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Found 21 papers, 7 papers with code
Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction
To bridge that gap, this work proposes to model two concurrent mechanisms that jointly control human motion: the Persistent process that runs continually on the global scale, and the Transient sub-processes that operate intermittently on the local context of the human while interacting with objects.
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.
Persistent-Transient Duality in Human Behavior Modeling
We propose to model the persistent-transient duality in human behavior using a parent-child multi-channel neural network, which features a parent persistent channel that manages the global dynamics and children transient channels that are initiated and terminated on-demand to handle detailed interactive actions.
A Field Guide to Scientific XAI: Transparent and Interpretable Deep Learning for Bioinformatics Research
This article is a field guide to transparent model design.
Neural Latent Traversal with Semantic Constraints
Whilst Generative Adversarial Networks (GANs) generate visually appealing high resolution images, the latent representations (or codes) of these models do not allow controllable changes on the semantic attributes of the generated images.
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.
A Spatio-temporal Attention-based Model for Infant Movement Assessment from Videos
The absence or abnormality of fidgety movements of joints or limbs is strongly indicative of cerebral palsy in infants.
Object-Centric Representation Learning for Video Question Answering
Video question answering (Video QA) presents a powerful testbed for human-like intelligent behaviors.
Learning Asynchronous and Sparse Human-Object Interaction in Videos
Their interactions are sparse in time hence more faithful to the true underlying nature and more robust in inference and learning.
The Three Ghosts of Medical AI: Can the Black-Box Present Deliver?
Our title alludes to the three Christmas ghosts encountered by Ebenezer Scrooge in \textit{A Christmas Carol}, who guide Ebenezer through the past, present, and future of Christmas holiday events.
Goal-driven Long-Term Trajectory Prediction
We design Goal-driven Trajectory Prediction model - a dual-channel neural network that realizes such intuition.
Hierarchical Conditional Relation Networks for Multimodal Video Question Answering
Video QA challenges modelers in multiple fronts.
Learning to Abstract and Predict Human Actions
We propose Hierarchical Encoder-Refresher-Anticipator, a multi-level neural machine that can learn the structure of human activities by observing a partial hierarchy of events and roll-out such structure into a future prediction in multiple levels of abstraction.
Trust and Medical AI: The challenges we face and the expertise needed to overcome them
These consequences could erode public trust in AI, which could in turn undermine trust in our healthcare institutions.
Scalable Backdoor Detection in Neural Networks
Recently, it has been shown that deep learning models are vulnerable to Trojan attacks, where an attacker can install a backdoor during training time to make the resultant model misidentify samples contaminated with a small trigger patch.
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.
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection
At the test stage, the learned memory will be fixed, and the reconstruction is obtained from a few selected memory records of the normal data.
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos
Appearance features have been widely used in video anomaly detection even though they contain complex entangled factors.
Ranked #6 on
Video Anomaly Detection
on HR-ShanghaiTech
