Results 11 to 20 of about 2,585,016 (323)
DINOv2: Learning Robust Visual Features without Supervision [PDF]
Trans. Mach. Learn. Res., 2023 The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision.
M. Oquab +25 more
semanticscholar +1 more source
GroupViT: Semantic Segmentation Emerges from Text Supervision [PDF]
Computer Vision and Pattern Recognition, 2022 Grouping and recognition are important components of visual scene understanding, e.g., for object detection and semantic segmentation. With end-to-end deep learning systems, grouping of image regions usually happens implicitly via top-down supervision ...
Jiarui Xu +6 more
semanticscholar +1 more source
Discovering Latent Knowledge in Language Models Without Supervision [PDF]
International Conference on Learning Representations, 2022 Existing techniques for training language models can be misaligned with the truth: if we train models with imitation learning, they may reproduce errors that humans make; if we train them to generate text that humans rate highly, they may output errors ...
Collin Burns +3 more
semanticscholar +1 more source
Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision [PDF]
Computer Vision and Pattern Recognition, 2021 In this paper, we study the semi-supervised semantic segmentation problem via exploring both labeled data and extra unlabeled data. We propose a novel consistency regularization approach, called cross pseudo supervision (CPS).
Xiaokang Chen +3 more
semanticscholar +1 more source
CLAP Learning Audio Concepts from Natural Language Supervision
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023 Mainstream machine listening models are trained to learn audio concepts under the paradigm of one class label to many recordings focusing on one task.
Benjamin Elizalde +3 more
semanticscholar +1 more source
Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation [PDF]
Computer Vision and Pattern Recognition, 2021 Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level weak supervision have several limitations: sparse object coverage, inaccurate object boundaries, and co-occurring pixels from non-target objects.
Seungho Lee +3 more
semanticscholar +1 more source
EnlightenGAN: Deep Light Enhancement Without Paired Supervision [PDF]
IEEE Transactions on Image Processing, 2019 Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?
Yifan Jiang +8 more
semanticscholar +1 more source
Learning Pixel-Level Semantic Affinity with Image-Level Supervision for Weakly Supervised Semantic Segmentation [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 The deficiency of segmentation labels is one of the main obstacles to semantic segmentation in the wild. To alleviate this issue, we present a novel framework that generates segmentation labels of images given their image-level class labels.
Jiwoon Ahn, Suha Kwak
semanticscholar +1 more source
Co-learning: Learning from Noisy Labels with Self-supervision [PDF]
ACM Multimedia, 2021 Noisy labels, resulting from mistakes in manual labeling or webly data collecting for supervised learning, can cause neural networks to overfit the misleading information and degrade the generalization performance.
Cheng Tan +3 more
semanticscholar +1 more source
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Face anti-spoofing is crucial to prevent face recognition systems from a security breach. Previous deep learning approaches formulate face anti-spoofing as a binary classification problem.
Yaojie Liu, Amin Jourabloo, Xiaoming Liu
semanticscholar +1 more source