3 citations · 25 across the 55 of their papers we have counts for
5 papers · 2 filters
PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge For Detailed Image Descriptions
Amith Ananthram, Elias Stengel-Eskin, Lorena A. Bradford +7
While vision-language models (VLMs) have advanced into detailed image description, evaluation remains a challenge. Standard metrics (e.g. CIDEr, SPICE) were designed for short text…
RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation
Tianyi Niu, Jaemin Cho, Elias Stengel-Eskin +1
We investigate to what extent Multimodal Large Language Models (MLLMs) can accurately identify the orientation of input images rotated 0°, 90°, 180°, and 270°. This task demands ro…
How Well Do Vision-Language Models Understand Movies?
Emmanouil Zaranis, António Farinhas, Saul Santos +28
Vision-language models (VLMs) can increasingly handle extended contexts and answer questions about long videos--but can they understand the narrative structure of movies as humans…
CLaMR: Contextualized Late-Interaction for Multimodal Content Retrieval
David Wan, Han Wang, Elias Stengel-Eskin +2
Online video web content is richly multimodal: a single video blends vision, speech, ambient audio, and on-screen text. Retrieval systems typically treat these modalities as indepe…
CAPTURe: Evaluating Spatial Reasoning in Vision Language Models via Occluded Object Counting
Atin Pothiraj, Elias Stengel-Eskin, Jaemin Cho +1
Recognizing and reasoning about occluded (partially or fully hidden) objects is vital to understanding visual scenes, as occlusions frequently occur in real-world environments and…