Fine-grained Few-shot Recognition by Deep Object Parsing

• URL: http://arxiv.org/abs/2207.07110v1
• Date: Thu, 14 Jul 2022 17:59:05 GMT
• Title: Fine-grained Few-shot Recognition by Deep Object Parsing
• Authors: Pengkai Zhu, Ruizhao Zhu, Samarth Mishra, Venkatesh Saligrama
• Abstract summary: We parse a test instance by inferring the K parts, where each part occupies a distinct location in the feature space. We recognize test instances by comparing its active templates and the relative geometry of its part locations.
• Score: 43.61794876834115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In our framework, an object is made up of K distinct parts or units, and we parse a test instance by inferring the K parts, where each part occupies a distinct location in the feature space, and the instance features at this location, manifest as an active subset of part templates shared across all instances. We recognize test instances by comparing its active templates and the relative geometry of its part locations against those of the presented few-shot instances. We propose an end-to-end training method to learn part templates on-top of a convolutional backbone. To combat visual distortions such as orientation, pose and size, we learn multi-scale templates, and at test-time parse and match instances across these scales. We show that our method is competitive with the state-of-the-art, and by virtue of parsing enjoys interpretability as well.

Related papers

• Dynamic Template Initialization for Part-Aware Person Re-ID [0.640781528166787]
  spatial attention-based Dynamic Part template Initialization module. Part-level features of the backbone are used to extract the templates of diverse human body parts. Tests on holistic, occluded, and partial Re-ID task benchmarks.
  arXiv  Detail & Related papers  (2022-08-24T11:20:48Z)
• Sparse Instance Activation for Real-Time Instance Segmentation [72.23597664935684]
  We propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation. SparseInst has extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO benchmark.
  arXiv  Detail & Related papers  (2022-03-24T03:15:39Z)
• Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes [17.217954254022573]
  We introduce a meta-learning-based method for few-shot 3D shape segmentation where only a few labeled samples are provided for the unseen classes. We demonstrate the superior performance of our proposed on the ShapeNet part dataset under the few-shot scenario, compared with well-established baseline and state-of-the-art semi-supervised methods.
  arXiv  Detail & Related papers  (2021-07-07T01:47:00Z)
• SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes [56.387647750094466]
  Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples. Most of advanced solutions exploit a metric learning framework that performs segmentation through matching each pixel to a learned foreground prototype. This framework suffers from biased classification due to incomplete construction of sample pairs with the foreground prototype only.
  arXiv  Detail & Related papers  (2021-04-19T11:21:47Z)
• FAPIS: A Few-shot Anchor-free Part-based Instance Segmenter [35.103437828235826]
  We evaluate a new few-shot anchor-free part-based instance segmenter FAPIS. Key novelty is in explicit modeling of latent object parts shared across training object classes. Our evaluation on the benchmark COCO-20i dataset demonstrates that we significantly outperform the state of the art.
  arXiv  Detail & Related papers  (2021-03-31T19:09:43Z)
• 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)
• 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)
• 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.