Search Results for author:
Found 21 papers, 10 papers with code
HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition
Specifically, we first utilize a two-phase module to generate span representations by aggregating context information based on a bottom-up and top-down transformer network.
Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection
Further, we propose a learnable text prompt mechanism with the assist of a normality visual prompt to further improve the matching accuracy of video event description text and video frames.
Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation
When activating class objects, we argue that the false activation stems from the bias to the ambiguous regions during the feature extraction.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation
In this work, we devise a 'Separate and Conquer' scheme SeCo to tackle this issue from dimensions of image space and feature space.
Towards Arbitrary-Scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework via Implicit Self-texture Enhancement
To the best of our knowledge, this is the first work to achieve arbitrary-scale super-resolution in pathology images.
WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom
This model only requires bag-level labels for training but is capable of inferring both sentence-level misinformation and article-level veracity, aided by relevant social media conversations that are attentively contextualized with news sentences.
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision Models
EndoKED automates the transformation of raw colonoscopy records into image datasets with pixel-level annotation.
OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification
In this paper, we formulate this scenario as an open-set AL problem and propose an efficient framework, OpenAL, to address the challenge of querying samples from an unlabeled pool with both target class and non-target class samples.
Exploring the Nexus between Exhaustible Human Resources and Economic Development in China: An Application of the Hotelling Model
This paper applies the Hotelling model to the context of exhaustible human resources in China.
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification
Focusing on more effective and comprehensive anomaly detection, we propose a network based on self-supervised learning and self-attentive graph convolution (SLSG) for anomaly detection.
Ranked #3 on
Anomaly Detection
on MVTec LOCO AD
Video Event Restoration Based on Keyframes for Video Anomaly Detection
Video anomaly detection (VAD) is a significant computer vision problem.
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement
Existing super-resolution models for pathology images can only work in fixed integer magnifications and have limited performance.
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection
The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics.
Boosting Whole Slide Image Classification from the Perspectives of Distribution, Correlation and Magnification
However, there are still three important issues that have not been fully addressed: (1) positive bags with a low positive instance ratio are prone to the influence of a large number of negative instances; (2) the correlation between local and global features of pathology images has not been fully modeled; and (3) there is a lack of effective information interaction between different magnifications.
A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection
Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances.
Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection
Existing methods for anomaly detection based on memory-augmented autoencoder (AE) have the following drawbacks: (1) Establishing a memory bank requires additional memory space.
The Sufficiency of Off-Policyness and Soft Clipping: PPO is still Insufficient according to an Off-Policy Measure
The popular Proximal Policy Optimization (PPO) algorithm approximates the solution in a clipped policy space.
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks
In contrast, Transformer Decoder with the causal attention masks is naturally sensitive to the word order.
Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning
Massive false rumors emerging along with breaking news or trending topics severely hinder the truth.
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks
Rumors are rampant in the era of social media.
Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision
Violence detection has been studied in computer vision for years.
