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Found 130 papers, 44 papers with code
What’s in a Name? Answer Equivalence For Open-Domain Question Answering
We incorporate answers for two settings: evaluation with additional answers and model training with equivalent answers.
Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering without Evidence Annotation
This paper investigates whether models can learn to find evidence from a large corpus, with only distant supervision from answer labels for model training, thereby generating no additional annotation cost.
Sparse-to-Dense Depth Completion Revisited: Sampling Strategy and Graph Construction
Depth completion is a widely studied problem of predicting a dense depth map from a sparse set of measurements and a single RGB image.
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion
Text-to-SQL parsers map natural language questions to programs that are executable over tables to generate answers, and are typically evaluated on large-scale datasets like Spider (Yu et al., 2018).
Towards Automated Movie Trailer Generation
Movie trailers are an essential tool for promoting films and attracting audiences.
Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World
Language agents that interact with the world on their own have great potential for automating digital tasks.
Invertible Diffusion Models for Compressed Sensing
To enable such memory-intensive end-to-end finetuning, we propose a novel two-level invertible design to transform both (1) the multi-step sampling process and (2) the noise estimation U-Net in each step into invertible networks.
Graphs Generalization under Distribution Shifts
Traditional machine learning methods heavily rely on the independent and identically distribution assumption, which imposes limitations when the test distribution deviates from the training distribution.
TexRO: Generating Delicate Textures of 3D Models by Recursive Optimization
We propose an optimal viewpoint selection strategy, that finds the most miniature set of viewpoints covering all the faces of a mesh.
DVMNet: Computing Relative Pose for Unseen Objects Beyond Hypotheses
Determining the relative pose of an object between two images is pivotal to the success of generalizable object pose estimation.
GGRt: Towards Pose-free Generalizable 3D Gaussian Splatting in Real-time
Specifically, we design a novel joint learning framework that consists of an Iterative Pose Optimization Network (IPO-Net) and a Generalizable 3D-Gaussians (G-3DG) model.
Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement
Our designed PPG branch is a plug-and-play network to produce the physics prior, which can be integrated into any deep framework.
GVA: Reconstructing Vivid 3D Gaussian Avatars from Monocular Videos
In this paper, we present a novel method that facilitates the creation of vivid 3D Gaussian avatars from monocular video inputs (GVA).
HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields
These representations are typically explicit, such as 3D point clouds or meshes, and thus provide information in the direct surroundings of the intermediate hand pose estimate.
Ranked #1 on
hand-object pose
on HO-3D
(using extra training data)
Multi-graph Graph Matching for Coronary Artery Semantic Labeling
However, deep learning-based methods encounter challenges in generating semantic labels for arterial segments, primarily due to the morphological similarity between arterial branches.
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness
Theoretical analysis yields sub-linear upper bounds for both loss regret and the cumulative violation of fairness constraints.
Parallel Structures in Pre-training Data Yield In-Context Learning
Pre-trained language models (LMs) are capable of in-context learning (ICL): they can adapt to a task with only a few examples given in the prompt without any parameter update.
Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA
Fourthly, we employed ICP, SICP or CPD algorithm to achieve a fine registration for the point clouds (together with the special points of APIGs) of the LV epicardial surfaces (LVERs) in SPECT and CTA images.
Vision Transformer-based Multimodal Feature Fusion Network for Lymphoma Segmentation on PET/CT Images
Methods: Our lymphoma segmentation approach combines a vision transformer with dual encoders, adeptly fusing PET and CT data via multimodal cross-attention fusion (MMCAF) module.
Supervised Algorithmic Fairness in Distribution Shifts: A Survey
Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced with changes in data distributions from source to target domains.
CMFN: Cross-Modal Fusion Network for Irregular Scene Text Recognition
However, the guidance of visual cues is ignored in the process of semantic mining, which limits the performance of the algorithm in recognizing irregular scene text.
BPDO:Boundary Points Dynamic Optimization for Arbitrary Shape Scene Text Detection
Arbitrary shape scene text detection is of great importance in scene understanding tasks.
Text Region Multiple Information Perception Network for Scene Text Detection
Segmentation-based scene text detection algorithms can handle arbitrary shape scene texts and have strong robustness and adaptability, so it has attracted wide attention.
Solving Continual Offline Reinforcement Learning with Decision Transformer
We aim to investigate whether Decision Transformer (DT), another offline RL paradigm, can serve as a more suitable offline continuous learner to address these issues.
Dr$^2$Net: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuning
We use two coefficients on either type of residual connections respectively, and introduce a dynamic training strategy that seamlessly transitions the pretrained model to a reversible network with much higher numerical precision.
GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization
This paper presents GIR, a 3D Gaussian Inverse Rendering method for relightable scene factorization.
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
End-to-End Temporal Action Detection with 1B Parameters Across 1000 Frames
Recently, temporal action detection (TAD) has seen significant performance improvement with end-to-end training.
Ranked #1 on
Temporal Action Localization
on EPIC-KITCHENS-100
Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration
Underwater images are subject to intricate and diverse degradation, inevitably affecting the effectiveness of underwater visual tasks.
Fairness-Aware Domain Generalization under Covariate and Dependence Shifts
Achieving the generalization of an invariant classifier from source domains to shifted target domains while simultaneously considering model fairness is a substantial and complex challenge in machine learning.
KnowledgeMath: Knowledge-Intensive Math Word Problem Solving in Finance Domains
We introduce KnowledgeMath, a novel benchmark designed to evaluate LLMs' capabilities in applying financial knowledge to solve complex math word problems.
A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images
By using five-fold cross-validation, a deep learning model incorporating uncertainty estimation was developed to classify RCC subtypes into clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC).
Retrieval-Augmented Chain-of-Thought in Semi-structured Domains
Applying existing question answering (QA) systems to specialized domains like law and finance presents challenges that necessitate domain expertise.
Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong
To reduce over-reliance on LLMs, we ask LLMs to provide contrastive information - explain both why the claim is true and false, and then we present both sides of the explanation to users.
Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics
By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information.
3D-Aware Hypothesis & Verification for Generalizable Relative Object Pose Estimation
Our goal then is to estimate the relative object pose between this reference view and a query image that depicts the object in a different pose.
Pursuing Counterfactual Fairness via Sequential Autoencoder Across Domains
This approach effectively separates environmental information and sensitive attributes from the embedded representation of classification features.
Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective
In this study, we introduce a novel problem, semantic OOD detection across domains, which simultaneously addresses both distributional shifts.
A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification
Uncertainty quantification from Ensemble 1 allowed for multi-stage decision-making to determine if the acquisition of SPECT data for a patient is necessary.
Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement
Underwater images suffer from complex and diverse degradation, which inevitably affects the performance of underwater visual tasks.
Transformer Compression via Subspace Projection
By projecting the whole transform model into a subspace, we enable matrix operations between the weight matrices in the model and features in a reduced-dimensional space, leading to significant reductions in model parameters and computing resources.
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs
As the basic element of graph-structured data, node has been recognized as the main object of study in graph representation learning.
Adaptation Speed Analysis for Fairness-aware Causal Models
In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable.
Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling
Coronary artery disease (CAD) is one of the primary causes leading to death worldwide.
HD-Fusion: Detailed Text-to-3D Generation Leveraging Multiple Noise Estimation
In this paper, we study Text-to-3D content generation leveraging 2D diffusion priors to enhance the quality and detail of the generated 3D models.
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
To answer these questions, we propose to evaluate $\textbf{counterfactual simulatability}$ of natural language explanations: whether an explanation can enable humans to precisely infer the model's outputs on diverse counterfactuals of the explained input.
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations
Despite significant progress having been made in question answering on tabular data (Table QA), it's unclear whether, and to what extent existing Table QA models are robust to task-specific perturbations, e. g., replacing key question entities or shuffling table columns.
Towards Fair Disentangled Online Learning for Changing Environments
To this end, in this paper, we propose a novel algorithm under the assumption that data collected at each time can be disentangled with two representations, an environment-invariant semantic factor and an environment-specific variation factor.
Just a Glimpse: Rethinking Temporal Information for Video Continual Learning
Studying continual learning in the video domain poses even more challenges, as video data contains a large number of frames, which places a higher burden on the replay memory.
Getting MoRE out of Mixture of Language Model Reasoning Experts
Beyond generalizability, the interpretable design of MoRE improves selective question answering results compared to baselines without incorporating inter-expert agreement.
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs
Large language models (LLMs) have achieved widespread success on a variety of in-context few-shot tasks, but this success is typically evaluated via correctness rather than consistency.
Coronary Artery Semantic Labeling using Edge Attention Graph Matching Network
The presence of atherosclerotic lesions in coronary arteries is the underlying pathophysiological basis of CAD, and accurate extraction of individual arterial branches using invasive coronary angiography (ICA) is crucial for stenosis detection and CAD diagnosis.
A new method using deep learning to predict the response to cardiac resynchronization therapy
The DL model outperformed the ML models, showcasing the additional predictive benefit of utilizing SPECT MPI polarmaps.
Large-capacity and Flexible Video Steganography via Invertible Neural Network
For large-capacity, we present a reversible pipeline to perform multiple videos hiding and recovering through a single invertible neural network (INN).
Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation
Recently, unsupervised image-to-image translation methods based on contrastive learning have achieved state-of-the-art results in many tasks.
Spectral Normalization and Dual Contrastive Regularization for Image-to-Image Translation
Similarly, we design a style contrastive loss to improve the global texture information of the generated images.
CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data
Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data.
FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model
In this work, we propose a training-Free conditional Diffusion Model (FreeDoM) used for various conditions.
A Unified Continual Learning Framework with General Parameter-Efficient Tuning
1) Learning: the pre-trained model adapts to the new task by tuning an online PET module, along with our adaptation speed calibration to align different PET modules, 2) Accumulation: the task-specific knowledge learned by the online PET module is accumulated into an offline PET module through momentum update, 3) Ensemble: During inference, we respectively construct two experts with online/offline PET modules (which are favored by the novel/historical tasks) for prediction ensemble.
Open Set Action Recognition via Multi-Label Evidential Learning
Existing methods for open-set action recognition focus on novelty detection that assumes video clips show a single action, which is unrealistic in the real world.
Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection
For severity detection, the hybrid model with radiomics features of both lungs and EAT showed improvements in AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) compared to the model with only lung radiomics features.
An Automated Vulnerability Detection Framework for Smart Contracts
With the increase of the adoption of blockchain technology in providing decentralized solutions to various problems, smart contracts have become more popular to the point that billions of US Dollars are currently exchanged every day through such technology.
AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms
Semantic labeling of coronary arterial segments in invasive coronary angiography (ICA) is important for automated assessment and report generation of coronary artery stenosis in the computer-aided diagnosis of coronary artery disease (CAD).
Re2TAL: Rewiring Pretrained Video Backbones for Reversible Temporal Action Localization
Temporal action localization (TAL) requires long-form reasoning to predict actions of various durations and complex content.
xFBD: Focused Building Damage Dataset and Analysis
This new challenge involves a new dataset and metrics indicating solution performance when damage is more local and limited than in xBD.
EgoLoc: Revisiting 3D Object Localization from Egocentric Videos with Visual Queries
Yet, we point out that the low number of camera poses caused by camera re-localization from previous VQ3D methods severally hinders their overall success rate.
LocPoseNet: Robust Location Prior for Unseen Object Pose Estimation
The prior can be used to initialize the 3D object translation and facilitate 3D object rotation estimation.
Re^2TAL: Rewiring Pretrained Video Backbones for Reversible Temporal Action Localization
Temporal action localization (TAL) requires long-form reasoning to predict actions of various durations and complex content.
Unsupervised 3D Keypoint Discovery with Multi-View Geometry
Analyzing and training 3D body posture models depend heavily on the availability of joint labels that are commonly acquired through laborious manual annotation of body joints or via marker-based joint localization using carefully curated markers and capturing systems.
Estimating more camera poses for ego-centric videos is essential for VQ3D
Visual queries 3D localization (VQ3D) is a task in the Ego4D Episodic Memory Benchmark.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
Multi-view information fusion using multi-view variational autoencoders to predict proximal femoral strength
The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion.
A New Hip Fracture Risk Index Derived from FEA-Computed Proximal Femur Fracture Loads and Energies-to-Failure
The results showed that the average of the area under the receive operating characteristic curve (AUC) using PC1 was always higher than that using all FE parameters combined in the male subjects.
DCE: Offline Reinforcement Learning With Double Conservative Estimates
Previous conservative estimation methods are usually difficult to avoid the impact of OOD actions on Q-value estimates.
On the Relation between Sensitivity and Accuracy in In-context Learning
In-context learning (ICL) suffers from oversensitivity to the prompt, making it unreliable in real-world scenarios.
Automatic reorientation by deep learning to generate short axis SPECT myocardial perfusion images
Five-fold cross-validation with 180 stress and 201 rest MPIs was used for training and internal validation; the remaining images were used for testing.
Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms
A deep learning model U-Net 3+, which incorporates the full-scale skip connections and deep supervisions, was proposed for automatic extraction of coronary arteries from ICAs.
A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images
Conclusion: Our proposed method achieved a high accuracy in extracting LV myocardial contours and assessing LV function.
Re-Examining Calibration: The Case of Question Answering
Building on those observations, we propose a new calibration metric, MacroCE, that better captures whether the model assigns low confidence to wrong predictions and high confidence to correct predictions.
Adaptive Fairness-Aware Online Meta-Learning for Changing Environments
Furthermore, to determine a good model parameter at each round, we propose a novel adaptive fairness-aware online meta-learning algorithm, namely FairSAOML, which is able to adapt to changing environments in both bias control and model precision.
