Papers by Anna Rohrbach
Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation (2022.naacl-main)
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| Challenge: | Detecting out-of-context media is a problem in domains of public significance . a method that leverages automatically generated hard image-text mismatches is proposed . |
| Approach: | They propose a method that leverages automatically generated hard image-text mismatches to detect out-of-context media . they analyze tweets relevant to topics such as COVID-19, Climate Change and Military Vehicles . |
| Outcome: | The proposed method improves detection accuracy over a strong baseline on a set of fakes created by humans. |
G3: Geolocation via Guidebook Grounding (2022.findings-emnlp)
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| Challenge: | a new task uses explicit knowledge from human-written guidebooks to improve geolocation accuracy . a state-of-the-art image-only method is unable to predict the location of an image . |
| Approach: | They propose a task that uses streetview images and a guidebook to predict a country for each image . they add clues from the guidebook and supervise attention with country-level pseudo labels . |
| Outcome: | The proposed method outperforms state-of-the-art image-only geolocation methods with 5% improvement in Top-1 accuracy. |
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension (2022.acl-long)
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| Challenge: | Visual referring expression comprehension (ReC) models can be trained for a domain, but it remains unclear if they can be applied in a zero-shot manner to more complex tasks like ReC. |
| Approach: | They propose a method that repurposes CLIP, a state-of-the-art large-scale model, for training a referring expression comprehension model for a new visual domain. |
| Outcome: | The proposed model reduces the gap between zero-shot baselines from prior work and supervised models by as much as 29% on RefCOCOg, and on ReFGTA (video game imagery), and its relative improvement over supervised ReC models is 8%. |
Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation (P19-1)
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| Challenge: | Existing models that ground language into visual appearance and route structure are outperforming their visual counterparts in unseen new environments. |
| Approach: | They propose to decompose the grounding procedure into a set of expert models with access to different modalities and ensemble them at prediction time. |
| Outcome: | The proposed model outperforms models with only route structure and visual features on the benchmark Room-to-Room dataset. |
VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checking (2026.acl-long)
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| Challenge: | Existing benchmarks for evaluating AFC systems are limited in terms of task scope, modalities, domain, language diversity, realism, or coverage of misinformation types. |
| Approach: | They propose to use Verified Theses and Statements (VeriTaS) to evaluate AFC systems that are static and subject to data leakage as claims enter pretraining corpora. |
| Outcome: | The proposed system is robust under large-scale pretraining of foundation models and can be updated in the future. |
Exposing the Limits of Video-Text Models through Contrast Sets (2022.naacl-main)
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| Challenge: | Recent video-text models can retrieve relevant videos based on text with high accuracy, but to what extent do they comprehend the semantics of the text? |
| Approach: | They propose a framework that probes video-text models with hard negatives . they leverage a pre-trained language model and a set of heuristics to create verb and person entity focused contrast sets. |
| Outcome: | The proposed framework erases the performance gap between CLIP-based methods and the earlier methods. |
Focus! Relevant and Sufficient Context Selection for News Image Captioning (2022.findings-emnlp)
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| Challenge: | Recent work only coarsely leverages the article to extract the necessary context, which makes it difficult for models to identify relevant events and named entities. |
| Approach: | They propose to use a vision and language retrieval model CLIP to localize the visually grounded entities in the news article and then capture the non-visual entities via an open relation extraction model. |
| Outcome: | The proposed model significantly improves on existing models and achieves state-of-the-art on multiple benchmarks. |
Object Hallucination in Image Captioning (D18-1)
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| Challenge: | Existing image captioning metrics do not capture image relevance . current metrics only measure similarity to ground truth captions . |
| Approach: | They propose a new image relevance metric to evaluate captioning models with veridical visual labels and assess their rate of object hallucination. |
| Outcome: | The proposed metrics show that models with veridical visual labels have higher hallucination rates than models with lower hallucinosity. |
NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media (2021.emnlp-main)
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| Challenge: | a threat scenario where an image is used out of context to support a narrative is proposed. |
| Approach: | They propose a dataset where both image and text are unmanipulated but mismatched . they benchmark several state-of-the-art multimodal models on their dataset . |
| Outcome: | The proposed dataset shows that machine-driven image repurposing is now a realistic threat . it provides samples that represent challenging instances of mismatch between text and image . |
A vision-grounded dataset for predicting typical locations for verbs (L18-1)
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| Challenge: | Existing models for inferring location from text are often underestimating the probability of the most typical role fillers. |
| Approach: | They propose a dataset which contains thematic fit judgments for 2,000 verb/location pairs. |
| Outcome: | The proposed dataset can be used to evaluate text-based, vision-based or multimodal inference systems for the typicality of an event's location. |