Search Results for author: Asim Kadav

Found 20 papers, 8 papers with code

SplitBrain: Hybrid Data and Model Parallel Deep Learning

no code implementations31 Dec 2021 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 huge datasets.

Dual Projection Generative Adversarial Networks for Conditional Image Generation

1 code implementation ICCV 2021 Ligong Han, Martin Renqiang Min, Anastasis Stathopoulos, Yu Tian, Ruijiang Gao, Asim Kadav, Dimitris Metaxas

We then propose an improved cGAN model with Auxiliary Classification that directly aligns the fake and real conditionals $P(\text{class}|\text{image})$ by minimizing their $f$-divergence.

Conditional Image Generation

S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation

no code implementations CVPR 2020 Yizhe Zhu, Martin Renqiang Min, Asim Kadav, Hans Peter Graf

We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e. g., videos and audios) under self-supervision.


15 Keypoints Is All You Need

no code implementations CVPR 2020 Michael Snower, Asim Kadav, Farley Lai, Hans Peter Graf

Keypoints are tracked using our Pose Entailment method, in which, first, a pair of pose estimates is sampled from different frames in a video and tokenized.

Binary Classification Optical Flow Estimation +2

Contextual Grounding of Natural Language Entities in Images

1 code implementation5 Nov 2019 Farley Lai, Ning Xie, Derek Doran, Asim Kadav

Next, the model learns the contextual representations of the text tokens and image objects through layers of high-order interaction respectively.

Language Modelling Masked Language Modeling

Tripping through time: Efficient Localization of Activities in Videos

no code implementations22 Apr 2019 Meera Hahn, Asim Kadav, James M. Rehg, Hans Peter Graf

Localizing moments in untrimmed videos via language queries is a new and interesting task that requires the ability to accurately ground language into video.

Visual Entailment: A Novel Task for Fine-Grained Image Understanding

1 code implementation20 Jan 2019 Ning Xie, Farley Lai, Derek Doran, Asim Kadav

We evaluate various existing VQA baselines and build a model called Explainable Visual Entailment (EVE) system to address the VE task.

Question Answering Sentence +3

Visual Entailment Task for Visually-Grounded Language Learning

1 code implementation26 Nov 2018 Ning Xie, Farley Lai, Derek Doran, Asim Kadav

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 natural language sentence as in TE tasks.

Grounded language learning Question Answering +3

Teaching Syntax by Adversarial Distraction

no code implementations WS 2018 Ju-ho Kim, Christopher Malon, Asim Kadav

Existing entailment datasets mainly pose problems which can be answered without attention to grammar or word order.

General Classification

Adaptive Memory Networks

no code implementations ICLR 2018 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 Answering (QA) tasks.

Decision Making Question Answering

Grounded Objects and Interactions for Video Captioning

no code implementations16 Nov 2017 Chih-Yao Ma, Asim Kadav, Iain Melvin, Zsolt Kira, Ghassan AlRegib, Hans Peter Graf

We address the problem of video captioning by grounding language generation on object interactions in the video.

Object Scene Understanding +3

ASAP: Asynchronous Approximate Data-Parallel Computation

no code implementations27 Dec 2016 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.

BIG-bench Machine Learning

A Context-aware Attention Network for Interactive Question Answering

no code implementations22 Dec 2016 Huayu Li, Martin Renqiang Min, Yong Ge, Asim Kadav

Employing these attention mechanisms, our model accurately understands when it can output an answer or when it requires generating a supplementary question for additional input depending on different contexts.

Decoder Question Answering +1

Pruning Filters for Efficient ConvNets

21 code implementations31 Aug 2016 Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, Hans Peter Graf

However, magnitude-based pruning of weights reduces a significant number of parameters from the fully connected layers and may not adequately reduce the computation costs in the convolutional layers due to irregular sparsity in the pruned networks.

Image Classification Network Pruning

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