682 citations · 978 across the 20 of their papers we have counts for
20 papers
Learning Higher-order Object Interactions for Keypoint-based Video Understanding
Yi Huang, Asim Kadav, Farley Lai +2
Action recognition is an important problem that requires identifying actions in video by learning complex interactions across scene actors and objects. However, modern deep-learnin…
Self-supervised Video Representation Learning with Cascade Positive Retrieval
Cheng-En Wu, Farley Lai, Yu Hen Hu +1
Self-supervised video representation learning has been shown to effectively improve downstream tasks such as video retrieval and action recognition. In this paper, we present the C…
SplitBrain: Hybrid Data and Model Parallel Deep Learning
Farley Lai, Asim Kadav, Erik Kruus
The recent success of deep learning applications has coincided with those widely available powerful computational resources for training sophisticated machine learning models with…
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality
Honglu Zhou, Asim Kadav, Aviv Shamsian +6
Group Activity Recognition detects the activity collectively performed by a group of actors, which requires compositional reasoning of actors and objects. We approach the task by m…
Dual Projection Generative Adversarial Networks for Conditional Image Generation
Ligong Han, Martin Renqiang Min, Anastasis Stathopoulos +4
Conditional Generative Adversarial Networks (cGANs) extend the standard unconditional GAN framework to learning joint data-label distributions from samples, and have been establish…
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning
Honglu Zhou, Asim Kadav, Farley Lai +4
This paper considers the problem of spatiotemporal object-centric reasoning in videos. Central to our approach is the notion of object permanence, i.e., the ability to reason about…
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
Yizhe Zhu, Martin Renqiang Min, Asim Kadav +1
We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e.g., videos and audios) under self-supervision. Specifically, we exploit…
15 Keypoints Is All You Need
Michael Snower, Asim Kadav, Farley Lai +1
Pose tracking is an important problem that requires identifying unique human pose-instances and matching them temporally across different frames of a video. However, existing pose…
Contextual Grounding of Natural Language Entities in Images
Farley Lai, Ning Xie, Derek Doran +1
In this paper, we introduce a contextual grounding approach that captures the context in corresponding text entities and image regions to improve the grounding accuracy. Specifical…
Tripping through time: Efficient Localization of Activities in Videos
Meera Hahn, Asim Kadav, James M. Rehg +1
Localizing moments in untrimmed videos via language queries is a new and interesting task that requires the ability to accurately ground language into video. Previous works have ap…
Visual Entailment: A Novel Task for Fine-Grained Image Understanding
Ning Xie, Farley Lai, Derek Doran +1
Existing visual reasoning datasets such as Visual Question Answering (VQA), often suffer from biases conditioned on the question, image or answer distributions. The recently propos…
Visual Entailment Task for Visually-Grounded Language Learning
Ning Xie, Farley Lai, Derek Doran +1
We introduce a new inference task - Visual Entailment (VE) - which differs from traditional Textual Entailment (TE) tasks whereby a premise is defined by an image, rather than a na…
Teaching Syntax by Adversarial Distraction
Juho Kim, Christopher Malon, Asim Kadav
Existing entailment datasets mainly pose problems which can be answered without attention to grammar or word order. Learning syntax requires comparing examples where different gram…
Adaptive Memory Networks
Daniel Li, Asim Kadav
We present Adaptive Memory Networks (AMN) that processes input-question pairs to dynamically construct a network architecture optimized for lower inference times for Question Answe…
DeepConfig: Automating Data Center Network Topologies Management with Machine Learning
Christopher Streiffer, Huan Chen, Theophilus Benson +1
In recent years, many techniques have been developed to improve the performance and efficiency of data center networks. While these techniques provide high accuracy, they are often…
Grounded Objects and Interactions for Video Captioning
Chih-Yao Ma, Asim Kadav, Iain Melvin +3
We address the problem of video captioning by grounding language generation on object interactions in the video. Existing work mostly focuses on overall scene understanding with of…
Attend and Interact: Higher-Order Object Interactions for Video Understanding
Chih-Yao Ma, Asim Kadav, Iain Melvin +3
Human actions often involve complex interactions across several inter-related objects in the scene. However, existing approaches to fine-grained video understanding or visual relat…
ASAP: Asynchronous Approximate Data-Parallel Computation
Asim Kadav, Erik Kruus
Emerging workloads, such as graph processing and machine learning are approximate because of the scale of data involved and the stochastic nature of the underlying algorithms. Thes…
A Context-aware Attention Network for Interactive Question Answering
Huayu Li, Martin Renqiang Min, Yong Ge +1
Neural network based sequence-to-sequence models in an encoder-decoder framework have been successfully applied to solve Question Answering (QA) problems, predicting answers from s…
Pruning Filters for Efficient ConvNets
Hao Li, Asim Kadav, Igor Durdanovic +2
The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Recent efforts toward reducing these overheads…