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Found 144 papers, 40 papers with code
Visual Prompt Tuning for Few-Shot Text Classification
In few-shot text classification, a feasible paradigm for deploying VL-PTMs is to align the input samples and their category names via the text encoders.
OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning
Understanding and modeling enzyme-substrate interactions is crucial for catalytic mechanism research, enzyme engineering, and metabolic engineering.
Near-Field Sensing Enabled Predictive Beamforming: Fundamentals, Framework, and Opportunities
The article proposes a novel near-field predictive beamforming framework for high-mobility wireless networks.
Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving
In contrast to existing VLMs with over 7B parameters and unstructured language processing(e. g., LLaVA-1. 5), FastDrive understands structured and concise descriptions and generates machine-friendly driving decisions with high efficiency.
SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformers
The generation and editing of audio-conditioned talking portraits guided by multimodal inputs, including text, images, and videos, remains under explored.
STDR: Spatio-Temporal Decoupling for Real-Time Dynamic Scene Rendering
Although dynamic scene reconstruction has long been a fundamental challenge in 3D vision, the recent emergence of 3D Gaussian Splatting (3DGS) offers a promising direction by enabling high-quality, real-time rendering through explicit Gaussian primitives.
CiUAV: A Multi-Task 3D Indoor Localization System for UAVs based on Channel State Information
The proposed system provides a cost-effective and scalable solution, demonstrating its usefulness for UAV applications in resource-constrained indoor environments.
DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving
Research interest in end-to-end autonomous driving has surged owing to its fully differentiable design integrating modular tasks, i. e. perception, prediction and planing, which enables optimization in pursuit of the ultimate goal.
ProFashion: Prototype-guided Fashion Video Generation with Multiple Reference Images
To address these issues, we propose ProFashion, a fashion video generation framework leveraging multiple reference images to achieve improved view consistency and temporal coherency.
Fast-Slow Thinking for Large Vision-Language Model Reasoning
Recent advances in large vision-language models (LVLMs) have revealed an \textit{overthinking} phenomenon, where models generate verbose reasoning across all tasks regardless of questions.
NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results
This paper provides a review of the NTIRE 2025 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes.
Pinching-Antenna System (PASS) Enhanced Covert Communications
Capitalizing on this high reconfigurable flexibility of antennas, the potential of PASS for covert communications is investigated.
Boosting MLLM Reasoning with Text-Debiased Hint-GRPO
Current reasoning methods fall into two types: PRM, which supervises the intermediate reasoning steps, and ORM, which supervises the final results.
SED-MVS: Segmentation-Driven and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint
Recently, patch-deformation methods have exhibited significant effectiveness in multi-view stereo owing to the deformable and expandable patches in reconstructing textureless areas.
Optimizing Ride-Pooling Operations with Extended Pickup and Drop-Off Flexibility
The Ride-Pool Matching Problem (RMP) is central to on-demand ride-pooling services, where vehicles must be matched with multiple requests while adhering to service constraints such as pickup delays, detour limits, and vehicle capacity.
CMMCoT: Enhancing Complex Multi-Image Comprehension via Multi-Modal Chain-of-Thought and Memory Augmentation
The construction of interleaved multimodal multi-step reasoning chains, which utilize critical visual region tokens, extracted from intermediate reasoning steps, as supervisory signals.
Streaming Video Question-Answering with In-context Video KV-Cache Retrieval
ReKV enables the separation of video encoding and question-answering across different processes and GPUs, significantly enhancing the efficiency of StreamingVQA.
UniGO: A Unified Graph Neural Network for Modeling Opinion Dynamics on Graphs
Polarization and fragmentation in social media amplify user biases, making it increasingly important to understand the evolution of opinions.
Integrated Scheduling Model for Arrivals and Departures in Metroplex Terminal Area
The genetic algorithm is employed to solve the proposed model.
HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation
To effectively learn the HealthGPT, we devise a comprehensive medical domain-specific comprehension and generation dataset called VL-Health.
Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation
In retrospective cohorts, Smart-CCS achieved an overall area under the curve (AUC) value of 0. 965 and sensitivity of 0. 913 for cancer screening on 11 internal test datasets.
