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Found 198 papers, 74 papers with code
Beyond 3DMM Space: Towards Fine-grained 3D Face Reconstruction
However, most of their training data is constructed by 3D Morphable Model, whose space spanned is only a small part of the shape space.
Improving Evidence Retrieval with Claim-Evidence Entailment
Claim verification is challenging because it requires first to find textual evidence and then apply claim-evidence entailment to verify a claim.
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks
This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains.
MT-Speech at SemEval-2022 Task 10: Incorporating Data Augmentation and Auxiliary Task with Cross-Lingual Pretrained Language Model for Structured Sentiment Analysis
Furthermore, we leverage two data augment strategies and auxiliary tasks to improve the performance on few-label data and zero-shot cross-lingual settings.
XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks.
DESED: Dialogue-based Explanation for Sentence-level Event Detection
Many recent sentence-level event detection efforts focus on enriching sentence semantics, e. g., via multi-task or prompt-based learning.
Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity
Ensuring relevance quality in product search is a critical task as it impacts the customer’s ability to find intended products in the short-term as well as the general perception and trust of the e-commerce system in the long term.
Spelling Correction using Phonetics in E-commerce Search
In E-commerce search, spelling correction plays an important role to find desired products for customers in processing user-typed search queries.
Conditional generalized quantiles based on expected utility model and equivalent characterization of properties
As a counterpart to the (static) risk measures of generalized quantiles and motivated by Bellini et al. (2018), we propose a new kind of conditional risk measure called conditional generalized quantiles.
SparDA: Accelerating Dynamic Sparse Deep Neural Networks via Sparse-Dense Transformation
The property enables Spider (1) to extract dynamic sparsity patterns of tensors that are only known at runtime with little overhead; and (2) to transform the dynamic sparse computation into an equivalent dense computation which has been extremely optimized on commodity accelerators.
SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction
SuperScaler is a system that facilitates the design and generation of highly flexible parallelization plans.
Data-centric AI: Perspectives and Challenges
The role of data in building AI systems has recently been significantly magnified by the emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model advancements to ensuring data quality and reliability.
Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms
The CIN is composed of the invertible part to achieve high imperceptibility and the non-invertible part to strengthen the robustness against strong noise attacks.
Exploring Stochastic Autoregressive Image Modeling for Visual Representation
By introducing stochastic prediction and the parallel encoder-decoder, SAIM significantly improve the performance of autoregressive image modeling.
Instance-level Heterogeneous Domain Adaptation for Limited-labeled Sketch-to-Photo Retrieval
Therefore, previous works pre-train their models on rich-labeled photo retrieval data (i. e., source domain) and then fine-tune them on the limited-labeled sketch-to-photo retrieval data (i. e., target domain).
Ranked #1 on
Image Retrieval
on PKU-Reid
MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts
As a pioneering exploration that expands event detection to the scenarios involving informal and heterogeneous texts, we propose a new large-scale Chinese event detection dataset based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service.
Hard to Track Objects with Irregular Motions and Similar Appearances? Make It Easier by Buffering the Matching Space
To address this issue, our C-BIoU tracker adds buffers to expand the matching space of detections and tracks, which mitigates the effect of irregular motions in two aspects: one is to directly match identical but non-overlapping detections and tracks in adjacent frames, and the other is to compensate for the motion estimation bias in the matching space.
Ranked #3 on
Multi-Object Tracking
on DanceTrack
The Second-place Solution for CVPR 2022 SoccerNet Tracking Challenge
This is our second-place solution for CVPR 2022 SoccerNet Tracking Challenge.
The Second-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge
This is our 2nd-place solution for the ECCV 2022 Multiple People Tracking in Group Dance Challenge.
A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset
Furthermore, based on this dataset, we propose an end-to-end model that jointly optimizes the video understanding objective with knowledge graph embedding, which can not only better inject factual knowledge into video understanding but also generate effective multi-modal entity embedding for KG.