ETAD: Training Action Detection End to End on a Laptop
We propose to sequentially forward the snippet frame through the video encoder, and backward only a small necessary portion of gradients to update the encoder.
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search
TNAS performs a modified bi-level Breadth-First Search in the proposed trees to discover a high-performance architecture.
End-to-End Active Speaker Detection
Recent advances in the Active Speaker Detection (ASD) problem build upon a two-stage process: feature extraction and spatio-temporal context aggregation.
Ranked #4 on
Audio-Visual Active Speaker Detection
on AVA-ActiveSpeaker
(using extra training data)
R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning
After RRL, the classification head is refined with global class-balanced classification loss to address the data imbalance issue as well as learn the decision boundaries between new and previous classes.
Unsupervised Learning of 3D Semantic Keypoints with Mutual Reconstruction
To the best of our knowledge, the proposed method is the first to mine 3D semantic consistent keypoints from a mutual reconstruction view.
Fusing Local Similarities for Retrieval-based 3D Orientation Estimation of Unseen Objects
In this paper, we tackle the task of estimating the 3D orientation of previously-unseen objects from monocular images.
SegTAD: Precise Temporal Action Detection via Semantic Segmentation
To address these issues and precisely model temporal action detection, we formulate the task of temporal action detection in a novel perspective of semantic segmentation.
Layer Adaptive Deep Neural Networks for Out-of-distribution Detection
During the forward pass of Deep Neural Networks (DNNs), inputs gradually transformed from low-level features to high-level conceptual labels.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
OWL (Observe, Watch, Listen): Audiovisual Temporal Context for Localizing Actions in Egocentric Videos
We validate our approach in two large-scale datasets, EPIC-Kitchens, and HOMAGE.
MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions
The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques.
Ranked #2 on
Natural Language Moment Retrieval
on MAD
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
Spatial-temporal V-Net for automatic segmentation and quantification of right ventricles in gated myocardial perfusion SPECT images
By integrating the spatial features from each cardiac frame of the gated MPS and the temporal features from the sequential cardiac frames of the gated MPS, we developed a Spatial-Temporal V-Net (ST-VNet) for automatic extraction of RV endocardial and epicardial contours.
Distantly-Supervised Evidence Retrieval Enables Question Answering without Evidence Annotation
Open-domain question answering answers a question based on evidence retrieved from a large corpus.
Automatic Identification of the End-Diastolic and End-Systolic Cardiac Frames from Invasive Coronary Angiography Videos
More specifically, a detection algorithm is first used to detect the key points of coronary arteries, and then an optical flow method is employed to track the trajectories of the selected key points.
What's in a Name? Answer Equivalence For Open-Domain Question Answering
We incorporate answers for two settings: evaluation with additional answers and model training with equivalent answers.
Fairness-Aware Online Meta-learning
To overcome such issues and bridge the gap, in this paper for the first time we proposed a novel online meta-learning algorithm, namely FFML, which is under the setting of unfairness prevention.
A method using deep learning to discover new predictors of CRT response from mechanical dyssynchrony on gated SPECT MPI
Correlation analysis was used to explain the relationships between new parameters with conventional LVMD parameters.
FedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework
With the proliferation of edge smart devices and the Internet of Vehicles (IoV) technologies, intelligent fatigue detection has become one of the most-used methods in our daily driving.
Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval
Complex question answering often requires finding a reasoning chain that consists of multiple evidence pieces.
Complex Factoid Question Answering with a Free-Text Knowledge Graph
DELFT's advantage comes from both the high coverage of its free-text knowledge graph-more than double that of dbpedia relations-and the novel graph neural network which reasons on the rich but noisy free-text evidence.
A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images
In this paper, we aim to develop a deep learning-based method using an encoder-decoder deep architecture to automatically extract both lumen and MA border.
Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus
In this paper, we propose a general scheme to analyze the gradient vanishing phenomenon, also known as the barren plateau phenomenon, in training quantum neural networks with the ZX-calculus.
A Deep Learning-Based Approach to Extracting Periosteal and Endosteal Contours of Proximal Femur in Quantitative CT Images
In this study, we proposed an approach based on deep learning for the automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments.
A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms
We aim to develop an automatic algorithm by deep learning to extract coronary arteries from ICAs. In this study, a multi-input and multi-scale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation.
Progressive Correspondence Pruning by Consensus Learning
Correspondence selection aims to correctly select the consistent matches (inliers) from an initial set of putative correspondences.
Wetting equilibrium in a rectangular channel
When a capillary channel with corners is wetted by a fluid, there are regions where the fluid fills the whole cross-section and regions where only the corners are filled by the fluid.
