Search Results for author:
Found 215 papers, 87 papers with code
TopoGAN: A Topology-Aware Generative Adversarial Network
We show in experiments that our method generates synthetic images with realistic topology.
ImmunoDiff: A Diffusion Model for Immunotherapy Response Prediction in Lung Cancer
Accurately predicting immunotherapy response in Non-Small Cell Lung Cancer (NSCLC) remains a critical unmet need.
Human-centered Interactive Learning via MLLMs for Text-to-Image Person Re-identification
Despite remarkable advancements in text-to-image person re-identification (TIReID) facilitated by the breakthrough of cross-modal embedding models, existing methods often struggle to distinguish challenging candidate images due to intrinsic limitations, such as network architecture and data quality.
On phase in scaled graphs
The scaled graph has been introduced recently as a nonlinear extension of the classical Nyquist plot for linear time-invariant systems.
Graphical Dominance Analysis for Linear Systems: A Frequency-Domain Approach
We propose a frequency-domain approach to dominance analysis for multi-input multi-output (MIMO) linear time-invariant systems.
Soft and Hard Scaled Relative Graphs for Nonlinear Feedback Stability
This paper presents input-output stability analysis of nonlinear feedback systems based on the notion of soft and hard scaled relative graphs (SRGs).
NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection and few-shot detection models when applied across domains.
Cross-Domain Few-Shot
Cross-Domain Few-Shot Object Detection
+3
Constants of motion network revisited
Discovering constants of motion is meaningful in helping understand the dynamical systems, but inevitably needs proficient mathematical skills and keen analytical capabilities.
BrainMRDiff: A Diffusion Model for Anatomically Consistent Brain MRI Synthesis
In this work, we propose BrainMRDiff, a novel topology-preserving, anatomy-guided diffusion model for synthesizing brain MRI, leveraging brain and tumor anatomies as conditioning inputs.
RF-BayesPhysNet: A Bayesian rPPG Uncertainty Estimation Method for Complex Scenarios
Due to the current lack of uncertainty estimation metrics in the rPPG field, this paper also proposes a new set of methods, using Spearman correlation coefficient, prediction interval coverage, and confidence interval width, to measure the effectiveness of uncertainty estimation methods under different noise conditions.
Computational Efficiency
Photoplethysmography (PPG) heart rate estimation
+1
Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation
To parameterize a continuous surface from sparse points, we propose a bijective surface parameterization (BSP) that learns the global shape from local patches.
Wasserstein-regularized Conformal Prediction under General Distribution Shift
Conformal prediction yields a prediction set with guaranteed $1-\alpha$ coverage of the true target under the i. i. d.
Enhancing Graph Representation Learning with Localized Topological Features
Our method utilizes the topological features to enhance the representation learning of graph neural networks and achieve state-of-the-art performance on various node classification and link prediction benchmarks.
SKE-Layout: Spatial Knowledge Enhanced Layout Generation with LLMs
Generating layouts from textual descriptions by large language models (LLMs) plays a crucial role in precise spatial reasoning-induced domains such as robotic object rearrangement and text-to-image generation.
Sharpening Neural Implicit Functions with Frequency Consolidation Priors
To better generalize a learned prior to unseen shapes, we introduce to represent frequency components as embeddings and disentangle the embedding of the low frequency component from the embedding of the full frequency component.
Efficient Event-based Semantic Segmentation with Spike-driven Lightweight Transformer-based Networks
Event-based semantic segmentation has great potential in autonomous driving and robotics due to the advantages of event cameras, such as high dynamic range, low latency, and low power cost.
ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL
To address this challenge, we propose a novel RObust mUltitask Tuning and collaboration mEthod (ROUTE) to improve the comprehensive capabilities of open-source LLMs for Text2SQL, thereby providing a more practical solution.
TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model
Accurately modeling multi-class cell topology is crucial in digital pathology, as it provides critical insights into tissue structure and pathology.
MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Images
Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches.
FedAH: Aggregated Head for Personalized Federated Learning
Personalized Federated Learning (PFL), building upon FL, aims to address the issue of statistical heterogeneity and achieve personalization.
RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency
Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression within tissue, enabling detailed study of cellular heterogeneity and tissue organization.
