Search Results for author: Rick Siow Mong Goh

Found 38 papers, 15 papers with code

How Interpretable are Reasoning Explanations from Prompting Large Language Models?

2 code implementations19 Feb 2024 Wei Jie Yeo, Ranjan Satapathy, Rick Siow Mong Goh, Erik Cambria

We present a comprehensive and multifaceted evaluation of interpretability, examining not only faithfulness but also robustness and utility across multiple commonsense reasoning benchmarks.

Prompt Engineering

Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices

no code implementations17 Feb 2024 Hongye Zeng, Ke Zou, Zhihao Chen, Yuchong Gao, Hongbo Chen, Haibin Zhang, Kang Zhou, Meng Wang, Rick Siow Mong Goh, Yong liu, Chang Jiang, Rui Zheng, Huazhu Fu

Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data.

Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning

1 code implementation3 Jan 2024 Zitong Huang, Ze Chen, Zhixing Chen, Erjin Zhou, Xinxing Xu, Rick Siow Mong Goh, Yong liu, WangMeng Zuo, ChunMei Feng

When progressing to a new session, pseudo-features are sampled from old-class distributions combined with training images of the current session to optimize the prompt, thus enabling the model to learn new knowledge while retaining old knowledge.

Few-Shot Class-Incremental Learning Incremental Learning +1

VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering

1 code implementation19 Dec 2023 Chun-Mei Feng, Yang Bai, Tao Luo, Zhen Li, Salman Khan, WangMeng Zuo, Xinxing Xu, Rick Siow Mong Goh, Yong liu

By feeding the retrieved image and question to the VQA model, one can find the images inconsistent with relative caption when the answer by VQA is inconsistent with the answer in the QA pair.

Image Retrieval Question Answering +2

Sentence-level Prompts Benefit Composed Image Retrieval

1 code implementation9 Oct 2023 Yang Bai, Xinxing Xu, Yong liu, Salman Khan, Fahad Khan, WangMeng Zuo, Rick Siow Mong Goh, Chun-Mei Feng

Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption.

Attribute Composed Image Retrieval (CoIR) +2

Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction

no code implementations20 Aug 2023 Yunlu Yan, Chun-Mei Feng, Yuexiang Li, Rick Siow Mong Goh, Lei Zhu

In this paper, we propose a novel communication-efficient federated learning framework, namely Fed-PMG, to address the missing modality challenge in federated multi-modal MRI reconstruction.

Federated Learning MRI Reconstruction

Rethinking Client Drift in Federated Learning: A Logit Perspective

no code implementations20 Aug 2023 Yunlu Yan, Chun-Mei Feng, Mang Ye, WangMeng Zuo, Ping Li, Rick Siow Mong Goh, Lei Zhu, C. L. Philip Chen

Concretely, FedCSD introduces a class prototype similarity distillation to align the local logits with the refined global logits that are weighted by the similarity between local logits and the global prototype.

Federated Learning

DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs

no code implementations9 May 2023 Myat Thu Linn Aung, Daniel Gerlinghoff, Chuping Qu, Liwei Yang, Tian Huang, Rick Siow Mong Goh, Tao Luo, Weng-Fai Wong

Brain-inspired spiking neural networks (SNNs) replace the multiply-accumulate operations of traditional neural networks by integrate-and-fire neurons, with the goal of achieving greater energy efficiency.

Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging

no code implementations23 Mar 2023 Meng Wang, Lianyu Wang, Xinxing Xu, Ke Zou, Yiming Qian, Rick Siow Mong Goh, Yong liu, Huazhu Fu

Our TWEU employs an evidential deep layer to produce the uncertainty score with the DR staging results for client reliability evaluation.

Federated Learning

Reliable Federated Disentangling Network for Non-IID Domain Feature

no code implementations30 Jan 2023 Meng Wang, Kai Yu, Chun-Mei Feng, Yiming Qian, Ke Zou, Lianyu Wang, Rick Siow Mong Goh, Yong liu, Huazhu Fu

To the best of our knowledge, our proposed RFedDis is the first work to develop an FL approach based on evidential uncertainty combined with feature disentangling, which enhances the performance and reliability of FL in non-IID domain features.

