Search Results for author:
Found 128 papers, 62 papers with code
Towards Long-Range ENSO Prediction with an Explainable Deep Learning Model
El Ni\~no-Southern Oscillation (ENSO) is a prominent mode of interannual climate variability with far-reaching global impacts.
Collaborative Temporal Consistency Learning for Point-supervised Natural Language Video Localization
To address this problem, we propose a new COllaborative Temporal consistEncy Learning (COTEL) framework that leverages the synergy between saliency detection and moment localization to strengthen the video-language alignment.
Efficient Response Generation Method Selection for Fine-Tuning Large Language Models
Using this strategy, we can evaluate a small subset of the generated output from each response generation strategy option, then select the most effective strategy.
Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization
Large Language Models (LLMs) have shown significant capability across various tasks, with their real-world effectiveness often driven by prompt design.
MADP: Multi-Agent Deductive Planning for Enhanced Cognitive-Behavioral Mental Health Question Answer
To address this, we propose a framework named Multi-Agent Deductive Planning (MADP), which is based on the interactions between the various psychological elements of CBT.
Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models
We introduce Sigma, an efficient large language model specialized for the system domain, empowered by a novel architecture including DiffQKV attention, and pre-trained on our meticulously collected system domain data.
IPDN: Image-enhanced Prompt Decoding Network for 3D Referring Expression Segmentation
To tackle intent ambiguity, we designed a Prompt-Aware Decoder (PAD) that guides the decoding process by deriving task-driven signals from the interaction between the expression and visual features.
EpiCoder: Encompassing Diversity and Complexity in Code Generation
Effective instruction tuning is indispensable for optimizing code LLMs, aligning model behavior with user expectations and enhancing model performance in real-world applications.
ScaleMAI: Accelerating the Development of Trusted Datasets and AI Models
This process often delays AI benefits, as human-centric data creation and AI-centric model development are treated as separate, sequential steps.
Text-Driven Tumor Synthesis
Tumor synthesis can generate examples that AI often misses or over-detects, improving AI performance by training on these challenging cases.
Attention-driven GUI Grounding: Leveraging Pretrained Multimodal Large Language Models without Fine-Tuning
Additionally, we demonstrate that our attention map-based grounding technique significantly outperforms direct localization predictions from MiniCPM-Llama3-V 2. 5, highlighting the potential of using attention maps from pretrained MLLMs and paving the way for future innovations in this domain.
VQTalker: Towards Multilingual Talking Avatars through Facial Motion Tokenization
We present VQTalker, a Vector Quantization-based framework for multilingual talking head generation that addresses the challenges of lip synchronization and natural motion across diverse languages.
RG-SAN: Rule-Guided Spatial Awareness Network for End-to-End 3D Referring Expression Segmentation
The RG-SAN consists of the Text-driven Localization Module (TLM) and the Rule-guided Weak Supervision (RWS) strategy.
Global Estimation of Building-Integrated Facade and Rooftop Photovoltaic Potential by Integrating 3D Building Footprint and Spatio-Temporal Datasets
This research tackles the challenges of estimating Building-Integrated Photovoltaics (BIPV) potential across various temporal and spatial scales, accounting for different geographical climates and urban morphology.
A Survey of Medical Vision-and-Language Applications and Their Techniques
Unlike general vision-and-language models trained on diverse, non-specialized datasets, MVLMs are purpose-built for the medical domain, automatically extracting and interpreting critical information from medical images and textual reports to support clinical decision-making.
KMM: Key Frame Mask Mamba for Extended Motion Generation
The recent Mamba architecture shows promising results in efficiently modeling long and complex sequences, yet two significant challenges remain: Firstly, directly applying Mamba to extended motion generation is ineffective, as the limited capacity of the implicit memory leads to memory decay.
CIT: Rethinking Class-incremental Semantic Segmentation with a Class Independent Transformation
Class-incremental semantic segmentation (CSS) requires that a model learn to segment new classes without forgetting how to segment previous ones: this is typically achieved by distilling the current knowledge and incorporating the latest data.
