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Found 311 papers, 104 papers with code
Friendly Topic Assistant for Transformer Based Abstractive Summarization
Abstractive document summarization is a comprehensive task including document understanding and summary generation, in which area Transformer-based models have achieved the state-of-the-art performance.
Translate-Train Embracing Translationese Artifacts
Translate-train is a general training approach to multilingual tasks.
WordNet Troponymy and Extraction of “Manner-Result” Relations
The procedure of extraction includes three steps and the results are based on the analysis of the whole set of verbs in WordNet.
Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling
Specifically, we design 5-type templates as instructional prompts, and each template includes a question that acts as the driver to teach UGEN to grasp the paradigm, options that list the candidate intents or slots to reduce the answer search space, and the context denotes original utterance.
GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection
For Subtask B, framed as a multi-label classification problem, we utilize various improved multi-label cross-entropy loss functions and analyze the performance of our method.
BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging
This measurement and the modulation masks are fed into our Recurrent Neural Network (RNN) to reconstruct the desired high-speed frames.
FDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
The ability to create high-quality 3D faces from a single image has become increasingly important with wide applications in video conferencing, AR/VR, and advanced video editing in movie industries.
MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction
Given a query, the task of Natural Language Video Localization (NLVL) is to localize a temporal moment in an untrimmed video that semantically matches the query.
BRIGHT: Bi-level Feature Representation of Image Collections using Groups of Hash Tables
We present BRIGHT, a bi-level feature representation for an image collection, consisting of a per-image latent space on top of a multi-scale feature grid space.
Semantic Segmentation with Bidirectional Language Models Improves Long-form ASR
We address this limitation by distilling punctuation knowledge from a bidirectional teacher language model (LM) trained on written, punctuated text.
Mobile Safety Application for Pedestrians
The work we discuss in this paper is related to a mobile application that utilizes the mobile phone sensors and Bluetooth communication to implement Personal Safety Message (PSM) broadcast using the SAE J2735 standard to create a Pedestrian to Vehicle (P2V) based safety warning structure.
Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model
The detection of machine-generated text, especially from large language models (LLMs), is crucial in preventing serious social problems resulting from their misuse.
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing
To mitigate the effects of label noise, learning with noisy labels (LNL) methods are designed to achieve better generalization performance.
Hybrid AHS: A Hybrid of Kalman Filter and Deep Learning for Acoustic Howling Suppression
During offline training, a pre-processed signal obtained from the Kalman filter and an ideal microphone signal generated via teacher-forced training strategy are used to train the deep neural network (DNN).
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction
In FormNetV2, we introduce a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss.
Deep Learning for Joint Acoustic Echo and Acoustic Howling Suppression in Hybrid Meetings
In particular, the interplay between acoustic echo and acoustic howling in a hybrid meeting makes the joint suppression of them difficult.
A Strong and Reproducible Object Detector with Only Public Datasets
This work presents Focal-Stable-DINO, a strong and reproducible object detection model which achieves 64. 6 AP on COCO val2017 and 64. 8 AP on COCO test-dev using only 700M parameters without any test time augmentation.
Ranked #3 on
Object Detection
on COCO test-dev
Decouple Non-parametric Knowledge Distillation For End-to-end Speech Translation
Existing techniques often attempt to make knowledge transfer from a powerful machine translation (MT) to speech translation (ST) model with some elaborate techniques, which often requires transcription as extra input during training.
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning
However, the final model often performs worse on the MT task than the MT model trained alone, which means that the knowledge transfer ability of this method is also limited.
DropDim: A Regularization Method for Transformer Networks
In contrast to the general dropout method, which randomly drops neurons, DropDim drops part of the embedding dimensions.
MS-LSTM: Exploring Spatiotemporal Multiscale Representations in Video Prediction Domain
The drastic variation of motion in spatial and temporal dimensions makes the video prediction task extremely challenging.
Segment Everything Everywhere All at Once
Inspired by the development of prompt-based universal interfaces for LLMs, this paper presents SEEM, a promptable, interactive model for Segmenting Everything Everywhere all at once in an image.