A Multimodal Approach Combining Structural and Cross-domain Textual Guidance for Weakly Supervised OCT Segmentation
Accurate segmentation of Optical Coherence Tomography (OCT) images is crucial for diagnosing and monitoring retinal diseases.
Adversarial Vessel-Unveiling Semi-Supervised Segmentation for Retinopathy of Prematurity Diagnosis
To address the scarcity of annotations, we propose a semi supervised segmentation framework designed to advance ROP studies without the need for extensive manual vessel annotation.
TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs
We empirically validate \emph{TopoTxR} using the VICTRE phantom breast dataset, showing that the topological structures extracted by our model effectively approximate the breast parenchymal structures.
MultiPull: Detailing Signed Distance Functions by Pulling Multi-Level Queries at Multi-Step
Recent methods usually train neural networks to overfit on single point clouds to infer signed distance functions (SDFs).
NYC-Event-VPR: A Large-Scale High-Resolution Event-Based Visual Place Recognition Dataset in Dense Urban Environments
Visual place recognition (VPR) enables autonomous robots to identify previously visited locations, which contributes to tasks like simultaneous localization and mapping (SLAM).
Simultaneous Localization and Mapping
Visual Place Recognition
Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors
To resolve this issue, we propose a method to promote pros of both data-driven based and overfitting-based methods for better generalization, faster inference, and higher accuracy in learning neural SDFs.
A Theoretical Study of Neural Network Expressive Power via Manifold Topology
A prevalent assumption regarding real-world data is that it lies on or close to a low-dimensional manifold.
Long-Tailed Backdoor Attack Using Dynamic Data Augmentation Operations
Through simultaneous optimization of the backdoored model and trigger generator, guided by dynamic data augmentation operation selectors, we achieve significant advancements.
Filtering Discomforting Recommendations with Large Language Models
Personalized algorithms can inadvertently expose users to discomforting recommendations, potentially triggering negative consequences.
Hard Negative Sample Mining for Whole Slide Image Classification
Weakly supervised whole slide image (WSI) classification is challenging due to the lack of patch-level labels and high computational costs.
Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images
We achieve superior performance on complex pathology images, thus improving the interpretability and even generalization power of TIL detection deep learning models.
Backdooring Vision-Language Models with Out-Of-Distribution Data
The emergence of Vision-Language Models (VLMs) represents a significant advancement in integrating computer vision with Large Language Models (LLMs) to generate detailed text descriptions from visual inputs.
TrojVLM: Backdoor Attack Against Vision Language Models
The emergence of Vision Language Models (VLMs) is a significant advancement in integrating computer vision with Large Language Models (LLMs) to produce detailed text descriptions based on visual inputs, yet it introduces new security vulnerabilities.
An Efficient and Generalizable Symbolic Regression Method for Time Series Analysis
Time series analysis and prediction methods currently excel in quantitative analysis, offering accurate future predictions and diverse statistical indicators, but generally falling short in elucidating the underlying evolution patterns of time series.
Spatial Diffusion for Cell Layout Generation
In downstream tasks, we show that the generated cell layouts can be used to guide the generation of high-quality pathology images.
Learning Local Pattern Modularization for Point Cloud Reconstruction from Unseen Classes
Instead of object-centered coordinate system, current methods generalized global priors learned in seen classes to reconstruct 3D shapes from unseen classes in viewer-centered coordinate system.
AdaOcc: Adaptive-Resolution Occupancy Prediction
We conducted comprehensive experiments on the nuScenes dataset, demonstrating significant improvements over existing methods.
Intermittent Semi-working Mask: A New Masking Paradigm for LLMs
In this way, ISM is able to maintain the high quality of prefix LLM and low generation latency of causal LLM, simultaneously.
Enhancing LLM's Cognition via Structurization
Besides, we show the feasibility of distilling advanced LLMs' language processing abilities to a smaller yet effective StruXGPT-7B to execute structurization, addressing the practicality of our approach.
Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution
Detecting and rejecting unknown out-of-distribution (OOD) samples is critical for deployed neural networks to void unreliable predictions.
Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions
The key to OOD detection has two aspects: generalized feature representation and precise category description.
BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records
The advent of clinical language models integrated into electronic health records (EHR) for clinical decision support has marked a significant advancement, leveraging the depth of clinical notes for improved decision-making.
HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection
Most WSOD methods rely on traditional object proposals to generate candidate regions and are confronted with unstable training, which easily gets stuck in a poor local optimum.
Memorization in deep learning: A survey
Deep Learning (DL) powered by Deep Neural Networks (DNNs) has revolutionized various domains, yet understanding the intricacies of DNN decision-making and learning processes remains a significant challenge.
E-ICL: Enhancing Fine-Grained Emotion Recognition through the Lens of Prototype Theory
Specifically, we conduct extensive pilot experiments and find that ICL conforms to the prototype theory on fine-grained emotion recognition.
Large Language Model Watermark Stealing With Mixed Integer Programming
The Large Language Model (LLM) watermark is a newly emerging technique that shows promise in addressing concerns surrounding LLM copyright, monitoring AI-generated text, and preventing its misuse.
A Novel Wide-Area Multiobject Detection System with High-Probability Region Searching
To address these challenges, this paper presents a hybrid system that incorporates a wide-angle camera, a high-speed search camera, and a galvano-mirror.
Uncertainty Quantification on Graph Learning: A Survey
Graphical models, including Graph Neural Networks (GNNs) and Probabilistic Graphical Models (PGMs), have demonstrated their exceptional capabilities across numerous fields.
Detector Collapse: Physical-World Backdooring Object Detection to Catastrophic Overload or Blindness in Autonomous Driving
This paper is dedicated to bridging this gap by introducing Detector Collapse} (DC), a brand-new backdoor attack paradigm tailored for object detection.
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning
The technique is originated from physics, but is very effective in enabling RL agents to explore to continuously improve the solutions during test.
ROPO: Robust Preference Optimization for Large Language Models
Preference alignment is pivotal for empowering large language models (LLMs) to generate helpful and harmless responses.
Incorporating Domain Differential Equations into Graph Convolutional Networks to Lower Generalization Discrepancy
We theoretically derive conditions where GCNs incorporating such domain differential equations are robust to mismatched training and testing data compared to baseline domain agnostic models.
DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking
Multi-scenario route ranking (MSRR) is crucial in many industrial mapping systems.
Task-Agnostic Detector for Insertion-Based Backdoor Attacks
Textual backdoor attacks pose significant security threats.
Exploring Accurate 3D Phenotyping in Greenhouse through Neural Radiance Fields
This study investigates a learning-based phenotyping method using the Neural Radiance Field to achieve accurate in-situ phenotyping of pepper plants in greenhouse environments.
Robust Conformal Prediction under Distribution Shift via Physics-Informed Structural Causal Model
Uncertainty is critical to reliable decision-making with machine learning.
Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond
In this paper, we present a comprehensive review of the development and recent advances in deep learning for trajectory computing (DL4Traj).
Gradient-based Fuzzy System Optimisation via Automatic Differentiation -- FuzzyR as a Use Case
Since their introduction, fuzzy sets and systems have become an important area of research known for its versatility in modelling, knowledge representation and reasoning, and increasingly its potential within the context explainable AI.
An Iterative Associative Memory Model for Empathetic Response Generation
Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses.
Topological Analysis of Mouse Brain Vasculature via 3D Light-sheet Microscopy Images
In this paper, we study the morphology of brain vasculature through a topological lens.
FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting
Current research predominantly relies on historical solar power data or numerical weather prediction in a single-modality format, ignoring the complementary information provided in different modalities.
Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting
Time series analysis is vital for numerous applications, and transformers have become increasingly prominent in this domain.
Computational Efficiency
Multivariate Time Series Forecasting
+2
An invariance constrained deep learning network for PDE discovery
However, the discovery of governing equations from sparse data with high noise is still very challenging due to the difficulty of derivatives computation and the disturbance of noise.
INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection
Knowledge hallucination have raised widespread concerns for the security and reliability of deployed LLMs.
Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection
Most existing OOD detection methods focused on exploring advanced training skills or training-free tricks to prevent the model from yielding overconfident confidence score for unknown samples.
Sketch and Refine: Towards Fast and Accurate Lane Detection
At the "Sketch" stage, local directions of keypoints can be easily estimated by fast convolutional layers.
SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology
Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slides.
Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization Approach
Spatio-temporal forecasting is crucial in various fields and requires a careful balance between identifying subtle patterns and filtering out noise.
Ranked #5 on
Traffic Prediction
on BJTaxi
SO-NeRF: Active View Planning for NeRF using Surrogate Objectives
Despite the great success of Neural Radiance Fields (NeRF), its data-gathering process remains vague with only a general rule of thumb of sampling as densely as possible.
A Cyclic Small Phase Theorem
This paper introduces a brand-new phase definition called the segmental phase for multi-input multi-output linear time-invariant systems.
Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames
It aims to summarize a long video walkthrough of a scene into a small set of frames that are spatially diverse in the scene, which has many impotant applications, such as in surveillance, real estate, and robotics.
Semi-supervised Segmentation of Histopathology Images with Noise-Aware Topological Consistency
In digital pathology, segmenting densely distributed objects like glands and nuclei is crucial for downstream analysis.
Phase Preservation of N-Port Networks under General Connections
In addition, the inverse operations of the considered connections, that is, network subtractions with correspondences are examined.
Cycle Invariant Positional Encoding for Graph Representation Learning
To efficiently encode the space of all cycles, we start with a cycle basis (i. e., a minimal set of cycles generating the cycle space) which we compute via the kernel of the 1-dimensional Hodge Laplacian of the input graph.
High-fidelity 3D Reconstruction of Plants using Neural Radiance Field
Recently, a promising development has emerged in the form of Neural Radiance Field (NeRF), a novel method that utilises neural density fields.
Attention-Enhancing Backdoor Attacks Against BERT-based Models
Recent studies have revealed that \textit{Backdoor Attacks} can threaten the safety of natural language processing (NLP) models.
Learning to Abstain From Uninformative Data
Learning and decision-making in domains with naturally high noise-to-signal ratio, such as Finance or Healthcare, is often challenging, while the stakes are very high.
OWL: A Large Language Model for IT Operations
However, there is a lack of specialized LLMs for IT operations.
Client-side Gradient Inversion Against Federated Learning from Poisoning
For the first time, we show the feasibility of a client-side adversary with limited knowledge being able to recover the training samples from the aggregated global model.
GridPull: Towards Scalability in Learning Implicit Representations from 3D Point Clouds
However, these methods suffer from a slow inference due to the slow convergence of neural networks and the extensive calculation of distances to surface points, which limits them to small scale points.
Calibrating Uncertainty for Semi-Supervised Crowd Counting
A popular approach is to iteratively generate pseudo-labels for unlabeled data and add them to the training set.
Learning to Segment from Noisy Annotations: A Spatial Correction Approach
However, segmentation label noise usually has strong spatial correlation and has prominent bias in distribution.
Confidence Estimation Using Unlabeled Data
We stipulate that even with limited training labels, we can still reasonably approximate the confidence of model on unlabeled samples by inspecting the prediction consistency through the training process.
Robust Ranking Explanations
Robust explanations of machine learning models are critical to establish human trust in the models.
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis
To mitigate this problem, we propose a novel and simple counterfactual data augmentation method to generate opinion expressions with reversed sentiment polarity.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+5
Topology-Aware Uncertainty for Image Segmentation
Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology.
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning
Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS).
Security and Privacy Problems in Voice Assistant Applications: A Survey
The privacy issues include technical-wise information stealing and policy-wise privacy breaches.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Topology-Guided Multi-Class Cell Context Generation for Digital Pathology
In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis.
SoftED: Metrics for Soft Evaluation of Time Series Event Detection
They improved event detection evaluation by associating events and their representative detections, incorporating temporal tolerance in over 36\% of experiments compared to the usual classification metrics.
Mask and Restore: Blind Backdoor Defense at Test Time with Masked Autoencoder
It detects possible triggers in the token space using image structural similarity and label consistency between the test image and MAE restorations.
Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning Priors
To resolve this issue, we present a neural network to directly infer SDFs from single sparse point clouds without using signed distance supervision, learned priors or even normals.
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning
Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics directly in continuous time domain for its flexibility.
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution
Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros.
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation
We propose a novel approach to learn enhanced modality-agnostic representations by employing a meta-learning strategy in training, even when only limited full modality samples are available.
Rate-Perception Optimized Preprocessing for Video Coding
In the past decades, lots of progress have been done in the video compression field including traditional video codec and learning-based video codec.
