Reasoning about Object Affordances in a Knowledge Base Representation

By Yuke Zhu, Alireza Fathi, Li Fei-Fei. 2014

251 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Neural Logic Machines (2019, background): “Our work is also related to symbolic relational reasoning, which has a wide application in processing discrete data structures such as knowledge graphs and social graphs (Zhu et al., 2014; Kipf & Welling, 2017; Zeng et al., 2017; Yang et al., 2017).”
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge (2019, background): “Several knowledge bases have been created using visual data or for visual reasoning tasks [53, 9, 11, 39, 56, 55].”
Visual7W: Grounded Question Answering in Images (2015, background): “The proposed models range from probabilistic inference [27, 44, 51] and recurrent neural networks [1, 7, 28, 36] to convolutional networks [26].”
Logic Tensor Networks for Semantic Image Interpretation (2017, background): “ background knowledge can be of great help. Thus, recent SII systems have sought to combine, or even integrate, visual features obtained from data and symbolic knowledge in the form of logical axioms [30, 4, 8]. The area of Statistical Relational Learning (SRL), or Statistical Artiﬁcial Intelligence”
The More You Know: Using Knowledge Graphs for Image Classification (2016, background): “For example, [40] collects a knowledge base and then queries this knowledge base to do ﬁrst-order probabilistic reasoning to predict affordances.”
Weakly-Supervised Learning of Visual Relations (2017, background): “Triplet learning has been addressed in various tasks such as mining typical relations (knowledge extraction) [6, 42, 51, 53], reasoning [20, 34, 44], object detection [16, 43], image retrieval [22] or fact retrieval [10].”
Don't just listen, use your imagination: Leveraging visual common sense for non-visual tasks (2015, background): “By visual common sense we do not mean visual models of commonly occurring interactions between objects [11] or knowledge of visual relationships between objects, parts and attributes [9, 50].”
Neural Motifs: Scene Graph Parsing with Global Context (2017, neutral): “Much of the context we derive could be interpreted as commonsense priors, which have commonly been extracted using auxiliary means [59, 39, 5, 49, 55].”
Semantically-enriched 3D models for common-sense knowledge (2015, background): “• Where do I look for a desk lamp?”
Building a Large-scale Multimodal Knowledge Base System for Answering Visual Queries (2015, methodology): “Our paper is particularly inspired by these two works [7, 59], but focuses on addressing the following two key challenges.”
Representation Learning for Visual-Relational Knowledge Graphs (2017, background): “Recent years have witnessed a surge in reasoning about human-object, object-object, and object-attribute relationships [30,31,32,33,20,34,35,36].”
Predicting Motivations of Actions by Leveraging Text (2014, background): “Commonsense Knowledge: There are promising efforts in progress to acquire commonsense sense for use in computer vision tasks [43, 6, 7, 10, 42].”
3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera (2019, methodology): “[50] used a knowledge base representation for the task of object affordance reasoning that places edges between different nodes of objects, attributes, and affordances.”
Leveraging Visual Question Answering for Image-Caption Ranking (2016, background): “Recently there has been a surge of interest in visual reasoning tasks that require high-level reasoning such as physical reason-4 ing [34,35], future prediction [36,37,38], object affordance prediction [39] and textual tasks that require visual knowledge [40,41,42].”
Semantic Image Interpretation - Integration of Numerical Data and Logical Knowledge for Cognitive Vision (2018, unknown): “In [113, 73], visual features are combined with knowledge gathered from datasets, web resources or annotators, about object labels, properties (for example, shape, colour, size) and affordances, using a Markov Logic Network (MLN) [84].”
Situation Recognition: Visual Semantic Role Labeling for Image Understanding (2016, background): “Recent work has also explored other areas of cross pollination, including video recognition [20], cross modal mappings [42, 29, 16], coreference [8, 28], and affordances [52].”
Verb Physics: Relative Physical Knowledge of Actions and Objects (2017, background): “in recent work that requires visual reasoning and entailment (Izadinia et al., 2015; Zhu et al., 2014).”