Ranked #4 on
Personalized Segmentation
on PerSeg
RoSI: Recovering 3D Shape Interiors from Few Articulation Images
The dominant majority of 3D models that appear in gaming, VR/AR, and those we use to train geometric deep learning algorithms are incomplete, since they are modeled as surface meshes and missing their interior structures.
Detection Transformer with Stable Matching
We point out that the unstable matching in DETR is caused by a multi-optimization path problem, which is highlighted by the one-to-one matching design in DETR.
SpanRE: Entities and Overlapping Relations Extraction Based on Spans and Entity Attention
Then we present a labeled span mechanism to extract the objects and relations simultaneously, we use the labeled span mechanism to generate labeled spans whose start and end positions indicate the objects, and whose labels correspond to relations of subject and objects.
UKP-SQuARE v3: A Platform for Multi-Agent QA Research
To ease research in multi-agent models, we extend UKP-SQuARE, an online platform for QA research, to support three families of multi-agent systems: i) agent selection, ii) early-fusion of agents, and iii) late-fusion of agents.
Coarse-to-Fine Active Segmentation of Interactable Parts in Real Scene Images
We introduce the first active learning (AL) framework for high-accuracy instance segmentation of dynamic, interactable parts from RGB images of real indoor scenes.
Fine-Grained Regional Prompt Tuning for Visual Abductive Reasoning
To tackle this, we propose a simple yet effective Regional Prompt Tuning, which encodes "regional visual hints" and "global contexts" separately at fine and coarse-grained levels.
Ranked #1 on
Visual Abductive Reasoning
on SHERLOCK
SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments
Existing work has typically been tailored for particular environments or representations.
DS-Fusion: Artistic Typography via Discriminated and Stylized Diffusion
We introduce a novel method to automatically generate an artistic typography by stylizing one or more letter fonts to visually convey the semantics of an input word, while ensuring that the output remains readable.
A Simple Framework for Open-Vocabulary Segmentation and Detection
We present OpenSeeD, a simple Open-vocabulary Segmentation and Detection framework that jointly learns from different segmentation and detection datasets.
Ranked #1 on
Instance Segmentation
on ADE20K val
(using extra training data)
MP-Former: Mask-Piloted Transformer for Image Segmentation
We present a mask-piloted Transformer which improves masked-attention in Mask2Former for image segmentation.
Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR
Recent DEtection TRansformer-based (DETR) models have obtained remarkable performance.
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
To effectively fuse language and vision modalities, we conceptually divide a closed-set detector into three phases and propose a tight fusion solution, which includes a feature enhancer, a language-guided query selection, and a cross-modality decoder for cross-modality fusion.
Ranked #1 on
Zero-Shot Object Detection
on ODinW
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing
Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data.
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks.
Concept-Level Explanation for the Generalization of a DNN
Therefore, in this paper, we investigate the generalization power of each interactive concept, and we use the generalization power of different interactive concepts to explain the generalization power of the entire DNN.
Introducing Depth into Transformer-based 3D Object Detection
To address the second issue, we introduce an auxiliary learning task called Depth-aware Negative Suppression loss.
AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving
Model parallelism is conventionally viewed as a method to scale a single large deep learning model beyond the memory limits of a single device.
Deep AHS: A Deep Learning Approach to Acoustic Howling Suppression
In this paper, we formulate acoustic howling suppression (AHS) as a supervised learning problem and propose a deep learning approach, called Deep AHS, to address it.
Futuristic Variations and Analysis in Fundus Images Corresponding to Biological Traits
Our proposed DL models, named FAG-Net and FGC-Net, correspondingly estimate biological traits (age and gender) and generates fundus images.
NeuralKalman: A Learnable Kalman Filter for Acoustic Echo Cancellation
The Kalman filter is widely used for addressing acoustic echo cancellation (AEC) problems due to their robustness to double-talk and fast convergence.
