Visual Commonsense Reasoning
29 papers with code • 7 benchmarks • 7 datasets
Image source: Visual Commonsense Reasoning
Benchmarks
These leaderboards are used to track progress in Visual Commonsense Reasoning
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Datasets
VCR
ScienceQA
IconQA
GD-VCR
WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images
QLEVR
Visual Commonsense Immorality benchmark
Latest papers
Towards artificial general intelligence via a multimodal foundation model
To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks.
Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning
wadeyin9712/gd-vcr
•
•
EMNLP 2021
Commonsense is defined as the knowledge that is shared by everyone.
X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
yehli/xmodaler
•
•
Nevertheless, there has not been an open-source codebase in support of training and deploying numerous neural network models for cross-modal analytics in a unified and modular fashion.
Interpretable Visual Understanding with Cognitive Attention Network
tanjatang/CAN
•
•
While image understanding on recognition-level has achieved remarkable advancements, reliable visual scene understanding requires comprehensive image understanding on recognition-level but also cognition-level, which calls for exploiting the multi-source information as well as learning different levels of understanding and extensive commonsense knowledge.
Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory
tanjatang/DMVCR
•
•
Moreover, the proposed model provides intuitive interpretation into visual commonsense reasoning.
MERLOT: Multimodal Neural Script Knowledge Models
rowanz/merlot
•
•
NeurIPS 2021
As humans, we understand events in the visual world contextually, performing multimodal reasoning across time to make inferences about the past, present, and future.
Unifying Vision-and-Language Tasks via Text Generation
j-min/VL-T5
•
•
On 7 popular vision-and-language benchmarks, including visual question answering, referring expression comprehension, visual commonsense reasoning, most of which have been previously modeled as discriminative tasks, our generative approach (with a single unified architecture) reaches comparable performance to recent task-specific state-of-the-art vision-and-language models.
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs
allenai/visual-reasoning-rationalization
•
•
Findings of the Association for Computational Linguistics 2020
Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights.
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.
TAB-VCR: Tags and Attributes based VCR Baselines
Deanplayerljx/tab-vcr
•
•
NeurIPS 2019
Despite impressive recent progress that has been reported on tasks that necessitate reasoning, such as visual question answering and visual dialog, models often exploit biases in datasets.
