Search Results for author:
Found 334 papers, 142 papers with code
Sparse-to-Dense Depth Completion Revisited: Sampling Strategy and Graph Construction
Depth completion is a widely studied problem of predicting a dense depth map from a sparse set of measurements and a single RGB image.
DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition
We propose a Dynamic Directed Graph Convolutional Network (DDGCN) to model spatial and temporal features of human actions from their skeletal representations.
Aspect-based Sentiment Analysis in Question Answering Forums
This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
SeaLLMs -- Large Language Models for Southeast Asia
Despite the remarkable achievements of large language models (LLMs) in various tasks, there remains a linguistic bias that favors high-resource languages, such as English, often at the expense of low-resource and regional languages.
Brainformer: Modeling MRI Brain Functions to Machine Vision
In this work, we first present a simple yet effective Brainformer approach, a novel Transformer-based framework, to analyze the patterns of fMRI in the human perception system from the machine learning perspective.
Surf-D: High-Quality Surface Generation for Arbitrary Topologies using Diffusion Models
Since the learning difficulty for various shapes can differ, a curriculum learning strategy is employed to efficiently embed various surfaces, enhancing the whole embedding process.
Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
Large Vision-Language Models (LVLMs) have advanced considerably, intertwining visual recognition and language understanding to generate content that is not only coherent but also contextually attuned.
Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models
However, performance advancements are limited when relying solely on intricate algorithmic designs for a single model, even one exhibiting strong performance, e. g., CLIP-ViT-B/16.
Ranked #1 on
Prompt Engineering
on ImageNet
Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding
In precision agriculture, the detection and recognition of insects play an essential role in the ability of crops to grow healthy and produce a high-quality yield.
Neuro-Symbolic Integration Brings Causal and Reliable Reasoning Proofs
Though prompting LLMs with various reasoning structures produces reasoning proofs along with answers, these proofs are not ensured to be causal and reliable due to the inherent defects of LLMs.
On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving
This has been a significant bottleneck, particularly in the development of common sense reasoning and nuanced scene understanding necessary for safe and reliable autonomous driving.
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains.
Overhead Line Defect Recognition Based on Unsupervised Semantic Segmentation
Overhead line inspection greatly benefits from defect recognition using visible light imagery.
Towards Control-Centric Representations in Reinforcement Learning from Images
Image-based Reinforcement Learning is a practical yet challenging task.
CLEX: Continuous Length Extrapolation for Large Language Models
We generalise the PE scaling approaches to model the continuous dynamics by ordinary differential equations over the length scaling factor, thereby overcoming the constraints of current PE scaling methods designed for specific lengths.
Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations
Imitation Learning (IL) aims to discover a policy by minimizing the discrepancy between the agent's behavior and expert demonstrations.
Once Upon a $\textit{Time}$ in $\textit{Graph}$: Relative-Time Pretraining for Complex Temporal Reasoning
However, the knowledge-time association is usually insufficient for the downstream tasks that require reasoning over temporal dependencies between knowledge.
Diagnosis-oriented Medical Image Compression with Efficient Transfer Learning
Remote medical diagnosis has emerged as a critical and indispensable technique in practical medical systems, where medical data are required to be efficiently compressed and transmitted for diagnosis by either professional doctors or intelligent diagnosis devices.
Demystifying the Myths and Legends of Nonconvex Convergence of SGD
Additionally, our analyses allow us to measure the density of the $\epsilon$-stationary points in the final iterates of SGD, and we recover the classical $O(\frac{1}{\sqrt{T}})$ asymptotic rate under various existing assumptions on the objective function and the bounds on the stochastic gradient.
FreqAlign: Excavating Perception-oriented Transferability for Blind Image Quality Assessment from A Frequency Perspective
Based on this, we propose to improve the perception-oriented transferability of BIQA by performing feature frequency decomposition and selecting the frequency components that contained the most transferable perception knowledge for alignment.
Blind Image Quality Assessment
Unsupervised Domain Adaptation
GAFlow: Incorporating Gaussian Attention into Optical Flow
Moreover, for reliable motion analysis, we provide a new Gaussian-Guided Attention Module (GGAM) which not only inherits properties from Gaussian distribution to instinctively revolve around the neighbor fields of each point but also is empowered to put the emphasis on contextually related regions during matching.
DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models
Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretability.
3D Multiple Object Tracking on Autonomous Driving: A Literature Review
This paper undertakes a comprehensive examination, assessment, and synthesis of the research landscape in this domain, remaining attuned to the latest developments in 3D MOT while suggesting prospective avenues for future investigation.
GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph
To mitigate that, we propose an effective adapter-style tuning strategy, dubbed GraphAdapter, which performs the textual adapter by explicitly modeling the dual-modality structure knowledge (i. e., the correlation of different semantics/classes in textual and visual modalities) with a dual knowledge graph.
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase
Besides, we construct the OpenPCSeg codebase, which is the largest and most comprehensive outdoor LiDAR segmentation codebase.
VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generation
In this paper, we present VideoGen, a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency using reference-guided latent diffusion.
A Note on Randomized Kaczmarz Algorithm for Solving Doubly-Noisy Linear Systems
In this paper, we analyze the convergence of RK for noisy linear systems when the coefficient matrix, $A$, is corrupted with both additive and multiplicative noise, along with the noisy vector, $b$.
Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks
A method for measuring the synthesis complexity is proposed to automatically determine the synthesis order in our sequential GAN.
MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild
Detecting small scene text instances in the wild is particularly challenging, where the influence of irregular positions and nonideal lighting often leads to detection errors.
Ranked #1 on
Scene Text Detection
on SCUT-CTW1500
CiteTracker: Correlating Image and Text for Visual Tracking
Existing visual tracking methods typically take an image patch as the reference of the target to perform tracking.
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive Survey
Image restoration (IR) has been an indispensable and challenging task in the low-level vision field, which strives to improve the subjective quality of images distorted by various forms of degradation.
The Algonauts Project 2023 Challenge: UARK-UAlbany Team Solution
The goal is to predict brain responses across the entire visual brain, as it is the region where the most reliable responses to images have been observed.
Fast Full-frame Video Stabilization with Iterative Optimization
Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one.
Adaptive Control of Resource Flow to Optimize Construction Work and Cash Flow via Online Deep Reinforcement Learning
Therefore, this paper introducess a model and method to adaptive control the resource flows to optimize the work and cash flows of construction projects.
Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells
Learning-based multi-view stereo (MVS) methods deal with predicting accurate depth maps to achieve an accurate and complete 3D representation.
Drive Like a Human: Rethinking Autonomous Driving with Large Language Models
In this paper, we explore the potential of using a large language model (LLM) to understand the driving environment in a human-like manner and analyze its ability to reason, interpret, and memorize when facing complex scenarios.
Unveiling the Potential of Knowledge-Prompted ChatGPT for Enhancing Drug Trafficking Detection on Social Media
By integrating prior knowledge and the proposed prompts, ChatGPT can effectively identify and label drug trafficking activities on social networks, even in the presence of deceptive language and euphemisms used by drug dealers to evade detection.
TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction
When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available.
Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detection
We argue that this external or contextual information should already be embedded within the known classes.
Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification
Mammogram image is important for breast cancer screening, and typically obtained in a dual-view form, i. e., cranio-caudal (CC) and mediolateral oblique (MLO), to provide complementary information.
Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease
Validation analysis revealed that the AUCs of DLRP were 0. 91 for GEV (95% CI 0. 90 to 0. 93, p < 0. 05) and 0. 88 for HRV (95% CI 0. 86 to 0. 89, p < 0. 01), which were significantly and robustly better than canonical risk indicators, including the value of LSM and SSM.
Securing Visually-Aware Recommender Systems: An Adversarial Image Reconstruction and Detection Framework
Our proposed method can simultaneously (1) secure VARS from adversarial attacks characterized by local perturbations by image reconstruction based on global vision transformers; and (2) accurately detect adversarial examples using a novel contrastive learning approach.
DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds
Extensive experiments on Waymo Open Dataset show our DetZero outperforms all state-of-the-art onboard and offboard 3D detection methods.