D$^2$CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts
We present D$^2$CSG, a neural model composed of two dual and complementary network branches, with dropouts, for unsupervised learning of compact constructive solid geometry (CSG) representations of 3D CAD shapes.
HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling
We present the first active learning tool for fine-grained 3D part labeling, a problem which challenges even the most advanced deep learning (DL) methods due to the significant structural variations among the small and intricate parts.
A Method For Eliminating Contour Errors In Self-Encoder Reconstructed Images
In this paper, we propose a self-supervised twin network approach based on this a priori.
See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning
The see stage scans the image and grounds the visual concept candidates with a visual perception model.
CA$^2$T-Net: Category-Agnostic 3D Articulation Transfer from Single Image
We present a neural network approach to transfer the motion from a single image of an articulated object to a rest-state (i. e., unarticulated) 3D model.
Lite DETR: An Interleaved Multi-Scale Encoder for Efficient DETR
Recent DEtection TRansformer-based (DETR) models have obtained remarkable performance.
CC-FedAvg: Computationally Customized Federated Averaging
Federated learning (FL) is an emerging paradigm to train model with distributed data from numerous Internet of Things (IoT) devices.
Improved Long-Form Spoken Language Translation with Large Language Models
A challenge in spoken language translation is that plenty of spoken content is long-form, but short units are necessary for obtaining high-quality translations.
ARO-Net: Learning Implicit Fields from Anchored Radial Observations
We introduce anchored radial observations (ARO), a novel shape encoding for learning implicit field representation of 3D shapes that is category-agnostic and generalizable amid significant shape variations.
Coordinating Cross-modal Distillation for Molecular Property Prediction
The existing graph methods have demonstrated that 3D geometric information is significant for better performance in MPP.
DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding
As phrase extraction can be regarded as a $1$D text segmentation problem, we formulate PEG as a dual detection problem and propose a novel DQ-DETR model, which introduces dual queries to probe different features from image and text for object prediction and phrase mask prediction.
Ranked #6 on
Referring Expression Comprehension
on RefCOCO
FLNeRF: 3D Facial Landmarks Estimation in Neural Radiance Fields
This paper presents the first significant work on directly predicting 3D face landmarks on neural radiance fields (NeRFs), without using intermediate representations such as 2D images, depth maps, or point clouds.
A Unified Mutual Supervision Framework for Referring Expression Segmentation and Generation
Our mutual supervision contains two directions.
QueryForm: A Simple Zero-shot Form Entity Query Framework
Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities.
On Optimizing the Communication of Model Parallelism
This pattern emerges when the two paradigms of model parallelism - intra-operator and inter-operator parallelism - are combined to support large models on large clusters.
FF2: A Feature Fusion Two-Stream Framework for Punctuation Restoration
To accomplish punctuation restoration, most existing methods focus on introducing extra information (e. g., part-of-speech) or addressing the class imbalance problem.
MPCFormer: fast, performant and private Transformer inference with MPC
Through extensive evaluations, we show that MPCFORMER significantly speeds up Transformer inference in MPC settings while achieving similar ML performance to the input model.
Neural Eigenfunctions Are Structured Representation Learners
In this paper, we introduce a scalable method for learning structured, adaptive-length deep representations.
NIFT: Neural Interaction Field and Template for Object Manipulation
Given a few object manipulation demos, NIFT guides the generation of the interaction imitation for a new object instance by matching the Neural Interaction Template (NIT) extracted from the demos in the target Neural Interaction Field (NIF) defined for the new object.
Language Model Decomposition: Quantifying the Dependency and Correlation of Language Models
A goodness-of-fit metric for LMD similar to the coefficient of determination is defined and used to measure the linear dependency of a set of LMs.
Regularized Data Programming with Bayesian Priors
The cost of manual data labeling can be a significant obstacle in supervised learning.
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness
Scaling up model sizes can lead to fundamentally new capabilities in many machine learning (ML) tasks.
Application of Deep Learning on Single-Cell RNA-sequencing Data Analysis: A Review
Single-cell RNA-sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously.
