Search Results for author:
Found 46 papers, 16 papers with code
Learning to Generalize: Meta-Learning for Domain Generalization
We propose a novel {meta-learning} method for domain generalization.
Ranked #114 on
Domain Generalization
on PACS
Deeper, Broader and Artier Domain Generalization
In this paper, we make two main contributions: Firstly, we build upon the favorable domain shift-robust properties of deep learning methods, and develop a low-rank parameterized CNN model for end-to-end DG learning.
Ranked #117 on
Domain Generalization
on PACS
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
To this end, we explore few-shot learning from the perspective of neural network architecture, as well as a three stage pipeline of network updates under different data supplies, where unsupervised external data is considered for pre-training, base categories are used to simulate few-shot tasks for meta-training, and the scarcely labelled data of an novel task is taken for fine-tuning.
Ranked #1 on
Few-Shot Image Classification
on Meta-Dataset
Valley: Video Assistant with Large Language model Enhanced abilitY
Large language models (LLMs), with their remarkable conversational capabilities, have demonstrated impressive performance across various applications and have emerged as formidable AI assistants.
Episodic Training for Domain Generalization
In this paper, we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime.
Ranked #76 on
Domain Generalization
on PACS
Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style
This paper studies the problem of zero-short sketch-based image retrieval (ZS-SBIR), however with two significant differentiators to prior art (i) we tackle all variants (inter-category, intra-category, and cross datasets) of ZS-SBIR with just one network (``everything''), and (ii) we would really like to understand how this sketch-photo matching operates (``explainable'').
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn
Meta-learning and other approaches to few-shot learning are widely studied for image recognition, and are increasingly applied to other vision tasks such as pose estimation and dense prediction.
STSC-SNN: Spatio-Temporal Synaptic Connection with Temporal Convolution and Attention for Spiking Neural Networks
We show that endowing synaptic models with temporal dependencies can improve the performance of SNNs on classification tasks.
Ranked #3 on
Audio Classification
on SHD
Dynamic Instance Domain Adaptation
In this paper, we address both single-source and multi-source UDA from a completely different perspective, which is to view each instance as a fine domain.
A Channel Coding Benchmark for Meta-Learning
We use our MetaCC benchmark to study several aspects of meta-learning, including the impact of task distribution breadth and shift, which can be controlled in the coding problem.
Neural Fine-Tuning Search for Few-Shot Learning
In few-shot recognition, a classifier that has been trained on one set of classes is required to rapidly adapt and generalize to a disjoint, novel set of classes.
Weight-Covariance Alignment for Adversarially Robust Neural Networks
Stochastic Neural Networks (SNNs) that inject noise into their hidden layers have recently been shown to achieve strong robustness against adversarial attacks.
Attacking Adversarial Defences by Smoothing the Loss Landscape
This paper investigates a family of methods for defending against adversarial attacks that owe part of their success to creating a noisy, discontinuous, or otherwise rugged loss landscape that adversaries find difficult to navigate.
Multi-task Pre-training Language Model for Semantic Network Completion
To demonstrate the effectiveness of our method, we conduct extensive experiments on three widely-used datasets, WN18RR, FB15k-237, and UMLS.
Ranked #1 on
Link Prediction
on UMLS
Robust Target Training for Multi-Source Domain Adaptation
Given any existing fully-trained one-step MSDA model, BORT$^2$ turns it to a labeling function to generate pseudo-labels for the target data and trains a target model using pseudo-labeled target data only.
Sketch-a-Classifier: Sketch-based Photo Classifier Generation
Contemporary deep learning techniques have made image recognition a reasonably reliable technology.
A Large-scale Distributed Video Parsing and Evaluation Platform
Visual surveillance systems have become one of the largest data sources of Big Visual Data in real world.
Deep Factorised Inverse-Sketching
Instead there is a fundamental process of abstraction and iconic rendering, where overall geometry is warped and salient details are selectively included.
Large-Scale Pedestrian Retrieval Competition
The Large-Scale Pedestrian Retrieval Competition (LSPRC) mainly focuses on person retrieval which is an important end application in intelligent vision system of surveillance.
Sequential Learning for Domain Generalization
In DG this means encountering a sequence of domains and at each step training to maximise performance on the next domain.
Ranked #76 on
Domain Generalization
on PACS
Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation
Therefore we propose an online shortest-path meta-learning framework that is both computationally tractable and practically effective for improving DA performance.
Meta-Learning
Multi-Source Unsupervised Domain Adaptation
+2
Angstrom-wide conductive channels in black phosphorus by Cu intercalation
Intercalation is an effective method to improve and modulate properties of two-dimensional materials.
Materials Science
Learning Domain Invariant Representations for Generalizable Person Re-Identification
Generalizable person Re-Identification (ReID) has attracted growing attention in recent computer vision community.
A Simple Feature Augmentation for Domain Generalization
The topical domain generalization (DG) problem asks trained models to perform well on an unseen target domain with different data statistics from the source training domains.
Simple and Effective Stochastic Neural Networks
Stochastic neural networks (SNNs) are currently topical, with several paradigms being actively investigated including dropout, Bayesian neural networks, variational information bottleneck (VIB) and noise regularized learning.
Finding lost DG: Explaining domain generalization via model complexity
However much of the work in general purpose DG is heuristically motivated, as the DG problem is hard to model formally; and recent evaluations have cast doubt on existing methods' practical efficacy -- in particular compared to a well tuned empirical risk minimisation baseline.
AMS_ADRN at SemEval-2022 Task 5: A Suitable Image-text Multimodal Joint Modeling Method for Multi-task Misogyny Identification
Women are influential online, especially in image-based social media such as Twitter and Instagram.
Fisher SAM: Information Geometry and Sharpness Aware Minimisation
Recent sharpness-aware minimisation (SAM) is known to find flat minima which is beneficial for better generalisation with improved robustness.
Feed-Forward Latent Domain Adaptation
Recognizing that device's data are likely to come from multiple latent domains that include a mixture of unlabelled domain-relevant and domain-irrelevant examples, we focus on the comparatively under-studied problem of latent domain adaptation.
A Simple Test-Time Method for Out-of-Distribution Detection
In contrast, this paper proposes a simple Test-time Linear Training (ETLT) method for OOD detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Prediction Calibration for Generalized Few-shot Semantic Segmentation
To ensure that the fused scores are not biased to either the base or novel classes, a new Transformer-based calibration module is introduced.
Generalized Few-Shot Semantic Segmentation
Semantic Segmentation
Learning to Augment via Implicit Differentiation for Domain Generalization
Machine learning models are intrinsically vulnerable to domain shift between training and testing data, resulting in poor performance in novel domains.
Federated Learning for Inference at Anytime and Anywhere
Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities.
An Electromagnetic-Information-Theory Based Model for Efficient Characterization of MIMO Systems in Complex Space
In this manuscript, an Electromagnetic-Information-Theory (EMIT) based model is developed for efficient characterization of MIMO systems in complex space.
Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach
We tackle the domain generalisation (DG) problem by posing it as a domain adaptation (DA) task where we adversarially synthesise the worst-case target domain and adapt a model to that worst-case domain, thereby improving the model's robustness.
Generative Model Based Noise Robust Training for Unsupervised Domain Adaptation
D-CFA minimizes the domain gap by augmenting the source data with distribution-sampled target features, and trains a noise-robust discriminative classifier by using target domain knowledge from the generative models.
Co-Design for Spectral Coexistence between RIS-aided MIMO Radar and MIMO Communication Systems
Reconfigurable intelligent surface (RIS) refers to a signal reflection surface containing a large number of low-cost passive reflecting elements.
Multi-Spectrally Constrained Low-PAPR Waveform Optimization for MIMO Radar Space-Time Adaptive Processing
This paper focuses on the joint design of transmit waveforms and receive filters for airborne multiple-input-multiple-output (MIMO) radar systems in spectrally crowded environments.
Label Calibration for Semantic Segmentation Under Domain Shift
Performance of a pre-trained semantic segmentation model is likely to substantially decrease on data from a new domain.
Feed-Forward Source-Free Domain Adaptation via Class Prototypes
Source-free domain adaptation has become popular because of its practical usefulness and no need to access source data.
Better Practices for Domain Adaptation
However, the domain shift scenario raises a second more subtle challenge: the difficulty of performing hyperparameter optimisation (HPO) for these adaptation algorithms without access to a labelled validation set.
Quality Diversity for Visual Pre-Training
Notably, the augmentations used in both supervised and self-supervised training lead to features with high invariance to spatial and appearance transformations.
Sketch-based Video Object Segmentation: Benchmark and Analysis
Reference-based video object segmentation is an emerging topic which aims to segment the corresponding target object in each video frame referred by a given reference, such as a language expression or a photo mask.
Extraction of n = 0 pick-up by locked mode detectors based on neural networks in J-TEXT
A new method to extract this pick-up has been developed by predicting the n = 0 pick-up brn=0 by the LM detectors based on Neural Networks (NNs) in J-TEXT.
Ground-to-UAV 140 GHz channel measurement and modeling
Employing experimental measurements through an unmodulated channel setup and a geometry-based stochastic model (GBSM) that integrates three-dimensional positional coordinates and beamwidth, this work evaluates the impact of UAV dynamic movements and antenna orientation on channel performance.
