CHIP: Contrastive Hierarchical Image Pretraining
- URL: http://arxiv.org/abs/2310.08304v1
- Date: Thu, 12 Oct 2023 13:11:38 GMT
- Title: CHIP: Contrastive Hierarchical Image Pretraining
- Authors: Arpit Mittal, Harshil Jhaveri, Swapnil Mallick, Abhishek Ajmera
- Abstract summary: We propose a one-shot/few-shot classification model that can classify an object of any unseen class into a relatively general category.
Our model uses a three-level hierarchical contrastive loss based ResNet152 for classifying an object based on its features extracted from Image embedding.
- Score: 1.6454089842548987
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Few-shot object classification is the task of classifying objects in an image
with limited number of examples as supervision. We propose a one-shot/few-shot
classification model that can classify an object of any unseen class into a
relatively general category in an hierarchically based classification. Our
model uses a three-level hierarchical contrastive loss based ResNet152
classifier for classifying an object based on its features extracted from Image
embedding, not used during the training phase. For our experimentation, we have
used a subset of the ImageNet (ILSVRC-12) dataset that contains only the animal
classes for training our model and created our own dataset of unseen classes
for evaluating our trained model. Our model provides satisfactory results in
classifying the unknown objects into a generic category which has been later
discussed in greater detail.
Related papers
- Image-free Classifier Injection for Zero-Shot Classification [72.66409483088995]
Zero-shot learning models achieve remarkable results on image classification for samples from classes that were not seen during training.
We aim to equip pre-trained models with zero-shot classification capabilities without the use of image data.
We achieve this with our proposed Image-free Injection with Semantics (ICIS)
arXiv Detail & Related papers (2023-08-21T09:56:48Z) - Stable Attribute Group Editing for Reliable Few-shot Image Generation [88.59350889410794]
We present an editing-based'' framework Attribute Group Editing (AGE) for reliable few-shot image generation.
We find that class inconsistency is a common problem in GAN-generated images for downstream classification.
We propose to boost the downstream classification performance of SAGE by enhancing the pixel and frequency components.
arXiv Detail & Related papers (2023-02-01T01:51:47Z) - Exploiting Category Names for Few-Shot Classification with
Vision-Language Models [78.51975804319149]
Vision-language foundation models pretrained on large-scale data provide a powerful tool for many visual understanding tasks.
This paper shows that we can significantly improve the performance of few-shot classification by using the category names to initialize the classification head.
arXiv Detail & Related papers (2022-11-29T21:08:46Z) - Text2Model: Text-based Model Induction for Zero-shot Image Classification [38.704831945753284]
We address the challenge of building task-agnostic classifiers using only text descriptions.
We generate zero-shot classifiers using a hypernetwork that receives class descriptions and outputs a multi-class model.
We evaluate this approach in a series of zero-shot classification tasks, for image, point-cloud, and action recognition, using a range of text descriptions.
arXiv Detail & Related papers (2022-10-27T05:19:55Z) - Multi-Category Mesh Reconstruction From Image Collections [90.24365811344987]
We present an alternative approach that infers the textured mesh of objects combining a series of deformable 3D models and a set of instance-specific deformation, pose, and texture.
Our method is trained with images of multiple object categories using only foreground masks and rough camera poses as supervision.
Experiments show that the proposed framework can distinguish between different object categories and learn category-specific shape priors in an unsupervised manner.
arXiv Detail & Related papers (2021-10-21T16:32:31Z) - 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) - Improving Few-shot Learning with Weakly-supervised Object Localization [24.3569501375842]
We propose a novel framework that generates class representations by extracting features from class-relevant regions of the images.
Our method outperforms the baseline few-shot model in miniImageNet and tieredImageNet benchmarks.
arXiv Detail & Related papers (2021-05-25T07:39:32Z) - Prototypical Region Proposal Networks for Few-Shot Localization and
Classification [1.5100087942838936]
We develop a framework to unifysegmentation and classification into an end-to-end classification model -- PRoPnet.
We empirically demonstrate that our methods improve accuracy on image datasets with natural scenes containing multiple object classes.
arXiv Detail & Related papers (2021-04-08T04:03:30Z) - Rethinking Natural Adversarial Examples for Classification Models [43.87819913022369]
ImageNet-A is a famous dataset of natural adversarial examples.
We validated the hypothesis by reducing the background influence in ImageNet-A examples with object detection techniques.
Experiments showed that the object detection models with various classification models as backbones obtained much higher accuracy than their corresponding classification models.
arXiv Detail & Related papers (2021-02-23T14:46:48Z) - 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)
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.