Learning Probabilistic Coordinate Fields for Robust Correspondences
We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems.
A2B: Anchor to Barycentric Coordinate for Robust Correspondence
This approach offers a new perspective to alleviate the problem of repeated patterns and emphasizes the importance of choosing coordinate representations for feature correspondences.
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
We present Video-LLaMA a multi-modal framework that empowers Large Language Models (LLMs) with the capability of understanding both visual and auditory content in the video.
nnMobileNe: Rethinking CNN for Retinopathy Research
Over the past few decades, convolutional neural networks (CNNs) have been at the forefront of the detection and tracking of various retinal diseases (RD).
AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach
In this work, we for the first time propose a challenging argument quadruplet extraction task (AQE), which can provide an all-in-one extraction of four argumentative components, i. e., claims, evidence, evidence types, and stances.
Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation
This paper proposes a cross-supervised learning framework based on dual classifiers (DC-Net), including an evidential classifier and a vanilla classifier.
Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation
To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs.
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning
In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data.
mPMR: A Multilingual Pre-trained Machine Reader at Scale
mPMR aims to guide multilingual pre-trained language models (mPLMs) to perform natural language understanding (NLU) including both sequence classification and span extraction in multiple languages.
Two-Stream Regression Network for Dental Implant Position Prediction
In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue.
GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark
With a fast developing pace of geographic applications, automatable and intelligent models are essential to be designed to handle the large volume of information.
UPDExplainer: an Interpretable Transformer-based Framework for Urban Physical Disorder Detection Using Street View Imagery
Experimental results on the re-annotated Place Pulse 2. 0 dataset demonstrate promising detection performance of the proposed method, with an accuracy of 79. 9%.
MEDIC: A Multimodal Empathy Dataset in Counseling
We also propose three labels (i. e., expression of experience, emotional reaction, and cognitive reaction) to describe the degree of empathy between counselors and their clients.
SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition
This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data.
Video-based Contrastive Learning on Decision Trees: from Action Recognition to Autism Diagnosis
How can we teach a computer to recognize 10, 000 different actions?
CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation
They are insufficient to model both global and local structures of a given image, especially in small regions of tail classes.
ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesis
The ALR-GAN includes an Adaptive Layout Refinement (ALR) module and a Layout Visual Refinement (LVR) loss.
Micron-BERT: BERT-based Facial Micro-Expression Recognition
By incorporating these components into an end-to-end deep network, the proposed $\mu$-BERT significantly outperforms all previous work in various micro-expression tasks.
Ranked #1 on
Micro Expression Recognition
on SMIC
Micro Expression Recognition
Micro-Expression Recognition
+1
Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA
Different to conventional STVQA models which take the linguistic semantics and visual semantics in scene text as two separate features, in this paper, we propose a paradigm of "Locate Then Generate" (LTG), which explicitly unifies this two semantics with the spatial bounding box as a bridge connecting them.
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications.
MobileInst: Video Instance Segmentation on the Mobile
To address those issues, we present MobileInst, a lightweight and mobile-friendly framework for video instance segmentation on mobile devices.
Emotional Reaction Intensity Estimation Based on Multimodal Data
In this paper, 1. better features are extracted with the SOTA pretrained models.
Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation
Recently, Table Structure Recognition (TSR) task, aiming at identifying table structure into machine readable formats, has received increasing interest in the community.
Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective
In this paper, we are the first to propose a novel training strategy for image restoration from the causality perspective, to improve the generalization ability of DNNs for unknown degradations.
SCPNet: Semantic Scene Completion on Point Cloud
We propose a simple yet effective label rectification strategy, which uses off-the-shelf panoptic segmentation labels to remove the traces of dynamic objects in completion labels, greatly improving the performance of deep models especially for those moving objects.
Ranked #1 on
3D Semantic Scene Completion
on SemanticKITTI
LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-Resolution
To this end, we construct a large-scale, multi-reference super-resolution dataset, named LMR.
LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion
Notably, LoGoNet ranks 1st on Waymo 3D object detection leaderboard and obtains 81. 02 mAPH (L2) detection performance.