D$^2$-DPM: Dual Denoising for Quantized Diffusion Probabilistic Models
In this work, we propose D2-DPM, a dual denoising mechanism aimed at precisely mitigating the adverse effects of quantization noise on the noise estimation network.
Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework
Efficient multimodal large language models (EMLLMs), in contrast to multimodal large language models (MLLMs), reduce model size and computational costs and are often deployed on resource-constrained devices.
Cramér-Rao Bound Optimization for Near-Field Sensing with Continuous-Aperture Arrays
A Cram\'er-Rao bound (CRB) optimization framework for near-field sensing (NISE) with continuous-aperture arrays (CAPAs) is proposed.
All-in-One: Transferring Vision Foundation Models into Stereo Matching
Inspired by the ability of vision foundation models (VFMs) to extract general representations, in this work, we propose AIO-Stereo which can flexibly select and transfer knowledge from multiple heterogeneous VFMs to a single stereo matching model.
COMET: Benchmark for Comprehensive Biological Multi-omics Evaluation Tasks and Language Models
To address this, we introduce the first comprehensive multi-omics benchmark COMET (Benchmark for Biological COmprehensive Multi-omics Evaluation Tasks and Language Models), designed to evaluate models across single-omics, cross-omics, and multi-omics tasks.
Unsupervised Cross-Domain Regression for Fine-grained 3D Game Character Reconstruction
Meanwhile, the game is the carrier of the metaverse, in which players can freely edit the facial appearance of the game character.
Political Actor Agent: Simulating Legislative System for Roll Call Votes Prediction with Large Language Models
Predicting roll call votes through modeling political actors has emerged as a focus in quantitative political science and computer science.
From Principles to Practice: A Deep Dive into AI Ethics and Regulations
In the rapidly evolving domain of Artificial Intelligence (AI), the complex interaction between innovation and regulation has become an emerging focus of our society.
Environment Reconstruction with Multi-targets Reflectors-merged Sensing Method Based on THz Single-sided Channel Characteristics
Reconstructing the propagation environment is a vital step for THz ISAC, as it enhances the predictability of the communication channel to reduce communication overhead.
T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts
Evaluating the quality of synthesized images remains a significant challenge in the development of text-to-image (T2I) generation.
PatchDPO: Patch-level DPO for Finetuning-free Personalized Image Generation
To tackle this problem, this work proposes PatchDPO that estimates the quality of image patches within each generated image and accordingly trains the model.
Towards Universal Soccer Video Understanding
As a globally celebrated sport, soccer has attracted widespread interest from fans all over the world.
Multi-scale Vehicle Localization In Heterogeneous Mobile Communication Networks
Additionally, through SF matching by integrating the inclusion and adjacency position relationships, a multi-scale vehicle localization (MSVL) algorithm is proposed to identify vehicular road signal patterns and determine the real-time segment and coordinates.
AutoGameUI: Constructing High-Fidelity Game UIs via Multimodal Learning and Interactive Web-Based Tool
Our experimental results demonstrate the effectiveness of our system in maintaining coherence between the constructed interfaces and the original designs.
A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications
With the rapid advancement of large language models (LLMs), aligning policy models with human preferences has become increasingly critical.
LG-CAV: Train Any Concept Activation Vector with Language Guidance
To bridge the gap between vision-language model and the target model, we calculate the activation values of concept descriptions on a common pool of images (probe images) with vision-language model and utilize them as language guidance to train the LG-CAV.
High-Efficient Near-Field Channel Characteristics Analysis for Large-Scale MIMO Communication Systems
A comprehensive analysis is conducted to investigate the influence of the height of the BS, motion characteristics of the MR, and antenna configurations on the channel statistics.
CursorCore: Assist Programming through Aligning Anything
In this work, we propose a new conversational framework that comprehensively integrates these information sources, collect data to train our models and evaluate their performance.
Pyramidal Flow Matching for Efficient Video Generative Modeling
Video generation requires modeling a vast spatiotemporal space, which demands significant computational resources and data usage.
