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Found 101 papers, 32 papers with code
Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images
To address these issues, in this paper, we develop a novel Breast Ultrasound SAM Adapter, termed Breast Ultrasound Segment Anything Model (BUSSAM), which migrates the SAM to the field of breast ultrasound image segmentation by using the adapter technique.
State Space Model for New-Generation Network Alternative to Transformers: A Survey
In this paper, we give the first comprehensive review of these works and also provide experimental comparisons and analysis to better demonstrate the features and advantages of SSM.
A Spatial-Temporal Progressive Fusion Network for Breast Lesion Segmentation in Ultrasound Videos
The main challenge for ultrasound video-based breast lesion segmentation is how to exploit the lesion cues of both intra-frame and inter-frame simultaneously.
Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization
To tackle this challenge, we propose a weakly supervised Context-Semantic Quality Awareness Network (CSQA-Net) for FGVC.
Ranked #4 on
Fine-Grained Image Classification
on NABirds
Fine-Grained Image Classification
Fine-Grained Visual Categorization
Long-term Frame-Event Visual Tracking: Benchmark Dataset and Baseline
Current event-/frame-event based trackers undergo evaluation on short-term tracking datasets, however, the tracking of real-world scenarios involves long-term tracking, and the performance of existing tracking algorithms in these scenarios remains unclear.
Which Model to Transfer? A Survey on Transferability Estimation
In this survey, we present the first review of existing advances in this area and categorize them into two separate realms: source-free model transferability estimation and source-dependent model transferability estimation.
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
Learning a human-like driving policy from large-scale driving demonstrations is promising, but the uncertainty and non-deterministic nature of planning make it challenging.
Unifying Graph Contrastive Learning via Graph Message Augmentation
Based on the proposed GMA, we then propose a unified graph contrastive learning, termed Graph Message Contrastive Learning (GMCL), that employs attribution-guided universal GMA for graph contrastive learning.
Understanding Representation Learnability of Nonlinear Self-Supervised Learning
There are only a few theoretical works on data representation learnability, and many of those focus on final data representation, treating the nonlinear neural network as a ``black box".
CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras
In this work, we formally propose the task of object tracking using unaligned neuromorphic and visible cameras.
Unleashing the Power of CNN and Transformer for Balanced RGB-Event Video Recognition
It is intuitive to combine them for high-performance RGB-Event based video recognition, however, existing works fail to achieve a good balance between the accuracy and model parameters, as shown in Fig.~\ref{firstimage}.
Mathematical Language Models: A Survey
In recent years, there has been remarkable progress in leveraging Language Models (LMs), encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models (LLMs), within the domain of mathematics.
Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning
Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their future performance on new questions based on their past response records.
AnonPSI: An Anonymity Assessment Framework for PSI
We conducted a comprehensive performance evaluation of various attack strategies proposed utilizing two real datasets.
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation
Label propagation methods try to propagate the labels of support samples on the constructed graph encoding the relationships between both support and query samples.
Fovea Transformer: Efficient Long-Context Modeling with Structured Fine-to-Coarse Attention
The quadratic complexity of self-attention in Transformers has hindered the processing of long text.
VcT: Visual change Transformer for Remote Sensing Image Change Detection
Then, each pixel of feature map is regarded as a graph node and the graph neural network is proposed to model the structured information for coarse change map prediction.
Unleashing the power of Neural Collapse for Transferability Estimation
Transferability estimation aims to provide heuristics for quantifying how suitable a pre-trained model is for a specific downstream task, without fine-tuning them all.
Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline
Tracking using bio-inspired event cameras has drawn more and more attention in recent years.
Learning Point-wise Abstaining Penalty for Point Cloud Anomaly Detection
We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance.
A study on the impact of pre-trained model on Just-In-Time defect prediction
These findings emphasize the effectiveness of transformer-based pre-trained models in JIT defect prediction tasks, especially in scenarios with limited training data.
Cerberus: A Deep Learning Hybrid Model for Lithium-Ion Battery Aging Estimation and Prediction Based on Relaxation Voltage Curves
The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices, encompassing aspects such as performance delivery and cycling utilization.
MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction
We propose a unified permutation-equivalent modeling approach, \ie, modeling map element as a point set with a group of equivalent permutations, which accurately describes the shape of map element and stabilizes the learning process.
SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition
Secondly, they adopt either Spiking Neural Networks (SNN) for energy-efficient recognition with suboptimal results, or Artificial Neural Networks (ANN) for energy-intensive, high-performance recognition.
ReCoMIF: Reading comprehension based multi-source information fusion network for Chinese spoken language understanding
It usually includes slot filling and intent detection (SFID) tasks aiming at semantic parsing of utterances.