Federated Learning

Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty

3 code implementations1 Jan 2023 Ke Zou, Yidi Chen, Ling Huang, Xuedong Yuan, Xiaojing Shen, Meng Wang, Rick Siow Mong Goh, Yong liu, Huazhu Fu

DEviS not only enhances the calibration and robustness of baseline segmentation accuracy but also provides high-efficiency uncertainty estimation for reliable predictions.

Computational Efficiency Image Segmentation +3

Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images

no code implementations1 Dec 2022 Meng Wang, Kai Yu, Chun-Mei Feng, Ke Zou, Yanyu Xu, Qingquan Meng, Rick Siow Mong Goh, Yong liu, Huazhu Fu

Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed.


Desire Backpropagation: A Lightweight Training Algorithm for Multi-Layer Spiking Neural Networks based on Spike-Timing-Dependent Plasticity

1 code implementation10 Nov 2022 Daniel Gerlinghoff, Tao Luo, Rick Siow Mong Goh, Weng-Fai Wong

Spiking neural networks (SNNs) are a viable alternative to conventional artificial neural networks when resource efficiency and computational complexity are of importance.

Optimizing for In-memory Deep Learning with Emerging Memory Technology

no code implementations1 Dec 2021 Zhehui Wang, Tao Luo, Rick Siow Mong Goh, Wei zhang, Weng-Fai Wong

In-memory deep learning has already demonstrated orders of magnitude higher performance density and energy efficiency.

Efficient Sharpness-aware Minimization for Improved Training of Neural Networks

1 code implementation ICLR 2022 Jiawei Du, Hanshu Yan, Jiashi Feng, Joey Tianyi Zhou, Liangli Zhen, Rick Siow Mong Goh, Vincent Y. F. Tan

Recently, the relation between the sharpness of the loss landscape and the generalization error has been established by Foret et al. (2020), in which the Sharpness Aware Minimizer (SAM) was proposed to mitigate the degradation of the generalization.

Few-Shot Domain Adaptation with Polymorphic Transformers

1 code implementation10 Jul 2021 Shaohua Li, Xiuchao Sui, Jie Fu, Huazhu Fu, Xiangde Luo, Yangqin Feng, Xinxing Xu, Yong liu, Daniel Ting, Rick Siow Mong Goh

Thus, the chance of overfitting the annotations is greatly reduced, and the model can perform robustly on the target domain after being trained on a few annotated images.

Domain Adaptation Segmentation

Automated Deepfake Detection

no code implementations20 Jun 2021 Ping Liu, Yuewei Lin, Yang He, Yunchao Wei, Liangli Zhen, Joey Tianyi Zhou, Rick Siow Mong Goh, Jingen Liu

In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection.

BIG-bench Machine Learning DeepFake Detection +1

Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation

1 code implementation8 Jun 2021 Gabriel Tjio, Ping Liu, Joey Tianyi Zhou, Rick Siow Mong Goh

In this work, we propose an adversarial semantic hallucination approach (ASH), which combines a class-conditioned hallucination module and a semantic segmentation module.

Domain Generalization Hallucination +2

Parallel Attention Network with Sequence Matching for Video Grounding

no code implementations Findings (ACL) 2021 Hao Zhang, Aixin Sun, Wei Jing, Liangli Zhen, Joey Tianyi Zhou, Rick Siow Mong Goh

In this work, we propose a Parallel Attention Network with Sequence matching (SeqPAN) to address the challenges in this task: multi-modal representation learning, and target moment boundary prediction.

Representation Learning Video Grounding

Video Corpus Moment Retrieval with Contrastive Learning

1 code implementation13 May 2021 Hao Zhang, Aixin Sun, Wei Jing, Guoshun Nan, Liangli Zhen, Joey Tianyi Zhou, Rick Siow Mong Goh

We adopt the first approach and introduce two contrastive learning objectives to refine video encoder and text encoder to learn video and text representations separately but with better alignment for VCMR.