SAG: Style-Aligned Article Generation via Model Collaboration
In this paper, we present a novel collaborative training framework that leverages the strengths of both LLMs and SLMs for style article generation, surpassing the performance of either model alone.
Align$^2$LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction Curation
Recent advances in Multi-modal Large Language Models (MLLMs), such as LLaVA-series models, are driven by massive machine-generated instruction-following data tuning.
Resolving Multi-Condition Confusion for Finetuning-Free Personalized Image Generation
Personalized text-to-image generation methods can generate customized images based on the reference images, which have garnered wide research interest.
GAInS: Gradient Anomaly-aware Biomedical Instance Segmentation
Instance segmentation plays a vital role in the morphological quantification of biomedical entities such as tissues and cells, enabling precise identification and delineation of different structures.
Holistic and Historical Instance Comparison for Cervical Cell Detection
To this end, we propose a holistic and historical instance comparison approach for cervical cell detection.
Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysis
With the emergence of integrated sensing, communication, and computation (ISCC) in the upcoming 6G era, federated learning with ISCC (FL-ISCC), integrating sample collection, local training, and parameter exchange and aggregation, has garnered increasing interest for enhancing training efficiency.
LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation
We introduce LLaVA-MoD, a novel framework designed to enable the efficient training of small-scale Multimodal Language Models (s-MLLM) by distilling knowledge from large-scale MLLM (l-MLLM).
MRSE: An Efficient Multi-modality Retrieval System for Large Scale E-commerce
Providing high-quality item recall for text queries is crucial in large-scale e-commerce search systems.
RePair: Automated Program Repair with Process-based Feedback
The gap between the trepidation of program reliability and the expense of repairs underscores the indispensability of Automated Program Repair (APR).
TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition
Considering this, we introduce an innovative PEFT method, TeamLoRA, consisting of a collaboration and competition module for experts, and thus achieving the right balance of effectiveness and efficiency: (i) For collaboration, a novel knowledge-sharing and -organizing mechanism is devised to appropriately reduce the scale of matrix operations, thereby boosting the training and inference speed.
Ranked #196 on
Visual Question Answering
on MM-Vet
Near-Field Sensing: A Low-Complexity Wavenumber-Domain Method
A novel low-complexity wavenumber-domain method is proposed for near-field sensing (NISE).
Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs
This paper introduces the Dynamic Neural Dowker Network (DNDN), a novel framework specifically designed to approximate the results of dynamic Dowker filtration, aiming to capture the high-order topological features of dynamic directed graphs.
Improving Network Interpretability via Explanation Consistency Evaluation
The pursuit of greater interpretability in neural networks often results in a degradation of their original performance.
Near-Field Sensing Enabled Predictive Beamforming: From Estimation to Tracking
It is also revealed that: 1)the proposed AGD-AO can achieve stable descending with small gradients, thereby accelerating convergence; 2) compared to far-field predictive beamforming and feedback-based schemes, both of the proposed methods exhibit superior performance; and 3) by incorporating multiple CPIs, the EKF method exhibits greater robustness in low SNR regions.
On the Evaluation Consistency of Attribution-based Explanations
Our findings underscore the necessity for future research in this domain to conduct rigorous evaluations encompassing a broader range of models and datasets, and to reassess the assumptions underlying the empirical success of different attribution methods.
Dynamic neural network with memristive CIM and CAM for 2D and 3D vision
In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing.
MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis
Auto-regressive models have made significant progress in the realm of language generation, yet they do not perform on par with diffusion models in the domain of image synthesis.
Think-then-Act: A Dual-Angle Evaluated Retrieval-Augmented Generation
Despite their impressive capabilities, large language models (LLMs) often face challenges such as temporal misalignment and generating hallucinatory content.
MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance
Recent advancements in text-to-image generation models have dramatically enhanced the generation of photorealistic images from textual prompts, leading to an increased interest in personalized text-to-image applications, particularly in multi-subject scenarios.