Toward NeuroDM: Where Computational Neuroscience Meets Data Mining
At the intersection of computational neuroscience (CN) and data mining (DM), we advocate a holistic view toward their rich connections.
Toward a Geometric Theory of Manifold Untangling
It has been hypothesized that the ventral stream processing for object recognition is based on a mechanism called cortically local subspace untangling.
SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
In this paper, we propose a new, simple, and effective Self-supervised Spatio-temporal Transformers (SPARTAN) approach to Group Activity Recognition (GAR) using unlabeled video data.
Virtual Sparse Convolution for Multimodal 3D Object Detection
Finally, we develop a semi-supervised pipeline VirConv-S based on a pseudo-label framework.
MorphGANFormer: Transformer-based Face Morphing and De-Morphing
Semantic face image manipulation has received increasing attention in recent years.
Analysis of Biomass Sustainability Indicators from a Machine Learning Perspective
This study proposes a robust model for biomass sustainability prediction by analyzing sustainability indicators using machine learning models.
PointSmile: Point Self-supervised Learning via Curriculum Mutual Information
From the perspective of how-and-what-to-learn, PointSmile is designed to imitate human curriculum learning, i. e., starting with an easy curriculum and gradually increasing the difficulty of that curriculum.
Negative Flux Aggregation to Estimate Feature Attributions
There are increasing demands for understanding deep neural networks' (DNNs) behavior spurred by growing security and/or transparency concerns.
Multi-Constraint Molecular Generation using Sparsely Labelled Training Data for Localized High-Concentration Electrolyte Diluent Screening
Recently, machine learning methods have been used to propose molecules with desired properties, which is especially useful for exploring large chemical spaces efficiently.
MGeo: Multi-Modal Geographic Pre-Training Method
Single-modal PTMs can barely make use of the important GC and therefore have limited performance.
CoIn: Contrastive Instance Feature Mining for Outdoor 3D Object Detection with Very Limited Annotations
Combining CoIn with an iterative training strategy, we propose a CoIn++ pipeline, which requires only 2% annotations in the KITTI dataset to achieve performance comparable to the fully supervised methods.
Self-Supervised Non-Uniform Kernel Estimation With Flow-Based Motion Prior for Blind Image Deblurring
To address these issues, we propose to represent the field of motion blur kernels in a latent space by normalizing flows, and design CNNs to predict the latent codes instead of motion kernels.
Low-Light Image Enhancement with Multi-Stage Residue Quantization and Brightness-Aware Attention
Deep learning-based LLIE methods focus on learning a mapping function between low-light images and normal-light images that outperforms conventional LLIE methods.
Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security
The dual-functional radar and communication (DFRC) technique constitutes a promising next-generation wireless solution, due to its benefits in terms of power consumption, physical hardware, and spectrum exploitation.
Vector Quantization With Self-Attention for Quality-Independent Representation Learning
During training, we enforce the quantization of features from clean and corrupted images in the same discrete embedding space so that an invariant quality-independent feature representation can be learned to improve the recognition robustness of low-quality images.
WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning
Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space.
Representation Learning in Deep RL via Discrete Information Bottleneck
Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations.
Coarse-to-Fine Contrastive Learning on Graphs
Inspired by the impressive success of contrastive learning (CL), a variety of graph augmentation strategies have been employed to learn node representations in a self-supervised manner.
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine Reader
We present Pre-trained Machine Reader (PMR), a novel method for retrofitting pre-trained masked language models (MLMs) to pre-trained machine reading comprehension (MRC) models without acquiring labeled data.
Semantic-aware Message Broadcasting for Efficient Unsupervised Domain Adaptation
In this way, our message broadcasting encourages the group tokens to learn more informative and diverse information for effective domain alignment.
Ranked #1 on
Unsupervised Domain Adaptation
on VisDA2017
AdaCM: Adaptive ColorMLP for Real-Time Universal Photo-realistic Style Transfer
Then, in \textbf{AdaCM}, we adopt a CNN encoder to adaptively predict all parameters for the ColorMLP conditioned on each input content and style image pair.
