Relative attributes

By Devi Parikh, Kristen Grauman. 2011

1,007 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions (2015, background): “[22] explores relative attributes, which captures how images relate to each other along different attributes.”
Generative Adversarial Text to Image Synthesis (2016, background): “…in attribute representations - distinguishing characteristics the object category encoded into a vector (Farhadi et al., 2009; Kumar et al., 2009; Parikh & Grauman, 2011; Lampert et al., 2014), in particular to enable zero-shot visual recognition (Fu et al., 2014; Akata et al., 2015), and…”
Generative Visual Manipulation on the Natural Image Manifold (2016, background): “Similar to relative attributes [34], a user can express ideas like changing the handle of the handbag to be more red, or making the heel of the shoes slightly higher, without committing to a specific final state.”
Joint Discovery of Object States and Manipulation Actions (2017, background): “Prior work has addressed recognition of object attributes [15, 33, 34], which can be seen as different object states in some cases.”
Deep Learning Face Attributes in the Wild (2014, background): “Face attributes, such as expression, race, and hair style, are beneficial for many applications such as image tagging [19] and face verification [12].”
CIDEr: Consensus-based image description evaluation (2014, background): “Other works have modeled prepositional relationships [16], attributes (adjectives) [23, 34], and visual phrases (i.”
A Joint Model of Language and Perception for Grounded Attribute Learning (2012, background): “…& Hebert, 1999) attributes provide rich descriptions of objects, and have become a popular topic in the vision community (Farhadi et al., 2009; Parikh & Grauman, 2011); although very successful, we still lack a deep understanding of the design rules underlying them and how they measure…”
Transductive Multi-View Zero-Shot Learning (2015, background): “X S ∈ R n s × t and X T ∈ R n T × t denote the t − dimensional low-level feature vectors of auxiliary and target instances respectively. z S and z T are the auxiliary and target class label vectors.”
Fine-Grained Emotion Strength Transfer, Control and Prediction for Emotional Speech Synthesis (2020, methodology): “[17] proposed to estimate the w by solving the following problem through Newton’s method [21]:”
Learning Fine-Grained Image Similarity with Deep Ranking (2014, methodology): “Relative attribute [19] learns image attribute ranking among the images with the same attributes.”
PANDA: Pose Aligned Networks for Deep Attribute Modeling (2013, background): “[22] exploring the relative strength of attributes by learning a rank function for each attribute, which can be applied to zero-shot learning as well as to generate richer textual descriptions.”
Zero-Shot Learning by Convex Combination of Semantic Embeddings (2013, background): “More recently, relative attributes [15] are proposed to strengthen the attribute based representations.”
Evaluation of output embeddings for fine-grained image classification (2014, background): “Attributes, which are often collected manually [25, 41, 12], have shown promising results in various applications, i.”
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data (2015, background): “Zero-shot learning has received substantial attention in computer vision [27, 24, 19, 30, 7] since it becomes difficult to obtain sufficient labeled images as the number of object categories grows.”
Adversarial Ranking for Language Generation (2017, methodology): “The ranking technique has been proven effective for searching documents [12] and images [23].”
Boosting Image Captioning with Attributes (2016, background): “On the other hand, attributes are properties observed in images with rich semantic cues and have been proved to be effective in visual recognition [23].”
Latent Embeddings for Zero-Shot Classification (2016, background): “Combining visual information with attributes [7, 8, 11, 17, 20, 28, 27] has also supported fine grained classification.”
Discovering states and transformations in image collections (2015, background): “, [10], [24], [23], [11]), which sometimes include object and scene states.”
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild (2016, background): “This is often achieved by embedding both seen and unseen classes into a common semantic space, such as visual attributes [4,5,6] or word2vec representations of the class names [7,8,9].”