Provable Robust Saliency-based Explanations
To foster trust in machine learning models, explanations must be faithful and stable for consistent insights.
DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization
LiDAR mapping is important yet challenging in self-driving and mobile robotics.
FoPro: Few-Shot Guided Robust Webly-Supervised Prototypical Learning
Most existing methods focus on learning noise-robust models from web images while neglecting the performance drop caused by the differences between web domain and real-world domain.
From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-Resolution
In the fine area, we use an Intra-Patch Self-Attention (IPSA) module to model long-range pixel dependencies in a local patch, and then a $3\times3$ convolution is applied to process the finest details.
Backdoor Cleansing with Unlabeled Data
In experiments, we show that our method, trained without labels, is on-par with state-of-the-art defense methods trained using labels.
ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement
In this paper, we propose an efficient structure named Efficient Correspondence Transformer (ECO-TR) by finding correspondences in a coarse-to-fine manner, which significantly improves the efficiency of functional correspondence model.
Feedback Stability Analysis via Dissipativity with Dynamic Supply Rates
This extends classical dissipativity with static supply rates and dynamic supply rates of miscellaneous quadratic forms.
Deep Anomaly Detection and Search via Reinforcement Learning
Semi-supervised Anomaly Detection (AD) is a kind of data mining task which aims at learning features from partially-labeled datasets to help detect outliers.
Self-Supervised Place Recognition by Refining Temporal and Featural Pseudo Labels from Panoramic Data
Inspired by noisy label learning, we propose a novel self-supervised framework named TF-VPR that uses temporal neighborhoods and learnable feature neighborhoods to discover unknown spatial neighborhoods.
A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction
Deep-learning-based clinical decision support using structured electronic health records (EHR) has been an active research area for predicting risks of mortality and diseases.
HOB-CNN: Hallucination of Occluded Branches with a Convolutional Neural Network for 2D Fruit Trees
We further validated HOB-CNN against two different types of 2D trees, and HOB-CNN shows generalization across different trees and robustness under different occluded conditions.
Learning Topological Interactions for Multi-Class Medical Image Segmentation
In this paper, we introduce a novel topological interaction module to encode the topological interactions into a deep neural network.
UniFusion: Unified Multi-view Fusion Transformer for Spatial-Temporal Representation in Bird's-Eye-View
Bird's eye view (BEV) representation is a new perception formulation for autonomous driving, which is based on spatial fusion.
Latent Partition Implicit with Surface Codes for 3D Representation
Our insight here is that both the part learning and the part blending can be conducted much easier in the latent space than in the spatial space.
Hiding Your Signals: A Security Analysis of PPG-based Biometric Authentication
It reduces the success rate of rPPG spoofing attacks in user authentication to 0. 05.
MDOE: A Spatiotemporal Event Representation Considering the Magnitude and Density of Events
Event-based sensors (e. g., DVS cameras) are capable of higher dynamic range, higher temporal resolution, lower time latency, and better power efficiency compared to conventional devices (e. g., RGB cameras).
On the Convergence of Optimizing Persistent-Homology-Based Losses
Topological loss based on persistent homology has shown promise in various applications.
Learning Probabilistic Topological Representations Using Discrete Morse Theory
We use discrete Morse theory and persistent homology to construct an one-parameter family of structures as the topological/structural representation space.
Nuclear Norm Maximization Based Curiosity-Driven Learning
To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in handy for pursuing the rewards in the future, such as encouraging the agent to visit novel states.
A Study of the Attention Abnormality in Trojaned BERTs
Trojan attacks raise serious security concerns.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
A Novel Framework for Characterization of Tumor-Immune Spatial Relationships in Tumor Microenvironment
Understanding the impact of tumor biology on the composition of nearby cells often requires characterizing the impact of biologically distinct tumor regions.
Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation
Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time.
PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image Generation
Pixel synthesis is a promising research paradigm for image generation, which can well exploit pixel-wise prior knowledge for generation.
End-to-End Zero-Shot HOI Detection via Vision and Language Knowledge Distillation
Extensive experiments on HICO-Det dataset demonstrate that our model discovers potential interactive pairs and enables the recognition of unseen HOIs.
