Object Categorization

24 papers with code • 1 benchmarks • 2 datasets

Object categorization identifies which label, from a given set, best corresponds to an image region defined by an input image and bounding box.

Most implemented papers

Learning Transferable Visual Models From Natural Language Supervision

openai/CLIP 26 Feb 2021

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling

xahidbuffon/SUIM 27 May 2015

These methods lack a mechanism to map deep layer feature maps to input dimensions.

Cost-Effective Active Learning for Deep Image Classification

dhaalves/CEAL_keras 13 Jan 2017

In this paper, we propose a novel active learning framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner.

Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition

MRLoghmani/relative-rotation 21 Apr 2020

Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich source dataset to make predictions on an unlabeled target dataset by aligning the two data distributions.

Deep Learning Human Mind for Automated Visual Classification

AliAbyaneh/Extracting-Image-from-EEG-signals CVPR 2017

In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories.

Data augmentation instead of explicit regularization

alexhernandezgarcia/data-aug-invariance ICLR 2018

Despite the fact that some (explicit) regularization techniques, such as weight decay and dropout, require costly fine-tuning of sensitive hyperparameters, the interplay between them and other elements that provide implicit regularization is not well understood yet.

Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection Network

YBZh/PartNet 16 Jun 2018

In this work, we propose a Weakly Supervised Part Detection Network (PartNet) that is able to detect discriminative local parts for use of fine-grained categorization.

Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs

dicarlolab/cornet NeurIPS 2019

Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream.

OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

ofa-sys/ofa 7 Feb 2022

In this work, we pursue a unified paradigm for multimodal pretraining to break the scaffolds of complex task/modality-specific customization.

Collaborative Receptive Field Learning

aimerykong/coRFL 2 Feb 2014

However, measuring pairwise distance of RF's for building the similarity graph is a nontrivial problem.