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Found 12 papers, 4 papers with code
Deep Neural Networks for Visual Reasoning
Visual perception and language understanding are - fundamental components of human intelligence, enabling them to understand and reason about objects and their interactions.
Video Dialog as Conversation about Objects Living in Space-Time
To tackle these challenges we present a new object-centric framework for video dialog that supports neural reasoning dubbed COST - which stands for Conversation about Objects in Space-Time.
Guiding Visual Question Answering with Attention Priors
This grounding guides the attention mechanism inside VQA models through a duality of mechanisms: pre-training attention weight calculation and directly guiding the weights at inference time on a case-by-case basis.
Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering
Toward reaching this goal we propose an object-oriented reasoning approach in that video is abstracted as a dynamic stream of interacting objects.
Object-Centric Representation Learning for Video Question Answering
Video question answering (Video QA) presents a powerful testbed for human-like intelligent behaviors.
Hierarchical Conditional Relation Networks for Multimodal Video Question Answering
Video QA challenges modelers in multiple fronts.
GEFA: Early Fusion Approach in Drug-Target Affinity Prediction
In addition, previous DTA methods learn protein representation solely based on a small number of protein sequences in DTA datasets while neglecting the use of proteins outside of the DTA datasets.
Dynamic Language Binding in Relational Visual Reasoning
We present Language-binding Object Graph Network, the first neural reasoning method with dynamic relational structures across both visual and textual domains with applications in visual question answering.
Hierarchical Conditional Relation Networks for Video Question Answering
Video question answering (VideoQA) is challenging as it requires modeling capacity to distill dynamic visual artifacts and distant relations and to associate them with linguistic concepts.
Ranked #3 on
Audio-Visual Question Answering (AVQA)
on AVQA
Audio-Visual Question Answering (AVQA)
Question Answering
+4
Neural Reasoning, Fast and Slow, for Video Question Answering
While recent advances in lingual and visual question answering have enabled sophisticated representations and neural reasoning mechanisms, major challenges in Video QA remain on dynamic grounding of concepts, relations and actions to support the reasoning process.
Deep Learning Based Multi-modal Addressee Recognition in Visual Scenes with Utterances
With the widespread use of intelligent systems, such as smart speakers, addressee recognition has become a concern in human-computer interaction, as more and more people expect such systems to understand complicated social scenes, including those outdoors, in cafeterias, and hospitals.
A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition
This paper presents a new framework for human action recognition from a 3D skeleton sequence.
Ranked #99 on
Skeleton Based Action Recognition
on NTU RGB+D