Human-Object Interaction Detection
Knowledge Distillation
+4
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation
User interests are usually dynamic in the real world, which poses both theoretical and practical challenges for learning accurate preferences from rich behavior data.
SeqTR: A Simple yet Universal Network for Visual Grounding
In this paper, we propose a simple yet universal network termed SeqTR for visual grounding tasks, e. g., phrase localization, referring expression comprehension (REC) and segmentation (RES).
Ranked #11 on
Referring Expression Segmentation
on RefCOCO testB
Discovering Governing Equations by Machine Learning implemented with Invariance
The partial differential equation (PDE) plays a significantly important role in many fields of science and engineering.
A Manifold View of Adversarial Risk
Assuming data lies in a manifold, we investigate two new types of adversarial risk, the normal adversarial risk due to perturbation along normal direction, and the in-manifold adversarial risk due to perturbation within the manifold.
Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations
In our method, a self-attention based Temporal Convolutional Network (TCN) is used to learn a representation that is most reflective of the disease trajectory.
A Topology-Attention ConvLSTM Network and Its Application to EM Images
We propose a novel TopologyAttention ConvLSTM Network (TACNet) for 3D image segmentation in order to achieve high structural accuracy for 3D segmentation tasks.
Neural Approximation of Graph Topological Features
Topological features based on persistent homology capture high-order structural information so as to augment graph neural network methods.
Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation
To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation.
DIFNet: Boosting Visual Information Flow for Image Captioning
Current Image captioning (IC) methods predict textual words sequentially based on the input visual information from the visual feature extractor and the partially generated sentence information.
Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchards
It is vital for robots to recognise and localise fruits before the harvesting in natural orchards.
A General Framework for Debiasing in CTR Prediction
Most of the existing methods for debaising in click-through rate (CTR) prediction depend on an oversimplified assumption, i. e., the click probability is the product of observation probability and relevance probability.
Multi-objective Explanations of GNN Predictions
We design a user study to investigate such joint effects and use the findings to design a multi-objective optimization (MOO) algorithm to find Pareto optimal explanations that are well-balanced in simulatability and counterfactual.
Federated Learning with Domain Generalization
Federated Learning (FL) enables a group of clients to jointly train a machine learning model with the help of a centralized server.
R4: A Framework for Route Representation and Route Recommendation
For user representation, we utilize a series of historical navigation to extract user preference.
Trigger Hunting with a Topological Prior for Trojan Detection
Despite their success and popularity, deep neural networks (DNNs) are vulnerable when facing backdoor attacks.
Multi-Class Cell Detection Using Spatial Context Representation
In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks.
Cycle Representation Learning for Inductive Relation Prediction
In this paper, we consider rules as cycles and show that the space of cycles has a unique structure based on the mathematics of algebraic topology.
Graph Representation Learning
Inductive Relation Prediction
+3
A Frequency-Domain Approach to Nonlinear Negative Imaginary Systems Analysis
In this study, we extend the theory of negative imaginary (NI) systems to a nonlinear framework using a frequency-domain approach.
A Topological View of Rule Learning in Knowledge Graphs
We propose to collect cycle bases that span the space of cycles.
Learning to Abstain in the Presence of Uninformative Data
Learning and decision making in domains with naturally high noise-to-signal ratios – such as Finance or Public Health – can be challenging and yet extremely important.
Self-learn to Explain Siamese Networks Robustly
Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and imbalanced.
The Singular Angle of Nonlinear Systems
It is, thus, different from the recently appeared nonlinear system phase which adopts the complexification of real-valued signals using the Hilbert transform.
Topological Detection of Trojaned Neural Networks
Deep neural networks are known to have security issues.
Convolutional Block Design for Learned Fractional Downsampling
The layers of convolutional neural networks (CNNs) can be used to alter the resolution of their inputs, but the scaling factors are limited to integer values.
Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning
One-bit matrix completion is an important class of positiveunlabeled (PU) learning problems where the observations consist of only positive examples, eg, in top-N recommender systems.
TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer
Characterization of breast parenchyma on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures.
Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey
This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services.
Structure Guided Lane Detection
Next, multi-level structural constraints are used to improve the perception of lanes.
