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Found 155 papers, 48 papers with code
Deep High-Resolution Representation Learning for Visual Recognition
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.
Ranked #1 on
Object Detection
on COCO test-dev
(Hardware Burden metric)
High-Resolution Representations for Labeling Pixels and Regions
The proposed approach achieves superior results to existing single-model networks on COCO object detection.
Ranked #7 on
Semantic Segmentation
on LIP val
BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs
Our experiments show that BuboGPT achieves impressive multi-modality understanding and visual grounding abilities during the interaction with human.
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild
3D face reconstruction from a single 2D image is a challenging problem with broad applications.
Ranked #7 on
Face Alignment
on AFLW2000-3D
Feature Quantization Improves GAN Training
The instability in GAN training has been a long-standing problem despite remarkable research efforts.
Ranked #1 on
Conditional Image Generation
on CIFAR-100
Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study
Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables such as epidemiological time series data, viral biology, population demographics, and the intersection of public policy and human behavior.
HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark
To design HW-NAS-Bench, we carefully collected the measured/estimated hardware performance of all the networks in the search spaces of both NAS-Bench-201 and FBNet, on six hardware devices that fall into three categories (i. e., commercial edge devices, FPGA, and ASIC).
Hardware Aware Neural Architecture Search
Neural Architecture Search
ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design
Specifically, on the algorithm level, ViTCoD prunes and polarizes the attention maps to have either denser or sparser fixed patterns for regularizing two levels of workloads without hurting the accuracy, largely reducing the attention computations while leaving room for alleviating the remaining dominant data movements; on top of that, we further integrate a lightweight and learnable auto-encoder module to enable trading the dominant high-cost data movements for lower-cost computations.
Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued.
Synchronous Bidirectional Inference for Neural Sequence Generation
In this work, we propose a synchronous bidirectional inference model to generate outputs using both left-to-right and right-to-left decoding simultaneously and interactively.
AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs
Specifically, AutoDNNchip consists of two integrated enablers: (1) a Chip Predictor, built on top of a graph-based accelerator representation, which can accurately and efficiently predict a DNN accelerator's energy, throughput, and area based on the DNN model parameters, hardware configuration, technology-based IPs, and platform constraints; and (2) a Chip Builder, which can automatically explore the design space of DNN chips (including IP selection, block configuration, resource balancing, etc.
xFraud: Explainable Fraud Transaction Detection
At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss.
A Survey of Federated Unlearning: A Taxonomy, Challenges and Future Directions
Achieving an optimal equilibrium among these facets is crucial for maintaining the effectiveness and usability of FL systems while adhering to privacy and security standards.
Unpaired Image-to-Image Translation via Latent Energy Transport
Instead of explicitly extracting the two codes and applying adaptive instance normalization to combine them, our latent EBM can implicitly learn to transport the source style code to the target style code while preserving the content code, an advantage over existing image translation methods.
Automatic Spatially-aware Fashion Concept Discovery
This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites.
Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes
This paper presents Chat-3D, which combines the 3D visual perceptual ability of pre-trained 3D representations and the impressive reasoning and conversation capabilities of advanced LLMs to achieve the first universal dialogue systems for 3D scenes.
Structure-Aware Human-Action Generation
Generating long-range skeleton-based human actions has been a challenging problem since small deviations of one frame can cause a malformed action sequence.
Ranked #2 on
Human action generation
on NTU RGB+D 2D
FaultNet: A Deep Convolutional Neural Network for bearing fault classification
In this work, we analyze vibration signal data of mechanical systems with bearings by combining different signal processing methods and coupling them with machine learning techniques to classify different types of bearing faults.
Ranked #2 on
Classification
on CWRU Bearing Dataset
(using extra training data)
IRS-Aided SWIPT: Joint Waveform, Active and Passive Beamforming Design Under Nonlinear Harvester Model
To facilitate practical implementation, we also propose a low-complexity design based on closed-form adaptive waveform schemes.