Self-distillation with Online Diffusion on Batch Manifolds Improves Deep Metric Learning
However, this type of method normally ignores the crucial knowledge hidden in the data (e. g., intra-class information variation), which is harmful to the generalization of the trained model.
Deep-Learning-Empowered Inverse Design for Freeform Reconfigurable Metasurfaces
Recently, artificial neural network empowered inverse design for metasurfaces has been developed that can design on-demand meta-atoms with diverse shapes and high performance, where the design process based on artificial intelligence is fast and automatic.
Multimodal Learning for Non-small Cell Lung Cancer Prognosis
This paper focuses on the task of survival time analysis for lung cancer.
ISA-Net: Improved spatial attention network for PET-CT tumor segmentation
In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information.
Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection
To pick up the ground plane prior for M3OD, we propose a Ground Plane Enhanced Network (GPENet) which resolves both issues at one go.
Revisiting Attention Weights as Explanations from an Information Theoretic Perspective
Attention mechanisms have recently demonstrated impressive performance on a range of NLP tasks, and attention scores are often used as a proxy for model explainability.
SIMPLE-RC: Group Network Inference with Non-Sharp Nulls and Weak Signals
To address these practical challenges, in this paper we propose a SIMPLE method with random coupling (SIMPLE-RC) for testing the non-sharp null hypothesis that a group of given nodes share similar (not necessarily identical) membership profiles under weaker signals.
Forecasting Human Trajectory from Scene History
We categorize scene history information into two types: historical group trajectory and individual-surroundings interaction.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
Improving alignment of dialogue agents via targeted human judgements
We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines.
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
Obj2Seq is able to flexibly determine input categories to satisfy customized requirements, and be easily extended to different visual tasks.
Nesting Forward Automatic Differentiation for Memory-Efficient Deep Neural Network Training
An activation function is an element-wise mathematical function and plays a crucial role in deep neural networks (DNN).
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or adaptive sampling weights on each sample in the training dataset.
RGB-Event Fusion for Moving Object Detection in Autonomous Driving
Moving Object Detection (MOD) is a critical vision task for successfully achieving safe autonomous driving.
ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization
In this work, we propose a fixed-length adaptive numerical data type called ANT to achieve low-bit quantization with tiny hardware overheads.
Actor-identified Spatiotemporal Action Detection --- Detecting Who Is Doing What in Videos
By using MOT, the spatiotemporal boundary of each actor is obtained and assigned to a unique actor identity.
Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation
The main challenges of applying NAS to semantic segmentation arise from two aspects: (i) high-resolution images to be processed; (ii) additional requirement of real-time inference speed (i. e., real-time semantic segmentation) for applications such as autonomous driving.
Differentially Private Counterfactuals via Functional Mechanism
Counterfactual, serving as one emerging type of model explanation, has attracted tons of attentions recently from both industry and academia.
ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classification
We evaluate ReMix on two public datasets with two state-of-the-art MIL methods.
Improving Generalization of Metric Learning via Listwise Self-distillation
Most deep metric learning (DML) methods employ a strategy that forces all positive samples to be close in the embedding space while keeping them away from negative ones.
Accelerating Shapley Explanation via Contributive Cooperator Selection
Even though Shapley value provides an effective explanation for a DNN model prediction, the computation relies on the enumeration of all possible input feature coalitions, which leads to the exponentially growing complexity.
Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach
The learning problem is framed as a subset selection task in which a subset of all possible rules needs to be selected to form an accurate and interpretable rule set.
Tutel: Adaptive Mixture-of-Experts at Scale
On effectiveness, the SwinV2-MoE model achieves superior accuracy in both pre-training and down-stream computer vision tasks such as COCO object detection than the counterpart dense model, indicating the readiness of Tutel for end-to-end real-world model training and inference.
Geo-Localization via Ground-to-Satellite Cross-View Image Retrieval
The large variation of viewpoint and irrelevant content around the target always hinder accurate image retrieval and its subsequent tasks.
Demo: low-power communications based on RIS and AI for 6G
By jointly designing the hardware and software, this prototype can realize real-time 4K video transmission with much reduced power consumption.
