Referring Expression Comprehension

30 papers with code • 6 benchmarks • 4 datasets

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Most implemented papers

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

facebookresearch/vilbert-multi-task NeurIPS 2019

We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language.

Compositional Attention Networks for Machine Reasoning

stanfordnlp/mac-network ICLR 2018

We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning.

UNITER: UNiversal Image-TExt Representation Learning

ChenRocks/UNITER ECCV 2020

Different from previous work that applies joint random masking to both modalities, we use conditional masking on pre-training tasks (i. e., masked language/region modeling is conditioned on full observation of image/text).

CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions

ruotianluo/iep-ref CVPR 2019

Yet there has been evidence that current benchmark datasets suffer from bias, and current state-of-the-art models cannot be easily evaluated on their intermediate reasoning process.

VL-BERT: Pre-training of Generic Visual-Linguistic Representations

jackroos/VL-BERT ICLR 2020

We introduce a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short).

A Joint Speaker-Listener-Reinforcer Model for Referring Expressions

lichengunc/speaker_listener_reinforcer CVPR 2017

The speaker generates referring expressions, the listener comprehends referring expressions, and the reinforcer introduces a reward function to guide sampling of more discriminative expressions.

A Fast and Accurate One-Stage Approach to Visual Grounding

zyang-ur/onestage_grounding ICCV 2019

We propose a simple, fast, and accurate one-stage approach to visual grounding, inspired by the following insight.

Large-Scale Adversarial Training for Vision-and-Language Representation Learning

zhegan27/VILLA NeurIPS 2020

We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning.

TransVG: End-to-End Visual Grounding with Transformers

djiajunustc/TransVG ICCV 2021

In this paper, we present a neat yet effective transformer-based framework for visual grounding, namely TransVG, to address the task of grounding a language query to the corresponding region onto an image.

SeqTR: A Simple yet Universal Network for Visual Grounding

sean-zhuh/seqtr 30 Mar 2022

In this paper, we propose a simple yet universal network termed SeqTR for visual grounding tasks, e. g., phrase localization, referring expression comprehension (REC) and segmentation (RES).