Information Theory Signal Processing Information Theory
Learning an Efficient Multimodal Depth Completion Model
With the wide application of sparse ToF sensors in mobile devices, RGB image-guided sparse depth completion has attracted extensive attention recently, but still faces some problems.
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.
Extending Multi-modal Contrastive Representations
Inspired by recent C-MCR, this paper proposes Extending Multimodal Contrastive Representation (Ex-MCR), a training-efficient and paired-data-free method to flexibly learn unified contrastive representation space for more than three modalities by integrating the knowledge of existing MCR spaces.
Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions
In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality.
Ranked #4 on
Human action generation
on NTU RGB+D 2D
De novo Drug Design using Reinforcement Learning with Multiple GPT Agents
A central challenge in this field is to generate molecules with specific properties while also producing a wide range of diverse candidates.
ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation
The performance of a semantic segmentation model for remote sensing (RS) images pretrained on an annotated dataset would greatly decrease when testing on another unannotated dataset because of the domain gap.
Depth-Assisted ResiDualGAN for Cross-Domain Aerial Images Semantic Segmentation
Generative methods are common approaches to minimizing the domain gap of aerial images which improves the performance of the downstream tasks, e. g., cross-domain semantic segmentation.
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed.
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendous demand for intelligent edge devices featuring on-site learning, while the practical realization of such systems remains a challenge due to the limited resources available at the edge and the required massive training costs for state-of-the-art (SOTA) DNNs.
Q-learning with Language Model for Edit-based Unsupervised Summarization
Q-learning is leveraged to train the agent to produce proper edit actions.
Modelling graph dynamics in fraud detection with "Attention"
At online retail platforms, detecting fraudulent accounts and transactions is crucial to improve customer experience, minimize loss, and avoid unauthorized transactions.
Bayesian Meta Sampling for Fast Uncertainty Adaptation
Specifically, we propose a Bayesian meta sampling framework consisting of two main components: a meta sampler and a sample adapter.
Video-Guided Curriculum Learning for Spoken Video Grounding
To rectify the discriminative phonemes and extract video-related information from noisy audio, we develop a novel video-guided curriculum learning (VGCL) during the audio pre-training process, which can make use of the vital visual perceptions to help understand the spoken language and suppress the external noise.
Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems
For instance, specificity is mandatory in a food-ordering dialogue task, whereas fluency is preferred in a language-teaching dialogue system.
NASA: Neural Architecture Search and Acceleration for Hardware Inspired Hybrid Networks
Multiplication is arguably the most cost-dominant operation in modern deep neural networks (DNNs), limiting their achievable efficiency and thus more extensive deployment in resource-constrained applications.
EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation
The precise detection of blood vessels in retinal images is crucial to the early diagnosis of the retinal vascular diseases, e. g., diabetic, hypertensive and solar retinopathies.
Distilling Coarse-to-Fine Semantic Matching Knowledge for Weakly Supervised 3D Visual Grounding
To accomplish this, we design a novel semantic matching model that analyzes the semantic similarity between object proposals and sentences in a coarse-to-fine manner.
Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance
The proposed UFG image dataset and evaluation protocols is intended to serve as a benchmark platform that can advance research of visual classification from approaching human performance to beyond human ability, via facilitating benchmark data of artificial intelligence (AI) not to be limited by the labels of human intelligence (HI).
Patchy Image Structure Classification Using Multi-Orientation Region Transform
To address above limitations, this paper proposes a novel Multi-Orientation Region Transform (MORT), which can effectively characterize both contour and structure features simultaneously, for patchy image structure classification.
E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation
Furthermore, the ablation studies verify the generalization of our method, where the proposed modal adapter is effective to bridge various OCR and MT models.
SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training
Results show that: 1) applied to inference, SD achieves up to 2. 44x energy efficiency as evaluated via real hardware implementations; 2) applied to training, SD leads to 10. 56x and 4. 48x reduction in the storage and training energy, with negligible accuracy loss compared to state-of-the-art training baselines.