NMA: Neural Multi-slot Auctions with Externalities for Online Advertising
We design a list-wise deep rank module to guarantee incentive compatibility in end-to-end learning.
A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services as Example
Though widely used in industry, traditional task-oriented dialogue systems suffer from three bottlenecks: (i) difficult ontology construction (e. g., intents and slots); (ii) poor controllability and interpretability; (iii) annotation-hungry.
Limited-memory BFGS Optimisation of Phase-Only Computer-Generated Hologram for Fraunhofer Diffraction
We implement a novel limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimisation algorithm with cross entropy (CE) loss function, to produce phase-only computer-generated hologram (CGH) for holographic displays, with validation on a binary-phase modulation holographic projector.
Learning Individual Interactions from Population Dynamics with Discrete-Event Simulation Model
Traditional machine learning approaches capture complex system dynamics either with dynamic Bayesian networks and state space models, which is hard to scale because it is non-trivial to prescribe the dynamics with a sparse graph or a system of differential equations; or a deep neural networks, where the distributed representation of the learned dynamics is hard to interpret.
A Multi-Person Video Dataset Annotation Method of Spatio-Temporally Actions
Spatio-temporal action detection is an important and challenging problem in video understanding.
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings
We perform comprehensive explorations for the optimal conduct of knowledge distillation, which may provide useful insights for the learning of VQ based ANN index.
SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration
In this paper, we present a second order spatial compatibility (SC^2) measure based method for efficient and robust point cloud registration (PCR), called SC^2-PCR.
Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss
Experiments on the open datasets verify that our model outperforms the existing calibration methods and achieves a significant improvement on the calibration metric.
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training
Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2. 5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential.
Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification
We propose an algorithmic framework that leverages the advantages of the DNNs for data self-expression and task-specific predictions, to improve image classification.
Class-Aware Contrastive Semi-Supervised Learning
Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization.
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
This paper proposes an on-the-fly DFQ framework with sub-second quantization time, called SQuant, which can quantize networks on inference-only devices with low computation and memory requirements.
Learning Optical Flow with Adaptive Graph Reasoning
Our key idea is to decouple the context reasoning from the matching procedure, and exploit scene information to effectively assist motion estimation by learning to reason over the adaptive graph.
A comprehensive benchmark analysis for sand dust image reconstruction
In this paper, we presented a comprehensive perceptual study and analysis of real-world sand dust images, then constructed a Sand-dust Image Reconstruction Benchmark (SIRB) for training Convolutional Neural Networks (CNNs) and evaluating algorithms performance.
Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices
Experimental studies with 20 participants demonstrate that, thanks to the emotionship awareness, EMOShip not only achieves superior emotion recognition accuracy over existing methods (80. 2% vs. 69. 4%), but also provides a valuable understanding of the cause of emotions.
BBA-net: A bi-branch attention network for crowd counting
Our method can integrate the pedestrian's head and body information to enhance the feature expression ability of the density map.
SC2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration
In this paper, we present a second order spatial compatibility (SC^2) measure based method for efficient and robust point cloud registration (PCR), called SC^2-PCR.
Learning Optical Flow With Kernel Patch Attention
Optical flow is a fundamental method used for quantitative motion estimation on the image plane.
Multimodal Dynamics: Dynamical Fusion for Trustworthy Multimodal Classification
To the best of our knowledge, this is the first work to jointly model both feature and modality variation for different samples to provide trustworthy fusion in multi-modal classification.
DetarNet: Decoupling Translation and Rotation by Siamese Network for Point Cloud Registration
In this paper, we propose a neural network named DetarNet to decouple the translation $t$ and rotation $R$, so as to overcome the performance degradation due to their mutual interference in point cloud registration.
Neural Born Iteration Method For Solving Inverse Scattering Problems: 2D Cases
In this paper, we propose the neural Born iteration method (NeuralBIM) for solving 2D inverse scattering problems (ISPs) by drawing on the scheme of physics-informed supervised residual learning (PhiSRL) to emulate the computing process of the traditional Born iteration method (TBIM).