Contrastive Learning Moment Retrieval +2

Deep N-ary Error Correcting Output Codes

1 code implementation22 Sep 2020 Hao Zhang, Joey Tianyi Zhou, Tianying Wang, Ivor W. Tsang, Rick Siow Mong Goh

To facilitate the training of N-ary ECOC with deep learning base learners, we further propose three different variants of parameter sharing architectures for deep N-ary ECOC.

Ensemble Learning General Classification +3

EDCompress: Energy-Aware Model Compression for Dataflows

no code implementations8 Jun 2020 Zhehui Wang, Tao Luo, Joey Tianyi Zhou, Rick Siow Mong Goh

EDCompress could also find the optimal dataflow type for specific neural networks in terms of energy consumption, which can guide the deployment of CNN models on hardware systems.

Model Compression

Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning

no code implementations25 May 2020 Renuga Kanagavelu, Zengxiang Li, Juniarto Samsudin, Yechao Yang, Feng Yang, Rick Siow Mong Goh, Mervyn Cheah, Praewpiraya Wiwatphonthana, Khajonpong Akkarajitsakul, Shangguang Wangz

Countries across the globe have been pushing strict regulations on the protection of personal or private data collected.

Distributed, Parallel, and Cluster Computing

Feature Lenses: Plug-and-play Neural Modules for Transformation-Invariant Visual Representations

1 code implementation12 Apr 2020 Shaohua Li, Xiuchao Sui, Jie Fu, Yong liu, Rick Siow Mong Goh

To make CNNs more invariant to transformations, we propose "Feature Lenses", a set of ad-hoc modules that can be easily plugged into a trained model (referred to as the "host model").

Privacy-preserving Weighted Federated Learning within Oracle-Aided MPC Framework

no code implementations17 Mar 2020 Huafei Zhu, Zengxiang Li, Mervyn Cheah, Rick Siow Mong Goh

In the second fold, an oracle-aided MPC solution for computing weighted federated learning is formalized by decoupling the security of federated learning systems from that of underlying multi-party computations.

Cryptography and Security

Multi-Instance Multi-Scale CNN for Medical Image Classification

no code implementations4 Jul 2019 Shaohua Li, Yong liu, Xiuchao Sui, Cheng Chen, Gabriel Tjio, Daniel Shu Wei Ting, Rick Siow Mong Goh

Deep learning for medical image classification faces three major challenges: 1) the number of annotated medical images for training are usually small; 2) regions of interest (ROIs) are relatively small with unclear boundaries in the whole medical images, and may appear in arbitrary positions across the x, y (and also z in 3D images) dimensions.

General Classification Image Classification +2

Multi-Modal Hybrid Deep Neural Network for Speech Enhancement

no code implementations15 Jun 2016 Zhenzhou Wu, Sunil Sivadas, Yong Kiam Tan, Ma Bin, Rick Siow Mong Goh

Enhancement is achieved by learning a nonlinear mapping function from the features of the corrupted speech signal to that of the reference clean speech signal.

Speech Enhancement

Transfer Hashing with Privileged Information

no code implementations13 May 2016 Joey Tianyi Zhou, Xinxing Xu, Sinno Jialin Pan, Ivor W. Tsang, Zheng Qin, Rick Siow Mong Goh

Specifically, we extend the standard learning to hash method, Iterative Quantization (ITQ), in a transfer learning manner, namely ITQ+.

Quantization Transfer Learning

Simple and Efficient Learning using Privileged Information

no code implementations6 Apr 2016 Xinxing Xu, Joey Tianyi Zhou, IvorW. Tsang, Zheng Qin, Rick Siow Mong Goh, Yong liu

The Support Vector Machine using Privileged Information (SVM+) has been proposed to train a classifier to utilize the additional privileged information that is only available in the training phase but not available in the test phase.

Image Categorization

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