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Found 26 papers, 14 papers with code
D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction
Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the past decade.
Ranked #2 on
Road Segmentation
on DeepGlobe
(IoU metric)
Improving Abstractive Text Summarization with History Aggregation
Recent neural sequence to sequence models have provided feasible solutions for abstractive summarization.
C-DLinkNet: considering multi-level semantic features for human parsing
Human parsing is an essential branch of semantic segmentation, which is a fine-grained semantic segmentation task to identify the constituent parts of human.
FGSD: A Dataset for Fine-Grained Ship Detection in High Resolution Satellite Images
To further promote the research of ship detection, we introduced a new fine-grained ship detection datasets, which is named as FGSD.
GINet: Graph Interaction Network for Scene Parsing
GI unit is further improved by the SC-loss to enhance the semantic representations over the exemplar-based semantic graph.
Contextual Graph Reasoning Networks
Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks.
ConTNet: Why not use convolution and transformer at the same time?
It is also worth pointing that, given identical strong data augmentations, the performance improvement of ConTNet is more remarkable than that of ResNet.
Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation
Internet video delivery has undergone a tremendous explosion of growth over the past few years.
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution
However, existing methods mostly train the DNNs on uniformly sampled LR-HR patch pairs, which makes them fail to fully exploit informative patches within the image.
Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identification
Much of that is due to the notorious modality bias training issue brought by the single-modality ImageNet pre-training, which might yield RGB-biased representations that severely hinder the cross-modality image retrieval.
Contrastive Learning
Cross-Modality Person Re-identification
+3
Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention
In this paper, we succeed in introducing multi-scale representations into semantic segmentation ViT via window attention mechanism and further improves the performance and efficiency.
Ranked #14 on
Semantic Segmentation
on DADA-seg
Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimation
Further, by defining a new form of data centroid, we transform the recovery problem of a label-dependent part to a centroid estimation problem.
CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summaries
In order to solve this problem, we paraphrase the reference summaries in CLTS, the Chinese Long Text Summarization dataset, correct errors of factual inconsistencies, and propose the first Chinese Long Text Summarization dataset with a high level of abstractiveness, CLTS+, which contains more than 180K article-summary pairs and is available online.
Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels
To exploit this effect, the model prediction-based methods have been widely adopted, which aim to exploit the outputs of DNNs in the early stage of learning to correct noisy labels.
Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection
Besides, by leveraging full training set and the additional 48K raw images of KITTI, it can further improve the MonoFlex by +4. 65% improvement on AP@0. 7 for car detection, reaching 18. 54% AP@0. 7, which ranks the 1st place among all monocular based methods on KITTI test leaderboard.
Privileged Prior Information Distillation for Image Matting
Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance.
SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines
To this end, we propose SwiftAvatar, a novel avatar auto-creation framework that is evidently superior to previous works.
Cross-modal Contrastive Learning for Multimodal Fake News Detection
An attention mechanism with an attention guidance module is implemented to help effectively and interpretably aggregate the aligned unimodal representations and the cross-modality correlations.
PaRK-Detect: Towards Efficient Multi-Task Satellite Imagery Road Extraction via Patch-Wise Keypoints Detection
In this paper, we propose a new scheme for multi-task satellite imagery road extraction, Patch-wise Road Keypoints Detection (PaRK-Detect).
MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings
We discover that most existing monocular 3D object detectors rely on the ego-vehicle prior assumption that the optical axis of the camera is parallel to the ground.
UAV Swarm-enabled Collaborative Secure Relay Communications with Time-domain Colluding Eavesdropper
Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting Internet of Things (IoT) network.
VN-Net: Vision-Numerical Fusion Graph Convolutional Network for Sparse Spatio-Temporal Meteorological Forecasting
Sparse meteorological forecasting is indispensable for fine-grained weather forecasting and deserves extensive attention.
SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detection
Our method surpasses all previous methods by a significant margin in new scenes, including +42. 57% for vehicle, +5. 87% for pedestrian, and +14. 89% for cyclist compared to BEVHeight on the DAIR-V2X-I heterologous benchmark.
HIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal Knowledge Graph
In recent years, temporal knowledge graph (TKG) reasoning has received significant attention.
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge Distillation
Automatic detection of multimodal misinformation has gained a widespread attention recently.
Multi-Scale Representations by Varying Window Attention for Semantic Segmentation
VWA leverages the local window attention (LWA) and disentangles LWA into the query window and context window, allowing the context's scale to vary for the query to learn representations at multiple scales.