A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
In this paper, we comprehensively study three architecture design choices on ViT -- spatial reduction, doubled channels, and multiscale features -- and demonstrate that a vanilla ViT architecture can fulfill this goal without handcrafting multiscale features, maintaining the original ViT design philosophy.
NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
To cover language, image, and video at the same time for different scenarios, a 3D transformer encoder-decoder framework is designed, which can not only deal with videos as 3D data but also adapt to texts and images as 1D and 2D data, respectively.
Ranked #1 on
Text-to-Video Generation
on Kinetics
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
Towards Privacy-Preserving Affect Recognition: A Two-Level Deep Learning Architecture
Possible solutions to protect the privacy of users and avoid misuse of their identities are to: (1) extract anonymised facial features, namely action units (AU) from a database of images, discard the images and use AUs for processing and training, and (2) federated learning (FL) i. e. process raw images in users' local machines (local processing) and send the locally trained models to the main processing machine for aggregation (central processing).
Defense Against Explanation Manipulation
Explainable machine learning attracts increasing attention as it improves transparency of models, which is helpful for machine learning to be trusted in real applications.
DEGREE: Decomposition Based Explanation for Graph Neural Networks
Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data.
Generalized Demographic Parity for Group Fairness
We show the understanding of GDP from the probability perspective and theoretically reveal the connection between GDP regularizer and adversarial debiasing.
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression
Based on the implementation, we propose a memory-efficient framework called ``EXACT'', which for the first time demonstrate the potential and evaluate the feasibility of training GNNs with compressed activations.
Causal-TGAN: Causally-Aware Synthetic Tabular Data Generative Adversarial Network
The flexibility of this architecture is its capability to support different types of expert knowledge (e. g., complete or partial) about the causal nature of the underlying phenomenon.
LODE: Deep Local Deblurring and A New Benchmark
While recent deep deblurring algorithms have achieved remarkable progress, most existing methods focus on the global deblurring problem, where the image blur mostly arises from severe camera shake.
Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
In this paper, we propose a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (CAMoE) and a novel Dual Softmax Loss (DSL) to solve the two heterogeneity.
Ranked #5 on
Video Retrieval
on ActivityNet
(using extra training data)
LinEasyBO: Scalable Bayesian Optimization Approach for Analog Circuit Synthesis via One-Dimensional Subspaces
A large body of literature has proved that the Bayesian optimization framework is especially efficient and effective in analog circuit synthesis.
Actuarial-consistency and two-step actuarial valuations: a new paradigm to insurance valuation
This paper introduces new valuation schemes called actuarial-consistent valuations for insurance liabilities which depend on both financial and actuarial risks, which imposes that all actuarial risks are priced via standard actuarial principles.
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many domains.
Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions
Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence.
Density-Based Dynamic Curriculum Learning for Intent Detection
To handle this problem, we propose a density-based dynamic curriculum learning model.
RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting
Motion forecasting plays a significant role in various domains (e. g., autonomous driving, human-robot interaction), which aims to predict future motion sequences given a set of historical observations.
Opinion Prediction with User Fingerprinting
Experimental results show novel insights that were previously unknown such as better predictions for an increase in dynamic history length, the impact of the nature of the article on performance, thereby laying the foundation for further research.
An Efficient Asynchronous Batch Bayesian Optimization Approach for Analog Circuit Synthesis
A new strategy is proposed to better balance the exploration and exploitation and guarantee the diversity of the query points.
An Efficient Batch Constrained Bayesian Optimization Approach for Analog Circuit Synthesis via Multi-objective Acquisition Ensemble
After achieving the first feasible point, we favor the feasible region by adopting a specially designed penalization term to the acquisition function ensemble.
A Scalable 256-Elements E-Band Phased-Array Transceiver for Broadband Communication
For E-band wireless communications, a high gain steerable antenna with sub-arrays is desired to reduce the implementation complexity.
