神经场通过将坐标输入映射到采样值来模型信号。从视觉,图形到生物学和天文学的许多领域,它们正成为越来越重要的主链体系结构。在本文中,我们探讨了这些网络中常见的调理机制之间的差异,这是将神经场从信号的记忆转移到概括的基本要素,其中共同建模了位于歧管上的一组信号。特别是,我们对这些机制的缩放行为感兴趣,以对日益高维的调理变量感兴趣。正如我们在实验中显示的那样,高维条件是建模复杂数据分布的关键,因此,确定哪种体系结构在处理此类问题时最能实现哪种选择。为此,我们运行了使用串联,超网络和基于注意力的调理策略对2D,3D和4D信号进行建模的实验,这是文献中尚未进行的必要但费力的努力。我们发现,基于注意力的条件在各种环境中的其他方法都优于其他方法。
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我们建议在2D域中利用自我监督的技术来实现细粒度的3D形状分割任务。这是受到观察的启发:基于视图的表面表示比基于点云或体素占用率的3D对应物更有效地建模高分辨率表面细节和纹理。具体而言,给定3D形状,我们将其从多个视图中渲染,并在对比度学习框架内建立密集的对应学习任务。结果,与仅在2D或3D中使用自学的替代方案相比,学到的2D表示是视图不变和几何一致的,在对有限的标记形状进行培训时,可以更好地概括概括。对纹理(渲染peple)和未纹理(partnet)3D数据集的实验表明,我们的方法在细粒部分分割中优于最先进的替代方案。当仅一组稀疏的视图可供训练或形状纹理时,对基准的改进就会更大,这表明MVDecor受益于2D处理和3D几何推理。
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我们呈现FURTIT,这是一种简单的3D形状分割网络的高效学习方法。FURTIT基于自我监督的任务,可以将3D形状的表面分解成几何基元。可以很容易地应用于用于3D形状分割的现有网络架构,并提高了几张拍摄设置中的性能,因为我们在广泛使用的ShapEnet和Partnet基准中展示。FISHIT在这种环境中优于现有的现有技术,表明对基元的分解是在学习对语义部分预测的陈述之前的有用。我们提出了许多实验,改变了几何基元和下游任务的选择,以证明该方法的有效性。
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Arbitrary pattern formation (\textsc{Apf}) is well studied problem in swarm robotics. The problem has been considered in two different settings so far; one is in plane and another is in infinite grid. This work deals the problem in infinite rectangular grid setting. The previous works in literature dealing with \textsc{Apf} problem in infinite grid had a fundamental issue. These deterministic algorithms use a lot space of the grid to solve the problem mainly because of maintaining asymmetry of the configuration or to avoid collision. These solution techniques can not be useful if there is a space constrain in the application field. In this work, we consider luminous robots (with one light that can take two colors) in order to avoid symmetry, but we carefully designed a deterministic algorithm which solves the \textsc{Apf} problem using minimal required space in the grid. The robots are autonomous, identical, anonymous and they operate in Look-Compute-Move cycles under a fully asynchronous scheduler. The \textsc{Apf} algorithm proposed in [WALCOM'2019] by Bose et al. can be modified using luminous robots so that it uses minimal space but that algorithm is not move optimal. The algorithm proposed in this paper not only uses minimal space but also asymptotically move optimal. The algorithm proposed in this work is designed for infinite rectangular grid but it can be easily modified to work in a finite grid as well.
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Medical professionals frequently work in a data constrained setting to provide insights across a unique demographic. A few medical observations, for instance, informs the diagnosis and treatment of a patient. This suggests a unique setting for meta-learning, a method to learn models quickly on new tasks, to provide insights unattainable by other methods. We investigate the use of meta-learning and robustness techniques on a broad corpus of benchmark text and medical data. To do this, we developed new data pipelines, combined language models with meta-learning approaches, and extended existing meta-learning algorithms to minimize worst case loss. We find that meta-learning on text is a suitable framework for text-based data, providing better data efficiency and comparable performance to few-shot language models and can be successfully applied to medical note data. Furthermore, meta-learning models coupled with DRO can improve worst case loss across disease codes.