Ranked #37 on
Lane Detection
on CULane
NeuSE: A Neural Snapshot Ensemble Method for Collaborative Filtering
In collaborative filtering (CF) algorithms, the optimal models are usually learned by globally minimizing the empirical risks averaged over all the observed data.
Synthesizing MR Image Contrast Enhancement Using 3D High-resolution ConvNets
To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors.
Topology-Aware Segmentation Using Discrete Morse Theory
In the segmentation of fine-scale structures from natural and biomedical images, per-pixel accuracy is not the only metric of concern.
Learning with Feature-Dependent Label Noise: A Progressive Approach
Label noise is frequently observed in real-world large-scale datasets.
Ranked #14 on
Learning with noisy labels
on ANIMAL
Compiler-Guided Throughput Scheduling for Many-core Machines
Typical schedulers in multi-tenancy environments make use of reactive, feedback-oriented mechanisms based on performance counters to avoid resource contention but suffer from detection lag and loss of performance.
Distributed, Parallel, and Cluster Computing
Link Prediction with Persistent Homology: An Interactive View
Link prediction is an important learning task for graph-structured data.
Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Survey
Stealing attack against controlled information, along with the increasing number of information leakage incidents, has become an emerging cyber security threat in recent years.
Stability of SGD: Tightness Analysis and Improved Bounds
This raises the question: is the stability analysis of [18] tight for smooth functions, and if not, for what kind of loss functions and data distributions can the stability analysis be improved?
Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach
A federatedGAN jointly trains a centralized generator and multiple private discriminators hosted at different sites.
Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projections Matching
Our method pushes the neural network to generate a 3D point cloud whose 2D projections match the irregular point supervision from different view angles.
Ricci-GNN: Defending Against Structural Attacks Through a Geometric Approach
We propose a novel GNN defense algorithm against structural attacks that maliciously modify graph topology.
Revisiting the Stability of Stochastic Gradient Descent: A Tightness Analysis
We provide empirical evidence that this condition holds for several loss functions, and provide theoretical evidence that the known tight SGD stability bounds for convex and non-convex loss functions can be circumvented by HC loss functions, thus partially explaining the generalization of deep neural networks.
Localization in the Crowd with Topological Constraints
Due to various challenges, a localization method is prone to spatial semantic errors, i. e., predicting multiple dots within a same person or collapsing multiple dots in a cluttered region.
A Topological Filter for Learning with Label Noise
Noisy labels can impair the performance of deep neural networks.
Phase of Nonlinear Systems
A nonlinear small phase theorem is then established for feedback stability analysis of semi-sectorial systems.
Error-Bounded Correction of Noisy Labels
To be robust against label noise, many successful methods rely on the noisy classifiers (i. e., models trained on the noisy training data) to determine whether a label is trustworthy.
Ranked #41 on
Image Classification
on Clothing1M
RCHOL: Randomized Cholesky Factorization for Solving SDD Linear Systems
We introduce a randomized algorithm, namely RCHOL, to construct an approximate Cholesky factorization for a given Laplacian matrix (a. k. a., graph Laplacian).
Numerical Analysis Mathematical Software Numerical Analysis
Minimizing Labeling Effort for Tree Skeleton Segmentation using an Automated Iterative Training Methodology
It is shown that in our application, the new Automating-the-Loop method greatly reduces the labeling effort while producing comparable performance to both human-in-the-loop and complete manual labeling methods.
Semantic Segmentation for Partially Occluded Apple Trees Based on Deep Learning
DeepLabv3 outperforms the other models at Binary accuracy, Mean IoU and Boundary F1 score, but is surpassed by Pix2Pix (without discriminator) and U-Net in Occluded branch recall.
Deep Variational Instance Segmentation
Specifically, we propose a variational relaxation of instance segmentation as minimizing an optimization functional for a piecewise-constant segmentation problem, which can be used to train an FCN end-to-end.
Object Tracking by Least Spatiotemporal Searches
This paper proposes a strategy named IHMs (Intermediate Searching at Heuristic Moments): each step we figure out which moment is the best to search according to a heuristic indicator, then at that moment search locations one by one in descending order of predicted appearing probabilities, until a search hits; iterate this step until we get the object's current location.
Learn distributed GAN with Temporary Discriminators
Our proposed method tackles the challenge of training GAN in the federated learning manner: How to update the generator with a flow of temporary discriminators?