Exploiting Transformer in Reinforcement Learning for Interpretable Temporal Logic Motion Planning
Automaton based approaches have enabled robots to perform various complex tasks.
Physical Interaction: Reconstructing Hand-object Interactions with Physics
Single view-based reconstruction of hand-object interaction is challenging due to the severe observation missing caused by occlusions.
Learning Reconstructability for Drone Aerial Path Planning
To reconstruct a new urban scene, we first build the 3D scene proxy, then rely on the predicted reconstruction quality and uncertainty measures by our network, based off of the proxy geometry, to guide the drone path planning.
DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination
To relieve this issue, curriculum learning is proposed to improve model performance and generalization by ordering training samples in a meaningful (e. g., easy to hard) sequence.
UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA
In this paper, we introduce SQuARE v2, the new version of SQuARE, to provide an explainability infrastructure for comparing models based on methods such as saliency maps and graph-based explanations.
PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network
Solving partial differential equations (PDEs) is an important research means in the fields of physics, biology, and chemistry.
Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP
They use token-mixing layers to capture cross-token interactions, as opposed to the multi-head self-attention mechanism used by Transformers.
Long-term Leap Attention, Short-term Periodic Shift for Video Classification
By replacing a vanilla 2D attention with the LAPS, we could adapt a static transformer into a video one, with zero extra parameters and neglectable computation overhead ($\sim$2. 6\%).
Data-and-Knowledge Dual-Driven Automatic Modulation Recognition for Wireless Communication Networks
In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of different modulations.
Wavelet Regularization Benefits Adversarial Training
Since adversarial vulnerability can be regarded as a high-frequency phenomenon, it is essential to regulate the adversarially-trained neural network models in the frequency domain.
MS-RNN: A Flexible Multi-Scale Framework for Spatiotemporal Predictive Learning
Spatiotemporal predictive learning, which predicts future frames through historical prior knowledge with the aid of deep learning, is widely used in many fields.
DETR++: Taming Your Multi-Scale Detection Transformer
Convolutional Neural Networks (CNN) have dominated the field of detection ever since the success of AlexNet in ImageNet classification [12].
Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
In this paper we present Mask DINO, a unified object detection and segmentation framework.
Ranked #1 on
Panoptic Segmentation
on COCO test-dev
(using extra training data)
Why Adversarial Training of ReLU Networks Is Difficult?
We also prove that adversarial training tends to strengthen the influence of unconfident input samples with large gradient norms in an exponential manner.
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis
In this paper, we combine the above two paradigms together and propose a novel Generalizable Logic Synthesis (GALOIS) framework to synthesize hierarchical and strict cause-effect logic programs.
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation
A simple pixel selection strategy followed with the construction of multi-level contrastive units is introduced to optimize the model for both domain adaptation and active supervised learning.
Downstream Transformer Generation of Question-Answer Pairs with Preprocessing and Postprocessing Pipelines
We present a system called TP3 to perform a downstream task of transformers on generating question-answer pairs (QAPs) from a given article.
Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms
We consider the image and video compression on resource limited platforms.
New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography
We found ECG-based assessment outperforms the ADA Risk test, achieving a higher area under the curve (0. 80 vs. 0. 68) and positive predictive value (13% vs. 9%) -- 2. 6 times the prevalence of diabetes in the cohort.
Adaptive Split-Fusion Transformer
Neural networks for visual content understanding have recently evolved from convolutional ones (CNNs) to transformers.
Ranked #1 on
Image Classification
on CIFAR-100
(Accuracy metric)
FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance Improvement
On the other hand, it enlarges the distances between local models, resulting in an aggregated global model with poor performance.
Heterogeneous Autoencoder Empowered by Quadratic Neurons
Inspired by the complexity and diversity of biological neurons, a quadratic neuron is proposed to replace the inner product in the current neuron with a simplified quadratic function.
Randomized Sharpness-Aware Training for Boosting Computational Efficiency in Deep Learning
Optimizers in RST would perform a Bernoulli trial at each iteration to choose randomly from base algorithms (SGD) and sharpness-aware algorithms (SAM) with a probability arranged by a predefined scheduling function.
