Cordelia Schmid
Cordelia Schmid holds a M.S. degree in Computer Science from the
University of Karlsruhe and a Doctorate, also in Computer Science,
from the Institut National Polytechnique de Grenoble (INPG). Her
doctoral thesis received the best thesis award from INPG in 1996.
Dr. Schmid was a post-doctoral research assistant in the Robotics
Research Group of Oxford University in 1996--1997. Since 1997 she has
held a permanent research position at Inria Grenoble Rhone-Alpes,
where she is a research director and directs an Inria team. Dr. Schmid
has been an Associate Editor for IEEE PAMI (2001--2005) and for IJCV
(2004--2012), editor-in-chief for IJCV (2013---), a program chair of
IEEE CVPR 2005 and ECCV 2012 as well as a general chair of IEEE CVPR
2015 and ECCV 2020. In 2006, 2014 and 2016, she was awarded the
Longuet-Higgins prize for fundamental contributions in computer vision
that have withstood the test of time. She is a fellow of IEEE. She was
awarded an ERC advanced grant in 2013, the Humbolt research award in
2015 and the Inria & French Academy of Science Grand Prix in 2016. She
was elected to the German National Academy of Sciences, Leopoldina, in 2017.
She is working for Google France starting Feb. 2018 part-time (50%).
For more information see http://thoth.inrialpes.fr/people/schmid.
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UnLoc: a unified framework for video localization tasks
Shen Yan
Xuehan Xiong
Anurag Arnab
Zhonghao Wang
Weina Ge
International Conference on Computer Vision (2023)
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We adapt large-scale image-text pretrained models such as CLIP for temporal localization tasks in untrimmed videos, which is still a relatively unexplored task. We do so by designing a new approach called UnLoc, which uses a pretrained image and text tower, and feeds tokens to a video-text fusion model. The output of the fusion module are then used to construct a feature pyramid in which each level connects to a head to predict a per-frame relevancy score and start/end time displacements. Unlike previous works, our architecture enables zero-shot Moment Retrieval, TAL and action segmentation with a single stage model, without the need for action proposals or representation masking. Unlike specialised models, we achieve state of the art results on three different localization tasks with a unified approach - in some cases outperforming previous works by large margins.
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Recent video and language pretraining frameworks lack the ability to generate sentences, and are limited in transferring to generative tasks such as multimodal video captioning. We present Multimodal Video Generative Pretraining (MV-GPT), a new pretraining framework for learning from unlabelled instructional videos where the pretrained model is effectively transferred to video captioning tasks. Unlike recent video-language pretraining frameworks, our framework trains both a multimodal video encoder and a sentence decoder jointly. To overcome the lack of the captions in the unlabelled videos, we leverage the future utterance as an additional text source and propose a bidirectional generation objective -- we generate future utterances given the present mulitmodal context, and also the present utterance given future observations. We use this objective to train an encoder-decoder model end-to-end to generate a caption from raw pixels and transcribed speech directly. Our model achieves state-of-the-art performance for video captioning on four standard benchmarks, as well as on other video understanding tasks such as VideoQA, video retrieval and action classification.
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AVATAR: Unconstrained Audiovisual Speech Recognition
Valentin Gabeur
Paul Hongsuck Seo
Karteek Alahari
Interspeech (2022)
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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 focus on the lip motion, we investigate the contribution of entire visual frames (visual actions, objects, background etc.). This is particularly useful for unconstrained videos, where the speaker is not necessarily visible. To solve this task, we propose a new sequence-to-sequence AudioVisual ASR TrAnsformeR (AVATAR) which is trained end-to-end from spectrograms and full-frame RGB. To prevent the audio stream from dominating training, we propose different word-masking strategies, thereby encouraging our model to pay attention to the visual stream. We demonstrate the contribution of the visual modality on the How2 AV-ASR benchmark, especially in the presence of simulated noise, and show that our model outperforms all other prior work by a large margin. Finally, we also create a new, real-world test bed for AV-ASR called VisSpeech, which demonstrates the contribution of the visual modality under challenging audio conditions.
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Learning Audio-Video Modalities from Image Captions
Paul Hongsuck Seo
Anja Hauth
Santiago Manen
European Conference on Computer Vision (2022)
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There has been a recent explosion of large-scale image-text datasets, as images with alt-text captions can be easily obtained online.Obtaining large-scale, high quality data for video in the form of text-video and text-audio pairs however, is more challenging. To close this gap we propose a new video mining pipeline which involves transferring captions from image captioning datasets to video clips with no additional manual effort. Using this pipeline, we create a new large-scale, weakly labelled audio-video captioning dataset consisting of millions of paired clips and captions. We show that training a multimodal transformer based model on this data achieves competitive performance on video retrieval and video captioning, matching or even outperforming HowTo100M pretraining with 20x fewer clips. We also show that our mined clips are suitable for text-audio pretraining, and achieve state of the art results for the task of audio retrieval.
