an open-vocabulary scenario. To achieve this goal, we make the following contributions: (i) we start with a naive two-stage approach for open-vocabulary object detection and attribute classification, termed CLIP-Attr. The candidate objects are first proposed with an offline RPN and later classified for semantic category and attributes;(ii) we combine all available datasets and train with a federated strategy to fine-tune the CLIP model, aligning the visual representation with attributes, additionally, we investigate the efficacy of leveraging freely available online image-caption pairs under weakly supervised learning;(iii) in pursuit of efficiency, we train a Faster-RCNN type model end-to-end with knowledge distillation, that performs class-agnostic object proposals and classification on semantic categories and attributes with classifiers generated from a text encoder;Finally, (iv) we conduct extensive experiments on VAW, MSCOCO, LSA, and OVAD datasets, and show that recognition of semantic category and attributes is complementary for visual scene understanding, i.e., jointly training object detection and attributes prediction largely outperform existing approaches that treat the two tasks independently, demonstrating strong generalization ability to novel attributes and categories.

关键词： detection recognition: Categorization retrieval

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SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution vision Transformer

SparseViT: Revisiting Activation Sparsity for Efficient High...

引用

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

作者： Chen, Xuanyao Liu, Zhijian Tang, Haotian Yi, Li Zhao, Hang Han, Song Shanghai Qi Zhi Inst Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China MIT Cambridge MA USA

ISBN: (纸本)9798350301298

High-resolution images enable neural networks to learn richer visual representations. However, this improved performance comes at the cost of growing computational complexity, hindering their usage in latency-sensitive applications. As not all pixels are equal, skipping computations for less-important regions offers a simple and effective measure to reduce the computation. This, however, is hard to be translated into actual speedup for CNNs since it breaks the regularity of the dense convolution workload. In this paper, we introduce SparseViT that revisits activation sparsity for recent window-based vision transformers (ViTs). As window attentions are naturally batched over blocks, actual speedup with window activation pruning becomes possible: i.e., similar to 50% latency reduction with 60% sparsity. Different layers should be assigned with different pruning ratios due to their diverse sensitivities and computational costs. We introduce sparsity-aware adaptation and apply the evolutionary search to efficiently find the optimal layerwise sparsity configuration within the vast search space. SparseViT achieves speedups of 1.5x, 1.4x, and 1.3x compared to its dense counterpart in monocular 3D object detection, 2D instance segmentation, and 2D semantic segmentation, respectively, with negligible to no loss of accuracy.

关键词： Efficient and scalable vision

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Good is Bad: Causality Inspired Cloth-debiasing for Cloth-changing Person Re-identification

Good is Bad: Causality Inspired Cloth-debiasing for Cloth-ch...

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

作者： Yang, Zhengwei Lin, Meng Zhong, Xian Wu, Yu Wang, Zheng Wuhan Univ Inst Artificial Intelligence Natl Engn Res Ctr Multimedia Software Wuhan Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ Technol Hubei Key Lab Multimedia & Network Commun Engn Wuhan Peoples R China Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China

ISBN: (纸本)9798350301298

Entangled representation of clothing and identity (ID)intrinsic clues are potentially concomitant in conventional person Re-IDentification (ReID). Nevertheless, eliminating the negative impact of clothing on ID remains challenging due to the lack of theory and the difficulty of isolating the exact implications. In this paper, a causality-based Auto-Intervention Model, referred to as AIM1, is first proposed to mitigate clothing bias for robust cloth-changing person ReID (CC-ReID). Specifically, we analyze the effect of clothing on the model inference and adopt a dual-branch model to simulate causal intervention. Progressively, clothing bias is eliminated automatically with model training. AIM is encouraged to learn more discriminative ID clues that are free from clothing bias. Extensive experiments on two standard CC-ReID datasets demonstrate the superiority of the proposed AIM over other state-of-the-art methods.

关键词： detection recognition: Categorization retrieval

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GENIE: Show Me the Data for Quantization

GENIE: Show Me the Data for Quantization

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

作者： Jeon, Yongkweon Lee, Chungman Kim, Ho-young Samsung Res Berkeley Hts NJ 07922 USA

ISBN: (纸本)9798350301298

Zero-shot quantization is a promising approach for developing lightweight deep neural networks when data is inaccessible owing to various reasons, including cost and issues related to privacy. By exploiting the learned parameters (mu and sigma) of batch normalization layers in an FP32-pre-trained model, zero-shot quantization schemes focus on generating synthetic data. Subsequently, they distill knowledge from the pre-trained model (teacher) to the quantized model (student) such that the quantized model can be optimized with the synthetic dataset. However, thus far, zero-shot quantization has primarily been discussed in the context of quantization-aware training methods, which require task-specific losses and long-term optimization as much as retraining. We thus introduce a post-training quantization scheme for zero-shot quantization that produces high-quality quantized networks within a few hours. Furthermore, we propose a framework called GENIE that generates data suited for quantization. With the data synthesized by GENIE, we can produce robust quantized models without real datasets, which is comparable to few-shot quantization. We also propose a post-training quantization algorithm to enhance the performance of quantized models. By combining them, we can bridge the gap between zero-shot and few-shot quantization while significantly improving the quantization performance compared to that of existing approaches. In other words, we can obtain a unique state-of-the-art zero-shot quantization approach. The code is available at https://***/SamsungLabs/Genie.

关键词： Efficient and scalable vision

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Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution

Better "CMOS" Produces Clearer Images: Learning Space-Varian...

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

作者： Chen, Xuhai Zhang, Jiangning Xu, Chao Wang, Yabiao Wang, Chengjie Liu, Yong Zhejiang Univ APRIL Lab Hangzhou Peoples R China Tencent Youtu Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant. However, the blur involved in real applications are usually space-variant due to object motion, out-of-focus, etc., resulting in severe performance drop of the advanced SR methods. To address this problem, we firstly introduce two new datasets with out-of-focus blur, i.e., NYUv2-BSR and Cityscapes-BSR, to support further researches of blind SR with space-variant blur. Based on the datasets, we design a novel Cross-MOdal fuSion network (CMOS) that estimate both blur and semantics simultaneously, which leads to improved SR results. It involves a feature Grouping Interactive Attention (GIA) module to make the two modalities interact more effectively and avoid inconsistency. GIA can also be used for the interaction of other features because of the universality of its structure. Qualitative and quantitative experiments compared with state-of-the-art methods on above datasets and real-world images demonstrate the superiority of our method, e.g., obtaining PSNR/SSIM by +1.91./+0.0048. on NYUv2-BSR than MANet.

关键词： Low-level vision

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