The scientific ambition of RobotLearn is to train robots to acquire the capacity to look, listen, learn, move and speak in a socially acceptable manner. This will be achieved via a fine tuning between scientific findings, development of practical algorithms and associated software packages, and thorough experimental validation. It is planned to endow robotic platforms with the ability to perform physically-unconstrained and open-domain multi-person interaction and communication. The roadmap of RobotLearn is twofold: (i) to build on the recent achievements of the Perception team, in particular, machine learning techniques for the temporal and spatial alignment of audio and visual data, variational Bayesian methods for unimodal and multimodal tracking of humans, and deep learning architectures for audio and audio-visual speech enhancement, and (ii) to explore novel scientific research opportunities at the crossroads of discriminative and generative deep learning architectures, Bayesian learning and inference, computer vision, audio/speech signal processing, spoken dialog systems, and robotics. The paramount applicative domain of RobotLearn is the development of multimodal and multi-party interactive methodologies and technologies for social (companion) robots. RobotLearn is a Research Team at Inria Grenoble and Université Grenoble Alpes, and is associated with Laboratoire Jean Kuntzman.
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- DAUMOT: Domain Adaptation for Unsupervised Multiple Object Tracking
By Guillaume Delorme*, Yihong Xu*, Luis G. Camara, Elisa Ricci, Radu Horaud, Xavier Alameda Pineda
Existing works on multiple object tracking (MOT) are developed under the traditional supervised learning setting, where the training and test data are drawn from the same distribution. This hinders the development of MOT in real-world applications since collecting and annotating a tracking dataset for each deployment scenario is often very time-consuming or even unrealistic. Motivated by this limitation, we investigate MOT in unsupervised settings and introduce DAUMOT, a general MOT training framework designed to adapt an existing pre-trained MOT method to a target dataset without annotations. DAUMOT alternates between tracking and adaptation. During tracking, a model pre-trained on source data is used to track on the target dataset and to generate pseudo-labels. During adaptation, both the source labels and the target pseudo-labels are used to update ...
- Continual Attentive Fusion for Incremental Learning in Semantic Segmentation
Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Hao Tang, Xavier Alameda-Pineda, Elisa Ricci
IEEE Transactions on Multimedia
Abstract. Over the past years, semantic segmentation, similar to many other tasks in computer vision, has benefited from the progress in deep neural networks, resulting in significantly improved performance. However, deep architectures trained with gradient-based techniques suffer from catastrophic forgetting, which is the tendency to forget previously learned knowledge while learning new tasks. Aiming at devising strategies to counteract this effect, incremental learning approaches have gained popularity over the past years. However, the first incremental learning methods for semantic segmentation appeared only recently. While effective, these approaches do not account for a crucial aspect in pixel-level dense prediction problems, i.e., the role of attention
mechanisms. To fill this gap, in this paper, we introduce a novel attentive feature distillation approach to mitigate catastrophic forgetting while accounting for semantic spatial- and channellevel ...
- A Proposal-based Paradigm for Self-supervised Sound Source Localization in Videos
Hanyu Xuan, Zhiliang Wu, Jian Yang, Yan Yan, Xavier Alameda-Pineda
IEEE/CVF International Conference on Computer Vision (CVPR) 2022, New Orleans, US
Abstract. Humans can easily recognize where and how the sound is produced via watching a scene and listening to corresponding audio cues. To achieve such cross-modal perception on machines, existing methods only use the maps generated by interpolation operations to localize the sound source. As semantic object-level localization is more attractive for potential practical applications, we argue that these existing map-based approaches only provide a coarse-grained and indirect description of the sound source. In this paper, we advocate a novel proposal-based paradigm that can directly perform semantic object-level localization, without
any manual annotations. We incorporate the global response map as an unsupervised spatial constraint to weight the proposals according to how well they cover the estimated global shape of the sound source. As a result, our proposal-based ...
