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Found 50 papers, 28 papers with code
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling
The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets.
Extracting COVID-19 Events from Twitter
Language Modelling
+4
CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers
Specifically, the proposed \textit{CrossGET} has two key designs:1) \textit{Cross-Guided Matching and Ensemble}.
Prompt Engineering for Healthcare: Methodologies and Applications
This review will introduce the latest advances in prompt engineering in the field of natural language processing (NLP) for the medical domain.
ImpressionGPT: An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT
The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians, and it is typically written by radiologists based on the 'Findings' section.
V3Det: Vast Vocabulary Visual Detection Dataset
To facilitate the development of more general visual object detection, we propose V3Det, a vast vocabulary visual detection dataset with precisely annotated bounding boxes on massive images.
Dense Distinct Query for End-to-End Object Detection
Concretely, we first lay dense queries like traditional detectors and then select distinct ones for one-to-one assignments.
Ranked #3 on
Object Detection
on CrowdHuman (full body)
Semantics-Aware Dynamic Localization and Refinement for Referring Image Segmentation
Referring image segmentation segments an image from a language expression.
Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT
This is a new yet practical scenario in federated learning, i. e., labels-at-server semi-supervised federated learning (SemiFL).
Deep Generative Mixture Model for Robust Imbalance Classification
In this paper, a deep generative classifier is proposed to mitigate this issue via both model perturbation and data perturbation.
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences
Specifically, we construct a negative-sample-free triplet steam structure that is composed of an anchor stream without any masking, a spatial masking stream with Central Spatial Masking (CSM), and a temporal masking stream with Motion Attention Temporal Masking (MATM).
UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers
Real-world data contains a vast amount of multimodal information, among which vision and language are the two most representative modalities.
Siamese transformer with hierarchical concept embedding for fine-grained image recognition
In particular, one subnetwork is for coarse-scale patches to learn the discriminative regions with the aid of the innate multi-head self-attention mechanism of the transformer.
Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant
Semi-Supervised Semantic Segmentation aims at training the segmentation model with limited labeled data and a large amount of unlabeled data.
OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation
Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of large-scale realscanned 3D databases.
Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus Image Quality Enhancement
Most of the existing image enhancement methods mainly focus on improving the image quality by leveraging the guidance of high-quality images, which is difficult to be collected in medical applications.
Multi-Level Logit Distillation
Through this framework, the prediction alignment is not only conducted at the instance level, but also at the batch and class level, through which the student model learns instance prediction, input correlation, and category correlation simultaneously.
BUOL: A Bottom-Up Framework With Occupancy-Aware Lifting for Panoptic 3D Scene Reconstruction From a Single Image
Understanding and modeling the 3D scene from a single image is a practical problem.
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
Graph convolution networks (GCN) have been widely used in skeleton-based action recognition.
Ranked #4 on
Skeleton Based Action Recognition
on NTU RGB+D
In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning
When learning from sensitive data, care must be taken to ensure that training algorithms address privacy concerns.
Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction
Previous methods based on neural volume rendering mostly train a fully implicit model with MLPs, which typically require hours of training for a single scene.
An Understanding-Oriented Robust Machine Reading Comprehension Model
In this paper, we propose an understanding-oriented machine reading comprehension model to address three kinds of robustness issues, which are over sensitivity, over stability and generalization.
Deep Amortized Relational Model with Group-Wise Hierarchical Generative Process
In this paper, we propose Deep amortized Relational Model (DaRM) with group-wise hierarchical generative process for community discovery and link prediction on relational data (e. g., graph, network).
What Are Expected Queries in End-to-End Object Detection?
As both sparse and dense queries are imperfect, then \emph{what are expected queries in end-to-end object detection}?
PYSKL: Towards Good Practices for Skeleton Action Recognition
The toolbox supports a wide variety of skeleton action recognition algorithms, including approaches based on GCN and CNN.
Ranked #11 on
Skeleton Based Action Recognition
on NTU RGB+D
MINI: Mining Implicit Novel Instances for Few-Shot Object Detection
The offline mining mechanism leverages a self-supervised discriminative model to collaboratively mine implicit novel instances with a trained FSOD network.
Deep Understanding based Multi-Document Machine Reading Comprehension
Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings.
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation
Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image.
