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Found 41 papers, 4 papers with code
Attention-Based Graph Neural Network with Global Context Awareness for Document Understanding
Information extraction from documents such as receipts or invoices is a fundamental and crucial step for office automation.
Deep Surface Normal Estimation on the 2-Sphere with Confidence Guided Semantic Attention
We propose a deep convolutional neural network (CNN) to estimate surface normal from a single color image accompanied with a low-quality depth channel.
Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation
Mis- and disinformation online have become a major societal problem as major sources of online harms of different kinds.
Manifold Path Guiding for Importance Sampling Specular Chains
We verify that importance sampling the seed chain in the continuous space reaches the goal of importance sampling the discrete admissible specular chain.
ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution
Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation.
Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation
The main idea of multimodal recommendation is the rational utilization of the item's multimodal information to improve the recommendation performance.
MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation
CLIP (Contrastive Language-Image Pretraining) is well-developed for open-vocabulary zero-shot image-level recognition, while its applications in pixel-level tasks are less investigated, where most efforts directly adopt CLIP features without deliberative adaptations.
Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction
Recent methods mostly rely on convolutional neural networks (CNNs) to fill the missing contents in the warped panorama.
Self-NeRF: A Self-Training Pipeline for Few-Shot Neural Radiance Fields
Recently, Neural Radiance Fields (NeRF) have emerged as a potent method for synthesizing novel views from a dense set of images.
UHDNeRF: Ultra-High-Definition Neural Radiance Fields
We propose UHDNeRF, a new framework for novel view synthesis on the challenging ultra-high-resolution (e. g., 4K) real-world scenes.
Symmetric Shape-Preserving Autoencoder for Unsupervised Real Scene Point Cloud Completion
Extensive experiments and comparisons demonstrate our superiority and generalization and show that our method achieves state-of-the-art performance on unsupervised completion of real scene objects.
HGAN: Hierarchical Graph Alignment Network for Image-Text Retrieval
Then, a multi-granularity shared space is established with a designed Multi-granularity Feature Aggregation and Rearrangement (MFAR) module, which enhances the semantic corresponding relations between the local and global information, and obtains more accurate feature representations for the image and text modalities.
Improving Performance of Automatic Keyword Extraction (AKE) Methods Using PoS-Tagging and Enhanced Semantic-Awareness
Automatic keyword extraction (AKE) has gained more importance with the increasing amount of digital textual data that modern computing systems process.
TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction
This technical report presents an effective method for motion prediction in autonomous driving.
Ranked #12 on
Motion Forecasting
on Argoverse CVPR 2020
Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentation
We propose in this paper an end-to-end network, named CS-Net, to complete the point clouds contaminated by noises or containing outliers.
Deep Point Cloud Simplification for High-quality Surface Reconstruction
In this paper, we propose a novel point cloud simplification network (PCS-Net) dedicated to high-quality surface mesh reconstruction while maintaining geometric fidelity.
Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction
At its core is a new lighting model (dubbed DSGLight) based on depth-augmented Spherical Gaussians (SG) and a Graph Convolutional Network (GCN) that infers the new lighting representation from a single LDR image of limited field-of-view.
GLPanoDepth: Global-to-Local Panoramic Depth Estimation
In this paper, we propose a learning-based method for predicting dense depth values of a scene from a monocular omnidirectional image.
Ranked #7 on
Depth Estimation
on Stanford2D3D Panoramic
ISNet: Shape Matters for Infrared Small Target Detection
TOAA block calculates the low-level information with attention mechanism in both row and column directions and fuses it with the high-level information to capture the shape characteristic of targets and suppress noises.
Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems
The vigorous developments of Internet of Things make it possible to extend its computing and storage capabilities to computing tasks in the aerial system with collaboration of cloud and edge, especially for artificial intelligence (AI) tasks based on deep learning (DL).
