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Found 68 papers, 36 papers with code
Robust Visual Tracking Using Oblique Random Forests
Unlike conventional orthogonal decision trees that use a single feature and heuristic measures to obtain a split at each node, we propose to use a more powerful proximal SVM to obtain oblique hyperplanes to capture the geometric structure of the data better.
Image Matching: An Application-oriented Benchmark
To this end, we present a uniform benchmark with novel evaluation metrics and a large-scale dataset for evaluating the overall performance of image matching methods.
Kernel Cross-Correlator
Cross-correlator plays a significant role in many visual perception tasks, such as object detection and tracking.
Semantic Edge Detection with Diverse Deep Supervision
Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition.
A Deep Network for Arousal-Valence Emotion Prediction with Acoustic-Visual Cues
In this paper, we comprehensively describe the methodology of our submissions to the One-Minute Gradual-Emotion Behavior Challenge 2018.
Learning Pixel-wise Labeling from the Internet without Human Interaction
Collecting sufficient annotated data is very expensive in many applications, especially for pixel-level prediction tasks such as semantic segmentation.
Crowd Counting With Deep Negative Correlation Learning
Deep convolutional networks (ConvNets) have achieved unprecedented performances on many computer vision tasks.
Ranked #9 on
Crowd Counting
on WorldExpo’10
MatchBench: An Evaluation of Feature Matchers
This leads to a critical absence in this field that there is no standard datasets and evaluation metrics to evaluate different feature matchers fairly.
Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN
Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment.
PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship
Apparent personality and emotion analysis are both central to affective computing.
Salient Object Detection via High-to-Low Hierarchical Context Aggregation
In this paper, we observe that the contexts of a natural image can be well expressed by a high-to-low self-learning of side-output convolutional features.
High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort
We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset.
Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation
Segmentation stands at the forefront of many high-level vision tasks.
A Deep Framework for Bone Age Assessment based on Finger Joint Localization
In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.
Robust Regression via Deep Negative Correlation Learning
Nonlinear regression has been extensively employed in many computer vision problems (e. g., crowd counting, age estimation, affective computing).
An Evaluation of Feature Matchers for Fundamental Matrix Estimation
According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator.
A novel centroid update approach for clustering-based superpixel methods and superpixel-based edge detection
Then according to the statistical features of noise, we propose a novel centroid update approach to enhance the robustness of clustering-based superpixel methods.
AdaSample: Adaptive Sampling of Hard Positives for Descriptor Learning
The effectiveness of the triplet loss heavily relies on the triplet selection, in which a common practice is to first sample intra-class patches (positives) from the dataset for batch construction and then mine in-batch negatives to form triplets.
Ordered or Orderless: A Revisit for Video based Person Re-Identification
Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation.
The 2019 BBN Cross-lingual Information Retrieval System
In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English.
Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
Recent years have seen increasing use of supervised learning methods for segmentation tasks.
Regularized Densely-connected Pyramid Network for Salient Instance Segmentation
Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels.
Generalized Zero-Shot Learning via VAE-Conditioned Generative Flow
Recent generative methods formulate GZSL as a missing data problem, which mainly adopts GANs or VAEs to generate visual features for unseen classes.
Disentangling Human Error from Ground Truth in Segmentation of Medical Images
In all cases, our method outperforms competing methods and relevant baselines particularly in cases where the number of annotations is small and the amount of disagreement is large.
EDN: Salient Object Detection via Extremely-Downsampled Network
In this paper, we tap into this gap and show that enhancing high- level features is essential for SOD as well.
Unsupervised Scale-consistent Depth Learning from Video
We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time.
Vision Transformers with Hierarchical Attention
This paper tackles the high computational/space complexity associated with Multi-Head Self-Attention (MHSA) in vanilla vision transformers.
Free Lunch for Co-Saliency Detection: Context Adjustment
We hope that the scale, diversity, and quality of our dataset can benefit researchers in this area and beyond.
Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net
The asymmetric bilateral encoder has a transformer path and a lightweight CNN path, where the two paths communicate at each encoder stage to learn complementary global contexts and local spatial details, respectively.
Diverse and Consistent Multi-view Networks for Semi-supervised Regression
Label collection is costly in many applications, which poses the need for label-efficient learning.
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i. e., cyclic sequences).
Probing Simile Knowledge from Pre-trained Language Models
In this paper, we probe simile knowledge from PLMs to solve the SI and SG tasks in the unified framework of simile triple completion for the first time.
SUBS: Subtree Substitution for Compositional Semantic Parsing
Although sequence-to-sequence models often achieve good performance in semantic parsing for i. i. d.
TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding
Data augmentation is an effective approach to tackle over-fitting.
Efficient Harmonic Neural Networks with Compound Discrete Cosine Transform filters and Shared Reconstruction filters
First, the spectrum feature learned by HNN is insufficient and limited because the number of DCT filters is much smaller than that of feature maps.
Mining Relations among Cross-Frame Affinities for Video Semantic Segmentation
The essence of video semantic segmentation (VSS) is how to leverage temporal information for prediction.
Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning
In recent years, image generation has made great strides in improving the quality of images, producing high-fidelity ones.
Aesthetic Visual Question Answering of Photographs
In this paper, we propose a new task of aesthetic language assessment: aesthetic visual question and answering (AVQA) of images.
Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes
Current efficient LiDAR-based detection frameworks are lacking in exploiting object relations, which naturally present in both spatial and temporal manners.
AIA: Attention in Attention Within Collaborate Domains
Attention mechanisms can effectively improve the performance of the mobile networks with a limited computational complexity cost.
Deep Negative Correlation Classification
Ensemble learning serves as a straightforward way to improve the performance of almost any machine learning algorithm.
FedTADBench: Federated Time-Series Anomaly Detection Benchmark
We would like to answer the following questions: (1)How is the performance of time series anomaly detection algorithms when meeting federated learning?
Deep adaptive hiding network for image hiding using attentive frequency extraction and gradual depth extraction
Image hiding secures information security in multimedia communication.
GSB: Group Superposition Binarization for Vision Transformer with Limited Training Samples
Compared with the full-precision one, the model with the binarization method replaces complex tensor multiplication with simple bit-wise binary operations and represents full-precision model parameters and activations with only 1-bit ones, which potentially solves the problem of model size and computational complexity, respectively.
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation
The field of protein folding research has been greatly advanced by deep learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance and atomic-level precision.
CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
Motivated by these, we aim to improve the use efficiency of unlabeled data by designing two novel label propagation strategies.
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional Understanding
However, the compositional reasoning abilities of existing VLMs remains subpar.
Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering
In summary, our research sheds light on the intricacies of prompting strategies in VLMs for VQA, emphasizing the synergistic use of captions, templates, and pre-processing to enhance model efficacy.
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management.
Joint adjustment image steganography networks
Thus, the security and quality of stego and revealed secret images still have much room for promotion, especially for large-capacity image steganography.
Sudowoodo: a Chinese Lyric Imitation System with Source Lyrics
In this paper, we introduce \textbf{\textit{Sudowoodo}}, a Chinese lyrics imitation system that can generate new lyrics based on the text of source lyrics.
Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution
Transformer-based methods have exhibited remarkable potential in single image super-resolution (SISR) by effectively extracting long-range dependencies.
Ranked #26 on
Image Super-Resolution
on Set14 - 4x upscaling
AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation
In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics.
Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network
Accurate traffic forecasting at intersections governed by intelligent traffic signals is critical for the advancement of an effective intelligent traffic signal control system.
Multi-Task Cooperative Learning via Searching for Flat Minima
In this work, we propose to formulate MTL as a multi/bi-level optimization problem, and therefore force features to learn from each task in a cooperative approach.
Low-Resolution Self-Attention for Semantic Segmentation
Semantic segmentation tasks naturally require high-resolution information for pixel-wise segmentation and global context information for class prediction.
MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model
To enable LLMs to tackle the task in a zero-shot manner, we introduce MoqaGPT, a straightforward and flexible framework.
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography
The amygdala plays a vital role in emotional processing and exhibits structural diversity that necessitates fine-scale parcellation for a comprehensive understanding of its anatomico-functional correlations.
Learning Triangular Distribution in Visual World
Convolution neural network is successful in pervasive vision tasks, including label distribution learning, which usually takes the form of learning an injection from the non-linear visual features to the well-defined labels.
Learning to See Low-Light Images via Feature Domain Adaptation
To solve this problem, we propose a single-stage network empowered by Feature Domain Adaptation (FDA) to decouple the denoising and color mapping tasks in raw LLIE.
ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awareness
To address this, in this paper, we propose a novel Reliable Cognitive Diagnosis(ReliCD) framework, which can quantify the confidence of the diagnosis feedback and is flexible for different cognitive diagnostic functions.
MuGI: Enhancing Information Retrieval through Multi-Text Generation Integration with Large Language Models
Large Language Models (LLMs) have emerged as a pivotal force in language technology.
Increasing SAM Zero-Shot Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation
This study develops and evaluates a novel multimodal medical image zero-shot segmentation algorithm named Text-Visual-Prompt SAM (TV-SAM) without any manual annotations.
DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives
Graph Convolutional Networks (GCNs) have become pivotal in recommendation systems for learning user and item embeddings by leveraging the user-item interaction graph's node information and topology.
PASTA: Towards Flexible and Efficient HDR Imaging Via Progressively Aggregated Spatio-Temporal Alignment
Leveraging Transformer attention has led to great advancements in HDR deghosting.
RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection
In the dual-stream radar backbone, a point-based encoder and a transformer-based encoder are proposed to extract radar features, with an injection and extraction module to facilitate communication between the two encoders.
Ranked #2 on
3D Object Detection
on nuscenes Camera-Radar
QiuNiu: A Chinese Lyrics Generation System with Passage-Level Input
In this paper, we demonstrate the QiuNiu, a Chinese lyrics generation system which is conditioned on passage-level text rather than a few attributes or keywords.
