Search Results for author:
Found 50 papers, 26 papers with code
TSTTC: A Large-Scale Dataset for Time-to-Contact Estimation in Driving Scenarios
Time-to-Contact (TTC) estimation is a critical task for assessing collision risk and is widely used in various driver assistance and autonomous driving systems.
Practical Edge Detection via Robust Collaborative Learning
Edge detection, as a core component in a wide range of visionoriented tasks, is to identify object boundaries and prominent edges in natural images.
Single Image Reflection Separation via Component Synergy
The reflection superposition phenomenon is complex and widely distributed in the real world, which derives various simplified linear and nonlinear formulations of the problem.
Ranked #1 on
Reflection Removal
on SIR^2(Wild)
Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation
In the VPMCR setting, we propose a solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two components: Ambiguity-aware Soft Estimation (ASE) and Dynamism-aware Policy Learning (DPL).
Automated Code generation for Information Technology Tasks in YAML through Large Language Models
This work focuses on the generation of Ansible-YAML, a widely used markup language for IT Automation.
Adaptive Texture Filtering for Single-Domain Generalized Segmentation
Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features.
Face Inverse Rendering via Hierarchical Decoupling
Previous face inverse rendering methods often require synthetic data with ground truth and/or professional equipment like a lighting stage.
Pruning Before Training May Improve Generalization, Provably
It is shown that as long as the pruning fraction is below a certain threshold, gradient descent can drive the training loss toward zero and the network exhibits good generalization performance.
Multi-objective Deep Data Generation with Correlated Property Control
Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design.
YOLOV: Making Still Image Object Detectors Great at Video Object Detection
On the positive side, the detection in a certain frame of a video, compared with that in a still image, can draw support from other frames.
Ranked #7 on
Video Object Detection
on ImageNet VID
Controllable Data Generation by Deep Learning: A Review
This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation.
Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation
In this work, we propose PolarBEV for vision-based uneven BEV representation learning.
Automatic Controllable Product Copywriting for E-Commerce
Automatic product description generation for e-commerce has witnessed significant advancement in the past decade.
Disentangled Spatiotemporal Graph Generative Models
Spatiotemporal graph represents a crucial data structure where the nodes and edges are embedded in a geometric space and can evolve dynamically over time.
Interpretable Molecular Graph Generation via Monotonic Constraints
Recent advances in deep graph generative models treat molecule design as graph generation problems which provide new opportunities toward the breakthrough of this long-lasting problem.
Deep Generative Model for Periodic Graphs
To address them, this paper proposes Periodical-Graph Disentangled Variational Auto-encoder (PGD-VAE), a new deep generative models for periodic graphs that can automatically learn, disentangle, and generate local and global graph patterns.
Compact Graph Structure Learning via Mutual Information Compression
Furthermore, we maintain the performance of estimated views and the final view and reduce the mutual information of every two views.
Self-Augmented Unpaired Image Dehazing via Density and Depth Decomposition
With estimated scene depth, our method is capable of re-rendering hazy images with different thicknesses which further benefits the training of the dehazing network.
Intelligent Online Selling Point Extraction for E-Commerce Recommendation
In addition, this kind of product description should be eye-catching to the readers.
Low-light Image Enhancement via Breaking Down the Darkness
Assuming that an image can be decomposed into texture (with possible noise) and color components, one can specifically execute noise removal and color correction along with light adjustment.
Ranked #20 on
Low-Light Image Enhancement
on LOL
Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation
Single image reflection separation (SIRS), as a representative blind source separation task, aims to recover two layers, $\textit{i. e.}$, transmission and reflection, from one mixed observation, which is challenging due to the highly ill-posed nature.
Ranked #1 on
Reflection Removal
on Nature
Boosting Semantic Segmentation via Feature Enhancement
Semantic segmentation aims to map each pixel of an image into its correspond-ing semantic label.
GraphGT: Machine Learning Datasets for Graph Generation and Transformation
Graph generation, which learns from known graphs and discovers novel graphs, has great potential in numerous research topics like drug design and mobility synthesis and is one of the fastest-growing domains recently due to its promise for discovering new knowledge.
Graph Neural Networks for Natural Language Processing: A Survey
Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP).
Bilateral attention decoder: A lightweight decoder for real-time semantic segmentation
To verify the efficiency of the proposed bilateral attention decoder, we adopt a lightweight network as the backbone and compare our proposed method with other state-of-the-art real-time semantic segmentation methods on the Cityscapes and Camvid datasets.