Multi-Scale Context Aggregation Network with Attention-Guided for Crowd Counting
In this paper, we propose a multi-scale context aggregation network (MSCANet) based on single-column encoder-decoder architecture for crowd counting, which consists of an encoder based on a dense context-aware module (DCAM) and a hierarchical attention-guided decoder.
Improving End-to-End Text Image Translation From the Auxiliary Text Translation Task
End-to-end text image translation (TIT), which aims at translating the source language embedded in images to the target language, has attracted intensive attention in recent research.
Dilation-Erosion for Single-Frame Supervised Temporal Action Localization
To balance the annotation labor and the granularity of supervision, single-frame annotation has been introduced in temporal action localization.
Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation
Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes.
CoRTEx: Contrastive Learning for Representing Terms via Explanations with Applications on Constructing Biomedical Knowledge Graphs
Due to a lack of knowledge, previous contrastive learning models trained with Unified Medical Language System (UMLS) synonyms struggle at clustering difficult terms and do not generalize well beyond UMLS terms.
Empirical Evidence for the Fragment level Understanding on Drug Molecular Structure of LLMs
AI for drug discovery has been a research hotspot in recent years, and SMILES-based language models has been increasingly applied in drug molecular design.
Phrase Table as Recommendation Memory for Neural Machine Translation
Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently.
Multispectral Image Intrinsic Decomposition via Low Rank Constraint
In this paper, a Low Rank Multispectral Image Intrinsic Decomposition model (LRIID) is presented to decompose the shading and reflectance from a single multispectral image.
Towards Neural Machine Translation with Partially Aligned Corpora
While neural machine translation (NMT) has become the new paradigm, the parameter optimization requires large-scale parallel data which is scarce in many domains and language pairs.
A Conditional Variational Framework for Dialog Generation
Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems.
GUN: Gradual Upsampling Network for Single Image Super-Resolution
The GUN consists of an input layer, multiple upsampling and convolutional layers, and an output layer.
Local Patch Encoding-Based Method for Single Image Super-Resolution
The proposed method consists of a learning stage and a reconstructing stage.
Parallel Spectral Clustering Algorithm Based on Hadoop
Spectral clustering and cloud computing is emerging branch of computer science or related discipline.
Random Occlusion-recovery for Person Re-identification
As a basic task of multi-camera surveillance system, person re-identification aims to re-identify a query pedestrian observed from non-overlapping multiple cameras or across different time with a single camera.
Variance Reduction in Stochastic Particle-Optimization Sampling
Stochastic particle-optimization sampling (SPOS) is a recently-developed scalable Bayesian sampling framework that unifies stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) algorithms based on Wasserstein gradient flows.
Self-Adversarially Learned Bayesian Sampling
Scalable Bayesian sampling is playing an important role in modern machine learning, especially in the fast-developed unsupervised-(deep)-learning models.
Addressing Troublesome Words in Neural Machine Translation
One of the weaknesses of Neural Machine Translation (NMT) is in handling lowfrequency and ambiguous words, which we refer as troublesome words.
A Language Model based Evaluator for Sentence Compression
We herein present a language-model-based evaluator for deletion-based sentence compression and view this task as a series of deletion-and-evaluation operations using the evaluator.
Ranked #2 on
Sentence Compression
on Google Dataset
Multispectral Image Intrinsic Decomposition via Subspace Constraint
In this paper, a new Multispectral Image Intrinsic Decomposition model (MIID) is presented to decompose the shading and reflectance from a single multispectral image.
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data.
Unsupervised Rewriter for Multi-Sentence Compression
First, the word graph approach that simply concatenates fragments from multiple sentences may yield non-fluent or ungrammatical compression.
MobileFAN: Transferring Deep Hidden Representation for Face Alignment
To this end, we propose an effective lightweight model, namely Mobile Face Alignment Network (MobileFAN), using a simple backbone MobileNetV2 as the encoder and three deconvolutional layers as the decoder.
DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition
Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings
Extensive simulations and ablation studies, with real energy measurements from an FPGA board, confirm the superiority of our proposed strategies and demonstrate remarkable energy savings for training.
Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator
Pointer Generators have been the de facto standard for modern summarization systems.
A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation
To build connections among prediction branches, this paper introduces gradient boosting framework to deep classification model and proposes a gradient boosting network called BoostNet.
DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware Architectures
The recent breakthroughs in deep neural networks (DNNs) have spurred a tremendously increased demand for DNN accelerators.
A New MRAM-based Process In-Memory Accelerator for Efficient Neural Network Training with Floating Point Precision
The excellent performance of modern deep neural networks (DNNs) comes at an often prohibitive training cost, limiting the rapid development of DNN innovations and raising various environmental concerns.
Dual-discriminator GAN: A GAN way of profile face recognition
A wealth of angle problems occur when facial recognition is performed: At present, the feature extraction network presents eigenvectors with large differences between the frontal face and profile face recognition of the same person in many cases.
Local Differential Privacy based Federated Learning for Internet of Things
To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model.
Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck
Variational autoencoders (VAEs) are essential tools in end-to-end representation learning.
TIMELY: Pushing Data Movements and Interfaces in PIM Accelerators Towards Local and in Time Domain
Resistive-random-access-memory (ReRAM) based processing-in-memory (R$^2$PIM) accelerators show promise in bridging the gap between Internet of Thing devices' constrained resources and Convolutional/Deep Neural Networks' (CNNs/DNNs') prohibitive energy cost.
SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation
We present SmartExchange, an algorithm-hardware co-design framework to trade higher-cost memory storage/access for lower-cost computation, for energy-efficient inference of deep neural networks (DNNs).
Attacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients
In a case where the aggregator is untrusted and LDP is not applied to each user gradient, the aggregator can recover sensitive user data from these gradients.
CASIA's System for IWSLT 2020 Open Domain Translation
This paper describes the CASIA{'}s system for the IWSLT 2020 open domain translation task.
A Flexible Recurrent Residual Pyramid Network for Video Frame Interpolation
Inspired by classical pyramid energy minimization optical flow algorithms, this paper proposes a recurrent residual pyramid network (RRPN) for video frame interpolation.
HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark
To design HW-NAS-Bench, we carefully collected the measured/estimated hardware performance (e. g., energy cost and latency) of all the networks in the search space of both NAS-Bench-201 and FBNet, considering six hardware devices that fall into three categories (i. e., commercial edge devices, FPGA, and ASIC).
Hardware Aware Neural Architecture Search
Neural Architecture Search
Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling
Energy-based models (EBMs) for generative modeling parametrize a single net and can be directly trained by maximum likelihood estimation.
Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning
Specifically, we introduce a selection module that is independent of the translation module to score each candidate context sentence.
Role Taxonomy of Units in Deep Neural Networks
Identifying the role of network units in deep neural networks (DNNs) is critical in many aspects including giving understandings on the mechanisms of DNNs and building basic connections between deep learning and neuroscience.
Rethinking deinterlacing for early interlaced videos
Traditional deinterlacing approaches are mainly focused on early interlacing scanning systems and thus cannot handle the complex and complicated artifacts in real-world early interlaced videos.
ReMP: Rectified Metric Propagation for Few-Shot Learning
Few-shot learning features the capability of generalizing from a few examples.
Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks
Massive account registration has raised concerns on risk management in e-commerce companies, especially when registration increases rapidly within a short time frame.
Waveform and Beamforming Design for Intelligent Reflecting Surface Aided Wireless Power Transfer: Single-User and Multi-User Solutions
This paper highlights the fact that IRS can provide an extra passive beamforming gain on output DC power over conventional WPT designs and significantly influence the waveform design by leveraging the benefit of passive beamforming, frequency diversity and energy harvester nonlinearity.