Guardians of Discourse: Evaluating LLMs on Multilingual Offensive Language Detection
Identifying offensive language is essential for maintaining safety and sustainability in the social media era.
SPFresh: Incremental In-Place Update for Billion-Scale Vector Search
LIRE achieves low-overhead vector updates by only reassigning vectors at the boundary between partitions, where in a high-quality vector index the amount of such vectors are deemed small.
PAPL-SLAM: Principal Axis-Anchored Monocular Point-Line SLAM
In point-line SLAM systems, the utilization of line structural information and the optimization of lines are two significant problems.
Weak-eval-Strong: Evaluating and Eliciting Lateral Thinking of LLMs with Situation Puzzles
To address these challenges, we introduce SPLAT, a benchmark leveraging Situation Puzzles to evaluate and elicit LAteral Thinking of LLMs.
Integrative Decoding: Improve Factuality via Implicit Self-consistency
Self-consistency-based approaches, which involve repeatedly sampling multiple outputs and selecting the most consistent one as the final response, prove to be remarkably effective in improving the factual accuracy of large language models.
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning
To exploit complementary information from the auxiliary modality, we propose a Cross-Modal Selective fusion (CMS-fusion) module that selectively incorporate the frequency and spatial features from the auxiliary modality to enhance the corresponding branch of the target modality.
RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval
This paper proposes RetrievalAttention, a training-free approach to both accelerate attention computation and reduce GPU memory consumption.
Analyzing Tumors by Synthesis
We also present case studies in the liver, pancreas, and kidneys reveal that AI trained on synthetic tumors can achieve performance comparable to, or better than, AI only trained on real data.
HEAD: A Bandwidth-Efficient Cooperative Perception Approach for Heterogeneous Connected and Autonomous Vehicles
In cooperative perception studies, there is often a trade-off between communication bandwidth and perception performance.
XLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-Training
To this end, this paper proposes a XLIP (Masked modelling for medical Language-Image Pre-training) framework to enhance pathological learning and feature learning via unpaired data.
InfiniMotion: Mamba Boosts Memory in Transformer for Arbitrary Long Motion Generation
Text-to-motion generation holds potential for film, gaming, and robotics, yet current methods often prioritize short motion generation, making it challenging to produce long motion sequences effectively: (1) Current methods struggle to handle long motion sequences as a single input due to prohibitively high computational cost; (2) Breaking down the generation of long motion sequences into shorter segments can result in inconsistent transitions and requires interpolation or inpainting, which lacks entire sequence modeling.
MMR-Mamba: Multi-Modal MRI Reconstruction with Mamba and Spatial-Frequency Information Fusion
To accelerate the acquisition process, a practical approach is to reconstruct images of the target modality, which requires longer scanning times, from under-sampled k-space data using the fully-sampled reference modality with shorter scanning times as guidance.
Gradient-Mask Tuning Elevates the Upper Limits of LLM Performance
Large language models (LLMs) have revolutionized lots of fields of research.
Meta-Learning Neural Procedural Biases
The goal of few-shot learning is to generalize and achieve high performance on new unseen learning tasks, where each task has only a limited number of examples available.
Intersectional Unfairness Discovery
To further evaluate the generated unseen but possible unfair intersectional sensitive attributes, we formulate them as prompts and use modern generative AI to produce new texts and images.
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals.
Sharpness-Aware Minimization for Evolutionary Feature Construction in Regression
In recent years, genetic programming (GP)-based evolutionary feature construction has achieved significant success.
AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding
The paper introduces AniTalker, an innovative framework designed to generate lifelike talking faces from a single portrait.
STT: Stateful Tracking with Transformers for Autonomous Driving
In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately.
GSTalker: Real-time Audio-Driven Talking Face Generation via Deformable Gaussian Splatting
We present GStalker, a 3D audio-driven talking face generation model with Gaussian Splatting for both fast training (40 minutes) and real-time rendering (125 FPS) with a 3$\sim$5 minute video for training material, in comparison with previous 2D and 3D NeRF-based modeling frameworks which require hours of training and seconds of rendering per frame.
