Search Results for author: James J. Clark

Found 17 papers, 5 papers with code

Efficient Fine-Tuning of BERT Models on the Edge

no code implementations3 May 2022 Danilo Vucetic, Mohammadreza Tayaranian, Maryam Ziaeefard, James J. Clark, Brett H. Meyer, Warren J. Gross

FAR reduces fine-tuning time on the DistilBERT model and CoLA dataset by 30%, and time spent on memory operations by 47%.

Natural Language Processing

Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes

1 code implementation CVPR 2022 Samrudhdhi B. Rangrej, Chetan L. Srinidhi, James J. Clark

Most hard attention models initially observe a complete scene to locate and sense informative glimpses, and predict class-label of a scene based on glimpses.

Hard Attention

Standard Deviation-Based Quantization for Deep Neural Networks

no code implementations24 Feb 2022 Amir Ardakani, Arash Ardakani, Brett Meyer, James J. Clark, Warren J. Gross

Quantization of deep neural networks is a promising approach that reduces the inference cost, making it feasible to run deep networks on resource-restricted devices.

Quantization

A Probabilistic Hard Attention Model For Sequentially Observed Scenes

1 code implementation15 Nov 2021 Samrudhdhi B. Rangrej, James J. Clark

A visual hard attention model actively selects and observes a sequence of subregions in an image to make a prediction.

Hard Attention

Kronecker Decomposition for GPT Compression

no code implementations ACL 2022 Ali Edalati, Marzieh Tahaei, Ahmad Rashid, Vahid Partovi Nia, James J. Clark, Mehdi Rezagholizadeh

GPT is an auto-regressive Transformer-based pre-trained language model which has attracted a lot of attention in the natural language processing (NLP) domain due to its state-of-the-art performance in several downstream tasks.

Knowledge Distillation Language Modelling +2

Visual Attention in Imaginative Agents

no code implementations1 Apr 2021 Samrudhdhi B. Rangrej, James J. Clark

The next fixation is planned using uncertainty in the content of the imagined scenes.

HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images

1 code implementation30 Mar 2021 Saverio Vadacchino, Raghav Mehta, Nazanin Mohammadi Sepahvand, Brennan Nichyporuk, James J. Clark, Tal Arbel

The proposed network is trained and tested on the BraTS 2019 brain tumour segmentation challenge dataset, where it achieves performance improvements in the ranges of 16% - 26% over (a) recent modality-agnostic segmentation methods (U-HeMIS, U-HVED), (b) KD-Net adapted to this problem, (c) the pre-trained student network and (d) a non-hierarchical version of the network (AD-Net), in terms of Dice scores for enhancing tumour (ET).

Knowledge Distillation

Achieving Explainability in a Visual Hard Attention Model through Content Prediction

no code implementations1 Jan 2021 Samrudhdhi Bharatkumar Rangrej, James J. Clark

Unlike in the deep convolution network, in hard attention it is explainable which regions of the image contributed to the prediction.

Hard Attention Image Classification

Grow-Push-Prune: aligning deep discriminants for effective structural network compression

no code implementations29 Sep 2020 Qing Tian, Tal Arbel, James J. Clark

We also show that our grown Inception nets (without hard-coded dimension alignment) clearly outperform residual nets of similar complexities.

Instance Segmentation based Semantic Matting for Compositing Applications

1 code implementation10 Apr 2019 Guanqing Hu, James J. Clark

In order to achieve automatic compositing in natural scenes, we propose a fully automated method that integrates instance segmentation and image matting processes to generate high-quality semantic mattes that can be used for image editing task.

Instance Segmentation Semantic Image Matting +1

Going From Image to Video Saliency: Augmenting Image Salience With Dynamic Attentional Push

no code implementations CVPR 2018 Siavash Gorji, James J. Clark

We evaluate our model by comparing the performance of several augmented static saliency models with state-of-the-art in spatiotemporal saliency on three largest dynamic eye tracking datasets, HOLLYWOOD2, UCF-Sport and DIEM.

Task dependent Deep LDA pruning of neural networks

1 code implementation21 Mar 2018 Qing Tian, Tal Arbel, James J. Clark

Moreover, we examine our approach's potential in network architecture search for specific tasks and analyze the influence of our pruning on model robustness to noises and adversarial attacks.

WAYLA - Generating Images from Eye Movements

no code implementations21 Nov 2017 Bingqing Yu, James J. Clark

The WAYLA approach is based on the Conditional Generative Adversarial Network (Conditional GAN) image-to-image translation technique of Isola et al. We consider two specific applications - the first, of reconstructing newspaper images from gaze heat maps, and the second, of detailed reconstruction of images containing only text.

Image Reconstruction Image-to-Image Translation +1

Personalization of Saliency Estimation

no code implementations21 Nov 2017 Bingqing Yu, James J. Clark

The discriminator also has the observer label as an input, which contributes to the personalization ability of our approach.

Saliency Prediction

Attentional Push: A Deep Convolutional Network for Augmenting Image Salience With Shared Attention Modeling in Social Scenes

no code implementations CVPR 2017 Siavash Gorji, James J. Clark

The Attentional Push CNN is then fine-tuned along with the augmented saliency CNN to minimize the Euclidean distance between the augmented saliency and ground truth fixations using an eye-tracking dataset, annotated with the head and the gaze location of the scene actors.

Transfer Learning

Efficient Gender Classification Using a Deep LDA-Pruned Net

no code implementations20 Apr 2017 Qing Tian, Tal Arbel, James J. Clark

Many real-time tasks, such as human-computer interaction, require fast and efficient facial gender classification.

Classification General Classification

Attentional Push: Augmenting Salience with Shared Attention Modeling

no code implementations1 Sep 2016 Siavash Gorji, James J. Clark

We present a novel visual attention tracking technique based on Shared Attention modeling.

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