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
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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Multiview Transformers for Video Recognition
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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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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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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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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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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We focus on contrastive methods for self-supervised video representation learning. A common paradigm in contrastive learning is to construct positive pairs by sampling different data views for the same instance, with different data instances as negatives. These methods implicitly assume a set of representational invariances to the view selection mechanism (eg, sampling frames with temporal shifts), which may lead to poor performance on downstream tasks which violate these invariances (fine-grained video action recognition that would benefit from temporal information). To overcome this limitation, we propose an 'augmentation aware' contrastive learning framework, where we explicitly provide a sequence of augmentation parameterisations (such as the values of the time shifts used to create data views) as composable augmentation encodings (CATE) to our model when projecting the video representations for contrastive learning. We show that representations learned by our method encode valuable information about specified spatial or temporal augmentation, and in doing so also achieve state-of-the-art performance on a number of video benchmarks.
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While most conversational AI systems focus on textual dialogue only, conditioning utterances on visual context (when it's available) can lead to more realistic conversations. Unfortunately, a major challenge for incorporating visual context into conversational dialogue is the lack of large-scale labeled datasets. We provide a solution in the form of a new visually conditioned Future Utterance Prediction task. Our task involves predicting the next utterance in a video, using both visual frames and transcribed speech as context. By exploiting the large number of instructional videos online, we train a model to solve this task at scale, without the need for manual annotations. Leveraging recent advances in multimodal learning, our model consists of a novel co-attentional multimodal video transformer, and when trained on both textual and visual context, outperforms baselines that use textual inputs alone. Further, we demonstrate that our model trained for this task on unlabelled videos achieves state-of-the-art performance on a number of downstream VideoQA benchmarks such as MSRVTT-QA, MSVD-QA, ActivityNet-QA and How2QA.
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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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Despite the recent advances in video classification, progress in spatio-temporal action recognition has lagged behind.
A major contributing factor has been the prohibitive cost of annotating videos frame-by-frame.
In this paper, we present a spatio-temporal action recognition that is trained with only video-level labels, which are significantly easier to annotate, and can even be mined automatically (subject to some label noise).
Our method leverages per-frame person detectors which have been trained on large image datasets within a Multiple Instance Learning framework.
We show how we can apply our method in cases where the standard Multiple Instance Learning assumption, that each bag contains at least one instance with the specified label, is invalid using a novel probabilistic variant of MIL.
Furthermore, we report the first weakly-supervised results on the AVA dataset and state-of-the-art results among weakly-supervised methods on UCF101-24.
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AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection
Ondrej Klejch
Radhika Marvin
Liat Kaver
Sharadh Ramaswamy
Arkadiusz Stopczynski
ICASSP, IEEE (2020)
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Active speaker detection is an important component in video analysis algorithms for applications such as speaker diarization, video re-targeting for meetings, speech enhancement, and human-robot interaction. The absence of a large, carefully labeled audio-visual active speaker dataset has limited evaluation in terms of data diversity, environments, and accuracy. In this paper, we present the AVA Active Speaker detection dataset (AVA-ActiveSpeaker) which has been publicly released to facilitate algorithm development and comparison. It contains temporally labeled face tracks in videos, where each face instance is labeled as speaking or not, and whether the speech is audible. The dataset contains about 3.65 million human labeled frames spanning 38.5 hours. We also introduce a state-of-the-art, jointly trained audio-visual model for real-time active speaker detection and compare several variants. The evaluation clearly demonstrates a significant gain due to audio-visual modeling and temporal integration over multiple frames.
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What makes for good views for contrastive representation learning?
Dilip Krishnan
Phillip Isola
Yonglong Tian
NeurIPS 2020 (to appear)
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Contrastive learning between multiple views of the data has recently dominated the field of self-supervised representation learning. Despite its success, the influence of different views is less studied. In this paper, we step towards understanding the importance of view selection with empirical analysis, and argue that we should reduce the mutual information (MI) between contrasted views while keeping their information bits that are relevant to the downstream task. To verify it, we devise an unsupervised and a semi-supervised framework to learn good views from the perspective of color space. We also view data augmentation as a way to reduce MI, and show that increasing data augmentation leads to decreasing MI but improved downstream classification accuracy. As a by-product, a new state-of-the-art accuracy is achieved on ImageNet linear readoff benchmark with ResNet-50.
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In this work we consider the problem of learning a classifier from noisy labels when a few clean labeled examples are given. The structure of clean and noisy data is modeled by a graph per class and Graph Convolutional Networks (GCN) are used to predict class relevance of noisy examples. For each class, the GCN is treated as a binary classifier learning to discriminate clean from noisy examples using a weighted binary cross-entropy loss function, and then the GCN-inferred "clean" probability is exploited as a relevance measure. Each noisy example is weighted by its relevance when learning a classifier for the end task. We evaluate our method on an extended version of a few-shot learning problem, where the few clean examples of novel classes are supplemented with additional noisy data. Experimental results show that our GCN-based cleaning process significantly improves the classification accuracy over not cleaning the noisy data and standard few-shot classification where only few clean examples are used. The proposed GCN-based method outperforms the transductive approach (Douze et al., 2018)that is using the same additional data without labels.
