Search Results for author:
Found 45 papers, 9 papers with code
UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation
This paper introduces a novel framework for unified incremental few-shot object detection (iFSOD) and instance segmentation (iFSIS) using the Transformer architecture.
Medical AI for Early Detection of Lung Cancer: A Survey
The recent emergence of deep learning has revolutionized medical image analysis, driving substantial advancements in this field.
SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation
Medical imaging segmentation plays a significant role in the automatic recognition and analysis of lesions.
In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model
Inspired by in-context learning in natural language processing (NLP), we propose In-Context Prompt Learning (InCPL) for test-time visual recognition tasks, which empowers a pre-trained vision-language model with labeled examples as context information on downstream task.
A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation
Effectively addressing the challenge of industrial Anomaly Detection (AD) necessitates an ample supply of defective samples, a constraint often hindered by their scarcity in industrial contexts.
DART: Depth-Enhanced Accurate and Real-Time Background Matting
In this paper, we leverage the rich depth information provided by the RGB-Depth (RGB-D) cameras to enhance background matting performance in real-time, dubbed DART.
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
Further, we, for the first time, formulate the formalized adversarial training of deep hashing into a unified minimax optimization under the guidance of the generated mainstay codes.
DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction
Multivariate time series long-term prediction, which aims to predict the change of data in a long time, can provide references for decision-making.
The News Delivery Channel Recommendation Based on Granular Neural Network
As is known, traditional news recommendation systems mostly employ techniques based on collaborative filtering and deep learning, but the performance of these algorithms is constrained by the sparsity of the data and the scalability of the approaches.
Research on Multilingual News Clustering Based on Cross-Language Word Embeddings
(2) We use the LDA topic model to represent news as a combina-tion of cross-lingual vectors for headlines and topic probability distributions for con-tent, introducing concepts such as topic similarity to address the cross-lingual issue in news content representation.
LipFormer: Learning to Lipread Unseen Speakers based on Visual-Landmark Transformers
However, generalizing these methods to unseen speakers incurs catastrophic performance degradation due to the limited number of speakers in training bank and the evident visual variations caused by the shape/color of lips for different speakers.
Jacobian Norm with Selective Input Gradient Regularization for Improved and Interpretable Adversarial Defense
Moreover, adversarially training a defense model in general cannot produce interpretable predictions towards the inputs with perturbations, whilst a highly interpretable robust model is required by different domain experts to understand the behaviour of a DNN.
Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification
It is a challenging and practical problem since the query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras.
Pseudo-Pair based Self-Similarity Learning for Unsupervised Person Re-identification
In this paper, we present a pseudo-pair based self-similarity learning approach for unsupervised person re-ID without human annotations.
A coarse-to-fine approach for dynamic-to-static image translation
Using the coarse predicted image, we explicitly infer a more accurate dynamic mask to identify both dynamic objects and their shadows, so that the task could be effectively converted to an image inpainting problem.
Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech
In expressive speech synthesis, there are high requirements for emotion interpretation.
Multi-modal Visual Place Recognition in Dynamics-Invariant Perception Space
In this letter, we for the first time explore the use of multi-modal fusion of semantic and visual modalities in dynamics-invariant space to improve place recognition in dynamic environments.
Symmetry-enforced Band Nodes in 230 Space Groups
Crystallographic symmetries enforcing band touchings (BTs) in the Brillouin zone (BZ) have been utilized to classify and predict the topological semimetals.
Materials Science
Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation
To achieve gradient alignment, we propose Forward-Backward Cyclic Adaptation, which iteratively computes adaptation from source to target via backward hopping and from target to source via forward passing.
Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks
In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record.
CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels
This is because the system can ignore the attention mechanism by assigning equal weights for all members.
Deep Instance-Level Hard Negative Mining Model for Histopathology Images
Histopathology image analysis can be considered as a Multiple instance learning (MIL) problem, where the whole slide histopathology image (WSI) is regarded as a bag of instances (i. e, patches) and the task is to predict a single class label to the WSI.