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness
Safe and reliable natural language inference is critical for extracting insights from clinical trial reports but poses challenges due to biases in large pre-trained language models.
G-NeRF: Geometry-enhanced Novel View Synthesis from Single-View Images
Novel view synthesis aims to generate new view images of a given view image collection.
PairAug: What Can Augmented Image-Text Pairs Do for Radiology?
Acknowledging this limitation, our objective is to devise a framework capable of concurrently augmenting medical image and text data.
A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation
Multi-modality magnetic resonance imaging data with various sequences facilitate the early diagnosis, tumor segmentation, and disease staging in the management of nasopharyngeal carcinoma (NPC).
Fast and Efficient Local Search for Genetic Programming Based Loss Function Learning
In this paper, we develop upon the topic of loss function learning, an emergent meta-learning paradigm that aims to learn loss functions that significantly improve the performance of the models trained under them.
Towards Generalizable Tumor Synthesis
Tumor synthesis enables the creation of artificial tumors in medical images, facilitating the training of AI models for tumor detection and segmentation.
Understanding the Weakness of Large Language Model Agents within a Complex Android Environment
These challenges motivate AndroidArena, an environment and benchmark designed to evaluate LLM agents on a modern operating system.
Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation of Prediction Rationale
Source-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task where a model needs to be adapted to a new domain without access to target domain labels or source domain data.
CREAD: A Classification-Restoration Framework with Error Adaptive Discretization for Watch Time Prediction in Video Recommender Systems
These results highlight the effectiveness of the CREAD framework in watch time prediction in video recommender systems.
Attention-based Interactive Disentangling Network for Instance-level Emotional Voice Conversion
We introduce a two-stage pipeline to effectively train our network: Stage I utilizes inter-speech contrastive learning to model fine-grained emotion and intra-speech disentanglement learning to better separate emotion and content.
WebVLN: Vision-and-Language Navigation on Websites
Vision-and-Language Navigation (VLN) task aims to enable AI agents to accurately understand and follow natural language instructions to navigate through real-world environments, ultimately reaching specific target locations.
SiCP: Simultaneous Individual and Cooperative Perception for 3D Object Detection in Connected and Automated Vehicles
Cooperative perception for connected and automated vehicles is traditionally achieved through the fusion of feature maps from two or more vehicles.
Generating Valid and Natural Adversarial Examples with Large Language Models
Deep learning-based natural language processing (NLP) models, particularly pre-trained language models (PLMs), have been revealed to be vulnerable to adversarial attacks.
Zero-Shot Medical Information Retrieval via Knowledge Graph Embedding
In the era of the Internet of Things (IoT), the retrieval of relevant medical information has become essential for efficient clinical decision-making.
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning
By extracting semantic information from unlabeled data, self-supervised methods can improve the performance of downstream tasks, among which the mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Model-enhanced Vector Index
We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions.
Domain Adaptive Synapse Detection with Weak Point Annotations
In the second stage, we improve model generalizability on target data by regenerating square masks to get high-quality pseudo labels.
3D-STMN: Dependency-Driven Superpoint-Text Matching Network for End-to-End 3D Referring Expression Segmentation
In 3D Referring Expression Segmentation (3D-RES), the earlier approach adopts a two-stage paradigm, extracting segmentation proposals and then matching them with referring expressions.
Dynamic Strategy Chain: Dynamic Zero-Shot CoT for Long Mental Health Support Generation
The combination of chain-of-thought (CoT) prompting and Large Language Models (LLMs) is employed and get the SOTA performance on various NLP tasks, especially on text generation tasks.
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation
Instance segmentation in electron microscopy (EM) volumes is tough due to complex shapes and sparse annotations.
Likelihood-Based Text-to-Image Evaluation with Patch-Level Perceptual and Semantic Credit Assignment
In this paper, we propose to evaluate text-to-image generation performance by directly estimating the likelihood of the generated images using a pre-trained likelihood-based text-to-image generative model, i. e., a higher likelihood indicates better perceptual quality and better text-image alignment.