Property Controllable Variational Autoencoder via Invertible Mutual Dependence
Deep generative models have made important progress towards modeling complex, high dimensional data via learning latent representations.
A Systematic Survey on Deep Generative Models for Graph Generation
Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios.
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement
Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning.
Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder
Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function.
Deep Multi-attributed Graph Translation with Node-Edge Co-evolution
Then, a spectral graph regularization based on our non-parametric graph Laplacian is proposed in order to learn and maintain the consistency of the predicted nodes and edges.
LaFIn: Generative Landmark Guided Face Inpainting
It is challenging to inpaint face images in the wild, due to the large variation of appearance, such as different poses, expressions and occlusions.
EDIT: Exemplar-Domain Aware Image-to-Image Translation
The principle behind is that, for images from multiple domains, the content features can be obtained by a uniform extractor, while (re-)stylization is achieved by mapping the extracted features specifically to different purposes (domains and exemplars).
Generative Adversarial Network
Image-to-Image Translation
+1
DEEP GRAPH SPECTRAL EVOLUTION NETWORKS FOR GRAPH TOPOLOGICAL TRANSFORMATION
On the other hand, graph topological evolution has been investigated in the graph signal processing domain historically, but it involves intensive labors to manually determine suitable prescribed spectral models and prohibitive difficulty to fit their potential combinations and compositions.
Multi-stage Deep Classifier Cascades for Open World Recognition
At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase.
Kindling the Darkness: A Practical Low-light Image Enhancer
It is worth to note that our network is trained with paired images shot under different exposure conditions, instead of using any ground-truth reflectance and illumination information.
Ranked #24 on
Low-Light Image Enhancement
on LOL
Single Image Deraining: A Comprehensive Benchmark Analysis
We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images. This dataset highlights diverse data sources and image contents, and is divided into three subsets (rain streak, rain drop, rain and mist), each serving different training or evaluation purposes.
PFLD: A Practical Facial Landmark Detector
Being accurate, efficient, and compact is essential to a facial landmark detector for practical use.
Deep Graph Translation
To achieve this, we propose a novel Graph-Translation-Generative Adversarial Networks (GT-GAN) which will generate a graph translator from input to target graphs.
Ensemble Soft-Margin Softmax Loss for Image Classification
On the other hand, the learned classifier of softmax loss is weak.
Fast Single Image Rain Removal via a Deep Decomposition-Composition Network
Rain effect in images typically is annoying for many multimedia and computer vision tasks.
Exclusivity-Consistency Regularized Multi-View Subspace Clustering
Multi-view subspace clustering aims to partition a set of multi-source data into their underlying groups.
LIME: Low-light Image Enhancement via Illumination Map Estimation
When one captures images in low-light conditions, the images often suffer from low visibility.
Ranked #1 on
Low-Light Image Enhancement
on 10 Monkey Species
(using extra training data)
Rain Streak Removal Using Layer Priors
This paper addresses the problem of rain streak removal from a single image.
LIME: A Method for Low-light IMage Enhancement
When one captures images in low-light conditions, the images often suffer from low visibility.
Exclusivity Regularized Machine
It has been recognized that the diversity of base learners is of utmost importance to a good ensemble.
Adaptively Unified Semi-Supervised Dictionary Learning With Active Points
In this paper, we present a novel semi-supervised dictionary learning method, which uses the informative coding vectors of both labeled and unlabeled data, and adaptively emphasizes the high confidence coding vectors of unlabeled data to enhance the dictionary discriminative capability simultaneously.
Visual Data Deblocking using Structural Layer Priors
The blocking artifact frequently appears in compressed real-world images or video sequences, especially coded at low bit rates, which is visually annoying and likely hurts the performance of many computer vision algorithms.
Generalized Tensor Total Variation Minimization for Visual Data Recovery
In this paper, we propose a definition of Generalized Tensor Total Variation norm (GTV) that considers both the inhomogeneity and the multi-directionality of responses to derivative-like filters.
Robust Separation of Reflection from Multiple Images
When one records a video/image sequence through a transparent medium (e. g. glass), the image is often a superposition of a transmitted layer (scene behind the medium) and a reflected layer.
Video Editing with Temporal, Spatial and Appearance Consistency
Given an area of interest in a video sequence, one may want to manipulate or edit the area, e. g. remove occlusions from or replace with an advertisement on it.
