Suchergebnisse - "Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online)"

Masked-attention Mask Transformer for Universal Image Segmentation von Cheng, Bowen, Misra, Ishan, Schwing, Alexander G., Kirillov, Alexander, Girdhar, Rohit

“… Image segmentation groups pixels with different semantics, e.g., category or instance membership. Each choice of semantics defines a task. While only the …”
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GLIGEN: Open-Set Grounded Text-to-Image Generation von Li, Yuheng, Liu, Haotian, Wu, Qingyang, Mu, Fangzhou, Yang, Jianwei, Gao, Jianfeng, Li, Chunyuan, Lee, Yong Jae

“… Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In …”
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Bottleneck Transformers for Visual Recognition von Srinivas, Aravind, Lin, Tsung-Yi, Parmar, Niki, Shlens, Jonathon, Abbeel, Pieter, Vaswani, Ashish

“… We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image …”
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Dense Contrastive Learning for Self-Supervised Visual Pre-Training von Wang, Xinlong, Zhang, Rufeng, Shen, Chunhua, Kong, Tao, Li, Lei

“… To date, most existing self-supervised learning methods are designed and optimized for image classification. These pre-trained models can be sub-optimal for …”
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Deformable ConvNets V2: More Deformable, Better Results von Zhu, Xizhou, Hu, Han, Lin, Stephen, Dai, Jifeng

“… The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects. Through an examination …”
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Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection von Haliassos, Alexandros, Vougioukas, Konstantinos, Petridis, Stavros, Pantic, Maja

“… Although current deep learning-based face forgery detectors achieve impressive performance in constrained scenarios, they are vulnerable to samples created by …”
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Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks von Dong, Yinpeng, Pang, Tianyu, Su, Hang, Zhu, Jun

“… Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of …”
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TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation von Zhang, Wenqiang, Huang, Zilong, Luo, Guozhong, Chen, Tao, Wang, Xinggang, Liu, Wenyu, Yu, Gang, Shen, Chunhua

“… Although vision transformers (ViTs) have achieved great success in computer vision, the heavy computational cost hampers their applications to dense prediction …”
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Multi-class Token Transformer for Weakly Supervised Semantic Segmentation von Xu, Lian, Ouyang, Wanli, Bennamoun, Mohammed, Boussaid, Farid, Xu, Dan

“… This paper proposes a new transformer-based framework to learn class-specific object localization maps as pseudo labels for weakly supervised semantic …”
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Causality Inspired Representation Learning for Domain Generalization von Lv, Fangrui, Liang, Jian, Li, Shuang, Zang, Bin, Liu, Chi Harold, Wang, Ziteng, Liu, Di

“… Domain generalization (DG) is essentially an out-of-distribution problem, aiming to generalize the knowledge learned from multiple source domains to an unseen …”
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Deformable Siamese Attention Networks for Visual Object Tracking von Yu, Yuechen, Xiong, Yilei, Huang, Weilin, Scott, Matthew R.

“… Siamese-based trackers have achieved excellent performance on visual object tracking. However, the target template is not updated online, and the features of …”
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Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval von Weyand, Tobias, Araujo, Andre, Cao, Bingyi, Sim, Jack

“… While image retrieval and instance recognition techniques are progressing rapidly, there is a need for challenging datasets to accurately measure their …”
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MobileOne: An Improved One millisecond Mobile Backbone von Vasu, Pavan Kumar Anasosalu, Gabriel, James, Zhu, Jeff, Tuzel, Oncel, Ranjan, Anurag

“… Efficient neural network backbones for mobile devices are often optimized for metrics such as FLOPs or parameter count. However, these metrics may not …”
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Deep Snake for Real-Time Instance Segmentation von Peng, Sida, Jiang, Wen, Pi, Huaijin, Li, Xiuli, Bao, Hujun, Zhou, Xiaowei

“… This paper introduces a novel contour-based approach named deep snake for real-time instance segmentation. Unlike some recent methods that directly regress the …”
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Token Contrast for Weakly-Supervised Semantic Segmentation von Ru, Lixiang, Zheng, Heliang, Zhan, Yibing, Du, Bo

“… Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by …”
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Finding Task-Relevant Features for Few-Shot Learning by Category Traversal von Li, Hongyang, Eigen, David, Dodge, Samuel, Zeiler, Matthew, Wang, Xiaogang

“… Few-shot learning is an important area of research. Conceptually, humans are readily able to understand new concepts given just a few examples, while in more …”
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Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Prediction von Ma, Tiezheng, Nie, Yongwei, Long, Chengjiang, Zhang, Qing, Li, Guiqing

“… This paper presents a high-quality human motion pre-diction method that accurately predicts future human poses given observed ones. Our method is based on the …”
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Wavelet Integrated CNNs for Noise-Robust Image Classification von Li, Qiufu, Shen, Linlin, Guo, Sheng, Lai, Zhihui

“… Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress …”
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DG-Font: Deformable Generative Networks for Unsupervised Font Generation von Xie, Yangchen, Chen, Xinyuan, Sun, Li, Lu, Yue

“… Font generation is a challenging problem especially for some writing systems that consist of a large number of characters and has attracted a lot of attention …”
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Partial Order Pruning: For Best Speed/Accuracy Trade-Off in Neural Architecture Search von Li, Xin, Zhou, Yiming, Pan, Zheng, Feng, Jiashi

“… Achieving good speed and accuracy trade-off on a target platform is very important in deploying deep neural networks in real world scenarios. However, most …”
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