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Memes are powerful means for effective communication on social media. Their effortless amalgamation of viral visuals and compelling messages can have far-reaching implications with proper marketing. Previous research on memes has primarily focused on characterizing their affective spectrum and detecting whether the meme's message insinuates any intended harm, such as hate, offense, racism, etc. However, memes often use abstraction, which can be elusive. Here, we introduce a novel task - EXCLAIM, generating explanations for visual semantic role labeling in memes. To this end, we curate ExHVV, a novel dataset that offers natural language explanations of connotative roles for three types of entities - heroes, villains, and victims, encompassing 4,680 entities present in 3K memes. We also benchmark ExHVV with several strong unimodal and multimodal baselines. Moreover, we posit LUMEN, a novel multimodal, multi-task learning framework that endeavors to address EXCLAIM optimally by jointly learning to predict the correct semantic roles and correspondingly to generate suitable natural language explanations. LUMEN distinctly outperforms the best baseline across 18 standard natural language generation evaluation metrics. Our systematic evaluation and analyses demonstrate that characteristic multimodal cues required for adjudicating semantic roles are also helpful for generating suitable explanations.
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Active speaker detection in videos addresses associating a source face, visible in the video frames, with the underlying speech in the audio modality. The two primary sources of information to derive such a speech-face relationship are i) visual activity and its interaction with the speech signal and ii) co-occurrences of speakers' identities across modalities in the form of face and speech. The two approaches have their limitations: the audio-visual activity models get confused with other frequently occurring vocal activities, such as laughing and chewing, while the speakers' identity-based methods are limited to videos having enough disambiguating information to establish a speech-face association. Since the two approaches are independent, we investigate their complementary nature in this work. We propose a novel unsupervised framework to guide the speakers' cross-modal identity association with the audio-visual activity for active speaker detection. Through experiments on entertainment media videos from two benchmark datasets, the AVA active speaker (movies) and Visual Person Clustering Dataset (TV shows), we show that a simple late fusion of the two approaches enhances the active speaker detection performance.
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One of the recent advances in surgical AI is the recognition of surgical activities as triplets of (instrument, verb, target). Albeit providing detailed information for computer-assisted intervention, current triplet recognition approaches rely only on single frame features. Exploiting the temporal cues from earlier frames would improve the recognition of surgical action triplets from videos. In this paper, we propose Rendezvous in Time (RiT) - a deep learning model that extends the state-of-the-art model, Rendezvous, with temporal modeling. Focusing more on the verbs, our RiT explores the connectedness of current and past frames to learn temporal attention-based features for enhanced triplet recognition. We validate our proposal on the challenging surgical triplet dataset, CholecT45, demonstrating an improved recognition of the verb and triplet along with other interactions involving the verb such as (instrument, verb). Qualitative results show that the RiT produces smoother predictions for most triplet instances than the state-of-the-arts. We present a novel attention-based approach that leverages the temporal fusion of video frames to model the evolution of surgical actions and exploit their benefits for surgical triplet recognition.
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The adversarial input generation problem has become central in establishing the robustness and trustworthiness of deep neural nets, especially when they are used in safety-critical application domains such as autonomous vehicles and precision medicine. This is also practically challenging for multiple reasons-scalability is a common issue owing to large-sized networks, and the generated adversarial inputs often lack important qualities such as naturalness and output-impartiality. We relate this problem to the task of patching neural nets, i.e. applying small changes in some of the network$'$s weights so that the modified net satisfies a given property. Intuitively, a patch can be used to produce an adversarial input because the effect of changing the weights can also be brought about by changing the inputs instead. This work presents a novel technique to patch neural networks and an innovative approach of using it to produce perturbations of inputs which are adversarial for the original net. We note that the proposed solution is significantly more effective than the prior state-of-the-art techniques.
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The primary obstacle to developing technologies for low-resource languages is the lack of representative, usable data. In this paper, we report the deployment of technology-driven data collection methods for creating a corpus of more than 60,000 translations from Hindi to Gondi, a low-resource vulnerable language spoken by around 2.3 million tribal people in south and central India. During this process, we help expand information access in Gondi across 2 different dimensions (a) The creation of linguistic resources that can be used by the community, such as a dictionary, children's stories, Gondi translations from multiple sources and an Interactive Voice Response (IVR) based mass awareness platform; (b) Enabling its use in the digital domain by developing a Hindi-Gondi machine translation model, which is compressed by nearly 4 times to enable it's edge deployment on low-resource edge devices and in areas of little to no internet connectivity. We also present preliminary evaluations of utilizing the developed machine translation model to provide assistance to volunteers who are involved in collecting more data for the target language. Through these interventions, we not only created a refined and evaluated corpus of 26,240 Hindi-Gondi translations that was used for building the translation model but also engaged nearly 850 community members who can help take Gondi onto the internet.
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