ReSSL: Relational Self-Supervised Learning with Weak Augmentation

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   On the Discriminability of Self-Supervised Representation Learning

   Zeen Song, Wenwen Qiang, Changwen Zheng et al. · 2024 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Self-supervised learning (SSL) has recently achieved significant success in downstream visual tasks. However, a notable gap still exists between SSL and supervised learning (SL), especially in complex downstream tasks. In this paper, we show that the features learned by SSL methods suffer from the crowding problem, where features of different classes are not distinctly separated, and features within the same class exhibit large intra-class variance. In contrast, SL ensures a clear separation between classes. We analyze this phenomenon and conclude that SSL objectives do not constrain the relationships between different samples and their augmentations. Our theoretical analysis delves into how…

2. Close Not relevant

   Barlow Twins: Self-Supervised Learning via Redundancy Reduction

   Jure Zbontar, Li Jing, Ishan Misra et al. · 2021 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn representations which are invariant to distortions of the input sample. However, a recurring issue with this approach is the existence of trivial constant representations. Most current methods avoid such collapsed solutions by careful implementation details. We propose an objective function that naturally avoids such collapse by measuring the cross-correlation matrix between the outputs of two identical networks fed with distorted versions of a sample, and making it as close to the identity matrix as possible. This causes the represen…

3. Close Not relevant

   Patch-level Representation Learning for Self-supervised Vision Transformers

   Sukmin Yun, Hankook Lee, Jaehyung Kim et al. · 2022 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Recent self-supervised learning (SSL) methods have shown impressive results in learning visual representations from unlabeled images. This paper aims to improve their performance further by utilizing the architectural advantages of the underlying neural network, as the current state-of-the-art visual pretext tasks for SSL do not enjoy the benefit, i.e., they are architecture-agnostic. In particular, we focus on Vision Transformers (ViTs), which have gained much attention recently as a better architectural choice, often outperforming convolutional networks for various visual tasks. The unique characteristic of ViT is that it takes a sequence of disjoint patches from an image and processes pat…

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   Self-supervised Geometric Features Discovery via Interpretable Attentio for Vehicle Re-Identification and Beyond (Complete Version)

   Ming Li, Xinming Huang, Ziming Zhang · 2023 · Computer Vision and Pattern Recognition · Not summarized yet

   Abstract

   To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requires prohibitive human labors. In this paper, we seek to achieve the similar goal but do not involve more human efforts. To this end, we introduce a novel framework, which successfully encodes both geometric local features and global representations to distinguish vehicle instances, optimized only by the supervision from official ID labels. Specifically, given our insight that objects in ReID share similar geometric characteristics, we propose to borrow self-supervised representation learning to facilitate geometric …

5. Related Not relevant

   Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation

   Zhen Zhao, Lihe Yang, Sifan Long et al. · 2022 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Recent studies on semi-supervised semantic segmentation (SSS) have seen fast progress. Despite their promising performance, current state-of-the-art methods tend to increasingly complex designs at the cost of introducing more network components and additional training procedures. Differently, in this work, we follow a standard teacher-student framework and propose AugSeg, a simple and clean approach that focuses mainly on data perturbations to boost the SSS performance. We argue that various data augmentations should be adjusted to better adapt to the semi-supervised scenarios instead of directly applying these techniques from supervised learning. Specifically, we adopt a simplified intensit…

6. Related Not relevant

   RelTR: Relation Transformer for Scene Graph Generation

   Yuren Cong, Michael Ying Yang, Bodo Rosenhahn · 2022 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Different objects in the same scene are more or less related to each other, but only a limited number of these relationships are noteworthy. Inspired by DETR, which excels in object detection, we view scene graph generation as a set prediction problem and propose an end-to-end scene graph generation model RelTR which has an encoder-decoder architecture. The encoder reasons about the visual feature context while the decoder infers a fixed-size set of triplets subject-predicate-object using different types of attention mechanisms with coupled subject and object queries. We design a set prediction loss performing the matching between the ground truth and predicted triplets for the end-to-end tr…

7. Related Not relevant

   Self-Supervised Correspondence Estimation via Multiview Registration

   Mohamed El Banani, Ignacio Rocco, David Novotny et al. · 2022 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Video provides us with the spatio-temporal consistency needed for visual learning. Recent approaches have utilized this signal to learn correspondence estimation from close-by frame pairs. However, by only relying on close-by frame pairs, those approaches miss out on the richer long-range consistency between distant overlapping frames. To address this, we propose a self-supervised approach for correspondence estimation that learns from multiview consistency in short RGB-D video sequences. Our approach combines pairwise correspondence estimation and registration with a novel SE(3) transformation synchronization algorithm. Our key insight is that self-supervised multiview registration allows u…

8. Related Not relevant

   Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation

   Xiangyi Yan, Junayed Naushad, Chenyu You et al. · 2023 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Recent advancements in self-supervised learning have demonstrated that effective visual representations can be learned from unlabeled images. This has led to increased interest in applying self-supervised learning to the medical domain, where unlabeled images are abundant and labeled images are difficult to obtain. However, most self-supervised learning approaches are modeled as image level discriminative or generative proxy tasks, which may not capture the finer level representations necessary for dense prediction tasks like multi-organ segmentation. In this paper, we propose a novel contrastive learning framework that integrates Localized Region Contrast (LRC) to enhance existing self-supe…