Visual Commonsense in Pretrained Unimodal and Multimodal Models (2022.naacl-main)
Copied to clipboard
| Challenge: | Fig. 1 shows how text-only and image-only models can capture commonsense visual attributes, but reporting bias affects their performance. |
| Approach: | They use a Visual Commonsense Tests dataset to validate their findings . they find multimodal models better reconstruct attribute distributions, but are still subject to reporting bias . |
| Outcome: | The proposed model improves on the unimodal and multimodal models, but is still subject to reporting bias. |
Similar Papers
What do Models Learn From Training on More Than Text? Measuring Visual Commonsense Knowledge (2022.acl-srw)
Copied to clipboard
| Challenge: | Existing evaluation methods to measure what language models learn from multimodal training are lacking. |
| Approach: | They propose two evaluation tasks to measure commonsense knowledge in language models by using visual data to evaluate multimodal models and unimodal baselines. |
| Outcome: | The proposed evaluation tasks show that training on a visual modality improves on the visual commonsense knowledge in language models. |
Seeing What Tastes Good: Revisiting Multimodal Distributional Semantics in the Billion Parameter Era (2025.findings-acl)
Copied to clipboard
| Challenge: | danoneata, et al., 2021): human learning and conceptual representation is grounded in sensorimotor experience. |
| Approach: | They evaluate image encoders and language-only models to learn which attributes are salient to the models. |
| Outcome: | The proposed models outperform language-only models on attributes predicting extended denser McRae norms and newer Binder datasets. |
VCD: A Dataset for Visual Commonsense Discovery in Images (2025.findings-acl)
Copied to clipboard
| Challenge: | Visual commonsense data sets lack visual grounded representations of commonsensense . existing knowledge bases lack visual-based knowledge tied to actual visual scenes . |
| Approach: | They present a large-scale visual commonsense dataset with over 100,000 images and 14 million object-commonsense pairs that integrates both Seen (directly observable) and Unseen (inferrable) commonsens. |
| Outcome: | The proposed model integrates Seen (directly observable) and Unseen (inferrable) commonsense across Property, Action, and Space aspects. |
Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing models that understand image and text but also cross-reference in-between are lacking in evaluation data resources. |
| Approach: | They propose a multimodal evaluation pipeline to automatically generate question-answer pairs to test models’ understanding of the visual scene, text, and related knowledge. |
| Outcome: | The proposed model can answer the highly semantic VCR question correctly but fails to answer related visual question (Q2), textual question (q3), and background knowledge question ( Q4) as shallow mappings with language priors and unbalanced utilization of information between modalities. |
Are Visual-Linguistic Models Commonsense Knowledge Bases? (2022.coling-1)
Copied to clipboard
| Challenge: | PTLMs are used to extract knowledge from text on demand. |
| Approach: | They compare visual-linguistic and language-only visual-language models in a zero-shot commonsense question answering inference task. |
| Outcome: | The proposed models are highly promising on certain types of commonsense knowledge associated with the visual world. |
The World of an Octopus: How Reporting Bias Influences a Language Model’s Perception of Color (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent work has raised concerns about the inherent limitations of text-only pretraining. |
| Approach: | They first generate a color dataset of human-perceived color distributions for 521 common objects and then use it to analyze and compare the color distribution found in text and the distribution captured by language models. |
| Outcome: | The proposed model improves on the CoDa color distribution, while the language model improve on the ground-truth distribution. |
LaMI: Augmenting Large Language Models via Late Multi-Image Fusion (2026.acl-short)
Copied to clipboard
| Challenge: | Large Language Models lack visual grounding on visual reasoning, despite training on text alone. |
| Approach: | They propose a late multi-image fusion method that augments LLMs with test-time visual signals. |
| Outcome: | Using a late multi-image fusion method, the proposed model outperforms LLMs on visual reasoning and matches VLMs in vision-based tasks. |
Find Someone Who: Visual Commonsense Understanding in Human-Centric Grounding (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Visual scenes often involve multiple people and humans can distinguish between them based on context descriptions about what happened before, their mental/physical states, and intentions. |
| Approach: | They propose a task that tests human-centric commonsense grounding models' ability to distinguish individuals given context descriptions about what happened before and their mental/physical states or intentions. |
| Outcome: | The proposed model outperforms pre-trained and non-pretrained models on 130k commonsense descriptions annotated on 67k images. |
VLIS: Unimodal Language Models Guide Multimodal Language Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing vision-language models face challenges in tasks that require complex linguistic understanding. |
| Approach: | They propose a framework that combines visual conditioning and linguistic understanding of unimodal text-only language models without further training to improve vision-language models. |
| Outcome: | The proposed framework improves vision-language models on diverse tasks including commonsense understanding and complex text generation. |
The Effects of Unimodal Representation Choices on Multimodal Learning (L18-1)
Copied to clipboard
| Challenge: | In the real world, multiple modes of information are gathered to create knowledge in a way humans can understand. |
| Approach: | They propose to combine unimodal representations to map multiple modes of information to a single space . they argue that the way they are combined can affect performance and classification metrics . |
| Outcome: | The proposed model can be used to correlate words in a textual description of an object with multimodal representations. |