Data Valuation by Leveraging Global and Local Statistical Information
In this paper, we demonstrate that both global and local statistical information of value distributions hold significant potential for data valuation within the context of machine learning.
RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance
Our study shows that based on a recent rectified flow framework, the major limitation of vanilla classifier guidance in requiring a special classifier can be resolved with a simple fixed-point solution, allowing flexible personalization with off-the-shelf image discriminators.
Time Matters: Enhancing Pre-trained News Recommendation Models with Robust User Dwell Time Injection
Despite this, accurately modeling user preferences remains challenging due to the inherent uncertainty of click behaviors.
An Embarrassingly Simple Approach to Enhance Transformer Performance in Genomic Selection for Crop Breeding
Genomic selection (GS), as a critical crop breeding strategy, plays a key role in enhancing food production and addressing the global hunger crisis.
Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback
The rapidly developing Large Vision Language Models (LVLMs) have shown notable capabilities on a range of multi-modal tasks, but still face the hallucination phenomena where the generated texts do not align with the given contexts, significantly restricting the usages of LVLMs.
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
Recent advancements indicate that scaling up Multimodal Large Language Models (MLLMs) effectively enhances performance on downstream multimodal tasks.
Ranked #198 on
Visual Question Answering
on MM-Vet
An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models
Based on these findings, we then propose several simple document pruning methods to reduce the storage overhead and compare the effectiveness of different pruning methods on different late-interaction models.
Large-Scale RIS Enabled Air-Ground Channels: Near-Field Modeling and Analysis
Then, we develop a beam domain channel model based on the proposed sub-array partition framework for large-scale RIS-enabled UAV-to-vehicle communication systems, which can be used to efficiently capture the sparse features in RIS-enabled UAV-to-vehicle channels in both near-field and far-field ranges.
Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Semi-supervised Cross-domain Neural Networks for Indoor Environment
The label classifier obtained from training the source domain data improves the recognition of target domain activities due to the semi-supervised learning utilized in training the target domain data.
PowerSkel: A Device-Free Framework Using CSI Signal for Human Skeleton Estimation in Power Station
WiFi-based human pose estimation is a suitable method for monitoring power operations due to its low cost, device-free, and robustness to various illumination conditions. In this paper, a novel Channel State Information (CSI)-based pose estimation framework, namely PowerSkel, is developed to address these challenges.
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization
In light of recent advances in multimodal Large Language Models (LLMs), there is increasing attention to scaling them from image-text data to more informative real-world videos.
Ranked #3 on
Text-to-Video Generation
on MSR-VTT
SD-MVS: Segmentation-Driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization
In this paper, we introduce Segmentation-Driven Deformation Multi-View Stereo (SD-MVS), a method that can effectively tackle challenges in 3D reconstruction of textureless areas.
Hybrid Vector Message Passing for Generalized Bilinear Factorization
In this paper, we propose a new message passing algorithm that utilizes hybrid vector message passing (HVMP) to solve the generalized bilinear factorization (GBF) problem.
Ink Dot-Oriented Differentiable Optimization for Neural Image Halftoning
In this paper we propose an innovative halftoning method termed "neural dot-controllable halftoning".
The Audio-Visual Conversational Graph: From an Egocentric-Exocentric Perspective
We propose a unified multi-modal framework -- Audio-Visual Conversational Attention (AV-CONV), for the joint prediction of conversation behaviors -- speaking and listening -- for both the camera wearer as well as all other social partners present in the egocentric video.
CT-MVSNet: Efficient Multi-View Stereo with Cross-scale Transformer
Recent deep multi-view stereo (MVS) methods have widely incorporated transformers into cascade network for high-resolution depth estimation, achieving impressive results.
Random resistive memory-based deep extreme point learning machine for unified visual processing
Our random resistive memory-based deep extreme point learning machine may pave the way for energy-efficient and training-friendly edge AI across various data modalities and tasks.
Audio-Visual LLM for Video Understanding
This paper presents Audio-Visual LLM, a Multimodal Large Language Model that takes both visual and auditory inputs for holistic video understanding.