Information Theory Signal Processing Information Theory
SDA: Improving Text Generation with Self Data Augmentation
Data augmentation has been widely used to improve deep neural networks in many research fields, such as computer vision.
Interaction between optical pulse and tumor using finite element analysis
As a non-destructive biological tissue imaging technology, photoacoustic imaging has important application value in the field of biomedicine.
Biological Physics
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.
Super-Resolving Compressed Video in Coding Chain
Scaling and lossy coding are widely used in video transmission and storage.
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.
A Comprehensive Survey of 6G Wireless Communications
First, we give an overview of 6G from perspectives of technologies, security and privacy, and applications.
Cascaded Prediction Network via Segment Tree for Temporal Video Grounding
Temporal video grounding aims to localize the target segment which is semantically aligned with the given sentence in an untrimmed video.
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency
The recent breakthroughs of deep neural networks (DNNs) and the advent of billions of Internet of Things (IoT) devices have excited an explosive demand for intelligent IoT devices equipped with domain-specific DNN accelerators.
Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity
Previous studies combining knowledge graph (KG) with neural machine translation (NMT) have two problems: i) Knowledge under-utilization: they only focus on the entities that appear in both KG and training sentence pairs, making much knowledge in KG unable to be fully utilized.
Multi-frame Joint Enhancement for Early Interlaced Videos
Early interlaced videos usually contain multiple and interlacing and complex compression artifacts, which significantly reduce the visual quality.
Ranked #1 on
Video Deinterlacing
on MSU Deinterlacer Benchmark
D$^2$-GCN: Data-Dependent GCNs for Boosting Both Efficiency and Scalability
Graph Convolutional Networks (GCNs) have gained an increasing attention thanks to their state-of-the-art (SOTA) performance in graph-based learning tasks.
Rethinking Deep Face Restoration
While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features and cannot authentically reconstruct the faces.
TransTCN: An Attention-based TCN Framework for Sequential Modeling
The model was evaluated across a wide range of the tasks in time series, which are commonly used to the benchmark of TCN and recurrent networks.
Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture
Transaction checkout fraud detection is an essential risk control components for E-commerce marketplaces.
Rethinking Sentiment Style Transfer
To overcome this issue, we propose a graph-based method to extract attribute content and attribute-independent content from input sentences in the YELP dataset and IMDB dataset.
EnergyNet: Energy-Efficient Dynamic Inference
The prohibitive energy cost of running high-performance Convolutional Neural Networks (CNNs) has been limiting their deployment on resource-constrained platforms including mobile and wearable devices.
Equivalence between algorithmic instability and transition to replica symmetry breaking in perceptron learning systems
Binary perceptron is a fundamental model of supervised learning for the non-convex optimization, which is a root of the popular deep learning.
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.
Spatio-temporal Gait Feature with Global Distance Alignment
In this paper, we try to enhance the discrimination of spatio-temporal gait features from two aspects: effective extraction of spatio-temporal gait features and reasonable refinement of extracted features.
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.
Indoor simultaneous localization and mapping based on fringe projection profilometry
Simultaneous Localization and Mapping (SLAM) plays an important role in outdoor and indoor applications ranging from autonomous driving to indoor robotics.
Age Minimization in Outdoor and Indoor Communications with Relay-aided Dual RIS
In this paper, we investigate an outdoor and indoor wireless communication network with the assistance of a novel relay-aided double-sided reconfigurable intelligent surface (RIS).
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection
Apart from rule-based and machine learning filters that are already deployed in production, we want to enable efficient real-time inference with graph neural networks (GNNs), which is useful to catch multihop risk propagation in a transaction graph.
AntPivot: Livestream Highlight Detection via Hierarchical Attention Mechanism
In recent days, streaming technology has greatly promoted the development in the field of livestream.
Turning to a Teacher for Timestamp Supervised Temporal Action Segmentation
To obtain more information to optimize the model, the existing method generated pseudo frame-wise labels iteratively based on the output of a segmentation model and the timestamp annotations.
