Latent Noise Segmentation: How Neural Noise Leads to the Emergence of Segmentation and Grouping
- URL: http://arxiv.org/abs/2309.16515v3
- Date: Wed, 23 Oct 2024 11:56:02 GMT
- Title: Latent Noise Segmentation: How Neural Noise Leads to the Emergence of Segmentation and Grouping
- Authors: Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog,
- Abstract summary: Humans are able to segment images effortlessly without supervision using perceptual grouping.
We propose a counter-intuitive computational approach to solving unsupervised perceptual grouping.
We show that under realistic assumptions, neural noise can be used to separate objects from each other.
- Score: 0.24578723416255752
- License:
- Abstract: Humans are able to segment images effortlessly without supervision using perceptual grouping. Here, we propose a counter-intuitive computational approach to solving unsupervised perceptual grouping and segmentation: that they arise because of neural noise, rather than in spite of it. We (1) mathematically demonstrate that under realistic assumptions, neural noise can be used to separate objects from each other; (2) that adding noise in a DNN enables the network to segment images even though it was never trained on any segmentation labels; and (3) that segmenting objects using noise results in segmentation performance that aligns with the perceptual grouping phenomena observed in humans, and is sample-efficient. We introduce the Good Gestalt (GG) datasets -- six datasets designed to specifically test perceptual grouping, and show that our DNN models reproduce many important phenomena in human perception, such as illusory contours, closure, continuity, proximity, and occlusion. Finally, we (4) show that our model improves performance on our GG datasets compared to other tested unsupervised models by $24.9\%$. Together, our results suggest a novel unsupervised segmentation method requiring few assumptions, a new explanation for the formation of perceptual grouping, and a novel potential benefit of neural noise.
Related papers
- Enhance Vision-Language Alignment with Noise [59.2608298578913]
We investigate whether the frozen model can be fine-tuned by customized noise.
We propose Positive-incentive Noise (PiNI) which can fine-tune CLIP via injecting noise into both visual and text encoders.
arXiv Detail & Related papers (2024-12-14T12:58:15Z) - UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation [64.01742988773745]
An increasing privacy concern exists regarding training large-scale image segmentation models on unauthorized private data.
We exploit the concept of unlearnable examples to make images unusable to model training by generating and adding unlearnable noise into the original images.
We empirically verify the effectiveness of UnSeg across 6 mainstream image segmentation tasks, 10 widely used datasets, and 7 different network architectures.
arXiv Detail & Related papers (2024-10-13T16:34:46Z) - Combating Bilateral Edge Noise for Robust Link Prediction [56.43882298843564]
We propose an information-theory-guided principle, Robust Graph Information Bottleneck (RGIB), to extract reliable supervision signals and avoid representation collapse.
Two instantiations, RGIB-SSL and RGIB-REP, are explored to leverage the merits of different methodologies.
Experiments on six datasets and three GNNs with diverse noisy scenarios verify the effectiveness of our RGIB instantiations.
arXiv Detail & Related papers (2023-11-02T12:47:49Z) - Factorized Diffusion Architectures for Unsupervised Image Generation and
Segmentation [24.436957604430678]
We develop a neural network architecture which, trained in an unsupervised manner as a denoising diffusion model, simultaneously learns to both generate and segment images.
Experiments demonstrate that our model achieves accurate unsupervised image segmentation and high-quality synthetic image generation across multiple datasets.
arXiv Detail & Related papers (2023-09-27T15:32:46Z) - Learning Confident Classifiers in the Presence of Label Noise [5.551384206194696]
This paper proposes a probabilistic model for noisy observations that allows us to build a confident classification and segmentation models.
Our experiments show that our algorithm outperforms state-of-the-art solutions for the considered classification and segmentation problems.
arXiv Detail & Related papers (2023-01-02T04:27:25Z) - Embedding contrastive unsupervised features to cluster in- and
out-of-distribution noise in corrupted image datasets [18.19216557948184]
Using search engines for web image retrieval is a tempting alternative to manual curation when creating an image dataset.
Their main drawback remains the proportion of incorrect (noisy) samples retrieved.
We propose a two stage algorithm starting with a detection step where we use unsupervised contrastive feature learning.
We find that the alignment and uniformity principles of contrastive learning allow OOD samples to be linearly separated from ID samples on the unit hypersphere.
arXiv Detail & Related papers (2022-07-04T16:51:56Z) - Synergy Between Semantic Segmentation and Image Denoising via Alternate
Boosting [102.19116213923614]
We propose a boosting network to perform denoising and segmentation alternately.
We observe that not only denoising helps combat the drop of segmentation accuracy due to noise, but also pixel-wise semantic information boosts the capability of denoising.
Experimental results show that the denoised image quality is improved substantially and the segmentation accuracy is improved to close to that of clean images.
arXiv Detail & Related papers (2021-02-24T06:48:45Z) - Joint self-supervised blind denoising and noise estimation [0.0]
Two neural networks jointly predict the clean signal and infer the noise distribution.
We show empirically with synthetic noisy data that our model captures the noise distribution efficiently.
arXiv Detail & Related papers (2021-02-16T08:37:47Z) - Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation [49.90178055521207]
This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation.
We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths.
In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes.
arXiv Detail & Related papers (2020-12-09T12:40:13Z) - DenoiSeg: Joint Denoising and Segmentation [75.91760529986958]
We propose DenoiSeg, a new method that can be trained end-to-end on only a few annotated ground truth segmentations.
We achieve this by extending Noise2Void, a self-supervised denoising scheme that can be trained on noisy images alone, to also predict dense 3-class segmentations.
arXiv Detail & Related papers (2020-05-06T17:42:54Z)
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.