Soft Condensed Matter
Video Self-Stitching Graph Network for Temporal Action Localization
We have two key components in VSGN: video self-stitching (VSS) and cross-scale graph pyramid network (xGPN).
Ranked #16 on
Temporal Action Localization
on ActivityNet-1.3
A Nested Bi-level Optimization Framework for Robust Few Shot Learning
Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal.
On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries
Large-scale semantic parsing datasets annotated with logical forms have enabled major advances in supervised approaches.
A Primal-Dual Subgradient Approachfor Fair Meta Learning
In this work, we propose a Primal-Dual Fair Meta-learning framework, namely PDFM, which learns to train fair machine learning models using only a few examples based on data from related tasks.
Fair Meta-Learning For Few-Shot Classification
Artificial intelligence nowadays plays an increasingly prominent role in our life since decisions that were once made by humans are now delegated to automated systems.
Rank-Based Multi-task Learning for Fair Regression
In this work, we develop a novel fairness learning approach for multi-task regression models based on a biased training dataset, using a popular rank-based non-parametric independence test, i. e., Mann Whitney U statistic, for measuring the dependency between target variable and protected variables.
Unfairness Discovery and Prevention For Few-Shot Regression
In this work, we first discover discrimination from data using a causal Bayesian knowledge graph which not only demonstrates the dependency of the protected variable on target but also indicates causal effects between all variables.
A Novel Method for ECG Signal Classification via One-Dimensional Convolutional Neural Network
This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals.
A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images
During the experiments for the entire cohort then for male and female subjects separately, 90% of the subjects were used in 10-fold cross-validation for training and internal validation, and to select the optimal parameters of the proposed models; the rest of the subjects were used to evaluate the performance of models.
Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention
Transformers have achieved new heights modeling natural language as a sequence of text tokens.
Ranked #42 on
Question Answering
on HotpotQA
QDNN: DNN with Quantum Neural Network Layers
In this paper, we introduce a quantum extension of classical DNN, QDNN.
Learning Semantic Neural Tree for Human Parsing
In this paper, we design a novel semantic neural tree for human parsing, which uses a tree architecture to encode physiological structure of human body, and designs a coarse to fine process in a cascade manner to generate accurate results.
G-TAD: Sub-Graph Localization for Temporal Action Detection
In this work, we propose a graph convolutional network (GCN) model to adaptively incorporate multi-level semantic context into video features and cast temporal action detection as a sub-graph localization problem.
Ranked #5 on
Temporal Action Localization
on EPIC-KITCHENS-100
Rotation Invariant Point Cloud Classification: Where Local Geometry Meets Global Topology
To the best of our knowledge, this work is the first principled approach toward adaptively combining global and local information under the context of RI point cloud analysis.
Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization
Specifically, we incorporate convolutional operations along edges of the tree structure, and use the routing functions in each node to determine the root-to-leaf computational paths within the tree.
Ranked #33 on
Fine-Grained Image Classification
on Stanford Cars
Fine-Grained Image Classification
Fine-Grained Visual Categorization
Iterative Clustering with Game-Theoretic Matching for Robust Multi-consistency Correspondence
Matching corresponding features between two images is a fundamental task to computer vision with numerous applications in object recognition, robotics, and 3D reconstruction.
Comparative evaluation of 2D feature correspondence selection algorithms
Correspondence selection aiming at seeking correct feature correspondences from raw feature matches is pivotal for a number of feature-matching-based tasks.
Learning to Fuse Local Geometric Features for 3D Rigid Data Matching
This paper presents a simple yet very effective data-driven approach to fuse both low-level and high-level local geometric features for 3D rigid data matching.
ThumbNet: One Thumbnail Image Contains All You Need for Recognition
Although deep convolutional neural networks (CNNs) have achieved great success in computer vision tasks, its real-world application is still impeded by its voracious demand of computational resources.
NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences
Feature correspondence selection is pivotal to many feature-matching based tasks in computer vision.
A dataset and baselines for sequential open-domain question answering
Previous work on question-answering systems mainly focuses on answering individual questions, assuming they are independent and devoid of context.
LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning
LightNet is a lightweight, versatile and purely Matlab-based deep learning framework.
Analyzing Time Series Changes of Correlation between Market Share and Concerns on Companies measured through Search Engine Suggests
We finally propose how to predict the market share of a specific product genre based on the rates of concerns of those who search for Web pages.
Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization
From many fewer acquired measurements than suggested by the Nyquist sampling theory, compressive sensing (CS) theory demonstrates that, a signal can be reconstructed with high probability when it exhibits sparsity in some domain.