Group Contextualization for Video Recognition
By utilizing calibrators to embed feature with four different kinds of contexts in parallel, the learnt representation is expected to be more resilient to diverse types of activities.
Ranked #2 on
Egocentric Activity Recognition
on EGTEA
Contextual Networks and Unsupervised Ranking of Sentences
We construct a contextual network to represent a document with syntactic and semantic relations between word-sentence pairs, based on which we devise an unsupervised algorithm called CNATAR (Contextual Network And Text Analysis Rank) to score sentences, and rank them through a bi-objective 0-1 knapsack maximization problem over topic analysis and sentence scores.
Boilerplate Detection via Semantic Classification of TextBlocks
We present a hierarchical neural network model called SemText to detect HTML boilerplate based on a novel semantic representation of HTML tags, class names, and text blocks.
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
Compared to other models on the leaderboard, DINO significantly reduces its model size and pre-training data size while achieving better results.
Ranked #1 on
Object Detection
on COCO 2017 val
(box AP metric)
Vision-Language Intelligence: Tasks, Representation Learning, and Large Models
This survey is inspired by the remarkable progress in both computer vision and natural language processing, and recent trends shifting from single modality processing to multiple modality comprehension.
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising
Our method is universal and can be easily plugged into any DETR-like methods by adding dozens of lines of code to achieve a remarkable improvement.
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model
Capitalization normalization (truecasing) is the task of restoring the correct case (uppercase or lowercase) of noisy text.
Hierarchical Point Cloud Encoding and Decoding with Lightweight Self-Attention based Model
In this paper we present SA-CNN, a hierarchical and lightweight self-attention based encoding and decoding architecture for representation learning of point cloud data.
Multi-relation Message Passing for Multi-label Text Classification
These examples motivate the modelling of multiple types of bi-directional relationships between labels.
Multi-Label Classification
Multi-Label Image Classification
+3
Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning
In this paper, we propose an effective method to improve the model generalization by additionally penalizing the gradient norm of loss function during optimization.
A Variational Edge Partition Model for Supervised Graph Representation Learning
This paper introduces a graph generative process to model how the observed edges are generated by aggregating the node interactions over a set of overlapping node communities, each of which contributes to the edges via a logical OR mechanism.
Neural Dual Contouring
We introduce neural dual contouring (NDC), a new data-driven approach to mesh reconstruction based on dual contouring (DC).
RIM-Net: Recursive Implicit Fields for Unsupervised Learning of Hierarchical Shape Structures
Each level of the tree corresponds to an assembly of shape parts, represented as implicit functions, to reconstruct the input shape.
Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning
Existing model-parallel training systems either require users to manually create a parallelization plan or automatically generate one from a limited space of model parallelism configurations.
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR
We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR.
Temporal Sentence Grounding in Videos: A Survey and Future Directions
Temporal sentence grounding in videos (TSGV), \aka natural language video localization (NLVL) or video moment retrieval (VMR), aims to retrieve a temporal moment that semantically corresponds to a language query from an untrimmed video.
A Privacy-Preserving Unsupervised Domain Adaptation Framework for Clinical Text Analysis
To evaluate our proposed method's utility and privacy loss, we apply our model on a medical report disease label classification task using two noisy challenging clinical text datasets.
Neighborhood Region Smoothing Regularization for Finding Flat Minima In Deep Neural Networks
NRS leverages the finding that models would benefit from converging to flat minima, and tries to regularize the neighborhood region in weight space to yield approximate outputs.
Manifoldron: Direct Space Partition via Manifold Discovery
A neural network with the widely-used ReLU activation has been shown to partition the sample space into many convex polytopes for prediction.
Face Deblurring Based on Separable Normalization and Adaptive Denormalization
Face deblurring aims to restore a clear face image from a blurred input image with more explicit structure and facial details.