Probabilistic Model Distillation for Semantic Correspondence
We address this problem with the use of a novel Probabilistic Model Distillation (PMD) approach which transfers knowledge learned by a probabilistic teacher model on synthetic data to a static student model with the use of unlabeled real image pairs.
Model-Based Counterfactual Synthesizer for Interpretation
To address these limitations, we propose a Model-based Counterfactual Synthesizer (MCS) framework for interpreting machine learning models.
From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding
During synchronous decoding: the utterance paraphrasing is constrained by the structure of the logical form, therefore the canonical utterance can be paraphrased controlledly; the semantic decoding is guided by the semantics of the canonical utterance, therefore its logical form can be generated unsupervisedly.
MlTr: Multi-label Classification with Transformer
The task of multi-label image classification is to recognize all the object labels presented in an image.
Ranked #10 on
Multi-Label Classification
on MS-COCO
CAT: Cross Attention in Vision Transformer
In this paper, we propose a new attention mechanism in Transformer termed Cross Attention, which alternates attention inner the image patch instead of the whole image to capture local information and apply attention between image patches which are divided from single-channel feature maps capture global information.
MST: Masked Self-Supervised Transformer for Visual Representation
More importantly, the masked tokens together with the remaining tokens are further recovered by a global image decoder, which preserves the spatial information of the image and is more friendly to the downstream dense prediction tasks.
Calibrating multi-dimensional complex ODE from noisy data via deep neural networks
Our method is able to recover the ODE system without being subject to the curse of dimensionality and complicated ODE structure.
Towards Compact CNNs via Collaborative Compression
Channel pruning and tensor decomposition have received extensive attention in convolutional neural network compression.
ModelPS: An Interactive and Collaborative Platform for Editing Pre-trained Models at Scale
AI engineering has emerged as a crucial discipline to democratize deep neural network (DNN) models among software developers with a diverse background.
CT-Net: Complementary Transfering Network for Garment Transfer with Arbitrary Geometric Changes
Garment transfer shows great potential in realistic applications with the goal of transfering outfits across different people images.
GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions
Generating videos from text is a challenging task due to its high computational requirements for training and infinite possible answers for evaluation.
Mutual Graph Learning for Camouflaged Object Detection
Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models.
Improving Authorship Verification using Linguistic Divergence
It is also one of the first to use Deep Language Models in this setting.
ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation
Interpreting human actions requires understanding the spatial and temporal context of the scenes.
CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning
Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied.
Superresolving second-order correlation imaging using synthesized colored noise speckles
We present a novel method to synthesize non-trivial speckles that can enable superresolving second-order correlation imaging.
Optics Image and Video Processing
Sample canonical correlation coefficients of high-dimensional random vectors with finite rank correlations
If the entries of $\mathbf x$, $\mathbf y$ and $\mathbf z$ are i. i. d.
Probability
Cascade Network with Guided Loss and Hybrid Attention for Finding Good Correspondences
We then propose a hybrid attention block to extract feature, which integrates the Bayesian attentive context normalization (BACN) and channel-wise attention (CA).
Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation
With the wide use of deep neural networks (DNN), model interpretability has become a critical concern, since explainable decisions are preferred in high-stake scenarios.
Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection
Spotting objects that are visually adapted to their surroundings is challenging for both humans and AI.
Possible evidence of hydrogen emission in the first-overtone and multi-mode RR Lyrae variables
The nature of shock waves in non-fundamental mode RR Lyrae stars remains a mystery because of limited spectroscopic observations.
Solar and Stellar Astrophysics
A Polynomial Roth Theorem for Corners in Finite Fields
We prove a Roth type theorem for polynomial corners in the finite field setting.
Classical Analysis and ODEs Combinatorics Number Theory
Pattern-aware Data Augmentation for Query Rewriting in Voice Assistant Systems
Query rewriting (QR) systems are widely used to reduce the friction caused by errors in a spoken language understanding pipeline.
Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution
In this paper, we introduce a new framework called the sentiment-aspect attribution module (SAAM).
