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20232026
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cs.CV2026

MINERVA-Cultural: A Benchmark for Cultural and Multilingual Long Video Reasoning

Darshan Singh, Arsha Nagrani, Kawshik Manikantan +6

Recent advancements in video models have shown tremendous progress, particularly in long video understanding. However, current benchmarks predominantly feature western-centric data…

cs.CV2025

CAViAR: Critic-Augmented Video Agentic Reasoning

Sachit Menon, Ahmet Iscen, Arsha Nagrani +3

Video understanding has seen significant progress in recent years, with models' performance on perception from short clips continuing to rise. Yet, multiple recent benchmarks, such…

cs.CV2025

VoCap: Video Object Captioning and Segmentation from Any Prompt

Jasper Uijlings, Xingyi Zhou, Xiuye Gu +5

Understanding objects in videos in terms of fine-grained localization masks and detailed semantic properties is a fundamental task in video understanding. In this paper, we propose…

cs.CV2025

OVFact: Measuring and Improving Open-Vocabulary Factuality for Long Caption Models

Monika Wysoczańska, Shyamal Buch, Anurag Arnab +1

Large vision-language models (VLMs) often struggle to generate long and factual captions. However, traditional measures for hallucination and factuality are not well suited for eva…

cs.CV2025

Continual Learning in Vision-Language Models via Aligned Model Merging

Ghada Sokar, Gintare Karolina Dziugaite, Anurag Arnab +3

Continual learning is conventionally tackled through sequential fine-tuning, a process that, while enabling adaptation, inherently favors plasticity over the stability needed to re…

cs.CV2025

InteractVLM: 3D Interaction Reasoning from 2D Foundational Models

Sai Kumar Dwivedi, Dimitrije Antić, Shashank Tripathi +4

We introduce InteractVLM, a novel method to estimate 3D contact points on human bodies and objects from single in-the-wild images, enabling accurate human-object joint reconstructi…

cs.CV2025

What Are You Doing? A Closer Look at Controllable Human Video Generation

Emanuele Bugliarello, Anurag Arnab, Roni Paiss +3

High-quality benchmarks are crucial for driving progress in machine learning research. However, despite the growing interest in video generation, there is no comprehensive dataset…

cs.CV2024

Streaming Dense Video Captioning

Xingyi Zhou, Anurag Arnab, Shyamal Buch +5

An ideal model for dense video captioning -- predicting captions localized temporally in a video -- should be able to handle long input videos, predict rich, detailed textual descr…

cs.CV2024

Time-, Memory- and Parameter-Efficient Visual Adaptation

Otniel-Bogdan Mercea, Alexey Gritsenko, Cordelia Schmid +1

As foundation models become more popular, there is a growing need to efficiently finetune them for downstream tasks. Although numerous adaptation methods have been proposed, they a…

cs.CV2023

Pixel Aligned Language Models

Jiarui Xu, Xingyi Zhou, Shen Yan +5

Large language models have achieved great success in recent years, so as their variants in vision. Existing vision-language models can describe images in natural languages, answer…