Predicate Classification

8 papers with code • 3 benchmarks • 3 datasets

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Most implemented papers

SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences

ShunChengWu/SceneGraphFusion CVPR 2021

Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks.

1st Place Solution for PSG competition with ECCV'22 SenseHuman Workshop

wangqixun/mfpsg 6 Feb 2023

Panoptic Scene Graph (PSG) generation aims to generate scene graph representations based on panoptic segmentation instead of rigid bounding boxes.

Visual Distant Supervision for Scene Graph Generation

thunlp/visualds ICCV 2021

In this work, we propose visual distant supervision, a novel paradigm of visual relation learning, which can train scene graph models without any human-labeled data.

Tackling the Challenges in Scene Graph Generation with Local-to-Global Interactions

sangminwoo/Local-to-Global-Interaction-Networks-SGG 16 Jun 2021

To quantify how much LOGIN is aware of relational direction, a new diagnostic task called Bidirectional Relationship Classification (BRC) is also proposed.

Classification-Then-Grounding: Reformulating Video Scene Graphs as Temporal Bipartite Graphs

Dawn-LX/VidSGG-BIG CVPR 2022

To this end, we propose a new classification-then-grounding framework for VidSGG, which can avoid all the three overlooked drawbacks.

Biasing Like Human: A Cognitive Bias Framework for Scene Graph Generation

rafa-cxg/PySGG-cxg 17 Mar 2022

Scene graph generation is a sophisticated task because there is no specific recognition pattern (e. g., "looking at" and "near" have no conspicuous difference concerning vision, whereas "near" could occur between entities with different morphology).

Fine-Grained Scene Graph Generation with Data Transfer

waxnkw/ietrans-sgg.pytorch 22 Mar 2022

Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images.

Fine-Grained Predicates Learning for Scene Graph Generation

xinyulyu/fgpl CVPR 2022

The performance of current Scene Graph Generation models is severely hampered by some hard-to-distinguish predicates, e. g., "woman-on/standing on/walking on-beach" or "woman-near/looking at/in front of-child".