Bidirectional Semi-supervised Dual-branch CNN for Robust 3D Reconstruction of Stereo Endoscopic Images via Adaptive Cross and Parallel Supervisions

• URL: http://arxiv.org/abs/2210.08291v5
• Date: Fri, 26 May 2023 02:33:12 GMT
• Title: Bidirectional Semi-supervised Dual-branch CNN for Robust 3D Reconstruction of Stereo Endoscopic Images via Adaptive Cross and Parallel Supervisions
• Abstract summary: We propose a novel fashion of bidirectional learning between two learners, each of which can play both roles of teacher and student concurrently. Specifically, we introduce two self-supervisions, i.e., Adaptive Cross Supervision (ACS) and Adaptive Parallel Supervision (APS) The learned knowledge flows across branches along two directions: a cross direction (disparity guides distribution in ACS) and a parallel direction (disparity guides disparity in APS)
• Score: 15.879059896662723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised learning via teacher-student network can train a model effectively on a few labeled samples. It enables a student model to distill knowledge from the teacher's predictions of extra unlabeled data. However, such knowledge flow is typically unidirectional, having the performance vulnerable to the quality of teacher model. In this paper, we seek to robust 3D reconstruction of stereo endoscopic images by proposing a novel fashion of bidirectional learning between two learners, each of which can play both roles of teacher and student concurrently. Specifically, we introduce two self-supervisions, i.e., Adaptive Cross Supervision (ACS) and Adaptive Parallel Supervision (APS), to learn a dual-branch convolutional neural network. The two branches predict two different disparity probability distributions for the same position, and output their expectations as disparity values. The learned knowledge flows across branches along two directions: a cross direction (disparity guides distribution in ACS) and a parallel direction (disparity guides disparity in APS). Moreover, each branch also learns confidences to dynamically refine its provided supervisions. In ACS, the predicted disparity is softened into a unimodal distribution, and the lower the confidence, the smoother the distribution. In APS, the incorrect predictions are suppressed by lowering the weights of those with low confidence. With the adaptive bidirectional learning, the two branches enjoy well-tuned supervisions, and eventually converge on a consistent and more accurate disparity estimation. The extensive and comprehensive experimental results on four public datasets demonstrate our superior performance over other state-of-the-arts with a relative decrease of averaged disparity error by at least 9.76%.

