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Found 7 papers, 4 papers with code
Pose-aware Multi-level Feature Network for Human Object Interaction Detection
Reasoning human object interactions is a core problem in human-centric scene understanding and detecting such relations poses a unique challenge to vision systems due to large variations in human-object configurations, multiple co-occurring relation instances and subtle visual difference between relation categories.
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation
Scene graph generation is an important visual understanding task with a broad range of vision applications.
SGTR: Generating Scene Graph by Learning Compositional Triplets with Transformer
We develop a decoding-and-assembling paradigm for the end-to-end scene graph generation.
SGTR: End-to-end Scene Graph Generation with Transformer
Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property.
SGTR+: End-to-end Scene Graph Generation with Transformer
Moreover, we design a graph assembling module to infer the connectivity of the bipartite scene graph based on our entity-aware structure, enabling us to generate the scene graph in an end-to-end manner.
Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning
Generative vision-language models (VLMs) have shown impressive performance in zero-shot vision-language tasks like image captioning and visual question answering.
From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models
Scene graph generation (SGG) aims to parse a visual scene into an intermediate graph representation for downstream reasoning tasks.