Learning Compact Features via In-Training Representation Alignment
Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of the feature extractor (i. e., last hidden layer) and a linear classifier (i. e., output layer) that are trained jointly with stochastic gradient descent (SGD) on the loss function (e. g., cross-entropy).
Transformation-Equivariant 3D Object Detection for Autonomous Driving
However, it is difficult to apply such networks to 3D object detection in autonomous driving due to its large computation cost and slow reasoning speed.
ConNER: Consistency Training for Cross-lingual Named Entity Recognition
We propose ConNER as a novel consistency training framework for cross-lingual NER, which comprises of: (1) translation-based consistency training on unlabeled target-language data, and (2) dropoutbased consistency training on labeled source-language data.
SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking
Second, we develop a Similarity Matching Cascade (SMC) module with a novel GATE function for robust object matching across consecutive video frames, further enhancing MOT performance.
Ranked #1 on
Multi-Object Tracking
on MOT20
(using extra training data)
Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations
Due to the huge amount of parameters, fine-tuning of pretrained language models (PLMs) is prone to overfitting in the low resource scenarios.
Knowledge Distillation for Detection Transformer with Consistent Distillation Points Sampling
In this paper, we focus on the compression of DETR with knowledge distillation.
Batch-based Model Registration for Fast 3D Sherd Reconstruction
In this work, we aim to develop a portable, high-throughput, and accurate reconstruction system for efficient digitization of fragments excavated in archaeological sites.
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection
While previous state-of-the-art RefSR methods mainly focus on improving the efficacy and robustness of reference feature transfer, it is generally overlooked that a well reconstructed SR image should enable better SR reconstruction for its similar LR images when it is referred to as.
Behavior Prior Representation learning for Offline Reinforcement Learning
Our method, Behavior Prior Representation (BPR), learns state representations with an easy-to-integrate objective based on behavior cloning of the dataset: we first learn a state representation by mimicking actions from the dataset, and then train a policy on top of the fixed representation, using any off-the-shelf Offline RL algorithm.
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that are capable of solving multiple tasks and reach a diverse set of objectives.
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
We find that contemporary representation learning techniques can fail on datasets where the noise is a complex and time dependent process, which is prevalent in practical applications.
Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting
Our technical contributions include: 1) We propose a fusion-based few-shot learning (FSL) method to learn discriminative features that can generalize to unseen morphing attack types from predefined presentation attacks; 2) The proposed FSL based on the fusion of the PRNU model and Noiseprint network is extended from binary MAD to multiclass morphing attack fingerprinting (MAF).
Streaming Voice Conversion Via Intermediate Bottleneck Features And Non-streaming Teacher Guidance
In this paper, we propose to use intermediate bottleneck features (IBFs) to replace PPGs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-view Representation Learning from Malware to Defend Against Adversarial Variants
This provides an opportunity for the defenders (i. e., malware detectors) to detect the adversarial variants by utilizing more than one view of a malware file (e. g., source code view in addition to the binary view).
Development of a Hybrid Simulation and Experiment Test Platform for Dynamic Positioning Vessels
The test platform can test the performance of DP system and determine the operational time window.
Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field
NeRF-based SVCT methods represent the desired CT image as a continuous function of spatial coordinates and train a Multi-Layer Perceptron (MLP) to learn the function by minimizing loss on the SV sinogram.
Deep Learning-Based Channel Estimation for Double-RIS Aided Massive MIMO System
Reconfigurable Intelligent Surface (RIS) is considered as an energy-efficient solution for future wireless communication networks due to its fast and low-cost configuration.
Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection
To address these problems, we construct the homogeneous structure between the point cloud and images to avoid projective information loss by transforming the camera features into the LiDAR 3D space.
Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation
Unlike most prior work that only evaluates the ability to measure semantic similarity, we present a thorough evaluation of existing multilingual sentence embeddings and our improved versions, which include a collection of five transfer tasks in different downstream applications.
Cutting-Splicing data augmentation: A novel technology for medical image segmentation
Background: Medical images are more difficult to acquire and annotate than natural images, which results in data augmentation technologies often being used in medical image segmentation tasks.
PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks
Specifically, a novel Peer Data Augmentation (PeerDA) approach is proposed which employs span pairs with the PR relation as the augmentation data for training.
Toward an Over-parameterized Direct-Fit Model of Visual Perception
In this paper, we revisit the problem of computational modeling of simple and complex cells for an over-parameterized and direct-fit model of visual perception.
How Image Generation Helps Visible-to-Infrared Person Re-Identification?
To this end, we propose Flow2Flow, a unified framework that could jointly achieve training sample expansion and cross-modality image generation for V2I person ReID.
Uncertainty Aware Multitask Pyramid Vision Transformer For UAV-Based Object Re-Identification
Learning a robust and discriminative feature representation is a crucial challenge for object ReID.
Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System
We hypothesize that adversarial training can eliminate shortcut features whereas saliency guided training can filter out non-relevant features; both are nuisance features accounting for the performance degradation on OOD test sets.
Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network
We extracted ninety-one paper features from three dimensions as the input of the model, including twenty-one features in the paper dimension, thirty-five in the reference dimension, and thirty-five in the citing paper dimension.
Large-step neural network for learning the symplectic evolution from partitioned data
Based on Chen & Tao (2021), the symplectic mapping is represented by a generating function.
Learned Lossless JPEG Transcoding via Joint Lossy and Residual Compression
Our proposed transcoding architecture shows significant superiority in the compression of JPEG images thanks to the collaboration of learned lossy transform coding and residual entropy coding.
Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation
In this paper, we take a step forward to video semantic compression and propose the Hierarchical Reinforcement Learning based task-driven Video Semantic Coding, named as HRLVSC.
Hierarchical Reinforcement Learning
reinforcement-learning
+3
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
HST: Hierarchical Swin Transformer for Compressed Image Super-resolution
Compressed Image Super-resolution has achieved great attention in recent years, where images are degraded with compression artifacts and low-resolution artifacts.
Ranked #1 on
Compressed Image Super-resolution
on DIV2K-q40-x4
StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment
Specifically, we find a more compact and reliable space i. e., feature style space for perception-oriented UDA based on an interesting/amazing observation, that the feature style (i. e., the mean and variance) of the deep layer in DNNs is exactly associated with the quality score in NR-IQA.
Point Cloud Attacks in Graph Spectral Domain: When 3D Geometry Meets Graph Signal Processing
Instead, we propose point cloud attacks from a new perspective -- the graph spectral domain attack, aiming to perturb graph transform coefficients in the spectral domain that corresponds to varying certain geometric structure.
Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition
Following its canonical writing order, we first represent a Chinese character as a series of stroke images with a fixed writing order, and then our SAE model is trained to reconstruct this stroke image sequence.
Neural Color Operators for Sequential Image Retouching
The neural color operator mimics the behavior of traditional color operators and learns pixelwise color transformation while its strength is controlled by a scalar.
Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment
Thanks to the development of unsupervised domain adaptation (UDA), some works attempt to transfer the knowledge from a label-sufficient source domain to a label-free target domain under domain shift with UDA.
Blind Image Quality Assessment
Unsupervised Domain Adaptation
RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment
Therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA.
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compositions and world knowledge.
Ranked #1 on
Text-to-Image Generation
on LAION COCO
Formation Tracking for a Multi-Auv System Based on an Adaptive Sliding Mode Method in the Water Flow Environment
At last, a robust sliding mode controller with continuous model predictive control strategy for the multi-AUV system is developed to achieve leader-follower formation tracking under the presence of bounded flow disturbances, and simulations are implemented to confirm the effectiveness of the proposed method.
Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs
Vertebral landmark localization is a crucial step for variant spine-related clinical applications, which requires detecting the corner points of 17 vertebrae.
A Saliency-Guided Street View Image Inpainting Framework for Efficient Last-Meters Wayfinding
However, due to the large diversity of geographic context and acquisition conditions, the captured SVI always contains various distracting objects (e. g., pedestrians and vehicles), which will distract human visual attention from efficiently finding the destination in the last few meters.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment
In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space.
