Recurrent Models for Situation Recognition
Explore this paper's citation graph
- Type
- article
- Published
- 2017-03-18
- Cited by
- 34
- References
- 35
- Access
- Open access
- OpenAlex
- https://openalex.org/W2604673901
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:8774765
Keywords
CRFS, Conditional random field, Computer science, Artificial intelligence, Recurrent neural network
References
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- Recurrent Neural Network Regularization
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Show and tell: A neural image caption generator
- Long-term recurrent convolutional networks for visual recognition and description
- Describing Common Human Visual Actions in Images
- CIDEr: Consensus-based image description evaluation
- Background to Framenet
- The Pascal Visual Object Classes (VOC) Challenge
- Human action recognition by learning bases of action attributes and parts
- Long Short-Term Memory
- WordNet: A Lexical Database for English
- Bleu: a Method for Automatic Evaluation of Machine Translation
- Language Models for Image Captioning: The Quirks and What Works
- ImageNet Large Scale Visual Recognition Challenge
- Meteor Universal: Language Specific Translation Evaluation for Any Target Language
- HICO: A Benchmark for Recognizing Human-Object Interactions in Images
- Guiding the Long-Short Term Memory Model for Image Caption Generation
- Image Captioning with Semantic Attention
Cited by
- Convolutional Image Captioning
- Multimodal Frame Identification with Multilingual Evaluation
- Scene understanding using natural language description based on 3D semantic graph map
- Grounding Semantic Roles in Images
- Uni- and Multimodal and Structured Representations for Modeling Frame Semantics
- Weakly Supervised Visual Semantic Parsing
- Graph neural network for situation recognition
- Mixture-Kernel Graph Attention Network for Situation Recognition
- Grounded Situation Recognition
- Toward Characterizing Cities with Social Media Images Using Activity Recognition, Topic Modeling and Visualization
- Cross-media Structured Common Space for Multimedia Event Extraction
- Attention-Based Context Aware Reasoning for Situation Recognition
- Human-like Controllable Image Captioning with Verb-specific Semantic Roles
- Joint Multimedia Event Extraction from Video and Article
- Rethinking the Two-Stage Framework for Grounded Situation Recognition
- Beyond Grounding: Extracting Fine-Grained Event Hierarchies across Modalities
- GSRFormer: Grounded Situation Recognition Transformer with Alternate Semantic Attention Refinement
- Collaborative Transformers for Grounded Situation Recognition
- Knowledge-Aware Global Reasoning for Situation Recognition
- ClipSitu: Effectively Leveraging CLIP for Conditional Predictions in Situation Recognition
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