143 citations · 222 across the 15 of their papers we have counts for
22 papers
Finding the Trigger: Causal Abductive Reasoning on Video Events
Thao Minh Le, Vuong Le, Kien Do +3
This paper introduces a new problem, Causal Abductive Reasoning on Video Events (CARVE), which involves identifying causal relationships between events in a video and generating hy…
SADL: An Effective In-Context Learning Method for Compositional Visual QA
Long Hoang Dang, Thao Minh Le, Vuong Le +2
Large vision-language models (LVLMs) offer a novel capability for performing in-context learning (ICL) in Visual QA. When prompted with a few demonstrations of image-question-answe…
Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction
Hung Tran, Vuong Le, Svetha Venkatesh +1
Humans are highly adaptable, swiftly switching between different modes to progressively handle different tasks, situations and contexts. In Human-object interaction (HOI) activitie…
Video Dialog as Conversation about Objects Living in Space-Time
Hoang-Anh Pham, Thao Minh Le, Vuong Le +2
It would be a technological feat to be able to create a system that can hold a meaningful conversation with humans about what they watch. A setup toward that goal is presented as a…
Guiding Visual Question Answering with Attention Priors
Thao Minh Le, Vuong Le, Sunil Gupta +2
The current success of modern visual reasoning systems is arguably attributed to cross-modality attention mechanisms. However, in deliberative reasoning such as in VQA, attention i…
Persistent-Transient Duality in Human Behavior Modeling
Hung Tran, Vuong Le, Svetha Venkatesh +1
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 th…
A Field Guide to Scientific XAI: Transparent and Interpretable Deep Learning for Bioinformatics Research
Thomas P Quinn, Sunil Gupta, Svetha Venkatesh +1
Deep learning has become popular because of its potential to achieve high accuracy in prediction tasks. However, accuracy is not always the only goal of statistical modelling, espe…
Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering
Long Hoang Dang, Thao Minh Le, Vuong Le +1
Video Question Answering (Video QA) is a powerful testbed to develop new AI capabilities. This task necessitates learning to reason about objects, relations, and events across visu…
A Spatio-temporal Attention-based Model for Infant Movement Assessment from Videos
Binh Nguyen-Thai, Vuong Le, Catherine Morgan +3
The absence or abnormality of fidgety movements of joints or limbs is strongly indicative of cerebral palsy in infants. Developing computer-based methods for assessing infant movem…
Object-Centric Representation Learning for Video Question Answering
Long Hoang Dang, Thao Minh Le, Vuong Le +1
Video question answering (Video QA) presents a powerful testbed for human-like intelligent behaviors. The task demands new capabilities to integrate video processing, language unde…
Learning Asynchronous and Sparse Human-Object Interaction in Videos
Romero Morais, Vuong Le, Svetha Venkatesh +1
Human activities can be learned from video. With effective modeling it is possible to discover not only the action labels but also the temporal structures of the activities such as…
The Three Ghosts of Medical AI: Can the Black-Box Present Deliver?
Thomas P. Quinn, Stephan Jacobs, Manisha Senadeera +2
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…
Goal-driven Long-Term Trajectory Prediction
Hung Tran, Vuong Le, Truyen Tran
The prediction of humans' short-term trajectories has advanced significantly with the use of powerful sequential modeling and rich environment feature extraction. However, long-ter…
Hierarchical Conditional Relation Networks for Multimodal Video Question Answering
Thao Minh Le, Vuong Le, Svetha Venkatesh +1
Video QA challenges modelers in multiple fronts. Modeling video necessitates building not only spatio-temporal models for the dynamic visual channel but also multimodal structures…
Learning to Abstract and Predict Human Actions
Romero Morais, Vuong Le, Truyen Tran +1
Human activities are naturally structured as hierarchies unrolled over time. For action prediction, temporal relations in event sequences are widely exploited by current methods wh…
Trust and Medical AI: The challenges we face and the expertise needed to overcome them
Thomas P. Quinn, Manisha Senadeera, Stephan Jacobs +2
Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. However, failures of medical AI could have serious consequences for both clinical outcomes…
Scalable Backdoor Detection in Neural Networks
Haripriya Harikumar, Vuong Le, Santu Rana +3
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 mi…
Dynamic Language Binding in Relational Visual Reasoning
Thao Minh Le, Vuong Le, Svetha Venkatesh +1
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 v…
Hierarchical Conditional Relation Networks for Video Question Answering
Thao Minh Le, Vuong Le, Svetha Venkatesh +1
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 c…
Neural Reasoning, Fast and Slow, for Video Question Answering
Thao Minh Le, Vuong Le, Svetha Venkatesh +1
What does it take to design a machine that learns to answer natural questions about a video? A Video QA system must simultaneously understand language, represent visual content ove…
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos
Romero Morais, Vuong Le, Truyen Tran +3
Appearance features have been widely used in video anomaly detection even though they contain complex entangled factors. We propose a new method to model the normal patterns of hum…
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection
Dong Gong, Lingqiao Liu, Vuong Le +4
Deep autoencoder has been extensively used for anomaly detection. Training on the normal data, the autoencoder is expected to produce higher reconstruction error for the abnormal i…