Bayesian Multi-type Mean Field Multi-agent Imitation Learning
We demonstrate the performance of our algorithm through benchmarking with three state-of-the-art multi-agent imitation learning algorithms on several tasks, including solving a multi-agent traffic optimization problem in a real-world transportation network.
Predicting Personal Opinion on Future Events with Fingerprints
We evaluate the proposed model on this dataset.
Fault-Aware Robust Control via Adversarial Reinforcement Learning
Robots have limited adaptation ability compared to humans and animals in the case of damage.
PAMS: Quantized Super-Resolution via Parameterized Max Scale
Specifically, most state-of-the-art SR models without batch normalization have a large dynamic quantization range, which also serves as another cause of performance drop.
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
To recognize unseen relations at test time, we explore the problem of zero-shot relation classification.
Analysis of Information Transfer from Heterogeneous Sources via Precise High-dimensional Asymptotics
A fundamental question in transfer learning is whether combining the data of both tasks works better than using only the target task's data (equivalently, whether a "positive information transfer" happens).
Linear-time Temporal Logic with Team Semantics: Expressivity and Complexity
We study the expressivity and complexity of model checking linear temporal logic with team semantics (TeamLTL).
Logic in Computer Science Computational Complexity F.4.1; D.2.4
Towards Fast, Accurate and Stable 3D Dense Face Alignment
Firstly, on the basis of a lightweight backbone, we propose a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously.
Ranked #1 on
3D Face Reconstruction
on Florence
(Mean NME metric)
RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning
Despite the remarkable successes of Convolutional Neural Networks (CNNs) in computer vision, it is time-consuming and error-prone to manually design a CNN.
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
The average video length of LaSOT is around 2, 500 frames, where each video contains various challenge factors that exist in real world video footage, such as the targets disappearing and re-appearing.
Cascade Graph Neural Networks for RGB-D Salient Object Detection
Current works either simply distill prior knowledge from the corresponding depth map for handling the RGB-image or blindly fuse color and geometric information to generate the coarse depth-aware representations, hindering the performance of RGB-D saliency detectors. In this work, we introduceCascade Graph Neural Networks(Cas-Gnn), a unified framework which is capable of comprehensively distilling and reasoning the mutual benefits between these two data sources through a set of cascade graphs, to learn powerful representations for RGB-D salient object detection.
Ranked #5 on
RGB-D Salient Object Detection
on NJU2K
PIC-Net: Point Cloud and Image Collaboration Network for Large-Scale Place Recognition
Place recognition is one of the hot research fields in automation technology and is still an open issue, Camera and Lidar are two mainstream sensors used in this task, Camera-based methods are easily affected by illumination and season changes, LIDAR cannot get the rich data as the image could , In this paper, we propose the PIC-Net (Point cloud and Image Collaboration Network), which use attention mechanism to fuse the features of image and point cloud, and mine the complementary information between the two.
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Combating fake news and misinformation propagation is a challenging task in the post-truth era.
Cascade Network with Guided Loss and Hybrid Attention for Two-view Geometry
We then propose a hybrid attention block to extract feature, which integrates the bayesian attentive context normalization (BACN) and channel-wise attention (CA).
ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation
We aim to improve the performance of Multiple Object Tracking and Segmentation (MOTS) by refinement.
Ranked #1 on
Multi-Object Tracking
on MOTS20
Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation
In this work, we propose a method that differentiates the satirical news and true news.
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
In this paper, we investigate a specific security problem called trojan attack, which aims to attack deployed DNN systems relying on the hidden trigger patterns inserted by malicious hackers.
Defending SVMs against Poisoning Attacks: the Hardness and DBSCAN Approach
For the data sanitization defense, we link it to the intrinsic dimensionality of data; in particular, we provide a sampling theorem in doubling metrics for explaining the effectiveness of DBSCAN (as a density-based outlier removal method) for defending against poisoning attacks.
MSDU-net: A Multi-Scale Dilated U-net for Blur Detection
Blur detection is the separation of blurred and clear regions of an image, which is an important and challenging task in computer vision.