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images
To optimize 3D shape parameters, current renderers rely on pixel-wise losses between rendered images of 3D reconstructions and ground truth images from corresponding viewpoints.
End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19
Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus.
Local Causal Structure Learning and its Discovery Between Type 2 Diabetes and Bone Mineral Density
To illustrate the importance of prior knowledge, the result of the algorithm without prior knowledge is also investigated.
Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment
However, training with the global image underutilizes discriminative local information, while providing extra annotations is expensive and subjective.
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data
In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN).
Rigorous Explanation of Inference on Probabilistic Graphical Models
There is no existing method to rigorously attribute the inference outcomes to the contributing factors of the graphical models.
Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting
In this research, a fully neural network based visual perception framework for autonomous apple harvesting is proposed.
Visual Perception and Modelling in Unstructured Orchard for Apple Harvesting Robots
This paper develops a framework of visual perception and modelling for robotic harvesting of fruits in the orchard environments.
HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation
In particular, our proposed HoMM can perform arbitrary-order moment tensor matching, we show that the first-order HoMM is equivalent to Maximum Mean Discrepancy (MMD) and the second-order HoMM is equivalent to Correlation Alignment (CORAL).
Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards
The robustness and efficiency of the DaSNet-V2 in detection and segmentation are validated by the experiments in the real-environment of apple orchard.
Point Cloud Processing via Recurrent Set Encoding
The spatial layout of the beams is regular, and this allows the beam features to be further fed into an efficient 2D convolutional neural network (CNN) for hierarchical feature aggregation.
SSAH: Semi-supervised Adversarial Deep Hashing with Self-paced Hard Sample Generation
In this paper, we propose a novel Semi-supervised Self-pace Adversarial Hashing method, named SSAH to solve the above problems in a unified framework.
A Predictive On-Demand Placement of UAV Base Stations Using Echo State Network
In this paper, we propose a system framework consisting of UEs clustering, UAV-BS placement, UEs trajectories prediction, and UAV-BS reposition matching scheme, to serve the UEs seamlessly as well as minimize the energy cost of UAV-BSs' reposition trajectories.
Label Cleaning with Likelihood Ratio Test
To collect large scale annotated data, it is inevitable to introduce label noise, i. e., incorrect class labels.
Scalable Explanation of Inferences on Large Graphs
Probabilistic inferences distill knowledge from graphs to aid human make important decisions.
From Crowdsourcing to Crowdmining: Using Implicit Human Intelligence for Better Understanding of Crowdsourced Data
A generic model for CrowdMining is further proposed based on a set of existing studies.
ShapeCaptioner: Generative Caption Network for 3D Shapes by Learning a Mapping from Parts Detected in Multiple Views to Sentences
Specifically, ShapeCaptioner aggregates the parts detected in multiple colored views using our novel part class specific aggregation to represent a 3D shape, and then, employs a sequence to sequence model to generate the caption.
Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types
This allows for a much larger training set, that reflects visual variability across multiple cancer types and thus training of a single network which can be automatically applied to each cancer type without human adjustment.
DeepSquare: Boosting the Learning Power of Deep Convolutional Neural Networks with Elementwise Square Operators
In this paper, we pursue very efficient neural network modules which can significantly boost the learning power of deep convolutional neural networks with negligible extra computational cost.
Topology-Preserving Deep Image Segmentation
Segmentation algorithms are prone to make topological errors on fine-scale structures, e. g., broken connections.
ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis
Current neural networks for 3D object recognition are vulnerable to 3D rotation.
Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation
However, since the domain shift between source and target domains, only using the deep features for sample selection is defective.
Ranked #6 on
Partial Domain Adaptation
on Office-31
Automatic Long-Term Deception Detection in Group Interaction Videos
Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video.
Strain engineering of epitaxial oxide heterostructures beyond substrate limitations
The limitation of commercially available single-crystal substrates and the lack of continuous strain tunability preclude the ability to take full advantage of strain engineering for further exploring novel properties and exhaustively studying fundamental physics in complex oxides.
Materials Science
Towards Self-similarity Consistency and Feature Discrimination for Unsupervised Domain Adaptation
Recent advances in unsupervised domain adaptation mainly focus on learning shared representations by global distribution alignment without considering class information across domains.
