Suchergebnisse - Deep learning architectures and techniques; Recognition: detection

Equalized Focal Loss for Dense Long-Tailed Object Detection von Li, Bo, Yao, Yongqiang, Tan, Jingru, Zhang, Gang, Yu, Fengwei, Lu, Jianwei, Luo, Ye

“… Despite the recent success of long-tailed object detection, almost all long-tailed object detectors are developed based on the two-stage paradigm …”
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Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection von Yin, Li, Perez-Rua, Juan M, Liang, Kevin J

“… We study the challenging incremental few-shot object de-tection (iFSD) setting. Recently, hypernetwork-based approaches have been studied in the context of …”
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A ConvNet for the 2020s von Liu, Zhuang, Mao, Hanzi, Wu, Chao-Yuan, Feichtenhofer, Christoph, Darrell, Trevor, Xie, Saining

“… The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs …”
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Grounded Language-Image Pre-training von Li, Liunian Harold, Zhang, Pengchuan, Zhang, Haotian, Yang, Jianwei, Li, Chunyuan, Zhong, Yiwu, Wang, Lijuan, Yuan, Lu, Zhang, Lei, Hwang, Jenq-Neng, Chang, Kai-Wei, Gao, Jianfeng

“… This paper presents a grounded language-image pretraining (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations …”
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CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows von Dong, Xiaoyi, Bao, Jianmin, Chen, Dongdong, Zhang, Weiming, Yu, Nenghai, Yuan, Lu, Chen, Dong, Guo, Baining

“… We present CSWin Transformer, an efficient and effective Transformer-based backbone for general-purpose vision tasks. A challenging issue in Transformer design …”
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MetaFormer is Actually What You Need for Vision von Yu, Weihao, Luo, Mi, Zhou, Pan, Si, Chenyang, Zhou, Yichen, Wang, Xinchao, Feng, Jiashi, Yan, Shuicheng

“… Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance …”
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beta-DARTS: Beta-Decay Regularization for Differentiable Architecture Search von Ye, Peng, Li, Baopu, Li, Yikang, Chen, Tao, Fan, Jiayuan, Ouyang, Wanli

“… Neural Architecture Search (NAS) has attracted increasingly more attention in recent years because of its capability to design deep neural network automatically …”
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SLIC: Self-Supervised Learning with Iterative Clustering for Human Action Videos von Khorasgani, Salar Hosseini, Chen, Yuxuan, Shkurti, Florian

“… Self-supervised methods have significantly closed the gap with end-to-end supervised learning for image classification [13], [24 …”
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Revisiting Weakly Supervised Pre-Training of Visual Perception Models von Singh, Mannat, Gustafson, Laura, Adcock, Aaron, De Freitas Reis, Vinicius, Gedik, Bugra, Kosaraju, Raj Prateek, Mahajan, Dhruv, Girshick, Ross, Dollar, Piotr, Van Der Maaten, Laurens

“… Model pre-training is a cornerstone of modern visual recognition systems. Although fully supervised pre-training on datasets like ImageNet is still the de-facto …”
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Multimodal Token Fusion for Vision Transformers von Wang, Yikai, Chen, Xinghao, Cao, Lele, Huang, Wenbing, Sun, Fuchun, Wang, Yunhe

“… Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like …”
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Knowledge Distillation via the Target-aware Transformer von Lin, Sihao, Xie, Hongwei, Wang, Bing, Yu, Kaicheng, Chang, Xiaojun, Liang, Xiaodan, Wang, Gang

“… However, people tend to overlook the fact that, due to the architecture differences, the semantic information on the same spatial location usually vary …”
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Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation von Wu, Aming, Deng, Cheng

“… And we consider a realistic yet challenging scenario, namely Single-Domain Generalized Object Detection (Single-DGOD …”
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TransMix: Attend to Mix for Vision Transformers von Chen, Jie-Neng, Sun, Shuyang, He, Ju, Torr, Philip, Yuille, Alan, Bai, Song

“… Mixup-based augmentation has been found to be effective for generalizing models during training, especially for Vision Transformers (ViTs) since they can …”
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Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors von Liu, Yen-Cheng, Ma, Chih-Yao, Kira, Zsolt

“… With the recent development of Semi-Supervised Object Detection (SS-OD) techniques, object detectors can be improved by using a limited amount of labeled data and abundant unlabeled data …”
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MiniViT: Compressing Vision Transformers with Weight Multiplexing von Zhang, Jinnian, Peng, Houwen, Wu, Kan, Liu, Mengchen, Xiao, Bin, Fu, Jianlong, Yuan, Lu

“… Vision Transformer (ViT) models have recently drawn much attention in computer vision due to their high model capability. However, ViT models suffer from huge …”
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TableFormer: Table Structure Understanding with Transformers von Nassar, Ahmed, Livathinos, Nikolaos, Lysak, Maksym, Staar, Peter

“… In this paper, we present a new table-structure identification model. The latter improves the latest end-to-end deep learning model (i.e …”
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VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention von Deng, Shengheng, Liang, Zhihao, Sun, Lin, Jia, Kui

“… Detecting objects from LiDAR point clouds is of tremendous significance in autonomous driving …”
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Human-Object Interaction Detection via Disentangled Transformer von Zhou, Desen, Liu, Zhichao, Wang, Jian, Wang, Leshan, Hu, Tao, Ding, Errui, Wang, Jingdong

“… Human-Object Interaction Detection tackles the problem of joint localization and classification of human object interactions …”
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Progressive End-to-End Object Detection in Crowded Scenes von Zheng, Anlin, Zhang, Yuang, Zhang, Xiangyu, Qi, Xiaojuan, Sun, Jian

“… In this paper, we propose a new query-based detection framework for crowd detection …”
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DTA: Physical Camouflage Attacks using Differentiable Transformation Network von Suryanto, Naufal, Kim, Yongsu, Kang, Hyoeun, Larasati, Harashta Tatimma, Yun, Youngyeo, Le, Thi-Thu-Huong, Yang, Hunmin, Oh, Se-Yoon, Kim, Howon

“… In this paper, we propose the Differentiable Transformation Attack (DTA), a framework for generating a robust physical adversarial pattern on a target object to camouflage it against object detection models with a wide range of transformations …”
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