SAC-GAN: Structure-Aware Image Composition
Specifically, our network takes the semantic layout features from the input scene image, features encoded from the edges and silhouette in the input object patch, as well as a latent code as inputs, and generates a 2D spatial affine transform defining the translation and scaling of the object patch.
Hybrid Neural Networks for On-device Directional Hearing
On-device directional hearing requires audio source separation from a given direction while achieving stringent human-imperceptible latency requirements.
Ranked #1 on
Real-time Directional Hearing
on VCTK
UNIST: Unpaired Neural Implicit Shape Translation Network
We introduce UNIST, the first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains.
Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation
Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response.
Discovering and Explaining the Representation Bottleneck of DNNs
This paper explores the bottleneck of feature representations of deep neural networks (DNNs), from the perspective of the complexity of interactions between input variables encoded in DNNs.
Towards Debiasing Temporal Sentence Grounding in Video
In this paper, we propose two debiasing strategies, data debiasing and model debiasing, to "force" a TSGV model to capture cross-modal interactions.
Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation
In this paper, we introduce a novel approach that integrates uncertainty-aware spatiotemporal graph learning and model-based state estimation for a team of robots to collaboratively localize objects.
Clinical Evidence Engine: Proof-of-Concept For A Clinical-Domain-Agnostic Decision Support Infrastructure
As a result, clinicians cannot easily or rapidly scrutinize the CDSS recommendation when facing a difficult diagnosis or treatment decision in practice.
A Prototype-Oriented Framework for Unsupervised Domain Adaptation
Existing methods for unsupervised domain adaptation often rely on minimizing some statistical distance between the source and target samples in the latent space.
Edge Partition Modulated Graph Convolutional Networks
In this paper, we introduce a relational graph generative process to model how the observed edges are generated by aggregating the node interactions over multiple overlapping node communities, each of which represents a particular type of relation that contributes to the edges via a logical OR mechanism.
Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network
Truecasing is the task of restoring the correct case (uppercase or lowercase) of noisy text generated either by an automatic system for speech recognition or machine translation or by humans.
Detecting Small Objects in Thermal Images Using Single-Shot Detector
In this paper, we proposed DDSSD (Dilation and Deconvolution Single Shot Multibox Detector), an enhanced SSD with a novel feature fusion module which can improve the performance over SSD for small object detection.
Small Object Detection Based on Modified FSSD and Model Compression
Small objects have relatively low resolution, the unobvious visual features which are difficult to be extracted, so the existing object detection methods cannot effectively detect small objects, and the detection speed and stability are poor.
Automatic Modulation Classification Using Involution Enabled Residual Networks
Automatic modulation classification (AMC) is of crucial importance for realizing wireless intelligence communications.
Interpreting Attributions and Interactions of Adversarial Attacks
This paper aims to explain adversarial attacks in terms of how adversarial perturbations contribute to the attacking task.
Token Shift Transformer for Video Classification
It is worth noticing that our TokShift transformer is a pure convolutional-free video transformer pilot with computational efficiency for video understanding.
COSY: COunterfactual SYntax for Cross-Lingual Understanding
Pre-trained multilingual language models, e. g., multilingual-BERT, are widely used in cross-lingual tasks, yielding the state-of-the-art performance.
EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering
As a result, the backbone learns the shared knowledge among all clusters while modulated weights extract the cluster-specific features.
Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI
This workshop pays a special interest in theoretic foundations, limitations, and new application trends in the scope of XAI.
Turbulence-immune computational ghost imaging based on a multi-scale generative adversarial network
There is a consensus that turbulence-free images cannot be obtained by conventional computational ghost imaging (CGI) because the CGI is only a classic simulation, which does not satisfy the conditions of turbulence-free imaging.
MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis
With the rapid development of data-driven techniques, data has played an essential role in various computer vision tasks.
PhotoChat: A Human-Human Dialogue Dataset with Photo Sharing Behavior for Joint Image-Text Modeling
Based on this dataset, we propose two tasks to facilitate research on image-text modeling: a photo-sharing intent prediction task that predicts whether one intends to share a photo in the next conversation turn, and a photo retrieval task that retrieves the most relevant photo according to the dialogue context.
