https://github.com/dataminr-ai/hrdsattack
공식 코드A New Task and Dataset on Detecting Attacks on Human Rights Defenders
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
What is Human Pose Estimation? Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image. Background. Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years. The reason for its importance is the abundance of applications that can benefit from such a technology. For example, human pose estimation allows for higher-level reasoning in the context of human-computer interaction and activity recognition; it is also one of the basic building blocks for marker-less motion capture (MoCap) technology. MoCap technology is useful for applications ranging from character animation to clinical analysis of gait pathologies.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
A New Task and Dataset on Detecting Attacks on Human Rights Defenders
HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid
Contextualization Distillation from Large Language Model for Knowledge Graph Completion
Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder
Interpolated SelectionConv for Spherical Images and Surfaces
Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning
Characterizing Linguistic Shifts in Croatian News via Diachronic Word Embeddings
WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs
Cycle-consistent Generative Adversarial Networks for Neural Style Transfer using data from Chang'E-4
Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
Newswire: A Large-Scale Structured Database of a Century of Historical News
Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering
Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation
News Meets Microblog: Hashtag Annotation via Retriever-Generator
Temporal Aggregate Representations for Long-Range Video Understanding
Scalable Recommendation of Wikipedia Articles to Editors Using Representation Learning
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack
Improving Object Detection in Art Images Using Only Style Transfer
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.