Acme: A Research Framework for Distributed Reinforcement Learning
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
Learning to Detect Head Movement in Unconstrained Remote Gaze Estimation in the Wild
To better demonstrate the advantage of our methods, we further propose a new benchmark dataset with the most rich distribution of head-gaze combination reflecting real-world scenarios.
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks.
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
In this paper, we propose a generic trajectory forecasting framework (named EvolveGraph) with explicit relational structure recognition and prediction via latent interaction graphs among multiple heterogeneous, interactive agents.
Ranked #10 on
Trajectory Prediction
on Stanford Drone
Time Series Data Augmentation for Deep Learning: A Survey
In this paper, we systematically review different data augmentation methods for time series.
Hybrid Graph Neural Networks for Crowd Counting
In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph.
Tricritical physics in two-dimensional $p$-wave superfluids
When strong quantum fluctuations near resonance are taken into account, the line of continuous phase transitions terminates at two multicritical points near resonance, between which the transitions are expected to be first-order ones.
Quantum Gases
Relational State-Space Model for Stochastic Multi-Object Systems
Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other.
Game Design for Eliciting Distinguishable Behavior
The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems.
Dually Supervised Feature Pyramid for Object Detection and Segmentation
Feature pyramid architecture has been broadly adopted in object detection and segmentation to deal with multi-scale problem.
Bayesian Optimization Approach for Analog Circuit Synthesis Using Neural Network
Bayesian optimization with Gaussian process as surrogate model has been successfully applied to analog circuit synthesis.
Detecting Unknown Behaviors by Pre-defined Behaviours: An Bayesian Non-parametric Approach
An automatic mouse behavior recognition system can considerably reduce the workload of experimenters and facilitate the analysis process.
Using Panoramic Videos for Multi-person Localization and Tracking in a 3D Panoramic Coordinate
3D panoramic multi-person localization and tracking are prominent in many applications, however, conventional methods using LiDAR equipment could be economically expensive and also computationally inefficient due to the processing of point cloud data.
Ranked #1 on
Multi-Object Tracking
on MOT15_3D
(using extra training data)
TracKlinic: Diagnosis of Challenge Factors in Visual Tracking
For the analysis component, given the tracking results on all sequences, it investigates the behavior of the tracker under each individual factor and generates the report automatically.
XDeep: An Interpretation Tool for Deep Neural Networks
XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers.
A Hierarchical Mixture Density Network
One of the complicated relationships among three correlated variables could be a two-layer hierarchical many-to-many mapping.
TruNet: Short Videos Generation from Long Videos via Story-Preserving Truncation
In this work, we introduce a new problem, named as {\em story-preserving long video truncation}, that requires an algorithm to automatically truncate a long-duration video into multiple short and attractive sub-videos with each one containing an unbroken story.
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.
Contextual Local Explanation for Black Box Classifiers
We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE.
GLA-Net: An Attention Network with Guided Loss for Mismatch Removal
To establish the link between Fn-score and loss, we propose to guide the loss with the Fn-score directly.
Fairness in Deep Learning: A Computational Perspective
Deep learning is increasingly being used in high-stake decision making applications that affect individual lives.
Learning Credible Deep Neural Networks with Rationale Regularization
Recent explainability related studies have shown that state-of-the-art DNNs do not always adopt correct evidences to make decisions.
Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification
Interactions among users on social network platforms are usually positive, constructive and insightful.
Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images
Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible.
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed.
Ranked #1 on
Hand Gesture Recognition
on DHG-14
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
Interpretable Machine Learning (IML) has become increasingly important in many real-world applications, such as autonomous cars and medical diagnosis, where explanations are significantly preferred to help people better understand how machine learning systems work and further enhance their trust towards systems.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
XFake: Explainable Fake News Detector with Visualizations
In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility.
Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learning
In addition, we introduce a novel three-stage learning approach which enables the (cognitive) encoder to gradually distill useful knowledge from the paired (visual) encoder during the learning process.