Filter & Align: Leveraging Human Knowledge to Curate Image-Text Data
However, the vast scale of these datasets inevitably introduces significant variability in data quality, which can adversely affect the model performance.
Pruning random resistive memory for optimizing analogue AI
Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network.
TrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent System
We anticipate that our work will contribute to the advancement of research on TrainerAgent in both academic and industry communities, potentially establishing it as a new paradigm for model development in the field of AI.
Channel Modeling for Heterogeneous Vehicular ISAC System with Shared Clusters
In this paper, we consider the channel modeling of a heterogeneous vehicular integrated sensing and communication (ISAC) system, where a dual-functional multi-antenna base station (BS) intends to communicate with a multi-antenna vehicular receiver (MR) and sense the surrounding environments simultaneously.
Training Energy-Based Models with Diffusion Contrastive Divergences
In image generation experiments, the proposed DCD is capable of training an energy-based model for generating the Celab-A $32\times 32$ dataset, which is comparable to existing EBMs.
Video Compression with Arbitrary Rescaling Network
Most video platforms provide video streaming services with different qualities, and the quality of the services is usually adjusted by the resolution of the videos.
3GPP-Like GBSM THz Channel Characterization, Modeling, and Simulation Based on Experimental Observations
Finally, we propose the THz channel model and its simulation framework to reconstruct CIRs based on the obtained models, which aim at characterizing the sparser THz channels.
Pyramid Texture Filtering
We present a simple but effective technique to smooth out textures while preserving the prominent structures.
MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results
To evaluate the performance of different depth completion methods, we organized an RGB+sparse ToF depth completion competition.
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.
Egocentric Auditory Attention Localization in Conversations
In a noisy conversation environment such as a dinner party, people often exhibit selective auditory attention, or the ability to focus on a particular speaker while tuning out others.
DoNet: Deep De-overlapping Network for Cytology Instance Segmentation
Cell instance segmentation in cytology images has significant importance for biology analysis and cancer screening, while remains challenging due to 1) the extensive overlapping translucent cell clusters that cause the ambiguous boundaries, and 2) the confusion of mimics and debris as nuclei.
Future Aware Pricing and Matching for Sustainable On-demand Ride Pooling
Traditionally, both these challenges have been studied individually and using myopic approaches (considering only current requests), without considering the impact of current matching on addressing future requests.
Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties
Constrained Reinforcement Learning has been employed to enforce safety constraints on policy through the use of expected cost constraints.
Chat2Map: Efficient Scene Mapping from Multi-Ego Conversations
Can conversational videos captured from multiple egocentric viewpoints reveal the map of a scene in a cost-efficient way?
Multi-Modal Experience Inspired AI Creation
To model such human capabilities, in this paper, we define and solve a novel AI creation problem based on human experiences.
Joint Beamforming Coordination and User Selection for CoMP Enabled NR-U Networks
In this work, we introduce a CoMP based NR-U network, particularly for 6G enabled massive IoT scenarios.
PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN
Beyond topical relevance, passage ranking for open-domain factoid question answering also requires a passage to contain an answer (answerability).
Correction of out-of-focus microscopic images by deep learning
Results To solve the out-of-focus issue in microscopy, we developed a Cycle Generative Adversarial Network (CycleGAN) based model and a multi-component weighted loss function.
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering
To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR).
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning
Then, resorting to the prior, we split each few-shot task to a set of subtasks with different concept levels and then perform class prediction via a model of decision tree.
KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models
However, to figure out whether PLMs can be reliable knowledge sources and used as alternative knowledge bases (KBs), we need to further explore some critical features of PLMs.
Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions
Given that some of the calibrated parameters that show a good agreement with the experimental data can be nonphysical mercury properties, we need a more advanced two-phase flow model to capture bubble dynamics and mercury cavitation.
Deep Learning for Computational Cytology: A Survey
Computational cytology is a critical, rapid-developing, yet challenging topic in the field of medical image computing which analyzes the digitized cytology image by computer-aided technologies for cancer screening.
Egocentric Deep Multi-Channel Audio-Visual Active Speaker Localization
We propose a novel end-to-end deep learning approach that is able to give robust voice activity detection and localization results.
