Search Results for author:
Found 27 papers, 16 papers with code
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.
Learning Feature Interactions with Lorentzian Factorization Machine
Learning representations for feature interactions to model user behaviors is critical for recommendation system and click-trough rate (CTR) predictions.
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
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.
Ranked #29 on
Fine-Grained Image Classification
on FGVC Aircraft
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.
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.
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.
Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images
Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections.
Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations
To the best of our knowledge, our Weather2K is the first attempt to tackle weather forecasting task by taking full advantage of the strengths of observation data from ground weather stations.
Sound Event Localization and Detection for Real Spatial Sound Scenes: Event-Independent Network and Data Augmentation Chains
Our system submitted to the DCASE 2022 Task 3 is based on our previous proposed Event-Independent Network V2 (EINV2) with a novel data augmentation method.
Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation
How to learn highly compact yet effective sentence representation?
Pre-trained Language Models Can be Fully Zero-Shot Learners
How can we extend a pre-trained model to many language understanding tasks, without labeled or additional unlabeled data?
Doubly Robust Self-Training
Self-training is an important technique for solving semi-supervised learning problems.
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)
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.
Selective-Memory Meta-Learning with Environment Representations for Sound Event Localization and Detection
In addition, we introduce environment representations to characterize different acoustic settings, enhancing the adaptability of our attenuation approach to various environments.
RPC Considered Harmful: Fast Distributed Deep Learning on RDMA
Its computation is typically characterized by a simple tensor data abstraction to model multi-dimensional matrices, a data-flow graph to model computation, and iterative executions with relatively frequent synchronizations, thereby making it substantially different from Map/Reduce style distributed big data computation.
Towards Efficient Large-Scale Graph Neural Network Computing
This evolution has led to large graph-based irregular and sparse models that go beyond what existing deep learning frameworks are designed for.
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.
Adaptive Self-training for Neural Sequence Labeling with Few Labels
Neural sequence labeling is an important technique employed for many Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), slot tagging for dialog systems and semantic parsing.
Contextual Graph Reasoning Networks
Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks.
Adaptive Self-training for Few-shot Neural Sequence Labeling
While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.
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.
Zero-shot Clarifying Question Generation for Conversational Search
In this work, we innovatively explore generating clarifying questions in a zero-shot setting to overcome the cold start problem and we propose a constrained clarifying question generation system which uses both question templates and query facets to guide the effective and precise question generation.
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).
META-SELD: Meta-Learning for Fast Adaptation to the new environment in Sound Event Localization and Detection
For learning-based sound event localization and detection (SELD) methods, different acoustic environments in the training and test sets may result in large performance differences in the validation and evaluation stages.