A Versatile Adaptive Curriculum Learning Framework for Task-oriented Dialogue Policy Learning
It supports evaluating the difficulty of dialogue tasks only using the learning experiences of dialogue policy and skip-level selection according to their learning needs to maximize the learning efficiency.
AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments
The task of argument mining aims to detect all possible argumentative components and identify their relationships automatically.
e-G2C: A 0.14-to-8.31 $μ$J/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM
This work presents the first silicon-validated dedicated EGM-to-ECG (G2C) processor, dubbed e-G2C, featuring continuous lightweight anomaly detection, event-driven coarse/precise conversion, and on-chip adaptation.
A Simple Yet Effective Corpus Construction Method for Chinese Sentence Compression
To remedy this shortcoming, we present a dependency-tree-based method to construct a Chinese corpus with 151k pairs of sentences and compression based on Chinese language-specific characteristics.
Can We Really Trust Explanations? Evaluating the Stability of Feature Attribution Explanation Methods via Adversarial Attack
“Explanations can increase the transparency of neural networks and make them more trustworthy.
CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing
Leveraging supervised information can lead to superior retrieval performance in the image hashing domain but the performance degrades significantly without enough labeled data.
Behavioral graph fraud detection in E-commerce
Our experiments show that embedding features learned from similarity based behavioral graph have achieved significant performance increase to the baseline fraud detection model in various business scenarios.
Boosting Semi-Supervised 3D Object Detection with Semi-Sampling
When training a teacher-student semi-supervised framework, we randomly select gt samples and pseudo samples to both labeled frames and unlabeled frames, making a strong data augmentation for them.
Stereo Image Rain Removal via Dual-View Mutual Attention
Stereo images, containing left and right view images with disparity, are utilized in solving low-vision tasks recently, e. g., rain removal and super-resolution.
Life-long Learning for Multilingual Neural Machine Translation with Knowledge Distillation
To alleviate the CF, we investigate knowledge distillation based life-long learning methods.
Deformation measurement of a soil mixing retaining wall using terrestrial laser scanning
Retaining walls are often built to prevent excessive lateral movements of the ground surrounding an excavation site.
CoopInit: Initializing Generative Adversarial Networks via Cooperative Learning
The proposed algorithm consists of two learning stages: (i) Cooperative initialization stage: The discriminator of GAN is treated as an energy-based model (EBM) and is optimized via maximum likelihood estimation (MLE), with the help of the GAN's generator to provide synthetic data to approximate the learning gradients.
Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models
This paper proposes a method for generating images of customized objects specified by users.
DATE: Domain Adaptive Product Seeker for E-commerce
Then, we present two cooperative seekers to simultaneously search the image for PR and localize the product for PG.
Identity Encoder for Personalized Diffusion
Our approach greatly reduces the overhead for personalized image generation and is more applicable in many potential applications.
BCEdge: SLO-Aware DNN Inference Services with Adaptive Batching on Edge Platforms
As deep neural networks (DNNs) are being applied to a wide range of edge intelligent applications, it is critical for edge inference platforms to have both high-throughput and low-latency at the same time.
Multi-Teacher Knowledge Distillation For Text Image Machine Translation
Text image machine translation (TIMT) has been widely used in various real-world applications, which translates source language texts in images into another target language sentence.
Instant-NeRF: Instant On-Device Neural Radiance Field Training via Algorithm-Accelerator Co-Designed Near-Memory Processing
Instant on-device Neural Radiance Fields (NeRFs) are in growing demand for unleashing the promise of immersive AR/VR experiences, but are still limited by their prohibitive training time.
Pink-Eggs Dataset V1: A Step Toward Invasive Species Management Using Deep Learning Embedded Solutions
We introduce a novel dataset consisting of images depicting pink eggs that have been identified as Pomacea canaliculata eggs, accompanied by corresponding bounding box annotations.
Revisiting the Stack-Based Inverse Tone Mapping
In this paper, we revisit the stack-based ITM approaches and propose a novel method to reconstruct HDR radiance from a single image, which only needs to estimate two exposure images.
RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texture
The proposed framework consists of two significant components: Geometry Guided Diffusion and Mesh Optimization.
CLIP3Dstyler: Language Guided 3D Arbitrary Neural Style Transfer
In this paper, we propose a novel language-guided 3D arbitrary neural style transfer method (CLIP3Dstyler).
DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference
Due to limited resources on edge and different characteristics of deep neural network (DNN) models, it is a big challenge to optimize DNN inference performance in terms of energy consumption and end-to-end latency on edge devices.
Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond
An authentic face restoration system is becoming increasingly demanding in many computer vision applications, e. g., image enhancement, video communication, and taking portrait.
RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition
Palmprint recently shows great potential in recognition applications as it is a privacy-friendly and stable biometric.
Cross-Dataset-Robust Method for Blind Real-World Image Quality Assessment
First, many individual models based on popular and state-of-the-art (SOTA) Swin-Transformer (SwinT) are trained on different real-world BIQA datasets respectively.
TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework
Industrial systems demand reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime.
UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs
Text-to-image diffusion models have demonstrated remarkable capabilities in transforming textual prompts into coherent images, yet the computational cost of their inference remains a persistent challenge.
Cut-and-Paste: Subject-Driven Video Editing with Attention Control
This paper presents a novel framework termed Cut-and-Paste for real-word semantic video editing under the guidance of text prompt and additional reference image.
MobileDiffusion: Subsecond Text-to-Image Generation on Mobile Devices
The deployment of large-scale text-to-image diffusion models on mobile devices is impeded by their substantial model size and slow inference speed.
Connecting Multi-modal Contrastive Representations
This paper proposes a novel training-efficient method for learning MCR without paired data called Connecting Multi-modal Contrastive Representations (C-MCR).
HiFi Tuner: High-Fidelity Subject-Driven Fine-Tuning for Diffusion Models
We further extend our method to a novel image editing task: substituting the subject in an image through textual manipulations.
DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models
Our extensive experiments demonstrate the superior performance of our method in terms of visual quality, identity preservation, and text control, showcasing its effectiveness in the context of text-guided subject-driven image inpainting.
Adapting Vision Transformer for Efficient Change Detection
Most change detection models based on vision transformers currently follow a "pretraining then fine-tuning" strategy.
Multi-Modal Domain Adaptation Across Video Scenes for Temporal Video Grounding
Thus, to address this issue and enhance model performance on new scenes, we explore the TVG task in an unsupervised domain adaptation (UDA) setting across scenes for the first time, where the video-query pairs in the source scene (domain) are labeled with temporal boundaries, while those in the target scene are not.
Comparing roughness maps generated by five roughness descriptors for LiDAR-derived digital elevation models
Terrain surface roughness, often described abstractly, poses challenges in quantitative characterisation with various descriptors found in the literature.
Instruct-Imagen: Image Generation with Multi-modal Instruction
We introduce *multi-modal instruction* for image generation, a task representation articulating a range of generation intents with precision.
Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics
Second, the natural co-occurrence of audio and video is utilized to learn the color semantic correlations between audio and visual scenes.
A novel spatial-frequency domain network for zero-shot incremental learning
The proposed SFDNet has the ability to effectively extract spatial-frequency feature representation from input images, improve the accuracy of image classification, and fundamentally alleviate catastrophic forgetting.
Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning
Through pretraining on a corpus with various sources, Large Language Models (LLMs) have gained impressive performance.
OHTA: One-shot Hand Avatar via Data-driven Implicit Priors
In this paper, we delve into the creation of one-shot hand avatars, attaining high-fidelity and drivable hand representations swiftly from a single image.
MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising
Through extensive offline and online experiments, we demonstrate the effectiveness and efficiency of our method, and we obtain a 7. 01% lift in Gross Merchandise Volume, a 7. 42% lift in Return on Investment, and a 3. 26% lift in ad buy count.