Prompt Switch: Efficient CLIP Adaptation for Text-Video Retrieval
In text-video retrieval, recent works have benefited from the powerful learning capabilities of pre-trained text-image foundation models (e. g., CLIP) by adapting them to the video domain.
Chinese Financial Text Emotion Mining: GCGTS -- A Character Relationship-based Approach for Simultaneous Aspect-Opinion Pair Extraction
This innovative structure reduces the excessive reliance on pre-trained language models and emphasizes the modeling of structure and local relationships, thereby improving the performance of the model on Chinese financial texts.
Improving Tuning-Free Real Image Editing with Proximal Guidance
Null-text inversion (NTI) optimizes null embeddings to align the reconstruction and inversion trajectories with larger CFG scales, enabling real image editing with cross-attention control.
Act Like a Radiologist: Radiology Report Generation across Anatomical Regions
To address these issues, we propose X-RGen, a radiologist-minded report generation framework across six anatomical regions.
Ranked #1 on
Medical Report Generation
on IU X-Ray
(using extra training data)
Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation
We further provide algorithm-dependent generalization bounds for these two settings, where the generalization is characterized by the mutual information between the parameters and the data.
DAE-Talker: High Fidelity Speech-Driven Talking Face Generation with Diffusion Autoencoder
Additionally, we propose a novel method for generating continuous video frames with the DDIM image decoder trained on individual frames, eliminating the need for modelling the joint distribution of consecutive frames directly.
IRGen: Generative Modeling for Image Retrieval
While generative modeling has become prevalent across numerous research fields, its integration into the realm of image retrieval remains largely unexplored and underjustified.
AutoMatch: A Large-scale Audio Beat Matching Benchmark for Boosting Deep Learning Assistant Video Editing
In this paper, we investigate a novel and practical problem, namely audio beat matching (ABM), which aims to recommend the proper transition time stamps based on the background music.
SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous Driving
However, there is a lack of underground parking scenario datasets with multiple sensors and well-labeled images that support both SLAM tasks and perception tasks, such as semantic segmentation and parking slot detection.
Path Integral Method for Pricing Proportional Step Double-Barrier Option with Time Dependent Parameters
Path integral method in quantum mechanics provides a new thinking for barrier option pricing.
GMConv: Modulating Effective Receptive Fields for Convolutional Kernels
Inspired by the property that ERFs typically exhibit a Gaussian distribution, we propose a Gaussian Mask convolutional kernel (GMConv) in this work.
SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method
Ensemble learning methods have been widely used in machine learning in recent years due to their high predictive performance.
LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup
On-device Deep Neural Network (DNN) inference consumes significant computing resources and development efforts.
Online Loss Function Learning
Loss function learning is a new meta-learning paradigm that aims to automate the essential task of designing a loss function for a machine learning model.
StrokeGAN+: Few-Shot Semi-Supervised Chinese Font Generation with Stroke Encoding
The effectiveness of the proposed model for the zero-shot traditional Chinese font generation is also evaluated in this paper.
On Learning Fairness and Accuracy on Multiple Subgroups
In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors.
Pareto-aware Neural Architecture Generation for Diverse Computational Budgets
More critically, these independent search processes cannot share their learned knowledge (i. e., the distribution of good architectures) with each other and thus often result in limited search results.
A Hamiltonian Approach to Floating Barrier Option Pricing
Hamiltonian approach in quantum mechanics provides a new thinking for barrier option pricing.
Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning
In this paper, we develop upon the emerging topic of loss function learning, which aims to learn loss functions that significantly improve the performance of the models trained under them.
Learning Distinct and Representative Styles for Image Captioning
Our innovative idea is to explore the rich modes in the training caption corpus to learn a set of "mode embeddings", and further use them to control the mode of the generated captions for existing image captioning models.
