Object Counting: You Only Need to Look at One

• URL: http://arxiv.org/abs/2112.05993v1
• Date: Sat, 11 Dec 2021 14:22:05 GMT
• Title: Object Counting: You Only Need to Look at One
• Authors: Hui Lin, Xiaopeng Hong, Yabin Wang
• Abstract summary: This paper aims to tackle the challenging task of one-shot object counting. Given an image containing novel, previously unseen category objects, the goal of the task is to count all instances in the desired category with only one supporting bounding box example. We propose a counting model by which you only need to Look At One instance (LaoNet)
• Score: 28.49828300257672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper aims to tackle the challenging task of one-shot object counting. Given an image containing novel, previously unseen category objects, the goal of the task is to count all instances in the desired category with only one supporting bounding box example. To this end, we propose a counting model by which you only need to Look At One instance (LaoNet). First, a feature correlation module combines the Self-Attention and Correlative-Attention modules to learn both inner-relations and inter-relations. It enables the network to be robust to the inconsistency of rotations and sizes among different instances. Second, a Scale Aggregation mechanism is designed to help extract features with different scale information. Compared with existing few-shot counting methods, LaoNet achieves state-of-the-art results while learning with a high convergence speed. The code will be available soon.

Related papers

• Learning from Exemplars for Interactive Image Segmentation [15.37506525730218]
  We introduce novel interactive segmentation frameworks for both a single object and multiple objects in the same category. Our model reduces users' labor by around 15%, requiring two fewer clicks to achieve target IoUs 85% and 90%.
  arXiv  Detail & Related papers  (2024-06-17T12:38:01Z)
• OMG-Seg: Is One Model Good Enough For All Segmentation? [83.17068644513144]
  OMG-Seg is a transformer-based encoder-decoder architecture with task-specific queries and outputs. We show that OMG-Seg can support over ten distinct segmentation tasks and yet significantly reduce computational and parameter overhead.
  arXiv  Detail & Related papers  (2024-01-18T18:59:34Z)
• 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)
• Object-Centric Multiple Object Tracking [124.30650395969126]
  This paper proposes a video object-centric model for multiple-object tracking pipelines. It consists of an index-merge module that adapts the object-centric slots into detection outputs and an object memory module. Benefited from object-centric learning, we only require sparse detection labels for object localization and feature binding.
  arXiv  Detail & Related papers  (2023-09-01T03:34:12Z)
• 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)
• Learning To Count Everything [16.1159048148031]
  We are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. We present a novel method that takes a query image together with a few exemplar objects from the query image and predicts a density map for the presence of all objects of interest in the query image. We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category.
  arXiv  Detail & Related papers  (2021-04-16T22:45:58Z)
• Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting [18.733301622920102]
  Multi-class object counting expands the scope of application of object counting task. The multi-target detection task can achieve multi-class object counting in some scenarios. We propose a simple yet efficient counting network based on point-level annotations.
  arXiv  Detail & Related papers  (2020-12-15T08:38:28Z)
• 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)
• Object-Centric Image Generation from Layouts [93.10217725729468]
  We develop a layout-to-image-generation method to generate complex scenes with multiple objects. Our method learns representations of the spatial relationships between objects in the scene, which lead to our model's improved layout-fidelity. We introduce SceneFID, an object-centric adaptation of the popular Fr'echet Inception Distance metric, that is better suited for multi-object images.
  arXiv  Detail & Related papers  (2020-03-16T21:40:09Z)
• Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer [116.46533207849619]
  We study the impact of scale and location mismatch in the few-shot learning scenario. We propose a novel Spatially-aware Matching scheme to effectively perform matching across multiple scales and locations.
  arXiv  Detail & Related papers  (2020-01-06T14:10:20Z)

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.