- Continual Models are Self-Supervised Learners
by Enrico Fini, Victor G. Turrisi da Costa, Xavier Alameda-Pineda, Elisa Ricci, Karteek Alahari, Julien Mairal
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022, New Orleans, USA
Abstract. Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale. However, their efficacy is catastrophically reduced in a Continual Learning (CL) scenario where data is presented to the model sequentially. In this paper, we show that self-supervised loss functions can be seamlessly converted into distillation mechanisms for CL by adding a predictor network that maps the current state of the representations to their past state. This enables us to devise a framework for Continual self-supervised visual representation Learning that (i) significantly improves the quality of the learned representations, (ii) is compatible with several
state-of-the-art self-supervised objectives, and (iii) needs little to no hyperparameter tuning. We demonstrate the effectiveness of our approach empirically ...
- Multi Person Extreme Motion Prediction
by Wen Guo*, Xiaoyu Bie*, Xavier Alameda-Pineda and Francesc Moreno-Noguer
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022, New Orleans, USA
Abstract. Human motion prediction aims to forecast future poses given a sequence of past 3D skeletons. While this problem has recently received increasing attention, it has mostly been tackled by single humans in isolation. In this paper, we explore this problem when dealing with humans performing collaborative tasks, we seek to predict the future motion of two interacted persons given two sequences of their past skeletons.
We propose a novel cross-interaction attention mechanism that exploits historical information of both persons and learns to predict cross dependencies between the two pose sequences. Since no dataset to train such interactive situations is available, we collected ExPI (Extreme Pose Interaction), a new lab-based person interaction dataset of ...
- Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders
by Xiaoyu Bie, Simon Leglaive, Xavier Alameda-Pineda and Laurent Girin
Abstract. Dynamical variational autoencoders (DVAEs) are a class of deep generative models with latent variables, dedicated to model time series of high-dimensional data. DVAEs can be considered as extensions of the variational autoencoder (VAE) that include temporal dependencies between successive observed and/or latent vectors. Previous work has shown the interest of using DVAEs over the VAE for speech spectrograms modeling. Independently, the VAE has been successfully applied to speech enhancement in noise, in an unsupervised noise-agnostic set-up that requires neither noise samples nor noisy speech samples at training time, but only requires clean speech signals. In this paper, we extend these works to DVAE-based single-channel unsupervised speech enhancement, hence exploiting both speech signals unsupervised representation learning and dynamics modeling. We propose an unsupervised speech enhancement algorithm that combines a DVAE speech prior pre-trained on clean ...
- Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder
by Xiaoyu Lin, Laurent Girin and Xavier Alameda-Pineda
Multi-object tracking (MOT), or multi-target tracking, is a fundamental and very general pattern recognition task. Given an input time-series, the aim of MOT is to recover the trajectories of an unknown number of sources, that might appear and disappear at any point in time. There are four main challenges associated to MOT, namely: (i) extracting source observations (also called detections) at every time frame, (ii) modeling the dynamics of the sources’ movements, (iii) associating observations to sources consistently over time, and (iv) accounting for birth and death of source trajectories.
In computer vision, the tracking-by-detection paradigm has become extremely popular in the recent years. In this context, more and more powerful detection algorithms brought a significant increase of performance. Including motion information also improves the tracking performance. Among the tracking approaches based on motion models, ...
- The impact of removing head movements on audio-visual speech enhancement
by Zhiqi Kang, Mostafa Sadeghi, Radu Horaud, Xavier Alameda-Pineda, Jacob Donley, Anurag Kumar
Abstract. This paper investigates the impact of head movements on audio-visual speech enhancement (AVSE). Although being a common conversational feature, head movements have been ignored by past and recent studies: they challenge today’s learning-based methods as they often degrade the performance of models that are trained on clean, frontal, and steady face images. To alleviate this problem, we propose to use robust face frontalization (RFF) in combination with an AVSE method based on a variational auto-encoder (VAE) model. We briefly describe the basic ingredients of the proposed pipeline and we perform experiments with a recently released audio-visual dataset. In the light of these experiments, and based on three standard metrics, namely STOI, PESQ and SI-SDR, we conclude that RFF improves the performance of AVSE by a considerable margin.
by Davier Emukpere, Xavier Alameda-Pineda and Chris Reinke
Abstract. A longstanding goal in reinforcement learning is to build intelligent agents that show fast learning and a flexible transfer of skills akin to humans and animals. This paper investigates the integration of two frameworks for tackling those goals: episodic control and successor features. Episodic control is a cognitively inspired approach relying on episodic memory, an instance-based memory model of an agent’s experiences. Meanwhile, successor features and generalized policy improvement (SF&GPI) is a meta and transfer learning framework allowing to learn policies for tasks that can be efficiently reused for later tasks which have a different reward function. Individually, these two techniques have shown impressive results in vastly improving sample efficiency and the elegant reuse of previously learned policies. Thus, we outline a combination of both approaches in a single reinforcement learning framework and empirically illustrate ...
- ξ-Learning: Successor Feature Transfer Learning for General Reward Functions
by Chris Reinke and Xavier Alameda-Pineda
Abstract. Transfer in Reinforcement Learning aims to improve learning performance on target tasks using knowledge from experienced source tasks. Successor features (SF) are a prominent transfer mechanism in domains where the reward function changes between tasks. They reevaluate the expected return of previously learned policies in a new target task and to transfer their knowledge. A limiting factor of the SF framework is its assumption that rewards linearly decompose into successor features and a reward weight vector. We propose a novel SF mechanism, ξ-learning, based on learning the cumulative discounted probability of successor features. Crucially, ξ-learning allows to reevaluate the expected return of policies for general reward functions. We introduce two ξ-learning variations, prove its convergence, and provide a guarantee on ...
- Dynamical Variational AutoEncoders
by Laurent Girin, Simon Leglaive, Xiaoyu Bie, Julien Diard, Thomas Hueber, and Xavier Alameda-Pineda
Foundations and Trends in Machine Learning, 2021, Vol. 15, No. 1-2, pp 1–175.
Abstract. Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent space learned in an unsupervised manner. In the original VAE model, the input data vectors are processed independently. Recently, a series of papers have presented different extensions of the VAE to process sequential data, which model not only the latent space but also the temporal dependencies within a sequence of data vectors and corresponding latent vectors, relying on recurrent neural networks or state-space models. In this paper, we perform a literature review of these models. We introduce and discuss a general class of models, called dynamical variational autoencoders (DVAEs), which encompasses a large subset of these temporal ...
- SocialInteractionGAN: Multi-person Interaction Sequence Generation
by Louis Airale, Dominique Vaufreydaz and Xavier Alameda-Pineda
Abstract. Prediction of human actions in social interactions has important applications in the design of social robots or artificial avatars. In this paper, we model human interaction generation as a discrete multi-sequence generation problem and present SocialInteractionGAN, a novel adversarial architecture for conditional interaction generation. Our model builds on a recurrent encoder-decoder generator network and a dual-stream discriminator. This architecture allows the discriminator to jointly assess the realism of interactions and that of individual action sequences. Within each stream a recurrent network operating on short subsequences endows the output signal with local assessments, better guiding the forthcoming generation. Crucially, contextual information on interacting participants is shared among agents and reinjected in both the generation and the discriminator evaluation processes. We show that the proposed SocialInteractionGAN succeeds in producing high realism action sequences ...
- A Benchmark of Dynamical Variational Autoencoders applied to Speech Spectrogram Modeling
by Xiaoyu Bie, Laurent Girin, Simon Leglaive, Thomas Hueber and Xavier Alameda-Pineda
Interspeech’21, Brno, Czech Republic
Abstract. The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space learned in an unsupervised manner. In the original VAE model, input data vectors are processed independently. In recent years, a series of papers have presented different extensions of the VAE to process sequential data, that not only model the latent space, but also model the temporal dependencies within a sequence of data vectors and corresponding latent vectors, relying on recurrent neural networks. We recently performed a comprehensive review of those models and unified them into a general class called Dynamical Variational Autoencoders (DVAEs). In the present paper, we present the results of an experimental benchmark comparing ...
- PI-Net: Pose Interacting Network for Multi-Person Monocular 3D Pose Estimation
Abstract. Recent literature addressed the monocular 3D pose estimation task very satisfactorily. In these studies, different persons are usually treated as independent pose instances to estimate. However, in many everyday situations, people are interacting, and the pose of an individual depends on the pose of his/her interaction. In this paper, we investigate how to exploit this dependency to enhance current – and possibly future – deep networks for 3D monocular pose estimation. Our pose interacting network, or PI-Net, inputs the initial pose estimates of a variable number of interaction into a recurrent architecture used to refine the pose of the person-of-interest. Evaluating such a method is challenging due to the limited availability of public annotated multi-person 3D human pose datasets. ...
- Robust Face Frontalization For Visual Speech Recognition
by Zhiqi Kang, Radu Horaud and Mostafa Sadeghi
ICCV’21 Workshop on Traditional Computer Vision in the Age of Deep Learning (TradiCV’21)
Abstract. Face frontalization consists of synthesizing a frontally-viewed face from an arbitrarily-viewed one. The main contribution is a robust method that preserves non-rigid facial deformations, i.e. expressions. The method iteratively estimates the rigid transformation and the non-rigid deformation between 3D landmarks extracted from an arbitrarily-viewed face, and 3D vertices parameterized by a deformable shape model. The one merit of the method is its ability to deal with large Gaussian and non-Gaussian errors in the data. For that purpose, we use the generalized Student-t distribution. The associated EM algorithm assigns a weight to each observed landmark, the higher the weight the more important the landmark, thus favouring landmarks that are only affected by rigid head movements. We propose ...
- TransCenter: Transformers with Dense Representations for Multiple-Object Tracking
by Yihong Xu*, Yutong Ban*, Guillaume Delorme, Chuang Gan, Daniela Rus and Xavier Alameda-Pineda
Transformers have proven superior performance for a wide variety of tasks since they were introduced, which has drawn in recent years the attention of the vision community where efforts were made such as image classification and object detection. Despite this wave, building an accurate and efficient multiple-object tracking (MOT) method with transformers is not a trivial task. We argue that the direct application of a transformer architecture with quadratic complexity and insufficient noise-initialized sparse queries – is not optimal for MOT. Inspired by recent research, we propose TransCenter, a transformer-based MOT architecture with dense representations for accurately tracking all the objects while keeping a reasonable runtime. Methodologically, we propose the use of dense image-related multi-scale detection queries produced by an efficient transformer architecture. The queries allow ...
- Performance Analysis of 3D Face Alignment with a Statistically Robust Confidence TestAbstract: We address the problem of analyzing the performance of 3D face alignment (3DFA) algorithms. Traditionally, performance analysis relies on carefully annotated datasets. Here, these annotations correspond to the 3D coordinates of a set of pre-defined facial landmarks. However, this annotation process, be it manual or automatic, is rarely error-free, which strongly biases the analysis. In contrast, we propose a fully unsupervised methodology based on robust statistics and a parametric confidence test. We revisit the problem of robust estimation of the rigid transformation between two point sets and we describe two algorithms, one based on a mixture between a Gaussian and a uniform distribution, and another one based on the generalized Student’s t-distribution. We show that these methods are robust to up ...
- Fullsubnet: a full-band and sub-band fusion model for real-time single-channel speech enhancement
By Xiang Hao*,#, Xiangdong Su#, Radu Horaud and Xiaofei Li* (*Westlake University, #Inner Mongolia University, China)
Abstract. This paper proposes a full-band and sub-band fusion model, named as FullSubNet, for single-channel real-time speech enhancement. Full-band and sub-band refer to the models that input full-band and sub-band noisy spectral feature, output full-band and sub-band speech target, respectively. The sub-band model processes each frequency independently. Its input consists of one frequency and several context frequencies. The output is the prediction of the clean speech target for the corresponding frequency. These two types of models have distinct characteristics. The full-band model can capture the global spectral context and the long-distance cross- band dependencies. However, it lacks the ability to modeling signal stationarity and attending the local spectral pattern. The sub-band model is just the opposite. In our proposed FullSubNet, we connect a ...
- Mixture of Inference Networks for VAE-based Audio-visual Speech Enhancement
by Mostafa Sadeghi, Xavier Alameda-Pineda
IEEE TSP, 2021
Abstract. In this paper, we are interested in unsupervised (unknown noise) speech enhancement, where the probability distribution of clean speech spectrogram is simulated via a latent variable generative model, also called the decoder. Recently, variational autoencoders (VAEs) have gained much popularity as probabilistic generative models. In VAEs, the posterior of the latent variables is computationally intractable, and it is approximated by a so-called encoder network. Motivated by the fact that visual data, i.e. lip images of the speaker, provide helpful and complementary information about speech, some audio-visual architectures have been recently proposed. The initialization of the latent variables at test time is crucial as the overall inference problem is non-convex. This is usually done by using the output of the encoder where the noisy audio and clean video data are given as input. Current ...
- Variational Inference and Learning of Piecewise-linear Dynamical Systems
by Xavier Alameda-Pineda, Vincent Drouard, Radu Horaud
IEEE TNNLS 2021
Abstract Modeling the temporal behavior of data is of primordial importance in many scientific and engineering fields. Baseline methods assume that both the dynamic and observation equations follow linear-Gaussian models. However, there are many real-world processes that cannot be characterized by a single linear behavior. Alternatively, it is possible to consider a piecewise-linear model which, combined with a switching mechanism, is well suited when several modes of behavior are needed. Nevertheless, switching dynamical systems are intractable because their computational complexity increases exponentially with time. In this paper, we propose a variational approximation of piecewise linear dynamical systems. We provide full details of the derivation of two variational expectation-maximization algorithms, a filter and a smoother. We show that the model parameters can be split into two sets, static and dynamic parameters, and that the former parameters ...
- ODANet: Online Deep Appearance Network for Identity-Consistent Multi-Person Tracking
by Guillaume Delorme , Yutong Ban , Guillaume Sarrazin and Xavier Alameda-Pineda
ICPR’20 Workshop on Multimodal pattern recognition for social signal processing in human computer interaction
Abstract. The analysis of effective states through time in multi-person scenarii is very challenging, because it requires to consistently track all persons over time. This requires a robust visual appearance model capable of re-identifying people already tracked in the past, as well as spotting newcomers. In real-world applications, the appearance of the persons to be tracked is unknown in advance, and therefore on must devise methods that are both discriminative and flexible. Previous work in the literature proposed different tracking methods with fixed appearance models. These models allowed, up to a certain extent, to discriminate between appearance samples of two different people. We propose an online deep appearance network (ODANet), a method able to simultaneously track people and update the ...
- Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction
by Dan Xu, Xavier Alameda-Pineda, Wanli Ouyang, Elisa Ricci, Xiaogang Wang and Nicu Sebe
IEEE TPAMI, 2020
Abstract. Multi-scale representations deeply learned via convolutional neural networks have shown tremendous importance for various pixel-level prediction problems. In this paper we present a novel approach that advances the state of the art on pixel-level prediction in a fundamental aspect,i.e.structured multi-scale features learning and fusion. In contrast to previous works directly considering multi-scale feature maps obtained from the inner layers of a primary CNN architecture, and simply fusing the features with weighted averaging or concatenation, we propose a probabilistic graph attention network structure based on a novel Attention-Gated Conditional Random Fields(AG-CRFs) model for learning and fusing multi-scale representations in a principled manner. In order to further improve the learning capacity of the network structure, we propose to exploit feature dependant conditional kernels within the ...
- Switching Variational Auto-Encoders for Noise-Agnostic Audio-visual Speech Enhancement
by Mostafa Sadeghi and Xavier Alameda-Pineda
Presented at IEEE ICASSP 2021
Abstract: Recently, audio-visual speech enhancement has been tackled in the unsupervised settings based on variational auto-encoders (VAEs), where during training only clean data is used to train a generative model for speech, which at test time is combined with a noise model, e.g. nonnegative matrix factorization (NMF), whose parameters are learned without supervision. Consequently, the proposed model is agnostic to the noise type. When visual data is clean, audio-visual VAE-based architectures usually outperform the audio-only counterpart. The opposite happens when the visual data is corrupted by clutter, e.g. the speaker not facing the camera. In this paper, we propose to find the optimal combination of these two architectures through time. More precisely, we introduce the use of a latent sequential variable with Markovian dependencies to switch between different VAE architectures through time ...
- Deep Variational Generative Models for Audio-visual Speech Separation
by Viet-Nhat Nguyen, Mostafa Sadeghi, Elisa Ricci, and Xavier Alameda-Pineda
Presented at IEEE MLSP 2021
Abstract: In this paper, we are interested in audio-visual speech separation given a single-channel audio recording as well as visual information (lips movements) associated with each speaker. We propose an unsupervised technique based on audio-visual generative modeling of clean speech. More specifically, during training, a latent variable generative model is learned from clean speech spectrograms using a variational auto-encoder (VAE). To better utilize the visual information, the posteriors of the latent variables are inferred from mixed speech (instead of clean speech) as well as the visual data. The visual modality also serves as a prior for latent variables, through a visual network. At test time, the learned generative model (both for speaker-independent and speaker-dependent scenarios) is combined with an unsupervised non-negative matrix factorization (NMF) variance model for background ...
- Online Monaural Speech Enhancement using Delayed Subband LSTM
by Xiaofei Li and Radu Horaud
Abstract. This paper proposes a delayed subband LSTM network for online monaural (single-channel) speech enhancement. The proposed method is developed in the short time Fourier transform (STFT) domain. Online processing requires frame-by-frame signal reception and processing. A paramount feature of the proposed method is that the same LSTM is used across frequencies, which drastically reduces the number of network parameters, the amount of training data and the computational burden. Training is performed in a subband manner: the input consists of one frequency, together with a few context frequencies. The network learns a speech-to-noise discriminative function relying on the signal stationarity and on the local spectral pattern, based on which it predicts a clean-speech mask at each frequency. To exploit future information, i.e. look-ahead, we propose an output-delayed subband architecture, which allows ...
- CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification
by Guillaume Delorme, Stéphane Lathuilière, Radu Horaud and Xavier Alameda-Pineda
Presented at ICPR, 2021
Abstract: Unsupervised person re-ID is the task of identifying people on a target dataset for which the ID labels are unavailable during training. In this paper, we propose to unify two trends in unsupervised person re-ID: clustering & fine-tuning and adversarial learning. On one side, clustering is used to group the training images into pseudo-labels, and then use this pseudo-labels to fine-tune the feature extractor. On the other side, adversarial learning is used, inspired from domain adaptation, to match distributions from different domains. We propose to model each camera of the target dataset as a domain, and aim to learn domain-independent features. Straightforward adversarial learning yields negative transfer, and we introduce a conditioning vector to mitigate this undesirable effect. In our ...
- Learning Visual Voice Activity Detection with an Automatically Annotated Dataset
by Sylvain Guy, Stéphane Lathuilière, Pablo Mesejo and Radu Horaud
Presented at ICPR 2021
Abstract. Visual voice activity detection (V-VAD) uses visual features to predict whether a person is speaking or not. V-VAD is useful whenever audio VAD (A-VAD) is inefficient, either because the acoustic signal is difficult to analyze or because it is simply missing. We propose two deep architectures for V-VAD, one based on facial landmarks and one based on optical flow. Moreover, available datasets, used for learning and for testing V-VAD, lack content variability. We introduce a novel methodology to automatically create and annotate very large datasets in-the-wild – WildVVAD – based on combining A-VAD with face detection. A thorough empirical evaluation shows the advantage of training the proposed deep V-VAD models with this dataset.
Dataset. The automatically generated and annotated WildVVAD dataset is publicly available. It contains 12,000 video ...
- How To Train Your Deep Multi-Object Tracker
by Yihong Xu, Aljoša Ošep, Yutong Ban, Radu Horaud, Laura Leal-Taixé and Xavier Alameda-Pineda
Presented at IEEE CVPR 2020
Abstract: The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging the representational power of deep learning to jointly learn to detect and track objects. However, existing methods train only certain submodules using loss functions that often do not correlate with established tracking evaluation measures such as Multi-Object Tracking Accuracy (MOTA) and Precision (MOTP). As these measures are not differentiable, the choice of appropriate loss functions for end-to-end training of multiobject tracking methods is still an open research problem. In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multiobject trackers. As a key ingredient, we propose a Deep ...
- ξ-Learning: Successor Feature Transfer Learning for General Reward Functions