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Can we guess human action from dialogue alone? In this work we investigate the link between spoken words and actions in movies. We note that movie scripts describe actions, as well as contain the speech of characters and hence can be used to learn this correlation with no additional supervision. We train a speech to action classifier on 1k movie scripts downloaded from IMSDb and show that such a classifier performs well for certain classes, and when applied to the speech segments of a large \textit{unlabelled} movie corpus (288k videos, 188M speech segments), provides weak labels for over 800k video clips. By training on these video clips, we demonstrate superior action recognition performance on standard action recognition benchmarks, without using a single labelled action example.
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Multi-modal Transformer for Video Retrieval
Valentin Gabeur
Karteek Alahari
European Conference on Computer Vision (ECCV) (2020)
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The task of retrieving video content relevant to natural language queries plays a critical role in effectively handling internet-scale datasets. Most of the existing methods for this caption-to-video retrieval problem do not fully exploit cross-modal cues present in video. Furthermore, they aggregate per-frame visual features with limited or no temporal information. In this paper, we present a multi-modal transformer to jointly encode the different modalities in video, which allows each of them to attend to the others. The transformer architecture is also leveraged to encode and model the temporal information. On the natural language side, we investigate the best practices to jointly optimize the language embedding together with the multi-modal transformer. This novel framework allows us to establish state-of-the-art results for video retrieval on three datasets.
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We present a self-supervised framework, Consistency Guided Scene Flow Estimation (CGSF), to jointly estimate 3D scene structure and motion from stereo videos. The model takes two temporal stereo pairs as input, and predicts disparity and scene flow expressed as optical flow + disparity change. The model self-adapts at test time by iteratively refining its predictions. The refinement process is guided by a consistency loss, which combines stereo and temporal photo-consistency with a new geometric term that couples the disparity and 3D motion. To handle the noise in the consistency loss, we further propose a learned, output refinement network, which takes the initial predictions, the loss, and the gradient as input, and efficiently predicts a correlated output update. We perform extensive experimental validation on benchmark datasets and daily scenes captured by a stereo camera. We demonstrate the proposed model can reliably predict disparity and scene flow in many challenging scenarios, and achieves better generalization than the state-of-the-arts.
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A dominant paradigm for learning-based approaches in computer vision is training generic models, such as ResNet for image recognition, or I3D for video understanding, on large datasets and allowing them to discover the optimal representation for the problem at hand. While this is an obviously attractive approach, it is not applicable in all scenarios. We claim that action detection is one such challenging problem - the models that need to be trained are large, and the labeled data is expensive to obtain. To address this limitation, we propose to incorporate domain knowledge into the structure of the model to simplify optimization. In particular, we augment a standard I3D network with a tracking module to aggregate long term motion patterns, and use a graph convolutional network to reason about interactions between actors and objects. Evaluated on the challenging AVA dataset, the proposed approach improves over the I3D baseline by 5.5% mAP and over the state-of-the-art by 4.8% mAP.
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Relational Action Forecasting
Abhinav Shrivastava
Carl Martin Vondrick
CVPR 2019
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This paper focuses on multi-person action forecasting in videos. More precisely, given a history of H previous frames, the goal is to detect actors and to predict their future actions for the next T frames. Our approach jointly models temporal and spatial interactions among different actors by constructing a recurrent graph, using actor proposals obtained with Faster R-CNN as nodes. Our method learns to select a subset of discriminative relations without requiring explicit supervision, thus enabling us to tackle challenging visual data. We refer to our model as Discriminative Relational Recurrent Network (DRRN). Evaluation of action prediction on AVA demonstrates the effectiveness of our proposed method compared to simpler baselines. Furthermore, we significantly improve performance on the task of early action classification on J-HMDB, from the previous SOTA of 48% to 60%.
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We present GLNet, a self-supervised framework for learning depth, optical flow, camera pose and intrinsic parameters from monocular video – addressing the difficulty of acquiring realistic ground-truth for such processes under a variety of conditions where we would like them to operate. We propose three contributions for self-supervised systems: 1) we design new loss functions that capture multiple geometric constraints (e.g. epipolar geometry) as well as adaptive photometric costs that support multiple moving objects, rigid and non-rigid, 2) we extend the model such that it predicts camera intrinsics, making it applicable to uncalibrated images or video, and 3) we propose several online finetuning strategies that rely on the symmetry of our self-supervised loss in both training and testing, in particular optimizing both parameters and/or the output of different tasks and leveraging their mutual interactions. The idea of jointly optimizing the system output, under all geometric and photometric constraints can be viewed as a dense generalization of classical bundle adjustment. We demonstrate the effectiveness of our method on KITTI and Cityscapes, where we outperform previous self-supervised approaches. We also show good generalization for transfer learning.