Thompson Sampling under Bernoulli Rewards with Local Differential Privacy
This paper investigates the problem of regret minimization for multi-armed bandit (MAB) problems with local differential privacy (LDP) guarantee.
Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition
To address these issues, we propose a novel dual point-voxel absorbing graph representation learning for event stream data representation.
AMatFormer: Efficient Feature Matching via Anchor Matching Transformer
Second, AMatFormer adopts a shared FFN module to further embed the features of two images into the common domain and thus learn the consensus feature representations for the matching problem.
Prediction with Incomplete Data under Agnostic Mask Distribution Shift
We focus on the case where the underlying joint distribution of complete features and label is invariant, but the missing pattern, i. e., mask distribution may shift agnostically between training and testing.
AGFormer: Efficient Graph Representation with Anchor-Graph Transformer
To alleviate the local receptive issue of GCN, Transformers have been exploited to capture the long range dependences of nodes for graph data representation and learning.
Implicit Bayes Adaptation: A Collaborative Transport Approach
We integrate a metric correction term as well as a prior cluster structure in the source data of the OT-driven adaptation.
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation.
Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction
We present a path-based online lane graph construction method, termed LaneGAP, which end-to-end learns the path and recovers the lane graph via a Path2Graph algorithm.
Fine-Grained Visual Classification via Internal Ensemble Learning Transformer
The proposed IELT involves three main modules: multi-head voting (MHV) module, cross-layer refinement (CLR) module, and dynamic selection (DS) module.
MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection
Existing cross-domain keypoint detection methods always require accessing the source data during adaptation, which may violate the data privacy law and pose serious security concerns.
Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Prediction
Motion prediction is highly relevant to the perception of dynamic objects and static map elements in the scenarios of autonomous driving.
Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric
In this paper, we propose a single-stage backbone network for Color-Event Unified Tracking (CEUTrack), which achieves the above functions simultaneously.
Ranked #3 on
Object Tracking
on COESOT
Rethinking Batch Sample Relationships for Data Representation: A Batch-Graph Transformer based Approach
(1) It employs a flexible graph model, termed Batch Graph to jointly encode the visual and semantic relationships of samples within each mini-batch.
HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
The main streams of human activity recognition (HAR) algorithms are developed based on RGB cameras which are suffered from illumination, fast motion, privacy-preserving, and large energy consumption.
An efficient algorithm for the $\ell_{p}$ norm based metric nearness problem
Due to the high memory requirement for the storage of the matrix related to the metric constraints, we take advantage of the special structure of the matrix and do not need to store the corresponding constraint matrix.
Flexible Alignment Super-Resolution Network for Multi-Contrast MRI
To tackle this problem, we propose the Flexible Alignment Super-Resolution Network (FASR-Net) for multi-contrast MRI Super-Resolution.
On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification
For the emerging regularized system identification, the study on input design has just started, and it is often formulated as a non-convex optimization problem that minimizes a scalar measure of the Bayesian mean squared error matrix subject to certain constraints, and the state-of-art method is the so-called quadratic mapping and inverse embedding (QMIE) method, where a time domain inverse embedding (TDIE) is proposed to find the inverse of the quadratic mapping.
Spiking GATs: Learning Graph Attentions via Spiking Neural Network
Graph Attention Networks (GATs) have been intensively studied and widely used in graph data learning tasks.
Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification
sampleFormer aims to capture the dependence of samples in support and query sets for image representation.
DRSOM: A Dimension Reduced Second-Order Method
In this paper, we propose a Dimension-Reduced Second-Order Method (DRSOM) for convex and nonconvex (unconstrained) optimization.
HOPE: Hierarchical Spatial-temporal Network for Occupancy Flow Prediction
In this report, we introduce our solution to the Occupancy and Flow Prediction challenge in the Waymo Open Dataset Challenges at CVPR 2022, which ranks 1st on the leaderboard.
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search
To track the target in a video, current visual trackers usually adopt greedy search for target object localization in each frame, that is, the candidate region with the maximum response score will be selected as the tracking result of each frame.
Unified GCNs: Towards Connecting GCNs with CNNs
Graph Convolutional Networks (GCNs) have been widely demonstrated their powerful ability in graph data representation and learning.
Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection
Different from previous approaches that learn robust deep representations of scene text in a holistic manner, our method performs scene text detection based on a few representative features, which avoids the disturbance by background and reduces the computational cost.
Ranked #21 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Refining Self-Supervised Learning in Imaging: Beyond Linear Metric
We introduce in this paper a new statistical perspective, exploiting the Jaccard similarity metric, as a measure-based metric to effectively invoke non-linear features in the loss of self-supervised contrastive learning.
