Iterative Learning for Instance Segmentation

• URL: http://arxiv.org/abs/2202.09110v1
• Date: Fri, 18 Feb 2022 10:25:02 GMT
• Title: Iterative Learning for Instance Segmentation
• Authors: Tuomas Sormunen, Arttu L\"ams\"a, Miguel Bordallo Lopez
• Abstract summary: State-of-the-art deep neural network models require large amounts of labeled data in order to perform well in this task. We propose for the first time, an iterative learning and annotation method that is able to detect, segment and annotate instances in datasets composed of multiple similar objects. Experiments on two different datasets show the validity of the approach in different applications related to visual inspection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instance segmentation is a computer vision task where separate objects in an image are detected and segmented. State-of-the-art deep neural network models require large amounts of labeled data in order to perform well in this task. Making these annotations is time-consuming. We propose for the first time, an iterative learning and annotation method that is able to detect, segment and annotate instances in datasets composed of multiple similar objects. The approach requires minimal human intervention and needs only a bootstrapping set containing very few annotations. Experiments on two different datasets show the validity of the approach in different applications related to visual inspection.

Related papers

• ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification [24.709695178222862]
  We propose ISAR, a benchmark and baseline method for single- and few-shot object identification. We provide a semi-synthetic dataset of video sequences with ground-truth semantic annotations. Our benchmark aligns with the emerging research trend of unifying Multi-Object Tracking, Video Object, and Re-identification.
  arXiv  Detail & Related papers  (2023-11-05T18:51:33Z)
• Learning from Pseudo-labeled Segmentation for Multi-Class Object Counting [35.652092907690694]
  Class-agnostic counting (CAC) has numerous potential applications across various domains. The goal is to count objects of an arbitrary category during testing, based on only a few annotated exemplars. We show that the segmentation model trained on these pseudo-labeled masks can effectively localize objects of interest for an arbitrary multi-class image.
  arXiv  Detail & Related papers  (2023-07-15T01:33:19Z)
• Image Segmentation-based Unsupervised Multiple Objects Discovery [1.7674345486888503]
  Unsupervised object discovery aims to localize objects in images. We propose a fully unsupervised, bottom-up approach, for multiple objects discovery. We provide state-of-the-art results for both unsupervised class-agnostic object detection and unsupervised image segmentation.
  arXiv  Detail & Related papers  (2022-12-20T09:48:24Z)
• Tag-Based Attention Guided Bottom-Up Approach for Video Instance Segmentation [83.13610762450703]
  Video instance is a fundamental computer vision task that deals with segmenting and tracking object instances across a video sequence. We introduce a simple end-to-end train bottomable-up approach to achieve instance mask predictions at the pixel-level granularity, instead of the typical region-proposals-based approach. Our method provides competitive results on YouTube-VIS and DAVIS-19 datasets, and has minimum run-time compared to other contemporary state-of-the-art performance methods.
  arXiv  Detail & Related papers  (2022-04-22T15:32:46Z)
• Single Image Object Counting and Localizing using Active-Learning [4.56877715768796]
  We present a new method for counting and localizing repeating objects in single-image scenarios. Our method trains a CNN over a small set of labels carefully collected from the input image in few active-learning iterations. Compared with existing user-assisted counting methods, our active-learning iterations achieve state-of-the-art performance in terms of counting and localizing accuracy, number of user mouse clicks, and running-time.
  arXiv  Detail & Related papers  (2021-11-16T11:29:21Z)
• Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation [79.6596425920849]
  This paper addresses the task of unsupervised video multi-object segmentation. We introduce a novel approach for more accurate and efficient unseen-temporal segmentation. We evaluate the proposed approach on DAVIS$_17$ and YouTube-VIS, and the results demonstrate that it outperforms state-of-the-art methods both in segmentation accuracy and inference speed.
  arXiv  Detail & Related papers  (2021-04-10T14:39:44Z)
• DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping [72.84991726271024]
  We describe an unsupervised method to detect and segment portions of images of live scenes that are seen moving as a coherent whole. Our method first partitions the motion field by minimizing the mutual information between segments. It uses the segments to learn object models that can be used for detection in a static image.
  arXiv  Detail & Related papers  (2020-08-16T22:05:13Z)
• Part-aware Prototype Network for Few-shot Semantic Segmentation [50.581647306020095]
  We propose a novel few-shot semantic segmentation framework based on the prototype representation. Our key idea is to decompose the holistic class representation into a set of part-aware prototypes. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes.
  arXiv  Detail & Related papers  (2020-07-13T11:03:09Z)
• A Few-Shot Sequential Approach for Object Counting [63.82757025821265]
  We introduce a class attention mechanism that sequentially attends to objects in the image and extracts their relevant features. The proposed technique is trained on point-level annotations and uses a novel loss function that disentangles class-dependent and class-agnostic aspects of the model. We present our results on a variety of object-counting/detection datasets, including FSOD and MS COCO.
  arXiv  Detail & Related papers  (2020-07-03T18:23:39Z)
• UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation [52.487469544343305]
  Methods for object detection and segmentation rely on large scale instance-level annotations for training. We propose an intuitive and unified semi-supervised model that is applicable to a range of supervision.
  arXiv  Detail & Related papers  (2020-06-12T22:45:47Z)
• Reinforced Coloring for End-to-End Instance Segmentation [10.73460247817528]
  We propose a novel iterative deep reinforcement learning agent that learns how to differentiate multiple objects in parallel. Our reward function for the trainable agent is designed to favor grouping pixels belonging to the same object using a graph coloring algorithm.
  arXiv  Detail & Related papers  (2020-05-14T15:15:47Z)

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.