Ranked #1 on
Active Speaker Localization
on EasyCom
Joint Video Summarization and Moment Localization by Cross-Task Sample Transfer
To address it, this work explores a new solution for video summarization by transferring samples from a correlated task (i. e., video moment localization) equipped with abundant training data.
Ranked #4 on
Supervised Video Summarization
on SumMe
(using extra training data)
Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems
Cooperative Intelligent Transport System (C-ITS) is a promising network to provide safety, efficiency, sustainability, and comfortable services for automated vehicles and road infrastructures by taking advantages from participants.
Towards More Effective and Economic Sparsely-Activated Model
To increase the number of activated experts without an increase in computational cost, we propose SAM (Switch and Mixture) routing, an efficient hierarchical routing mechanism that activates multiple experts in a same device (GPU).
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
Towards Efficient NLP: A Standard Evaluation and A Strong Baseline
ELUE is dedicated to depict the Pareto Frontier for various language understanding tasks, such that it can tell whether and how much a method achieves Pareto improvement.
YES SIR!Optimizing Semantic Space of Negatives with Self-Involvement Ranker
Finally, supervisory signal in rear compressor is computed based on condition probability and thus can control sample dynamic and further enhance the model performance.
Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences.
Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need
In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval.
Millimeter-Wave NR-U and WiGig Coexistence: Joint User Grouping, Beam Coordination and Power Control
Our aim for the proposed coexistence network is to maximize the spectral efficiency while ensuring the strict NR-U delay requirement and the WiGig transmission performance in real time environments.
Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals
The final response is selected according to the predicted knowledge, the goal to achieve, and the context.
Graph-Embedded Multi-Agent Learning for Smart Reconfigurable THz MIMO-NOMA Networks
With the accelerated development of immersive applications and the explosive increment of internet-of-things (IoT) terminals, 6G would introduce terahertz (THz) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) technologies to meet the ultra-high-speed transmission and massive connectivity requirements.
Answer Complex Questions: Path Ranker Is All You Need
Currently, the most popular method for open-domain Question Answering (QA) adopts "Retriever and Reader" pipeline, where the retriever extracts a list of candidate documents from a large set of documents followed by a ranker to rank the most relevant documents and the reader extracts answer from the candidates.
EasyCom: An Augmented Reality Dataset to Support Algorithms for Easy Communication in Noisy Environments
In this work, we describe, evaluate and release a dataset that contains over 5 hours of multi-modal data useful for training and testing algorithms for the application of improving conversations for an AR glasses wearer.
Ranked #1 on
Speech Enhancement
on EasyCom
Early Exiting with Ensemble Internal Classifiers
In this paper, we show that a novel objective function for the training of the ensemble internal classifiers can be naturally induced from the perspective of ensemble learning and information theory.
Emotion Eliciting Machine: Emotion Eliciting Conversation Generation based on Dual Generator
Then we propose a dual encoder-decoder structure to model the generation of responses in both positive and negative side based on the changes of the user's emotion status in the conversation.
A Semi-Supervised Classification Method of Apicomplexan Parasites and Host Cell Using Contrastive Learning Strategy
A common shortfall of supervised learning for medical imaging is the greedy need for human annotations, which is often expensive and time-consuming to obtain.
Egocentric Pose Estimation from Human Vision Span
Existing approaches either use a narrow field of view front facing camera that barely captures the wearer, or an extruded head-mounted top-down camera for maximal wearer visibility.
Residual-Aided End-to-End Learning of Communication System without Known Channel
However, the gradient vanishing and overfitting problems of GAN will result in the serious performance degradation of E2E learning of communication system.
Market2Dish: Health-aware Food Recommendation
With the rising incidence of some diseases, such as obesity and diabetes, a healthy diet is arousing increasing attention.
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient
Deep Q-learning algorithms often suffer from poor gradient estimations with an excessive variance, resulting in unstable training and poor sampling efficiency.
Learning Differential Diagnosis of Skin Conditions with Co-occurrence Supervision using Graph Convolutional Networks
Skin conditions are reported the 4th leading cause of nonfatal disease burden worldwide.