Related papers

• DOPD: Dual On-policy Distillation [59.65986569617147]
  On-policy distillation offers superior capacity transfer by supervising student-sampled trajectories with dense token-level signals.<n>We propose DOPD, an advantage-aware dual distillation paradigm that dynamically routes token-level supervision between privileged teacher and privileged student policies.<n>Experiments on both large language model (LLM) and vision-language model (VLM) settings demonstrate that DOPD consistently outperforms Vanilla OPD.
  arXiv  Detail & Related papers  (2026-06-29T17:55:53Z)
• AVSD: Adaptive-View Self-Distillation by Balancing Consensus and Teacher-Specific Privileged Signals [65.71225905458182]
  Self-distillation enables language models to learn on-policy from their own trajectories by using the same model as both student and teacher.<n>This setup provides dense token-level feedback without relying on a separate external model.<n>We introduce AVSD, a novel method of self-distillation with multiple privileged-information views.
  arXiv  Detail & Related papers  (2026-05-20T03:06:36Z)
• DDAE++: Enhancing Diffusion Models Towards Unified Generative and Discriminative Learning [53.27049077100897]
  generative pre-training has been shown to yield discriminative representations, paving the way towards unified visual generation and understanding.<n>This work introduces self-conditioning, a mechanism that internally leverages the rich semantics inherent in denoising network to guide its own decoding layers.<n>Results are compelling: our method boosts both generation FID and recognition accuracy with 1% computational overhead and generalizes across diverse diffusion architectures.
  arXiv  Detail & Related papers  (2025-05-16T08:47:16Z)
• RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth Segmentation in CBCT [8.60376207420468]
  Region-Aware Instructive Learning (RAIL) is a dual-group dual-student, semi-supervised framework for CBCT tooth segmentation.<n>Our code will be available at https://github.com/Tournesol-Saturday/RAIL.
  arXiv  Detail & Related papers  (2025-05-06T13:50:57Z)
• A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets [9.413178499853156]
  Supervised contrastive learning (SupCon) has proven to be a powerful alternative to the standard cross-entropy loss for classification of balanced datasets. We show that SupCon's performance decreases with increasing class imbalance. We propose two new supervised contrastive learning strategies tailored to binary imbalanced datasets.
  arXiv  Detail & Related papers  (2025-03-21T10:34:51Z)
• Bridging Critical Gaps in Convergent Learning: How Representational Alignment Evolves Across Layers, Training, and Distribution Shifts [1.9458156037869137]
  convergent learning is the degree to which neural systems arrive at similar internal representations.<n>We present a large-scale audit of convergent learning spanning dozens of vision models and thousands of layer-pair comparisons.<n>Findings fill critical gaps in our understanding of representational convergence, with implications for neuroscience and AI.
  arXiv  Detail & Related papers  (2025-02-26T00:04:24Z)
• A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats Generation with 2D Supervision [65.33043028101471]
  We introduce a diffusion model for Gaussian Splats, SplatDiffusion, to enable generation of three-dimensional structures from single images. Existing methods rely on deterministic, feed-forward predictions, which limit their ability to handle the inherent ambiguity of 3D inference from 2D data.
  arXiv  Detail & Related papers  (2024-12-01T00:29:57Z)
• Towards Distribution-Agnostic Generalized Category Discovery [51.52673017664908]
  Data imbalance and open-ended distribution are intrinsic characteristics of the real visual world. We propose a Self-Balanced Co-Advice contrastive framework (BaCon) BaCon consists of a contrastive-learning branch and a pseudo-labeling branch, working collaboratively to provide interactive supervision to resolve the DA-GCD task.
  arXiv  Detail & Related papers  (2023-10-02T17:39:58Z)
• Amortised Invariance Learning for Contrastive Self-Supervision [11.042648980854485]
  We introduce the notion of amortised invariance learning for contrastive self supervision. We show that our amortised features provide a reliable way to learn diverse downstream tasks with different invariance requirements. This provides an exciting perspective that opens up new horizons in the field of general purpose representation learning.
  arXiv  Detail & Related papers  (2023-02-24T16:15:11Z)
• Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset Refinement [15.012694052674899]
  We propose two novel ideas to improve self-supervised monocular depth estimation. We use a parameter-optimized model as the teacher updated as the training epochs to provide additional supervision. We leverage the contextual consistency between high-scale and low-scale features to obtain multiscale disparity offsets.
  arXiv  Detail & Related papers  (2023-02-20T06:28:52Z)
• Robust Distributed Learning Against Both Distributional Shifts and Byzantine Attacks [29.34471516011148]
  In distributed learning systems, issues may arise from two sources. On one hand, due to distributional shifts between training data and test data, the model could exhibit poor out-of-sample performance. On the other hand, a portion of trained nodes might be subject to byzantine attacks which could invalidate the model.
  arXiv  Detail & Related papers  (2022-10-29T20:08:07Z)
• CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation [130.08432609780374]
  In 3D action recognition, there exists rich complementary information between skeleton modalities. We propose a new Cross-modal Mutual Distillation (CMD) framework with the following designs. Our approach outperforms existing self-supervised methods and sets a series of new records.
  arXiv  Detail & Related papers  (2022-08-26T06:06:09Z)
• Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap [64.60460828425502]
  We propose a new guarantee on the downstream performance of contrastive learning. Our new theory hinges on the insight that the support of different intra-class samples will become more overlapped under aggressive data augmentations. We propose an unsupervised model selection metric ARC that aligns well with downstream accuracy.
  arXiv  Detail & Related papers  (2022-03-25T05:36:26Z)
• Robust Self-Supervised LiDAR Odometry via Representative Structure Discovery and 3D Inherent Error Modeling [67.75095378830694]
  We develop a two-stage odometry estimation network, where we obtain the ego-motion by estimating a set of sub-region transformations. In this paper, we aim to alleviate the influence of unreliable structures in training, inference and mapping phases. Our two-frame odometry outperforms the previous state of the arts by 16%/12% in terms of translational/rotational errors.
  arXiv  Detail & Related papers  (2022-02-27T12:52:27Z)
• Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks [6.439477789066243]
  Adversarial defenses train deep neural networks to be invariant to the input perturbations from adversarial attacks. Although adversarial training is successful at mitigating adversarial attacks, the behavioral differences between adversarially-trained (AT) models and standard models are still poorly understood. We identify three logit characteristics essential to learning adversarial robustness.
  arXiv  Detail & Related papers  (2021-08-26T19:09:15Z)
• Robust Pre-Training by Adversarial Contrastive Learning [120.33706897927391]
  Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness. We improve robustness-aware self-supervised pre-training by learning representations consistent under both data augmentations and adversarial perturbations.
  arXiv  Detail & Related papers  (2020-10-26T04:44:43Z)
• Knowledge Distillation Meets Self-Supervision [109.6400639148393]
  Knowledge distillation involves extracting "dark knowledge" from a teacher network to guide the learning of a student network. We show that the seemingly different self-supervision task can serve as a simple yet powerful solution. By exploiting the similarity between those self-supervision signals as an auxiliary task, one can effectively transfer the hidden information from the teacher to the student.
  arXiv  Detail & Related papers  (2020-06-12T12:18:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.