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Masking Modalities for Cross-modal Video Retrieval
Valentin Gabeur
Karteek Alahari
Winter Conference on Applications of Computer Vision (WACV) (2022) (to appear)
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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 large-scale instructional video datasets, a common strategy for pre-training video encoders is to use the accompanying speech as weak supervision. However, as speech is used to supervise the pre-training, it is never seen by the video encoder, which does not learn to process that modality. We address this drawback of current pre-training methods, which fail to exploit the rich cues in spoken language. Our proposal is to pre-train a video encoder using all the available video modalities as supervision, namely, appearance, sound, and transcribed speech. We mask an entire modality in the input and predict it using the other two modalities. This encourages each modality to collaborate with the others, and our video encoder learns to process appearance and audio as well as speech. We show the superior performance of our `modality masking' pre-training approach for video retrieval on the How2R, YouCook2 and Condensed Movies datasets.
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TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency
Anna Rohrbach
Medhini Narasimhan
Trevor Darrell
European Conference on Computer Vision (2022)
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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 (abridged version of a video) provides viewers with a quick overview and massively reduces search time. In this work, we focus on summarizing nstructional videos, an under-explored area of video summarization. In comparison to generic videos, instructional videos can be parsed into semantically meaningful segments that correspond to important steps of the demonstrated task. Existing video summarization datasets rely on manual frame-level annotations, making them subjective and limited in size. To overcome this, we first automatically generate pseudo summaries for a corpus of instructional videos by exploiting two key assumptions: (i) relevant steps are likely to appear in multiple videos of the same task (Task Relevance), and (ii) they are more likely to be described by the demonstrator verbally (Cross-Modal Saliency). We propose an instructional video summarization network that combines a context-aware temporal video encoder and a segment scoring transformer. Using pseudo summaries as weak supervision, our network constructs a visual summary for an instructional video given only video and transcribed speech. To evaluate our model, we collect a high-quality test set, WikiHow Summaries, by scraping WikiHow articles that contain video demonstrations and visual depictions of steps allowing us to obtain the ground-truth summaries. We outperform several baselines and a state-of-the-art video summarization model on this new benchmark.
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This report describes the approach behind our submission to the 2022 Epic-Kitchens Action Recognition Challenge from team Google Research Grenoble. Our approach builds upon our recent work, Multiview Transformer for Video Recognition (MTV), and adapts it to multimodal inputs. Our final submission consists of an ensemble of Multimodal MTV (M\&M) models varying backbone sizes and input modalities. Our approach achieved 52.8% Top-1 accuracy on the test set in action classes, which is 4.1% higher than last year’s winning entry.
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Multiview Transformers for Video Recognition
Shen Yan
Xuehan Xiong
Anurag Arnab
Zhichao Lu
Mi Zhang
The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) (2022)
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Video understanding often requires reasoning at multiple spatiotemporal resolutions.
To this end, we present Multiview Transformers for Video Recognition (MTV).
Our model consists of separate encoders to represent different views of the input video with lateral connections to fuse information across views.
MTV consistently performs better than single-view counterparts in terms of accuracy and computational cost across a range of model sizes, and can effectively leverage different transformer encoder architectures.
We present thorough ablation studies of our model and achieve state-of-the-art results on five standard datasets.
We will release code and pretrained checkpoints to facilitate further research.
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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 contrast, are typically modality-specific and optimised for unimodal benchmarks. A common approach for building multimodal models is to simply combine multiple of these modality-specific architectures using late-stage fusion of final representations or predictions (\textit{`late-fusion'}). Instead, we propose a new architecture that learns to model both unimodal and cross-modal information at earlier stages, without imposing any modality specific priors. We investigate two pathways for the exchange of cross-modal information, \textit{vertical attention} (by restricting crossmodal fusion to certain layers) and \textit{horizontal attention}, via the use of `fusion bottleneck' tokens, that encourage the model to extract and exchange relevant information between modalities in an efficient manner.
We conduct thorough ablation studies, and achieve state-of-the-art results on multiple audio-visual classification benchmarks including Audioset, Epic-Kitchens and VGGSound. All code and models will be released.
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Real-world imagery is often characterized by a significant imbalance of the number of images per class, leading to long-tailed distributions. An effective and simple approach to long-tailed visual recognition is to learn feature representations and a classifier separately, with instance and class-balanced sampling, respectively. In this work, we introduce a new framework, by making the key observation that a feature representation learned with instance sampling is far from optimal in a long-tailed setting. Our main contribution is a new training method, referred to as Class-Balanced Distillation (CBD), that leverages knowledge distillation to enhance feature representations. CBD allows the feature representation to evolve in the second training stage, guided by the teacher learned in the first stage. The second stage uses class-balanced sampling, in order to focus on under-represented classes. This framework can naturally accommodate the usage of multiple teachers, unlocking the information from an ensemble of models to enhance recognition capabilities. Our experiments show that the proposed technique consistently outperforms the state of the art on long-tailed recognition benchmarks such as ImageNet-LT, iNaturalist17 and iNaturalist18.
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