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In this paper, we tackle the problem of 3D human shape estimation from single RGB images. While the recent progress in convolutional neural networks has allowed impressive results for 3D human pose estimation, estimating the full 3D shape of a person is still an open issue. Model-based approaches can output precise meshes of naked under-cloth human bodies but fail to estimate details and un-modelled elements such as hair or clothing. On the other hand, non-parametric volumetric approaches can potentially estimate complete shapes but, in practice, they are limited by the resolution of the output grid and cannot produce detailed estimates. In this work, we propose a non-parametric approach that employs a double depth map to represent the 3D shape of a person: a visible depth map and a ``hidden'' depth map are estimated and combined, to reconstruct the human 3D shape as done with a ``mould''. This representation through 2D depth maps allows a higher resolution output with a much lower dimension than voxel-based volumetric representations. Additionally, our fully derivable depth-based model allows us to efficiently incorporate a discriminator in an adversarial fashion to improve the accuracy and ``humanness'' of the 3D output. We train and quantitatively validate our approach on SURREAL and on 3D-HUMANS, a new photorealistic dataset made of semi-synthetic in-house images annotated with 3D ground truth surfaces.
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Self-supervised learning has become increasingly important to leverage the abundance of unlabeled data available on platforms like YouTube. Whereas most existing approaches learn low-level representations, we propose a joint visual-linguistic model to learn high-level features without any explicit supervision. In particular, inspired by its recent success in language modeling, we build upon the BERT model to learn bidirectional joint distributions over sequences of visual and linguistic tokens, derived from vector quantization of video data and off-the-shelf speech recognition outputs, respectively. We use VideoBERT in numerous tasks, including action classification and video captioning. We show that it can be applied directly to open-vocabulary classification, and confirm that large amounts of training data and cross-modal information are critical to performance. Furthermore, we outperform the state-of-the-art on video captioning, and quantitative results verify that the model learns high-level semantic features.
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Actor-Centric Relation Network
Abhinav Shrivastava
Carl Martin Vondrick
ECCV 2018
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Current state-of-the-art approaches for spatio-temporal action localization rely on detections at the frame level and model temporal context with 3D ConvNets. Here, we go one step further and model spatio-temporal relations to capture the interactions between human actors, relevant objects and scene elements essential to differentiate similar human actions. Our approach is weakly supervised and mines the relevant elements automatically with an actor-centric relational network (ACRN). ACRN computes and accumulates pair-wise relation information from actor and global scene features, and generates relation features for action classification. It is implemented as neural networks and can be trained jointly with an existing action detection system. We show that ACRN outperforms alternative approaches which capture relation information, and that the proposed framework improves upon the state-of-the-art performance on JHMDB and AVA. A visualization of the learned relation features confirms that our approach is able to attend to the relevant relations for each action.
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AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
Carl Martin Vondrick
Jitendra Malik
CVPR (2018)
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This paper introduces a video dataset of spatio-temporally localized Atomic Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual actions in 430 15-minute video clips, where actions are localized in space and time, resulting in 1.58M action labels with multiple labels per person occurring frequently. The key characteristics of our dataset are: (1) the definition of atomic visual actions, rather than composite actions; (2) precise spatio-temporal annotations with possibly multiple annotations for each person; (3) exhaustive annotation of these atomic actions over 15-minute video clips; (4) people temporally linked across consecutive segments; and (5) using movies to gather a varied set of action representations. This departs from existing datasets for spatio-temporal action recognition, which typically provide sparse annotations for composite actions in short video clips. We will release the dataset publicly.
AVA, with its realistic scene and action complexity, exposes the intrinsic difficulty of action recognition. To benchmark this, we present a novel approach for action localization that builds upon the current state-of-the-art methods, and demonstrates better performance on JHMDB and UCF101-24 categories. While setting a new state of the art on existing datasets, the overall results on AVA are low at 15.6% mAP, underscoring the need for developing new approaches for video understanding.
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Self-Supervised Learning of Structure and Motion from Video
Aikaterini Fragkiadaki
arxiv (2017)
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We propose SfM-Net, a geometry-aware neural network
for motion estimation in videos that decomposes frame-toframe
pixel motion in terms of scene and object depth, camera
motion and 3D object rotations and translations. Given
a sequence of frames, SfM-Net predicts depth, segmentation,
camera and rigid object motions, converts those into
a dense frame-to-frame motion field (optical flow), differentiably
warps frames in time to match pixels and backpropagates.
The model can be trained with various degrees
of supervision: 1) completely unsupervised, 2) supervised
by ego-motion (camera motion), 3) supervised by
depth (e.g., as provided by RGBD sensors), 4) supervised
by ground-truth optical flow. We show that SfM-Net successfully
estimates segmentation of the objects in the scene,
even though such supervision is never provided. It extracts
meaningful depth estimates or infills depth of RGBD sensors
and successfully estimates frame-to-frame camera displacements.
SfM-Net achieves state-of-the-art optical flow
performance. Our work is inspired by the long history of
research in geometry-aware motion estimation, Simultaneous
Localization and Mapping (SLAM) and Structure from
Motion (SfM). SfM-Net is an important first step towards
providing a learning-based approach for such tasks. A major
benefit over the existing optimization approaches is that
our proposed method can improve itself by processing more
videos, and by learning to explicitly model moving objects
in dynamic scenes.
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