Papers with vision and
Grounded, or a Good Guesser? A Per-Question Balanced Dataset to Separate Blind from Grounded Models for Embodied Question Answering (2025.acl-short)
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Miles Shelton, Nate Wingerd, Kritim K Rijal, Ayush Garg, Adelina Gutic, Brett Barnes, Catherine Finegan-Dollak
| Challenge: | Embodied question answering (EQA) is based on using perception and action in an environment to answer natural language questions. |
| Approach: | They propose a "per-question balanced" EQA dataset that uses two different environments to ground a model's answers in its environment. |
| Outcome: | The proposed model performs better than chance on the PQB-EQA benchmark, showing that it does not require the model to use perception, let alone to act in its environment to find the answer. |
Explicit Object Relation Alignment for Vision and Language Navigation (2022.acl-srw)
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| Challenge: | Existing work on vision and language navigation grounding the landmarks and spatial relations in textual instructions into visual modality is important. |
| Approach: | They propose a neural agent to explicitly align the spatial information in both instruction and visual environment, including landmarks and spatial relationships between the agent and landmarks. |
| Outcome: | The proposed method surpasses the baseline on the R2R dataset and shows that it can explain spatial reasoning and spatial relationships. |
Scene Graph Parsing as Dependency Parsing (N18-1)
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| Challenge: | Recent studies have focused on parsing structured knowledge graphs from textual descriptions. |
| Approach: | They propose an alternative but equivalent scene graph representation that connects to dependency parses. |
| Outcome: | The proposed model outperforms best approaches on image retrieval applications. |
Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality (2023.emnlp-main)
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| Challenge: | Recent studies have highlighted severe limitations of contrastive learning models in their ability to perform compositional reasoning over objects, attributes, and relations. |
| Approach: | They propose a graph decomposition framework and negative mining techniques to improve attribute binding and relation understanding of scene graphs. |
| Outcome: | The proposed approach improves attribute binding, relation understanding, generalization, and productivity on multiple benchmarks. |
IRFL: Image Recognition of Figurative Language (2023.findings-emnlp)
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| Challenge: | Figures of speech are ubiquitous in many forms of discourse, allowing people to convey complex, abstract ideas and evoke emotion. |
| Approach: | They develop a dataset for multimodal figurative language understanding using human annotation and an automatic pipeline to generate a multimodal dataset. |
| Outcome: | The proposed dataset performs better than human vision and language models compared with a human dataset . |
CPL: Counterfactual Prompt Learning for Vision and Language Models (2022.emnlp-main)
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Xuehai He, Diji Yang, Weixi Feng, Tsu-Jui Fu, Arjun Akula, Varun Jampani, Pradyumna Narayana, Sugato Basu, William Yang Wang, Xin Wang
| Challenge: | Existing prompt tuning methods tend to learn spurious or entangled representations, leading to poor generalization to unseen concepts. |
| Approach: | They propose a prompt tuning technique that tunes the learnable prompt for pre-trained vision and language models. |
| Outcome: | The proposed method improves few-shot performance on vision and language tasks over existing prompt tuning methods. |
Assessing News Thumbnail Representativeness: Counterfactual text can enhance the cross-modal matching ability (2024.findings-acl)
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| Challenge: | Existing models for visual and language processing struggle to match news actors’ visual and textual appearances. |
| Approach: | They propose a method that generates counterfactual news thumbnail images and text pairs to assess whether a news thumbnail image represents the actors discussed in the news text. |
| Outcome: | The proposed method can boost the performance for assessing news thumbnail representativeness, supporting the hypothesis. |
APoLLo : Unified Adapter and Prompt Learning for Vision Language Models (2023.emnlp-main)
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| Challenge: | APoLLo improves generalization capabilities of vision-language pretrained models . despite being largely successful in terms of generalization, these models are difficult to fine-tune for few-shot learning-based downstream tasks. |
| Approach: | They propose a multi-modal approach that combines Adapter and Prompt learning for Vision-Language models. |
| Outcome: | The proposed approach improves generalization capabilities of vision-language pretrained models . it achieves a relative gain of 6.03% over MaPLe on 10 diverse datasets . |
Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models (2023.emnlp-main)
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Geewook Kim, Hodong Lee, Daehee Kim, Haeji Jung, Sanghee Park, Yoonsik Kim, Sangdoo Yun, Taeho Kil, Bado Lee, Seunghyun Park
| Challenge: | Recent advances in Large Language Models (LLMs) have stimulated a surge of research aimed at extending their applications to the visual domain. |
| Approach: | They propose a novel neural architecture to enhance language-image understanding capability of LLMs by capturing intricate details that are often overlooked in existing methods. |
| Outcome: | The proposed model can achieve better comprehension of language information in visual contexts within images. |
Interpretable Composition Attribution Enhancement for Visio-linguistic Compositional Understanding (2024.emnlp-main)
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| Challenge: | Despite promising progress, vision-language models still exhibit significant challenges in understanding visio-linguistic concepts beyond object terms. |
| Approach: | They propose a framework that encourages the model to pay greater attention to composition words denoting relationships and attributes within the text. |
| Outcome: | The proposed framework improves the ability to discern intricate details and construct more sophisticated interpretations of combined visual and linguistic elements. |
An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models (2024.emnlp-main)
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| Challenge: | Large Multimodal Models (LMMs) have shown impressive generalization ability on vision and language tasks, but their spatial understanding is under-explored. |
| Approach: | They construct a VQA dataset to analyze LMMs' spatial reasoning capabilities. |
| Outcome: | The proposed model is stronger at basic object detection than complex spatial reasoning. |
FiRST: Finetuning Router-Selective Transformers for Input-Adaptive Latency Reduction (2025.findings-emnlp)
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| Challenge: | Existing approaches to improve latency via skipping layers have limitations . fiRST is a model-agnostic framework that reduces inference latency while maintaining quality . |
| Approach: | They propose a model-agnostic framework that skips transformer layers during decoding . it is fully compatible with KV caching, enabling faster decoding while maintaining quality . |
| Outcome: | a new framework reduces inference latency by using layer-specific routers to skip transformer layers during decoding. |
Can Hallucination Correction Improve Video-Language Alignment? (2025.findings-acl)
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| Challenge: | Existing work on hallucination correction for large vision-language models focuses on mitigating hallucisations, but a new approach is needed to improve video-language alignment. |
| Approach: | They propose a self-training framework learning to correct hallucinations in descriptions that do not align with the video content. |
| Outcome: | The proposed framework improves video-language alignment by identifying and correcting inconsistencies in descriptions that do not align with the video content. |
When Big Models Train Small Ones: Label-Free Model Parity Alignment for Efficient Visual Question Answering using Small VLMs (2025.emnlp-main)
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| Challenge: | Large vision and language models have demonstrated remarkable performance in visual question answering tasks. |
| Approach: | They introduce a framework to optimize L-VLMs by leveraging unlabeled images . they conduct extensive experiments on four diverse VQA benchmarks . |
| Outcome: | The proposed framework improves L-VLMs on four visual question answering benchmarks. |