Search Results for author:
Found 31 papers, 10 papers with code
Image Clustering with External Guidance
The core of clustering is incorporating prior knowledge to construct supervision signals.
Hybrid Representation Learning via Epistemic Graph
In recent years, deep models have achieved remarkable success in many vision tasks.
Multi-task Paired Masking with Alignment Modeling for Medical Vision-Language Pre-training
To address this limitation, we propose a unified Med-VLP framework based on Multi-task Paired Masking with Alignment (MPMA) to integrate the cross-modal alignment task into the joint image-text reconstruction framework to achieve more comprehensive cross-modal interaction, while a Global and Local Alignment (GLA) module is designed to assist self-supervised paradigm in obtaining semantic representations with rich domain knowledge.
ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos
A recent benchmark AGQA poses a promising paradigm to generate QA pairs automatically from pre-annotated scene graphs, enabling it to measure diverse reasoning abilities with granular control.
GLOW: Global Layout Aware Attacks on Object Detection
More desired attacks, to this end, should be able to fool defenses with such consistency checks.
Cross-domain recommendation via user interest alignment
Specifically, we first construct a unified cross-domain heterogeneous graph and redefine the message passing mechanism of graph convolutional networks to capture high-order similarity of users and items across domains.
Dual Pseudo-Labels Interactive Self-Training for Semi-Supervised Visible-Infrared Person Re-Identification
Visible-infrared person re-identification (VI-ReID) aims to match a specific person from a gallery of images captured from non-overlapping visible and infrared cameras.
Learning to Learn Domain-invariant Parameters for Domain Generalization
Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice.
SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency
To bridge the gap between the reference points of salient queries and Transformer detectors, we propose SAlient Point-based DETR (SAP-DETR) by treating object detection as a transformation from salient points to instance objects.
Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation
Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data, and multi-source domain adaptation (MSDA) is very attractive for real world applications.
Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answering
Recent advances in Transformer architectures [1] have brought remarkable improvements to visual question answering (VQA).
Graph Attention Transformer Network for Multi-Label Image Classification
Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain.
A Survey of Visual Transformers
Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP).
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities
Specifically, ACN adopts a novel co-training network, which enables a coupled learning process for both full modality and missing modality to supplement each other's domain and feature representations, and more importantly, to recover the `missing' information of absent modalities.
Trust It or Not: Confidence-Guided Automatic Radiology Report Generation
Experimental results have demonstrated that the proposed method for model uncertainty characterization and estimation can produce more reliable confidence scores for radiology report generation, and the modified loss function, which takes into account the uncertainties, leads to better model performance on two public radiology report datasets.
Cluster-level Feature Alignment for Person Re-identification
Instance-level alignment is widely exploited for person re-identification, e. g. spatial alignment, latent semantic alignment and triplet alignment.
Ranked #30 on
Person Re-Identification
on DukeMTMC-reID
Automatic Image Labelling at Pixel Level
A Guided Filter Network (GFN) is first developed to learn the segmentation knowledge from a source domain, and such GFN then transfers such segmentation knowledge to generate coarse object masks in the target domain.
Boundary-aware Context Neural Network for Medical Image Segmentation
In this paper, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information.
Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment
Specifically, the fractional dilated kernel is adaptively constructed according to the image aspect ratios, where the interpolation of nearest two integers dilated kernels is used to cope with the misalignment of fractional sampling.
MOD: A Deep Mixture Model with Online Knowledge Distillation for Large Scale Video Temporal Concept Localization
In this paper, we present and discuss a deep mixture model with online knowledge distillation (MOD) for large-scale video temporal concept localization, which is ranked 3rd in the 3rd YouTube-8M Video Understanding Challenge.
Imitating Targets from all sides: An Unsupervised Transfer Learning method for Person Re-identification
Person re-identification (Re-ID) models usually show a limited performance when they are trained on one dataset and tested on another dataset due to the inter-dataset bias (e. g. completely different identities and backgrounds) and the intra-dataset difference (e. g. camera invariance).
Learning Competitive and Discriminative Reconstructions for Anomaly Detection
Most of the existing methods for anomaly detection use only positive data to learn the data distribution, thus they usually need a pre-defined threshold at the detection stage to determine whether a test instance is an outlier.
NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification
This paper introduces a fast and efficient network architecture, NeXtVLAD, to aggregate frame-level features into a compact feature vector for large-scale video classification.
Deep Boosting of Diverse Experts
In this paper, a deep boosting algorithm is developed to learn more discriminative ensemble classifier by seamlessly combining a set of base deep CNNs (base experts) with diverse capabilities, e. g., these base deep CNNs are sequentially trained to recognize a set of object classes in an easy-to-hard way according to their learning complexities.
Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answering
For fine-grained image and question representations, a `co-attention' mechanism is developed by using a deep neural network architecture to jointly learn the attentions for both the image and the question, which can allow us to reduce the irrelevant features effectively and obtain more discriminative features for image and question representations.
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering
For multi-modal feature fusion, here we develop a Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi-modal features, which results in superior performance for VQA compared with other bilinear pooling approaches.
Embedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition
Our LMM model can provide an end-to-end approach for jointly learning: (a) the deep networks to extract more discriminative deep features for image and object class representation; (b) the tree classifier for recognizing large numbers of object classes hierarchically; and (c) the visual hierarchy adaptation for achieving more accurate indexing of large numbers of object classes hierarchically.
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition
In this paper, a deep mixture of diverse experts algorithm is developed for seamlessly combining a set of base deep CNNs (convolutional neural networks) with diverse outputs (task spaces), e. g., such base deep CNNs are trained to recognize different subsets of tens of thousands of atomic object classes.
Re-ranking Object Proposals for Object Detection in Automatic Driving
In this paper, we propose a semantic, class-specific approach to re-rank object proposals, which can consistently improve the recall performance even with less proposals.
Efficiently Detecting Overlapping Communities through Seeding and Semi-Supervised Learning
This paper proposes a new method that transforms a network into a corpus where each edge is treated as a document, and all nodes of the network are treated as terms of the corpus.
Correlative Multi-Label Multi-Instance Image Annotation
The cross-level label coherence en-codes the consistency between the labels at the image leveland the labels at the region level.
