A Unified Object Counting Network with Object Occupation Prior

• URL: http://arxiv.org/abs/2212.14193v3
• Date: Fri, 30 Jun 2023 12:26:50 GMT
• Title: A Unified Object Counting Network with Object Occupation Prior
• Authors: Shengqin Jiang, Qing Wang, Fengna Cheng, Yuankai Qi, Qingshan Liu
• Abstract summary: Existing object counting tasks are designed for a single object class. It is inevitable to encounter newly coming data with new classes in our real world. We build the first evolving object counting dataset and propose a unified object counting network.
• Score: 32.32999623924954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The counting task, which plays a fundamental role in numerous applications (e.g., crowd counting, traffic statistics), aims to predict the number of objects with various densities. Existing object counting tasks are designed for a single object class. However, it is inevitable to encounter newly coming data with new classes in our real world. We name this scenario as \textit{evolving object counting}. In this paper, we build the first evolving object counting dataset and propose a unified object counting network as the first attempt to address this task. The proposed model consists of two key components: a class-agnostic mask module and a class-incremental module. The class-agnostic mask module learns generic object occupation prior via predicting a class-agnostic binary mask (e.g., 1 denotes there exists an object at the considering position in an image and 0 otherwise). The class-incremental module is used to handle new coming classes and provides discriminative class guidance for density map prediction. The combined outputs of class-agnostic mask module and image feature extractor are used to predict the final density map. When new classes come, we first add new neural nodes into the last regression and classification layers of class-incremental module. Then, instead of retraining the model from scratch, we utilize knowledge distillation to help the model remember what have already learned about previous object classes. We also employ a support sample bank to store a small number of typical training samples of each class, which are used to prevent the model from forgetting key information of old data. With this design, our model can efficiently and effectively adapt to new coming classes while keeping good performance on already seen data without large-scale retraining. Extensive experiments on the collected dataset demonstrate the favorable performance.

Related papers

• Zero-Shot Object Counting with Language-Vision Models [50.1159882903028]
  Class-agnostic object counting aims to count object instances of an arbitrary class at test time. Current methods require human-annotated exemplars as inputs which are often unavailable for novel categories. We propose zero-shot object counting (ZSC), a new setting where only the class name is available during test time.
  arXiv  Detail & Related papers  (2023-09-22T14:48:42Z)
• 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)
• DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic Grasping [1.6317061277457001]
  We develop an object detector that requires no fine-tuning and can add any object as a class just by capturing a few images of the object. We evaluate our class-adaptive object detector on unseen datasets and compare it to a trained Mask R-CNN on those datasets.
  arXiv  Detail & Related papers  (2023-04-06T02:45:39Z)
• Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks [59.12108527904171]
  A model should recognize new classes and maintain discriminability over old classes. The task of recognizing few-shot new classes without forgetting old classes is called few-shot class-incremental learning (FSCIL) We propose a new paradigm for FSCIL based on meta-learning by LearnIng Multi-phase Incremental Tasks (LIMIT)
  arXiv  Detail & Related papers  (2022-03-31T13:46:41Z)
• Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection [56.22467011292147]
  Several incremental learning methods are proposed to mitigate catastrophic forgetting for object detection. Despite the effectiveness, these methods require co-occurrence of the unlabeled base classes in the training data of the novel classes. We propose the use of unlabeled in-the-wild data to bridge the non-occurrence caused by the missing base classes during the training of additional novel classes.
  arXiv  Detail & Related papers  (2021-10-28T10:57:25Z)
• Rectifying the Shortcut Learning of Background: Shared Object Concentration for Few-Shot Image Recognition [101.59989523028264]
  Few-Shot image classification aims to utilize pretrained knowledge learned from a large-scale dataset to tackle a series of downstream classification tasks. We propose COSOC, a novel Few-Shot Learning framework, to automatically figure out foreground objects at both pretraining and evaluation stage.
  arXiv  Detail & Related papers  (2021-07-16T07:46:41Z)
• Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning [1.0062040918634414]
  Few-shot learning algorithms are designed to generalize well to new tasks with limited data. We introduce a new few-shot model called Fuzzy Simplicial Networks (FSN) which leverages a construction from topology to more flexibly represent each class from limited data.
  arXiv  Detail & Related papers  (2020-09-23T17:01:09Z)
• 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)
• Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification [91.67977602992657]
  We propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training.
  arXiv  Detail & Related papers  (2020-03-20T15:44:17Z)

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.