Generalizing Aggregation Functions in GNNs:High-Capacity GNNs via Nonlinear Neighborhood Aggregators
(2) For max aggregator, it usually fails to be aware of the detailed information of node representations within neighborhood.
MutualFormer: Multi-Modality Representation Learning via Cross-Diffusion Attention
In this work, we re-think Transformer and extend it to MutualFormer for multi-modality data representation.
GAMnet: Robust Feature Matching via Graph Adversarial-Matching Network
Also, GAMnet exploits sparse GM optimization as correspondence solver which is differentiable and can also incorporate discrete one-to-one matching constraints approximately in natural in the final matching prediction.
Ranked #7 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
Networked Time Series Prediction with Incomplete Data via Generative Adversarial Network
An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph.
PRNet: A Progressive Regression Network for No-Reference User-Generated-Content Video Quality Assessment
The largest UGC video dataset---YouTube-UGC still faces a problem that the database has right-skewed MOS distribution.
Robust Graph Data Learning with Latent Graph Convolutional Representation
Given an input graph $\textbf{A}$, LatGCR aims to generate a flexible latent graph $\tilde{\textbf{A}}$ for graph convolutional representation which obviously enhances the representation capacity and also performs robustly w. r. t graph structural attacks and noises.
Human Pose Transfer with Augmented Disentangled Feature Consistency
Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others.
MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking
The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively.
Ranked #21 on
Rgb-T Tracking
on RGBT234
Feature importance recap and stacking models for forex price prediction
Traditionally, traders refer to technical analysis based on the historical data to make decisions and trade.
A proximal-proximal majorization-minimization algorithm for nonconvex tuning-free robust regression problems
In this paper, we introduce a proximal-proximal majorization-minimization (PPMM) algorithm for nonconvex tuning-free robust regression problems.
Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark
We believe this benchmark will greatly boost related researches on natural language guided tracking.
Ranked #3 on
Visual Object Tracking
on TNL2K
(precision metric)
Hierarchical Graph Attention Network for Few-Shot Visual-Semantic Learning
Deep learning has made tremendous success in computer vision, natural language processing and even visual-semantic learning, which requires a huge amount of labeled training data.
Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction
In this paper, we propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design which is more in line with the characteristics of surveillance videos.
Fast Object Detection with Latticed Multi-Scale Feature Fusion
In this work, we present a novel module, the Fluff block, to alleviate drawbacks of current multi-scale fusion methods and facilitate multi-scale object detection.
Visual Object Tracking by Segmentation with Graph Convolutional Network
The proposed model provides a general end-to-end framework which integrates i) label linear prediction, and ii) structure-aware feature information of each superpixel together to obtain object segmentation and further improves the performance of tracking.
WANA: Symbolic Execution of Wasm Bytecode for Cross-Platform Smart Contract Vulnerability Detection
Furthermore, WANA proposes a set of test oracles to detect the vulnerabilities in EOSIO and Ethereum smart contracts based on WebAssembly bytecode analysis.
Software Engineering D.2.5
EOSFuzzer: Fuzzing EOSIO Smart Contracts for Vulnerability Detection
Our fuzzing experiment on 3963 EOSIO smart contracts shows that EOSFuzzer is both effective and efficient to detect EOSIO smart contract vulnerabilities with high accuracy.
Software Engineering D.2.5
STADB: A Self-Thresholding Attention Guided ADB Network for Person Re-identification
Specifically, STADB first obtains an attention map by channel-wise pooling and returns a drop mask by thresholding the attention map.
Incomplete Graph Representation and Learning via Partial Graph Neural Networks
To address this problem, we develop a novel partial aggregation based GNNs, named Partial Graph Neural Networks (PaGNNs), for attribute-incomplete graph representation and learning.
\emph{cm}SalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks
Fusing complementary information of RGB and depth has been demonstrated to be effective for image salient object detection which is known as RGB-D salient object detection problem.
AttKGCN: Attribute Knowledge Graph Convolutional Network for Person Re-identification
Discriminative feature representation of person image is important for person re-identification (Re-ID) task.
GLMNet: Graph Learning-Matching Networks for Feature Matching
However, the matching graphs we feed to existing graph convolutional matching networks are generally fixed and independent of graph matching, which thus are not guaranteed to be optimal for the graph matching task.
Ranked #15 on
Graph Matching
on Willow Object Class
Context-Aware Graph Attention Networks
In particular, CaGAT conducts context-aware learning on both node feature representation and edge (weight) representation simultaneously and cooperatively in a unified manner which can boost their respective performance in network training.
GmCN: Graph Mask Convolutional Network
Graph Convolutional Networks (GCNs) have shown very powerful for graph data representation and learning tasks.