SVBRDF Recovery From a Single Image With Highlights using a Pretrained Generative Adversarial Network
For efficiency, we train the network in two stages: reusing a trained model to initialize the SVBRDFs and fine-tune it based on the input image.
Scale Invariant Domain Generalization Image Recapture Detection
However, most of them ignored the domain generalization scenario and scale variances, with an inferior performance on domain shift situations, and normally were exacerbated by intra-domain and inter-domain scale variances.
Temporal Alignment Prediction for Few-Shot Video Classification
In order to obtain the similarity of a pair of videos, we predict the alignment scores between all pairs of temporal positions in the two videos with the temporal alignment prediction function.
A Transductive Maximum Margin Classifier for Few-Shot Learning
We introduce a transductive maximum margin classifier for few-shot learning (FS-TMMC).
GLAVNet: Global-Local Audio-Visual Cues for Fine-Grained Material Recognition
We demonstrate that local geometry has a greater impact on the sound than the global geometry and offers more cues in material recognition.
Probabilistic Ranking-Aware Ensembles for Enhanced Object Detections
This leads to a new problem of confidence discrepancy for the detector ensembles.
A Survey on Natural Language Video Localization
Natural language video localization (NLVL), which aims to locate a target moment from a video that semantically corresponds to a text query, is a novel and challenging task.
Rendering Discrete Participating Media with Geometrical Optics Approximation
We consider the scattering of light in participating media composed of sparsely and randomly distributed discrete particles.
Graphics Optics
Hierarchical Disentangled Representation Learning for Outdoor Illumination Estimation and Editing
However, naively compressing an outdoor panorama into a low-dimensional latent vector, as existing models have done, causes two major problems.
Partially Observable Online Change Detection via Smooth-Sparse Decomposition
We consider online change detection of high dimensional data streams with sparse changes, where only a subset of data streams can be observed at each sensing time point due to limited sensing capacities.
Image Stitching Based on Planar Region Consensus
We specifically design a new module to make fully use of existing semantic segmentation networks to accommodate planar segmentation.
FA-GANs: Facial Attractiveness Enhancement with Generative Adversarial Networks on Frontal Faces
To the best of our knowledge, we are the first to enhance the facial attractiveness with GANs in both geometry and appearance aspects.
Hybrid Models for Open Set Recognition
We propose the OpenHybrid framework, which is composed of an encoder to encode the input data into a joint embedding space, a classifier to classify samples to inlier classes, and a flow-based density estimator to detect whether a sample belongs to the unknown category.
Learning Actor Relation Graphs for Group Activity Recognition
To this end, we propose to build a flexible and efficient Actor Relation Graph (ARG) to simultaneously capture the appearance and position relation between actors.
Ranked #3 on
Group Activity Recognition
on Collective Activity
Self-Selective Correlation Ship Tracking Method for Smart Ocean System
In recent years, with the development of the marine industry, navigation environment becomes more complicated.
Interest-Related Item Similarity Model Based on Multimodal Data for Top-N Recommendation
Specifically, the Multimodal IRIS model consists of three modules, i. e., multimodal feature learning module, the Interest-Related Network (IRN) module and item similarity recommendation module.
Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity
The sparsity and self-similarity of the image blocks are taken as the constraints.
Single Image Highlight Removal with a Sparse and Low-Rank Reflection Model
We propose a sparse and low-rank reflection model for specular highlight detection and removal using a single input image.
Stochastic Channel Decorrelation Network and Its Application to Visual Tracking
Deep convolutional neural networks (CNNs) have dominated many computer vision domains because of their great power to extract good features automatically.
FPAN: Fine-grained and Progressive Attention Localization Network for Data Retrieval
The Localization of the target object for data retrieval is a key issue in the Intelligent and Connected Transportation Systems (ICTS).
Fused Text Segmentation Networks for Multi-oriented Scene Text Detection
In this paper, we introduce a novel end-end framework for multi-oriented scene text detection from an instance-aware semantic segmentation perspective.
Ranked #12 on
Scene Text Detection
on MSRA-TD500