Real-time 3D Deep Multi-Camera Tracking
Our DMCT consists of 1) a fast and novel perspective-aware Deep GroudPoint Network, 2) a fusion procedure for ground-plane occupancy heatmap estimation, 3) a novel Deep Glimpse Network for person detection and 4) a fast and accurate online tracker.
Ranked #6 on
Multi-Object Tracking
on Wildtrack
Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology
With the aim of providing a comprehensive overview for researchers who are interested in developing a deep-learning-based analysis system for power lines inspection data, this paper conducts a thorough review of the current literature and identifies the challenges for future research.
Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation
It is due to two facts: first, as the model learns more tasks, the intersection of the low-error parameter subspace satisfying for these tasks becomes smaller or even does not exist; second, when the model learns a new task, the cumulative error keeps increasing as the model tries to protect the parameter configuration of previous tasks from interference.
Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China
First, we trained 1D CNNs, LSTM RNNs, and GRU RNNs based on the full images’ time series to attain three classifiers with optimal architectures and hyper-parameters.
Inertial nonconvex alternating minimizations for the image deblurring
In image processing, Total Variation (TV) regularization models are commonly used to recover blurred images.
Heavy-ball Algorithms Always Escape Saddle Points
In this paper, we answer a question: can the nonconvex heavy-ball algorithms with random initialization avoid saddle points?
Real-time Multiple People Hand Localization in 4D Point Clouds
Different from the traditional multiple view approaches, which find key points in 2D and then triangulate to recover the 3D locations, our method directly processes the dynamic 3D data that involve both clutter and crowd.
Iteratively reweighted penalty alternating minimization methods with continuation for image deblurring
In this paper, we consider a class of nonconvex problems with linear constraints appearing frequently in the area of image processing.
Ego-Downward and Ambient Video based Person Location Association
To provide a possible solution to this problem, this paper proposes a camera system with both ego-downward and third-static view to perform localization and tracking in a learning approach.
Non-ergodic Convergence Analysis of Heavy-Ball Algorithms
For objective functions satisfying a relaxed strongly convex condition, the linear convergence is established under weaker assumptions on the step size and inertial parameter than made in the existing literature.
A Detection and Segmentation Architecture for Skin Lesion Segmentation on Dermoscopy Images
This report summarises our method and validation results for the ISIC Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1: Lesion Segmentation.
Action4D: Real-time Action Recognition in the Crowd and Clutter
In this paper, we propose a real-time action recognition method, Action4D, which gives reliable and accurate results in the real-world settings.
Long short-term memory networks in memristor crossbars
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence.
Emerging Technologies Applied Physics
Densely Dilated Spatial Pooling Convolutional Network using benign loss functions for imbalanced volumetric prostate segmentation
Meanwhile, we adopt DSC loss and Jaccard loss functions to train our DDSP ConNet.
A convergence framework for inexact nonconvex and nonsmooth algorithms and its applications to several iterations
In fact, a lot of classical inexact nonconvex and nonsmooth algorithms allow these three conditions.
Iteratively Linearized Reweighted Alternating Direction Method of Multipliers for a Class of Nonconvex Problems
The traditional alternating direction method of multipliers encounters troubles in both mathematics and computations in solving the nonconvex and nonsmooth subproblem.
Research on Bi-mode Biometrics Based on Deep Learning
In view of the fact that biological characteristics have excellent independent distinguishing characteristics, biometric identification technology involves almost all the relevant areas of human distinction.
Detangling People: Individuating Multiple Close People and Their Body Parts via Region Assembly
In addition, we demonstrate its impact on a proxemics recognition task, which demands a precise representation of "whose body part is where" in crowded images.
Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Video
We propose to infer the "invisible pose" of a person behind the egocentric camera.
Matching Bags of Regions in RGBD images
We study the new problem of matching regions between a pair of RGBD images given a large set of overlapping region proposals.
A Linear Approach to Matching Cuboids in RGBD Images
We propose a novel linear method to match cuboids in indoor scenes using RGBD images from Kinect.