Semi-supervised Learning with Adaptive Neighborhood Graph Propagation Network
In this paper, we first re-interpret graph convolution operation in GCNs as a composition of feature propagation and (non-linear) transformation.
PH-GCN: Person Re-identification with Part-based Hierarchical Graph Convolutional Network
Given a person image, PH-GCN first constructs a hierarchical graph to represent the pairwise relationships among different parts.
Data Representation and Learning With Graph Diffusion-Embedding Networks
Recently, graph convolutional neural networks have been widely studied for graph-structured data representation and learning.
Semi-Supervised Learning With Graph Learning-Convolutional Networks
In this paper, we propose a novel Graph Learning-Convolutional Network (GLCN) for graph data representation and semi-supervised learning.
Robust Graph Data Learning via Latent Graph Convolutional Representation
Given an input graph $\textbf{A}$, LatGCR aims to generate a flexible latent graph $\widetilde{\textbf{A}}$ for graph convolutional representation which obviously enhances the representation capacity and also performs robustly w. r. t graph structural attacks and noises.
Ranked #29 on
Node Classification
on Cora
D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios
Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to boost the efficacy of such datasets to improve learning-based models.
MisGAN: Learning from Incomplete Data with Generative Adversarial Networks
Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks.
Multiple Graph Adversarial Learning
In this paper, we propose a novel Multiple Graph Adversarial Learning (MGAL) framework for multi-graph representation and learning.
Graph Learning-Convolutional Networks
Recently, graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks.
Graph Diffusion-Embedding Networks
We present a novel graph diffusion-embedding networks (GDEN) for graph structured data.
Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised Learning
Recently, graph convolutional network (GCN) has been widely used for semi-supervised classification and deep feature representation on graph-structured data.
Towards Discrete Solution: A Sparse Preserving Method for Correspondence Problem
Comparing with traditional relaxation models, SPM can incorporate the discrete one-to-one mapping constraint straightly via a sparse constraint and thus provides a tighter relaxation for original IQP matching problem.
Structured Quasi-Newton Methods for Optimization with Orthogonality Constraints
In particular, we are interested in applications that the Euclidean Hessian itself consists of a computational cheap part and a significantly expensive part.
Optimization and Control
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Decentralized cryptocurrencies feature the use of blockchain to transfer values among peers on networks without central agency.
Software Engineering Cryptography and Security
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Multi-Rate Multivariate Time Series (MR-MTS) are the multivariate time series observations which come with various sampling rates and encode multiple temporal dependencies.
Temporal Coherent and Graph Optimized Manifold Ranking for Visual Tracking
To address this problem, we propose a novel unified temporal coherence and graph optimized ranking model for weighted patch representation in visual tracking problem.
Context-aware Data Aggregation with Localized Information Privacy
The notion of context-awareness is incorporated in LIP by the introduction of priors, which enables the design of privacy-preserving data aggregation with knowledge of priors.
Highly accurate model for prediction of lung nodule malignancy with CT scans
Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN).
Relational Multi-Instance Learning for Concept Annotation from Medical Time Series
Recent advances in computing technology and sensor design have made it easier to collect longitudinal or time series data from patients, resulting in a gigantic amount of available medical data.
Graph Matching via Multiplicative Update Algorithm
As a fundamental problem in computer vision, graph matching problem can usually be formulated as a Quadratic Programming (QP) problem with doubly stochastic and discrete (integer) constraints.
Binary Constraint Preserving Graph Matching
There are three main contributions of the proposed method: (1) we propose a new graph matching relaxation model, called Binary Constraint Preserving Graph Matching (BPGM), which aims to incorporate the discrete binary mapping constraints more in graph matching relaxation.
Revisiting L21-norm Robustness with Vector Outlier Regularization
One interesting property of VOR is that how far an outlier lies away from its theoretically predicted value does not affect the final regularization and analysis results.
L1-norm Error Function Robustness and Outlier Regularization
(1) A key property of outlier regularization is that how far an outlier lies away from its theoretically predicted value does not affect the final regularization and analysis results.
Multiple Images Recovery Using a Single Affine Transformation
One approach to deal with noise image data is to use data recovery techniques which aim to recover the true uncorrupted signals from the observed noise images.
Structured Nonconvex and Nonsmooth Optimization: Algorithms and Iteration Complexity Analysis
In particular, we consider in this paper some constrained nonconvex optimization models in block decision variables, with or without coupled affine constraints.
Towards a solid solution of real-time fire and flame detection
Although the object detection and recognition has received growing attention for decades, a robust fire and flame detection method is rarely explored.
Graph-Laplacian PCA: Closed-Form Solution and Robustness
Principal Component Analysis (PCA) is a widely used to learn a low-dimensional representation.