Ranked #3 on
Referring Expression Segmentation
on RefCOCOg-test
Few-Shot Object Detection via Association and DIscrimination
1) In the association step, in contrast to implicitly leveraging multiple base classes, we construct a compact novel class feature space via explicitly imitating a specific base class feature space.
FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning
In particular, we propose to use three networks and a dynamic quality control mechanism to generate high-quality pseudo labels for unlabeled data, which are added to the training set.
UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
Specifically, the CTrans module is an alternate of the U-Net skip connections, which consists of a sub-module to conduct the multi-scale Channel Cross fusion with Transformer (named CCT) and a sub-module Channel-wise Cross-Attention (named CCA) to guide the fused multi-scale channel-wise information to effectively connect to the decoder features for eliminating the ambiguity.
Ranked #1 on
Medical Image Segmentation
on GlaS
(IoU metric)
FedCon: A Contrastive Framework for Federated Semi-Supervised Learning
Most existing FedSSL methods focus on the classical scenario, i. e, the labeled and unlabeled data are stored at the client side.
Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce
Second, we compare the accumulative rewards of the three MAB algorithms with more than 1, 000 trials using actual historical A/B test datasets.
Cluster-Wise Hierarchical Generative Model for Deep Amortized Clustering
In this paper, we propose Cluster-wise Hierarchical Generative Model for deep amortized clustering (CHiGac).
Interpretable Image Recognition by Constructing Transparent Embedding Space
This plug-in embedding space is spanned by transparent basis concepts which are constructed on the Grassmann manifold.
CARAFE++: Unified Content-Aware ReAssembly of FEatures
Feature reassembly, i. e. feature downsampling and upsampling, is a key operation in a number of modern convolutional network architectures, e. g., residual networks and feature pyramids.
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching
We adopt Deep Pyramid Convolutional Neural Network (DPCNN) for source code feature extraction and Graph Neural Network (GNN) for binary code feature extraction.
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text
The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets.
Extracting COVID-19 Events from Twitter
Language Modelling
+5
Texture Memory-Augmented Deep Patch-Based Image Inpainting
By bringing together the best of both paradigms, we propose a new deep inpainting framework where texture generation is guided by a texture memory of patch samples extracted from unmasked regions.
Active Learning for Product Type Ontology Enhancement in E-commerce
Entity-based semantic search has been widely adopted in modern search engines to improve search accuracy by understanding users' intent.
Seesaw Loss for Long-Tailed Instance Segmentation
Instances of head classes dominate a long-tailed dataset and they serve as negative samples of tail categories.
MMFashion: An Open-Source Toolbox for Visual Fashion Analysis
This toolbox supports a wide spectrum of fashion analysis tasks, including Fashion Attribute Prediction, Fashion Recognition and Retrieval, Fashion Landmark Detection, Fashion Parsing and Segmentation and Fashion Compatibility and Recommendation.
FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC
In MEC, edge nodes would not like to voluntarily participate in learning, and they differ in the provision of multi-dimensional resources, both of which might deteriorate the performance of federated learning.
Side-Aware Boundary Localization for More Precise Object Detection
To tackle the difficulty of precise localization in the presence of displacements with large variance, we further propose a two-step localization scheme, which first predicts a range of movement through bucket prediction and then pinpoints the precise position within the predicted bucket.
On the Robustness of the Backdoor-based Watermarking in Deep Neural Networks
We focus on backdoor-based watermarking and propose two -- a black-box and a white-box -- attacks that remove the watermark.
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
CARAFE: Content-Aware ReAssembly of FEatures
CARAFE introduces little computational overhead and can be readily integrated into modern network architectures.
Hierarchical Attention Generative Adversarial Networks for Cross-domain Sentiment Classification
Cross-domain sentiment classification (CDSC) is an importance task in domain adaptation and sentiment classification.
Hybrid Task Cascade for Instance Segmentation
In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation.
Ranked #57 on
Instance Segmentation
on COCO test-dev
Region Proposal by Guided Anchoring
State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the spatial domain with a predefined set of scales and aspect ratios.
Ranked #1 on
Region Proposal
on COCO test-dev
Optimizing Video Object Detection via a Scale-Time Lattice
High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e. g. those that require detecting objects from video streams in real time.