Learning Mesh Representations via Binary Space Partitioning Tree Networks
The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built over a set of planes, where the planes and convexes are both defined by learned network weights.
Interventional Video Grounding with Dual Contrastive Learning
2) Meanwhile, we introduce a dual contrastive learning approach (DCL) to better align the text and video by maximizing the mutual information (MI) between query and video clips, and the MI between start/end frames of a target moment and the others within a video to learn more informative visual representations.
Neural Marching Cubes
To tackle these challenges, we re-cast MC from a deep learning perspective, by designing tessellation templates more apt at preserving geometric features, and learning the vertex positions and mesh topologies from training meshes, to account for contextual information from nearby cubes.
D2IM-Net: Learning Detail Disentangled Implicit Fields From Single Images
We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features.
A Novel Automatic Modulation Classification Scheme Based on Multi-Scale Networks
Moreover, a novel loss function that combines the center loss and the cross entropy loss is exploited to learn both discriminative and separable features in order to further improve the classification performance.
SDNet: mutil-branch for single image deraining using swin
The former implements the basic rain pattern feature extraction, while the latter fuses different features to further extract and process the image features.
Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs
On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously.
Parallel Attention Network with Sequence Matching for Video Grounding
In this work, we propose a Parallel Attention Network with Sequence matching (SeqPAN) to address the challenges in this task: multi-modal representation learning, and target moment boundary prediction.
Video Corpus Moment Retrieval with Contrastive Learning
We adopt the first approach and introduce two contrastive learning objectives to refine video encoder and text encoder to learn video and text representations separately but with better alignment for VCMR.
Understanding Deep MIMO Detection
We demonstrate that data-driven DL detector asymptotically approaches to the maximum a posterior (MAP) detector in various scenarios but requires enough training samples to converge in time-varying channels.
Contrastive Attraction and Contrastive Repulsion for Representation Learning
We realize this strategy with contrastive attraction and contrastive repulsion (CACR), which makes the query not only exert a greater force to attract more distant positive samples but also do so to repel closer negative samples.
CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly
We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies.
RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes
We introduce RaidaR, a rich annotated image dataset of rainy street scenes, to support autonomous driving research.
Estimating the Generalization in Deep Neural Networks via Sparsity
By training DNNs with a wide range of generalization gap on popular datasets, we show that our key quantities and linear model could be efficient tools for estimating the generalization gap of DNNs.
Learning Multiscale Correlations for Human Motion Prediction
We evaluate our approach on two standard benchmark datasets for human motion prediction: Human3. 6M and CMU motion capture dataset.
Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation
We show that by investigating the feature entropy of units on only training data, it could give discrimination between networks with different generalization ability from the view of the effectiveness of feature representations.
In-plane selective area InSb-Al nanowire quantum networks
Strong spin-orbit semiconductor nanowires coupled to a superconductor are predicted to host Majorana zero modes.
Mesoscale and Nanoscale Physics
Memory-Efficient Network for Large-scale Video Compressive Sensing
With the knowledge of masks, optimization algorithms or deep learning methods are employed to reconstruct the desired high-speed video frames from this snapshot measurement.
Multi-Channel and Multi-Microphone Acoustic Echo Cancellation Using A Deep Learning Based Approach
Building on the deep learning based acoustic echo cancellation (AEC) in the single-loudspeaker (single-channel) and single-microphone setup, this paper investigates multi-channel AEC (MCAEC) and multi-microphone AEC (MMAEC).
MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing
To capture high-speed videos using a two-dimensional detector, video snapshot compressive imaging (SCI) is a promising system, where the video frames are coded by different masks and then compressed to a snapshot measurement.
Natural Language Video Localization: A Revisit in Span-based Question Answering Framework
Our study suggests that the span-based QA framework is an effective strategy to solve the NLVL problem.
TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models
With this key idea, we design TeraPipe, a high-performance token-level pipeline parallel algorithm for synchronous model-parallel training of Transformer-based language models.
