2.3k citations · 5.5k across the 42 of their papers we have counts for
41 papers · 1 filter
LOCI: A Locator-Critic with Refinement Loop
Walid Bousselham, Mathilde Caron, Arsha Nagrani +1
Vision-Language Models (VLMs) still struggle on tasks requiring complex visual understanding. We argue that the core issue is not high-level reasoning, but instead failing to locat…
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…
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…
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…
Unbiasing through Textual Descriptions: Mitigating Representation Bias in Video Benchmarks
Nina Shvetsova, Arsha Nagrani, Bernt Schiele +2
We propose a new "Unbiased through Textual Description (UTD)" video benchmark based on unbiased subsets of existing video classification and retrieval datasets to enable a more rob…
Mixture of Nested Experts: Adaptive Processing of Visual Tokens
Gagan Jain, Nidhi Hegde, Aditya Kusupati +5
The visual medium (images and videos) naturally contains a large amount of information redundancy, thereby providing a great opportunity for leveraging efficiency in processing. Wh…
MoReVQA: Exploring Modular Reasoning Models for Video Question Answering
Juhong Min, Shyamal Buch, Arsha Nagrani +2
This paper addresses the task of video question answering (videoQA) via a decomposed multi-stage, modular reasoning framework. Previous modular methods have shown promise with a si…
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…
Video Summarization: Towards Entity-Aware Captions
Hammad A. Ayyubi, Tianqi Liu, Arsha Nagrani +7
Existing popular video captioning benchmarks and models deal with generic captions devoid of specific person, place or organization named entities. In contrast, news videos present…
VidChapters-7M: Video Chapters at Scale
Antoine Yang, Arsha Nagrani, Ivan Laptev +2
Segmenting long videos into chapters enables users to quickly navigate to the information of their interest. This important topic has been understudied due to the lack of publicly…
UnLoc: A Unified Framework for Video Localization Tasks
Shen Yan, Xuehan Xiong, Arsha Nagrani +5
While large-scale image-text pretrained models such as CLIP have been used for multiple video-level tasks on trimmed videos, their use for temporal localization in untrimmed videos…
PaLI-X: On Scaling up a Multilingual Vision and Language Model
Xi Chen, Josip Djolonga, Piotr Padlewski +40
We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training t…
VicTR: Video-conditioned Text Representations for Activity Recognition
Kumara Kahatapitiya, Anurag Arnab, Arsha Nagrani +1
Vision-Language models (VLMs) have excelled in the image-domain -- especially in zero-shot settings -- thanks to the availability of vast pretraining data (i.e., paired image-text…
AVFormer: Injecting Vision into Frozen Speech Models for Zero-Shot AV-ASR
Paul Hongsuck Seo, Arsha Nagrani, Cordelia Schmid
Audiovisual automatic speech recognition (AV-ASR) aims to improve the robustness of a speech recognition system by incorporating visual information. Training fully supervised multi…
Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning
Antoine Yang, Arsha Nagrani, Paul Hongsuck Seo +5
In this work, we introduce Vid2Seq, a multi-modal single-stage dense event captioning model pretrained on narrated videos which are readily-available at scale. The Vid2Seq architec…
AVATAR submission to the Ego4D AV Transcription Challenge
Paul Hongsuck Seo, Arsha Nagrani, Cordelia Schmid
In this report, we describe our submission to the Ego4D AudioVisual (AV) Speech Transcription Challenge 2022. Our pipeline is based on AVATAR, a state of the art encoder-decoder mo…
TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency
Medhini Narasimhan, Arsha Nagrani, Chen Sun +4
YouTube users looking for instructions for a specific task may spend a long time browsing content trying to find the right video that matches their needs. Creating a visual summary…
M&M Mix: A Multimodal Multiview Transformer Ensemble
Xuehan Xiong, Anurag Arnab, Arsha Nagrani +1
This report describes the approach behind our winning solution to the 2022 Epic-Kitchens Action Recognition Challenge. Our approach builds upon our recent work, Multiview Transform…
AVATAR: Unconstrained Audiovisual Speech Recognition
Valentin Gabeur, Paul Hongsuck Seo, Arsha Nagrani +3
Audio-visual automatic speech recognition (AV-ASR) is an extension of ASR that incorporates visual cues, often from the movements of a speaker's mouth. Unlike works that simply foc…
Learning Audio-Video Modalities from Image Captions
Arsha Nagrani, Paul Hongsuck Seo, Bryan Seybold +4
A major challenge in text-video and text-audio retrieval is the lack of large-scale training data. This is unlike image-captioning, where datasets are in the order of millions of s…
End-to-end Generative Pretraining for Multimodal Video Captioning
Paul Hongsuck Seo, Arsha Nagrani, Anurag Arnab +1
Recent video and language pretraining frameworks lack the ability to generate sentences. We present Multimodal Video Generative Pretraining (MV-GPT), a new pretraining framework fo…
Masking Modalities for Cross-modal Video Retrieval
Valentin Gabeur, Arsha Nagrani, Chen Sun +2
Pre-training on large scale unlabelled datasets has shown impressive performance improvements in the fields of computer vision and natural language processing. Given the advent of…
With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition
Evangelos Kazakos, Jaesung Huh, Arsha Nagrani +2
In egocentric videos, actions occur in quick succession. We capitalise on the action's temporal context and propose a method that learns to attend to surrounding actions in order t…
Attention Bottlenecks for Multimodal Fusion
Arsha Nagrani, Shan Yang, Anurag Arnab +3
Humans perceive the world by concurrently processing and fusing high-dimensional inputs from multiple modalities such as vision and audio. Machine perception models, in stark contr…