Towards Lightweight Super-Resolution with Dual Regression Learning
Nevertheless, it is hard for existing model compression methods to accurately identify the redundant components due to the extremely large SR mapping space.
Optimization-Induced Graph Implicit Nonlinear Diffusion
Moreover, we show that the optimization-induced variants of our models can boost the performance and improve training stability and efficiency as well.
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.
Fair Representation Learning through Implicit Path Alignment
We consider a fair representation learning perspective, where optimal predictors, on top of the data representation, are ensured to be invariant with respect to different sub-groups.
Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search
The outcome of the competition was ranked leaderboards of algorithms in each track based on recall at a query throughput threshold.
Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity
We propose a simple yet effective proposal-free architecture for lidar panoptic segmentation.
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings
We perform comprehensive explorations for the optimal conduct of knowledge distillation, which may provide useful insights for the learning of VQ based ANN index.
Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels has attracted much attention due to low annotation costs.
Ranked #21 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Path Integral Method for Proportional Step and Proportional Double-Barrier Step Option Pricing
Path integral method in quantum mechanics provides a new thinking for barrier option pricing.
PolarStream: Streaming Object Detection and Segmentation with Polar Pillars
Recent works recognized lidars as an inherently streaming data source and showed that the end-to-end latency of lidar perception models can be reduced significantly by operating on wedge-shaped point cloud sectors rather then the full point cloud.
V2C: Visual Voice Cloning
Existing Voice Cloning (VC) tasks aim to convert a paragraph text to a speech with desired voice specified by a reference audio.
Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers
Multi-Object Tracking (MOT) has achieved aggressive progress and derived many excellent deep learning trackers.
SPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search
It stores the centroid points of the posting lists in the memory and the large posting lists in the disk.
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis
We derive a novel information-theoretic analysis of the generalization property of meta-learning algorithms.
PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillars
Recent works recognized lidars as an inherently streaming data source and showed that the end-to-end latency of lidar perception models can be reduced significantly by operating on wedge-shaped point cloud sectors rather then the full point cloud.
Ranked #24 on
LIDAR Semantic Segmentation
on nuScenes
SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search
It stores the centroid points of the posting lists in the memory and the large posting lists in the disk.
Contrastive Neural Architecture Search with Neural Architecture Comparators
One of the key steps in Neural Architecture Search (NAS) is to estimate the performance of candidate architectures.
Pareto-Frontier-aware Neural Architecture Generation for Diverse Budgets
To this end, we propose a Pareto-Frontier-aware Neural Architecture Generator (NAG) which takes an arbitrary budget as input and produces the Pareto optimal architecture for the target budget.
Towards Accurate and Compact Architectures via Neural Architecture Transformer
To address this issue, we propose a Neural Architecture Transformer++ (NAT++) method which further enlarges the set of candidate transitions to improve the performance of architecture optimization.
StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding
However, these deep generative models may suffer from the mode collapse issue, which significantly degrades the diversity and quality of generated results.
Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization
We proposed a pair of cross-view transformers to transform the feature maps into the other view and introduce cross-view consistency loss on them.
Modular Graph Attention Network for Complex Visual Relational Reasoning
Moreover, to capture the complex logic in a query, we construct a relational graph to represent the visual objects and their relationships, and propose a multi-step reasoning method to progressively understand the complex logic.
Ranked #2 on
Referring Expression Comprehension
on CLEVR-Ref+
CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles
To reduce the amount of transmitted data, feature map based fusion is recently proposed as a practical solution to cooperative 3D object detection by autonomous vehicles.
Beyond $\mathcal{H}$-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence
We reveal the incoherence between the widely-adopted empirical domain adversarial training and its generally-assumed theoretical counterpart based on $\mathcal{H}$-divergence.
GLOW : Global Weighted Self-Attention Network for Web Search
Deep matching models aim to facilitate search engines retrieving more relevant documents by mapping queries and documents into semantic vectors in the first-stage retrieval.
DCANet: Learning Connected Attentions for Convolutional Neural Networks
With DCANet, all attention blocks in a CNN model are trained jointly, which improves the ability of attention learning.