Relational Representation Learning in Visually-Rich Documents
To deal with the unpredictable definition of relations, we propose a novel contrastive learning task named Relational Consistency Modeling (RCM), which harnesses the fact that existing relations should be consistent in differently augmented positive views.
Global Mapping of Gene/Protein Interactions in PubMed Abstracts: A Framework and an Experiment with P53 Interactions
In this paper, we propose a comprehensive framework for constructing and analyzing large-scale gene functional networks based on the gene/protein interactions extracted from biomedical literature repositories using text mining tools.
Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach
Predicting gene functions is a challenge for biologists in the post genomic era.
The Devil is in the Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-Training
The self-supervised Masked Image Modeling (MIM) schema, following "mask-and-reconstruct" pipeline of recovering contents from masked image, has recently captured the increasing interest in the multimedia community, owing to the excellent ability of learning visual representation from unlabeled data.
DR-GAN: Distribution Regularization for Text-to-Image Generation
This paper presents a new Text-to-Image generation model, named Distribution Regularization Generative Adversarial Network (DR-GAN), to generate images from text descriptions from improved distribution learning.
Context-aware Visual Tracking with Joint Meta-updating
These deep trackers usually do not perform online update or update single sub-branch of the tracking model, for which they cannot adapt to the appearance variation of objects.
Unsupervised Learning of Accurate Siamese Tracking
As unlimited self-supervision signals can be obtained by tracking a video along a cycle in time, we investigate evolving a Siamese tracker by tracking videos forward-backward.
DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition
Various 3D-CNN based methods have been presented to tackle both the spatial and temporal dimensions in the task of video action recognition with competitive results.
Ranked #1 on
Action Recognition
on Jester (Gesture Recognition)
Aggregate effects of advertising decisions: a complex systems look at search engine advertising via an experimental study
Originality: This is one of the first research works to explore collective group decisions and resulting phenomena in the complex context of search engine advertising via developing and validating a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market.
A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges
More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence
Deep learning based single image super-resolution models have been widely studied and superb results are achieved in upscaling low-resolution images with fixed scale factor and downscaling degradation kernel.
Model Attribution of Face-swap Deepfake Videos
Then we take Deepfakes model attribution as a multiclass classification task and propose a spatial and temporal attention based method to explore the differences among Deepfakes in the new dataset.
Enhancing Cross-lingual Prompting with Dual Prompt Augmentation
Prompting shows promising results in few-shot scenarios.
Low-Rank Phase Retrieval with Structured Tensor Models
We study the low-rank phase retrieval problem, where the objective is to recover a sequence of signals (typically images) given the magnitude of linear measurements of those signals.
Learning Optical Flow with Adaptive Graph Reasoning
Our key idea is to decouple the context reasoning from the matching procedure, and exploit scene information to effectively assist motion estimation by learning to reason over the adaptive graph.
Multi-modal Sensor Fusion for Auto Driving Perception: A Survey
Multi-modal fusion is a fundamental task for the perception of an autonomous driving system, which has recently intrigued many researchers.
A multi-domain virtual network embedding algorithm with delay prediction
Virtual network embedding (VNE) is an crucial part of network virtualization (NV), which aims to map the virtual networks (VNs) to a shared substrate network (SN).
Cross-Domain Document Layout Analysis via Unsupervised Document Style Guide
Our framework is an unsupervised document layout analysis framework.
Machine learning prediction for mean motion resonance behaviour -- The planar case
Most recently, machine learning has been used to study the dynamics of integrable Hamiltonian systems and the chaotic 3-body problem.
A Survey on Applications of Digital Human Avatars toward Virtual Co-presence
This paper investigates different approaches to build and use digital human avatars toward interactive Virtual Co-presence (VCP) environments.
Learning Optical Flow With Kernel Patch Attention
Optical flow is a fundamental method used for quantitative motion estimation on the image plane.
Multi-Object Tracking Meets Moving UAV
Multi-object tracking in unmanned aerial vehicle (UAV) videos is an important vision task and can be applied in a wide range of applications.
SimSR: Simple Distance-based State Representation for Deep Reinforcement Learning
This work explores how to learn robust and generalizable state representation from image-based observations with deep reinforcement learning methods.
Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results
Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration.