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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.02256">arXiv:2411.02256</a> <span> [<a href="https://arxiv.org/pdf/2411.02256">pdf</a>, <a href="https://arxiv.org/format/2411.02256">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Unified Speech Recognition: A Single Model for Auditory, Visual, and Audiovisual Inputs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Haliassos%2C+A">Alexandros Haliassos</a>, <a href="/search/cs?searchtype=author&query=Mira%2C+R">Rodrigo Mira</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+H">Honglie Chen</a>, <a href="/search/cs?searchtype=author&query=Landgraf%2C+Z">Zoe Landgraf</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.02256v1-abstract-short" style="display: inline;"> Research in auditory, visual, and audiovisual speech recognition (ASR, VSR, and AVSR, respectively) has traditionally been conducted independently. Even recent self-supervised studies addressing two or all three tasks simultaneously tend to yield separate models, leading to disjoint inference pipelines with increased memory requirements and redundancies. This paper proposes unified training strate… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.02256v1-abstract-full').style.display = 'inline'; document.getElementById('2411.02256v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.02256v1-abstract-full" style="display: none;"> Research in auditory, visual, and audiovisual speech recognition (ASR, VSR, and AVSR, respectively) has traditionally been conducted independently. Even recent self-supervised studies addressing two or all three tasks simultaneously tend to yield separate models, leading to disjoint inference pipelines with increased memory requirements and redundancies. This paper proposes unified training strategies for these systems. We demonstrate that training a single model for all three tasks enhances VSR and AVSR performance, overcoming typical optimisation challenges when training from scratch. Moreover, we introduce a greedy pseudo-labelling approach to more effectively leverage unlabelled samples, addressing shortcomings in related self-supervised methods. Finally, we develop a self-supervised pre-training method within our framework, proving its effectiveness alongside our semi-supervised approach. Despite using a single model for all tasks, our unified approach achieves state-of-the-art performance compared to recent methods on LRS3 and LRS2 for ASR, VSR, and AVSR, as well as on the newly released WildVSR dataset. Code and models are available at https://github.com/ahaliassos/usr. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.02256v1-abstract-full').style.display = 'none'; document.getElementById('2411.02256v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">NeurIPS 2024. Code: https://github.com/ahaliassos/usr</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.19110">arXiv:2404.19110</a> <span> [<a href="https://arxiv.org/pdf/2404.19110">pdf</a>, <a href="https://arxiv.org/format/2404.19110">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> EMOPortraits: Emotion-enhanced Multimodal One-shot Head Avatars </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Drobyshev%2C+N">Nikita Drobyshev</a>, <a href="/search/cs?searchtype=author&query=Casademunt%2C+A+B">Antoni Bigata Casademunt</a>, <a href="/search/cs?searchtype=author&query=Vougioukas%2C+K">Konstantinos Vougioukas</a>, <a href="/search/cs?searchtype=author&query=Landgraf%2C+Z">Zoe Landgraf</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.19110v1-abstract-short" style="display: inline;"> Head avatars animated by visual signals have gained popularity, particularly in cross-driving synthesis where the driver differs from the animated character, a challenging but highly practical approach. The recently presented MegaPortraits model has demonstrated state-of-the-art results in this domain. We conduct a deep examination and evaluation of this model, with a particular focus on its laten… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.19110v1-abstract-full').style.display = 'inline'; document.getElementById('2404.19110v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.19110v1-abstract-full" style="display: none;"> Head avatars animated by visual signals have gained popularity, particularly in cross-driving synthesis where the driver differs from the animated character, a challenging but highly practical approach. The recently presented MegaPortraits model has demonstrated state-of-the-art results in this domain. We conduct a deep examination and evaluation of this model, with a particular focus on its latent space for facial expression descriptors, and uncover several limitations with its ability to express intense face motions. To address these limitations, we propose substantial changes in both training pipeline and model architecture, to introduce our EMOPortraits model, where we: Enhance the model's capability to faithfully support intense, asymmetric face expressions, setting a new state-of-the-art result in the emotion transfer task, surpassing previous methods in both metrics and quality. Incorporate speech-driven mode to our model, achieving top-tier performance in audio-driven facial animation, making it possible to drive source identity through diverse modalities, including visual signal, audio, or a blend of both. We propose a novel multi-view video dataset featuring a wide range of intense and asymmetric facial expressions, filling the gap with absence of such data in existing datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.19110v1-abstract-full').style.display = 'none'; document.getElementById('2404.19110v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.04713">arXiv:2202.04713</a> <span> [<a href="https://arxiv.org/pdf/2202.04713">pdf</a>, <a href="https://arxiv.org/format/2202.04713">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> PINs: Progressive Implicit Networks for Multi-Scale Neural Representations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Landgraf%2C+Z">Zoe Landgraf</a>, <a href="/search/cs?searchtype=author&query=Hornung%2C+A+S">Alexander Sorkine Hornung</a>, <a href="/search/cs?searchtype=author&query=Cabral%2C+R+S">Ricardo Silveira Cabral</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.04713v2-abstract-short" style="display: inline;"> Multi-layer perceptrons (MLP) have proven to be effective scene encoders when combined with higher-dimensional projections of the input, commonly referred to as \textit{positional encoding}. However, scenes with a wide frequency spectrum remain a challenge: choosing high frequencies for positional encoding introduces noise in low structure areas, while low frequencies result in poor fitting of det… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.04713v2-abstract-full').style.display = 'inline'; document.getElementById('2202.04713v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.04713v2-abstract-full" style="display: none;"> Multi-layer perceptrons (MLP) have proven to be effective scene encoders when combined with higher-dimensional projections of the input, commonly referred to as \textit{positional encoding}. However, scenes with a wide frequency spectrum remain a challenge: choosing high frequencies for positional encoding introduces noise in low structure areas, while low frequencies result in poor fitting of detailed regions. To address this, we propose a progressive positional encoding, exposing a hierarchical MLP structure to incremental sets of frequency encodings. Our model accurately reconstructs scenes with wide frequency bands and learns a scene representation at progressive level of detail \textit{without explicit per-level supervision}. The architecture is modular: each level encodes a continuous implicit representation that can be leveraged separately for its respective resolution, meaning a smaller network for coarser reconstructions. Experiments on several 2D and 3D datasets show improvements in reconstruction accuracy, representational capacity and training speed compared to baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.04713v2-abstract-full').style.display = 'none'; document.getElementById('2202.04713v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ICML 2022 (spotlight)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.16442">arXiv:2103.16442</a> <span> [<a href="https://arxiv.org/pdf/2103.16442">pdf</a>, <a href="https://arxiv.org/format/2103.16442">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> SIMstack: A Generative Shape and Instance Model for Unordered Object Stacks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Landgraf%2C+Z">Zoe Landgraf</a>, <a href="/search/cs?searchtype=author&query=Scona%2C+R">Raluca Scona</a>, <a href="/search/cs?searchtype=author&query=Laidlow%2C+T">Tristan Laidlow</a>, <a href="/search/cs?searchtype=author&query=James%2C+S">Stephen James</a>, <a href="/search/cs?searchtype=author&query=Leutenegger%2C+S">Stefan Leutenegger</a>, <a href="/search/cs?searchtype=author&query=Davison%2C+A+J">Andrew J. Davison</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2103.16442v2-abstract-short" style="display: inline;"> By estimating 3D shape and instances from a single view, we can capture information about an environment quickly, without the need for comprehensive scanning and multi-view fusion. Solving this task for composite scenes (such as object stacks) is challenging: occluded areas are not only ambiguous in shape but also in instance segmentation; multiple decompositions could be valid. We observe that ph… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.16442v2-abstract-full').style.display = 'inline'; document.getElementById('2103.16442v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.16442v2-abstract-full" style="display: none;"> By estimating 3D shape and instances from a single view, we can capture information about an environment quickly, without the need for comprehensive scanning and multi-view fusion. Solving this task for composite scenes (such as object stacks) is challenging: occluded areas are not only ambiguous in shape but also in instance segmentation; multiple decompositions could be valid. We observe that physics constrains decomposition as well as shape in occluded regions and hypothesise that a latent space learned from scenes built under physics simulation can serve as a prior to better predict shape and instances in occluded regions. To this end we propose SIMstack, a depth-conditioned Variational Auto-Encoder (VAE), trained on a dataset of objects stacked under physics simulation. We formulate instance segmentation as a centre voting task which allows for class-agnostic detection and doesn't require setting the maximum number of objects in the scene. At test time, our model can generate 3D shape and instance segmentation from a single depth view, probabilistically sampling proposals for the occluded region from the learned latent space. Our method has practical applications in providing robots some of the ability humans have to make rapid intuitive inferences of partially observed scenes. We demonstrate an application for precise (non-disruptive) object grasping of unknown objects from a single depth view. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.16442v2-abstract-full').style.display = 'none'; document.getElementById('2103.16442v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> ICCV 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.10342">arXiv:2002.10342</a> <span> [<a href="https://arxiv.org/pdf/2002.10342">pdf</a>, <a href="https://arxiv.org/format/2002.10342">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Comparing View-Based and Map-Based Semantic Labelling in Real-Time SLAM </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Landgraf%2C+Z">Zoe Landgraf</a>, <a href="/search/cs?searchtype=author&query=Falck%2C+F">Fabian Falck</a>, <a href="/search/cs?searchtype=author&query=Bloesch%2C+M">Michael Bloesch</a>, <a href="/search/cs?searchtype=author&query=Leutenegger%2C+S">Stefan Leutenegger</a>, <a href="/search/cs?searchtype=author&query=Davison%2C+A">Andrew Davison</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.10342v1-abstract-short" style="display: inline;"> Generally capable Spatial AI systems must build persistent scene representations where geometric models are combined with meaningful semantic labels. The many approaches to labelling scenes can be divided into two clear groups: view-based which estimate labels from the input view-wise data and then incrementally fuse them into the scene model as it is built; and map-based which label the generated… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.10342v1-abstract-full').style.display = 'inline'; document.getElementById('2002.10342v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.10342v1-abstract-full" style="display: none;"> Generally capable Spatial AI systems must build persistent scene representations where geometric models are combined with meaningful semantic labels. The many approaches to labelling scenes can be divided into two clear groups: view-based which estimate labels from the input view-wise data and then incrementally fuse them into the scene model as it is built; and map-based which label the generated scene model. However, there has so far been no attempt to quantitatively compare view-based and map-based labelling. Here, we present an experimental framework and comparison which uses real-time height map fusion as an accessible platform for a fair comparison, opening up the route to further systematic research in this area. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.10342v1-abstract-full').style.display = 'none'; document.getElementById('2002.10342v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ICRA 2020</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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