Attention-guided Context Feature Pyramid Network for Object Detection
For object detection, how to address the contradictory requirement between feature map resolution and receptive field on high-resolution inputs still remains an open question.
Ranked #74 on
Object Detection
on COCO test-dev
A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays
In this paper, we aim to review various adversarial attack and defense methods on chest X-rays.
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
Extensive experiments with paired training data and unpaired real-world data demonstrate our superiority over existing methods.
Intelligent Home 3D: Automatic 3D-House Design from Linguistic Descriptions Only
To this end, we propose a House Plan Generative Model (HPGM) that first translates the language input to a structural graph representation and then predicts the layout of rooms with a Graph Conditioned Layout Prediction Network (GC LPN) and generates the interior texture with a Language Conditioned Texture GAN (LCT-GAN).
Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots
Accurate 3D object detection in LiDAR based point clouds suffers from the challenges of data sparsity and irregularities.
Ranked #3 on
3D Object Detection
on KITTI Pedestrians Moderate
NAT: Neural Architecture Transformer for Accurate and Compact Architectures
To verify the effectiveness of the proposed strategies, we apply NAT on both hand-crafted architectures and NAS based architectures.
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
We present a new logic-based inference engine for natural language inference (NLI) called MonaLog, which is based on natural logic and the monotonicity calculus.
F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds
Autonomous vehicles are heavily reliant upon their sensors to perfect the perception of surrounding environments, however, with the current state of technology, the data which a vehicle uses is confined to that from its own sensors.
Ranked #4 on
3D Object Detection
on OPV2V
FPCNet: Fast Pavement Crack Detection Network Based on Encoder-Decoder Architecture
Specifically, we first propose the Multi-Dilation (MD) module, which can synthesize the crack features of multiple context sizes via dilated convolution with multiple rates.
Semi-parametric Bayesian variable selection for gene-environment interactions
Existing Bayesian methods for G$\times$E interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences.
Methodology
Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds
A point cloud based 3D object detection method is proposed to work on a diversity of aligned point clouds.
Ranked #3 on
3D Object Detection
on OPV2V
Natural Language Inference with Monotonicity
This paper describes a working system which performs natural language inference using polarity-marked parse trees.
Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis
To address this issue, we develop a novel GAN called Auto-Embedding Generative Adversarial Network (AEGAN), which simultaneously encodes the global structure features and captures the fine-grained details.
DUNet: A deformable network for retinal vessel segmentation
Results show that more detailed vessels are extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0. 9697/0. 9722/0. 9724 and AUC of 0. 9856/0. 9868/0. 9863 on DRIVE, STARE and CHASE_DB1 respectively.
Ranked #5 on
Retinal Vessel Segmentation
on STARE
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning
The lack of labeled data is one of the main challenges when building a task-oriented dialogue system.
Semantic Segmentation for Urban Planning Maps based on U-Net
The results indicate the proposed method can serve as a viable tool for urban planning map semantic segmentation task with high accuracy and efficiency.
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss
However, most deep learning methods employ feed-forward architectures, and thus the dependencies between LR and HR images are not fully exploited, leading to limited learning performance.
Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings
In this paper, we present a new large-scale benchmark dataset termed Aerial Imagery for Roof Segmentation (AIRS).
SampleAhead: Online Classifier-Sampler Communication for Learning from Synthesized Data
But, this raises an important problem in active vision: given an {\bf infinite} data space, how to effectively sample a {\bf finite} subset to train a visual classifier?
UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision
We generate a large synthetic image dataset with automatically computed hazardous regions and analyze algorithms on these regions.
A Hierarchical Distributed Processing Framework for Big Image Data
The proposed ICP framework consists of two mechanisms, i. e. SICP (Static ICP) and DICP (Dynamic ICP).
Data classification using the Dempster-Shafer method
In this paper, the Dempster-Shafer method is employed as the theoretical basis for creating data classification systems.
