Visual Reasoning
309 papers with code • 12 benchmarks • 44 datasets
Ability to understand actions and reasoning associated with any visual images
Benchmarks
These leaderboards are used to track progress in Visual Reasoning
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Libraries
Use these libraries to find Visual Reasoning models and implementations
RefCOCO
MathVista
SHAPES
SNLI-VE
RAVEN
AbstractReasoning
NLVR
VSR
Most implemented papers
Learning Transferable Visual Models From Natural Language Supervision
openai/CLIP
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State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
salesforce/lavis
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Conference 2023
The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models.
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
facebookresearch/vilbert-multi-task
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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.
Visual Instruction Tuning
haotian-liu/LLaVA
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NeurIPS 2023
Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field.
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
fartashf/vsepp
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We present a new technique for learning visual-semantic embeddings for cross-modal retrieval.
Compositional Attention Networks for Machine Reasoning
stanfordnlp/mac-network
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ICLR 2018
We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning.
VisualBERT: A Simple and Performant Baseline for Vision and Language
We propose VisualBERT, a simple and flexible framework for modeling a broad range of vision-and-language tasks.
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
airsplay/lxmert
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IJCNLP 2019
In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder.
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
salesforce/lavis
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Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision.
FiLM: Visual Reasoning with a General Conditioning Layer
ethanjperez/film
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We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation.