Cross-Entropy Adversarial View Adaptation for Person Re-identification
The main contribution is to learn coupled asymmetric mappings regarding view characteristics which are adversarially trained to address the view discrepancy by optimising the cross-entropy view confusion objective.
Few-Shot Deep Adversarial Learning for Video-based Person Re-identification
Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages.
3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification
In this paper, we introduce a global video representation to video-based person re-identification (re-ID) that aggregates local 3D features across the entire video extent.
Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification
Video-based person re-identification (re-id) is a central application in surveillance systems with significant concern in security.
Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification
Recent effective methods are developed in a pair-wise similarity learning system to detect a fixed set of features from distinct regions which are mapped to their vector embeddings for the distance measuring.
Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval
In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss.
Crossing Generative Adversarial Networks for Cross-View Person Re-identification
Given a pair of person images, the proposed model consists of the variational auto-encoder to encode the pair into respective latent variables, a proposed cross-view alignment to reduce the view disparity, and an adversarial layer to seek the joint distribution of latent representations.
Cross-Modal Person Re-Identification
Generative Adversarial Network
When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity
Inspired by point process in modeling temporal point process, in this paper we present a deep prediction method based on two recurrent neural networks (RNNs) to jointly model each user's continuous browsing history and asynchronous event sequences in the context of inter-user behavioral mutual infectivity.
Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition
Given an image, two different Convolutional Neural Networks (CNNs) are constructed, where the outputs of two CNNs are correlated through bilinear pooling to simultaneously focus on discriminative regions and extract relevant features.
Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization
However, as we observed, such classical paradigm still suffers from (1) overlooking the flexible local manifold structure, caused by (2) enforcing the low-rank data correlation agreement among all views; worse still, (3) LRR is not intuitively flexible to capture the latent data clustering structures.
Beyond Low-Rank Representations: Orthogonal Clustering Basis Reconstruction with Optimized Graph Structure for Multi-view Spectral Clustering
Low-Rank Representation (LRR) is arguably one of the most powerful paradigms for Multi-view spectral clustering, which elegantly encodes the multi-view local graph/manifold structures into an intrinsic low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts.
What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
To address \emph{what} to match, our deep network emphasizes common local patterns by learning joint representations in a multiplicative way.
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
To this end, a novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep embedding.
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection
The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time.
Finding Modes by Probabilistic Hypergraphs Shifting
Unlike the existing techniques to seek graph modes by shifting vertices based on pair-wise edges (i. e, an edge with $2$ ends), our paradigm is based on shifting high-order edges (hyperedges) to deliver graph modes.
Structured Deep Hashing with Convolutional Neural Networks for Fast Person Re-identification
Given a pedestrian image as a query, the purpose of person re-identification is to identify the correct match from a large collection of gallery images depicting the same person captured by disjoint camera views.
Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval
Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo.
Robust Hashing for Multi-View Data: Jointly Learning Low-Rank Kernelized Similarity Consensus and Hash Functions
To learn robust hash functions, a latent low-rank kernel function is used to construct hash functions in order to accommodate linearly inseparable data.
Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering
Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem.
Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach
In this paper, we present an end-to-end approach to simultaneously learn spatio-temporal features and corresponding similarity metric for video-based person re-identification.
Deep Linear Discriminant Analysis on Fisher Networks: A Hybrid Architecture for Person Re-identification
Person re-identification is to seek a correct match for a person of interest across views among a large number of imposters.
PersonNet: Person Re-identification with Deep Convolutional Neural Networks
In this paper, we propose a deep end-to-end neu- ral network to simultaneously learn high-level features and a corresponding similarity metric for person re-identification.
Structured learning of metric ensembles with application to person re-identification
However, seeking an optimal combination of visual features which is generic yet adaptive to different benchmarks is a unsoved problem, and metric learning models easily get over-fitted due to the scarcity of training data in person re-identification.