Fast Dynamics in a Model Metallic Glass-forming Material
We investigate the fast $\beta$- and Johari-Goldstein (JG) $\beta$-relaxation processes, along with the elastic scattering response of glass-forming (GF) liquids and the Boson peak, in a simulated Al-Sm GF material exhibiting a fragile-strong (FS) transition.
Materials Science
Dynamic Heterogeneity, Cooperative Motion, and Johari-Goldstein $β$-Relaxation in a Metallic Glass-Forming Material Exhibiting a Fragile to Strong Transition
We investigate the Johari-Goldstein (JG) $\beta$-relaxation process in a model metallic glass-forming (GF) material (Al90Sm10), previously studied extensively by both frequency-dependent mechanical measurements and simulation studies devoted to equilibrium properties, by molecular dynamics simulations based on validated and optimized interatomic potentials with the primary aim of better understanding the nature of this universal relaxation process from a dynamic heterogeneity (DH) perspective.
Materials Science
Large zero-bias peaks in InSb-Al hybrid semiconductor-superconductor nanowire devices
We report electron transport studies on InSb-Al hybrid semiconductor-superconductor nanowire devices.
Mesoscale and Nanoscale Physics
Data Association Between Perception and V2V Communication Sensors
The connectivity between vehicles, infrastructure, and other traffic participants brings a new dimension to automotive safety applications.
Decision Making
Robotics
Systems and Control
Systems and Control
Reinforcement Learning for Flexibility Design Problems
Flexibility design problems are a class of problems that appear in strategic decision-making across industries, where the objective is to design a ($e. g.$, manufacturing) network that affords flexibility and adaptivity.
Towards Understanding and Improving Dropout in Game Theory
Experimental results on various DNNs and datasets have shown that the interaction loss can effectively improve the utility of dropout and boost the performance of DNNs.
An Embarrassingly Simple Model for Dialogue Relation Extraction
Dialogue relation extraction (RE) is to predict the relation type of two entities mentioned in a dialogue.
Ranked #9 on
Dialog Relation Extraction
on DialogRE
Simultaneous View and Feature Selection for Collaborative Multi-Robot Perception
In this paper, we propose a novel approach to collaborative multi-robot perception that simultaneously integrates view selection, feature selection, and object recognition into a unified regularized optimization formulation, which uses sparsity-inducing norms to identify the robots with the most representative views and the modalities with the most discriminative features.
Roof-GAN: Learning to Generate Roof Geometry and Relations for Residential Houses
This paper presents Roof-GAN, a novel generative adversarial network that generates structured geometry of residential roof structures as a set of roof primitives and their relationships.
DECOR-GAN: 3D Shape Detailization by Conditional Refinement
During testing, a style code is fed into the generator to condition the refinement.
Unsupervised Image Segmentation using Mutual Mean-Teaching
Unsupervised image segmentation aims at assigning the pixels with similar feature into a same cluster without annotation, which is an important task in computer vision.
GDPNet: Refining Latent Multi-View Graph for Relation Extraction
Recent advances on RE task are from BERT-based sequence modeling and graph-based modeling of relationships among the tokens in the sequence.
Ranked #4 on
Dialog Relation Extraction
on DialogRE
(F1c (v1) metric)
D$^2$IM-Net: Learning Detail Disentangled Implicit Fields from Single Images
We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features.
On Learning the Right Attention Point for Feature Enhancement
We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e. g., classification and segmentation.
LayoutGMN: Neural Graph Matching for Structural Layout Similarity
In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-the-art method based on graph neural networks and image convolution.
Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation
Panoptic segmentation that unifies instance segmentation and semantic segmentation has recently attracted increasing attention.
Ranked #16 on
Panoptic Segmentation
on COCO test-dev
Bidirectional Convolutional Poisson Gamma Dynamical Systems
Incorporating the natural document-sentence-word structure into hierarchical Bayesian modeling, we propose convolutional Poisson gamma dynamical systems (PGDS) that introduce not only word-level probabilistic convolutions, but also sentence-level stochastic temporal transitions.
