United States

Occlusion and motion blur make it challenging to interpolate video frame, since estimating complex motions between two frames is hard and unreliable, especially in highly dynamic scenes. This paper aims to address these issues by exploiting spike stream as auxiliary visual information between frames to synthesize target frames. Instead of estimating motions by optical flow from RGB frames, we present a new dual-modal pipeline adopting both RGB frames and the corresponding spike stream as inputs (SVFI). It extracts the scene structure and objects&#39; outline feature maps of the target frames from spike stream. Those feature maps are fused with the color and texture feature maps extracted from RGB frames to synthesize target frames. Benefited by the spike stream that contains consecutive information between two frames, SVFI can directly extract the information in occlusion and motion blur areas of target frames from spike stream, thus it is more robust than previous optical flow-based methods. Experiments show SVFI outperforms the SOTA methods on wide variety of datasets. For instance, in 7 and 15 frame skip evaluations, it shows up to 5.58 dB and 6.56 dB improvements in terms of PSNR over the corresponding second best methods BMBC and DAIN. SVFI also shows visually impressive performance in real-world scenes.

AAAI 2023

SVFI: Spiking-based Video Frame Interpolation for High-Speed Motion

cv: low level

computational photography

video frame interpolation

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Occlusion and motion blur make it challenging to interpolate video frame, since estimating complex motions between two frames is hard and unreliable, especially in highly dynamic scenes. This paper aims to address these issues by exploiting spike stream as auxiliary visual information between frames to synthesize target frames. Instead of estimating motions by optical flow from RGB frames, we present a new dual-modal pipeline adopting both RGB frames and the corresponding spike stream as inputs (SVFI). It extracts the scene structure and objects' outline feature maps of the target frames from spike stream. Those feature maps are fused with the color and texture feature maps extracted from RGB frames to synthesize target frames. Benefited by the spike stream that contains consecutive information between two frames, SVFI can directly extract the information in occlusion and motion blur areas of target frames from spike stream, thus it is more robust than previous optical flow-based methods. Experiments show SVFI outperforms the SOTA methods on wide variety of datasets. For instance, in 7 and 15 frame skip evaluations, it shows up to 5.58 dB and 6.56 dB improvements in terms of PSNR over the corresponding second best methods BMBC and DAIN. SVFI also shows visually impressive performance in real-world scenes.

poster

The study of implicit regularization induced by gradient-based optimization in deep learning is a long-standing pursuit. In the present paper, we characterize the implicit regularization of momentum gradient descent (MGD) in the continuous-time view, so-called momentum gradient flow (MGF).   We show that the components of weight vector are learned for a depth-$N$ linear neural network at  the different evolution rates, and this evolution gap increases with the depth $N$. Firstly, we show that if $N=1$, the evolution gap between the weight components is linear, which is consistent with the performance of ridge. In particular, we establish a tight coupling between MGF and ridge which for least squares regression. In details, we prove that, under the calibration $t=\sqrt{2/\lambda}$, where $t$ is the time  parameter in MGF and $\lambda$ is the tuning parameter in ridge, the risk of MGF is no more than 1.54 times that of ridge and their relative Bayesian risk is hardly different. Secondly,  if the model becomes deeper, i.e. $N\geq2$, the evolution gap becomes more significant, which implies an implicit bias towards sparse solutions. The numerical experiments strongly support our theoretical results.

The Implicit Regularization of Momentum Gradient Descent in Overparametrized Models

Patient-independent detection of epileptic activities based on visual spectral representation of continuous EEG (cEEG) has been widely used for diagnosing epilepsy. However, precise detection remains a considerable challenge due to subtle variabilities across subjects, channels and time points. Thus, capturing fine-grained, discriminative features of EEG patterns, which is associated with high-frequency textural information, is yet to be resolved. In this work, we propose Scattering Transformer (ScatterFormer), an invariant scattering transform-based hierarchical Transformer that specifically pays attention to subtle features. In particular, the disentangled frequency-aware attention (FAA) enables the Transformer to capture clinically informative high-frequency components, offering a novel clinical explainability based on visual encoding of multichannel EEG signals. Evaluations on two distinct tasks of epileptiform detection demonstrate
the effectiveness our method. Our proposed model achieves median AUCROC and accuracy of 98.14%, 96.39% in patients with Rolandic epilepsy. On a neonatal seizure detection benchmark, it outperforms the state-of-the-art by 9% in terms of average AUCROC.

ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges

By adding exiting layers to the deep learning networks, early exit can terminate the inference earlier with accurate results. The passive decision-making of whether to exit or continue the next layer has to go through every pre-placed exiting layer until it exits. In addition, it is also hard to adjust the configurations of the computing platforms alongside the inference proceeds. By incorporating a low-cost prediction engine, we propose a Predictive Exit framework for computation- and energy-efficient deep learning applications. Predictive Exit can forecast where the network will exit (i.e., establish the number of remaining layers to finish the inference), which effectively reduces the network computation cost by exiting on time without running every pre-placed exiting layer. Moreover, according to the number of remaining layers, proper computing configurations (i.e., frequency and voltage) are selected to execute the network to further save energy. Extensive experimental results demonstrate that Predictive Exit achieves up to 96.2% computation reduction and 72.9% energy-saving compared with classic deep learning networks; and 12.8% computation reduction and 37.6% energy-saving compared with the early exit under state-of-the-art exiting strategies, given the same inference accuracy and latency.

Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference

Choreography refers to creation of dance motions according to both music and dance knowledge, where the created dances should be style-specific and consistent. However, most of the existing methods generate dances using the given music as the only reference, lacking the stylized dancing knowledge, namely, the flag motion patterns contained in different styles. Without the stylized prior knowledge, these approaches are not promising to generate controllable style or diverse moves for each dance style, nor new dances complying with stylized knowledge. To address this issue, we propose a novel music-to-dance generation framework guided by style embedding, considering both input music and stylized dancing knowledge. These style embeddings are learnt representations of style-consistent kinematic abstraction of reference dance videos, which can act as controllable factors to impose style constraints on dance generation in a latent manner. Hence, we can make the style embedding fit into any given style while allowing the flexibility to generate new compatible dance moves by modifying the style embedding according to the learnt representations of a certain style. We are the first to achieve knowledge-driven style control in dance generation tasks. To support this study, we build a large multi-style music-to-dance dataset referred to as I-Dance. The qualitative and quantitative evaluations demonstrate the advantage of the proposed framework, as well as the ability to synthesize diverse moves under a dance style directed by style embedding.

Mining and Applying Composition Knowledge of Dance Moves for Style-concentrated Dance Generation

Graph convolutional networks (GCNs) have been grabbing widespread attentions due to their encouraging performance and powerful generalizations. However, few works provide a general view to interpret various GCNs and guide GCNs' designs. In this paper, by revisiting the original GCN, we induce an interpretable regularizer-centered optimization framework, in which by building appropriate regularizers we could interpret most existing GCNs, such as APPNP, JKNet, DAGNN, and GNN-LF/HF. Further, under the proposed framework, we devise a dual-regularizer graph convolutional network (dubbed tsGCN) to capture both topological and semantic structures from graph data. Since the derived learning rule for tsGCN contains an inverse of a large matrix and thus is time-consuming, we borrow the Woodbury matrix identity and low-rank approximation tricks to successfully decrease the high computational complexity of computing infinite-order graph convolutions. Extensive experiments on eight public datasets demonstrate that tsGCN achieves superior performance against quite a few state-of-the-art competitors w.r.t. classification tasks.

Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework

Vaccine delivery in under-resourced locations with security
risks is not just challenging but also life threatening. The
COVID pandemic and the need to vaccinate added even more
urgency to this issue. Motivated by this problem, we propose
a general framework to set-up limited temporary (vaccination)
centers that balance physical security and desired (vaccine)
service coverage with limited resources. We set-up the
problem as a Stackelberg game between the centers operator
(defender) and an adversary, where the set of centers is not
fixed a priori but is part of the decision output. This results in
a mixed combinatorial and continuous optimization problem.
As part of our scalable approximation solution, we provide a
fundamental contribution by identifying general duality conditions
of switching max and min when both discrete and continuous
variables are involved. Via detailed experiments, we
show that the solution proposed is scalable in practice.

Securing Lifelines: Safe Delivery of Critical Services in Areas with Volatile Security Situation via a Stackelberg Game Approach

The opaqueness of the multi-hop fact verification model imposes imperative requirements for explainability. One feasible way is to extract rationales, a subset of inputs, where the performance of prediction drops dramatically when being removed. Though being explainable, most rationale extraction methods for multi-hop fact verification explore the semantic information within each piece of evidence individually, while ignoring the topological information interaction among different pieces of evidence. Intuitively, a faithful rationale bears complementary information being able to extract other rationales through the multi-hop reasoning process. To tackle such disadvantages, we cast explainable multi-hop fact verification as subgraph extraction, which can be solved based on graph convolutional network (GCN) with salience-aware graph learning. In specific, GCN is utilized to incorporate the topological interaction information among multiple pieces of evidence for learning evidence representation. Meanwhile, to alleviate the influence of noisy evidence, the salience-aware graph perturbation is induced into the message passing of GCN. Moreover, the multi-task model with three diagnostic properties of rationale is elaborately designed to improve the quality of an explanation without any explicit annotations. Experimental results on the FEVEROUS benchmark show significant gains over previous state-of-the-art methods for both rationale extraction and fact verification.

Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning

Ad hoc teamwork refers to collaboration in a team of agents without prior coordination. State of the art methods for this problem often include a data-driven component that uses a long history of prior observations to model the behaviour of other agents (or agent types) and to determine the ad hoc agent’s behaviour. In many practical domains, it is challenging to find large training datasets, and necessary to understand and incrementally extend the existing models to account for changes in team composition or domain attributes. Our architecture combines the principles of knowledge-based reasoning and data-driven learning. It enables the ad hoc agent to perform non-monotonic logical reasoning with prior commonsense domain knowledge and incrementally-updated simple predictive models of other agents’ behaviour. We use the benchmark simulated multiagent collaboration domain Fort Attack to demonstrate that our architecture enables an ad hoc agent to adapt to unforeseen changes, learn and revise its models of other agents’ behaviour from limited samples, support transparency in its decision making, and provide better performance than a data-driven baseline.

Back to the Future: Toward a Hybrid Architecture for Ad Hoc Teamwork

Yes. In this paper, we investigate strong lottery tickets in generative models, the subnetworks that achieve good generative performance without any weight update. Neural network pruning is considered the main cornerstone of model compression for reducing the costs of computation and memory. Unfortunately, pruning a generative model has not been extensively explored, and all existing pruning algorithms suffer from excessive weight-training costs, performance degradation, limited generalizability, or complicated training. To address these problems, we propose to find a strong lottery ticket via moment-matching scores. Our experimental results show that the discovered subnetwork can perform similarly or better than the trained dense model even when only 10% of the weights remain. To the best of our knowledge, we are the first to show the existence of strong lottery tickets in generative models and provide an algorithm to find it stably. Our code and supplementary materials are publicly available.

Can We Find Strong Lottery Tickets in Generative Models?

Normalizing flows (NF) build upon invertible neural networks and have wide applications in probabilistic modeling. Currently, building a powerful yet computationally efficient flow model relies on empirical fine-tuning over a large design space. While introducing neural architecture search (NAS) to NF is desirable, the invertibility constraint of NF brings new challenges to existing NAS methods whose application is limited to unstructured neural networks. Developing efficient NAS methods specifically for NF remains an open problem. We present AutoNF, the first automated NF architectural optimization framework. First, we present a new mixture distribution formulation that allows efficient differentiable architecture search of flow models without violating the invertibility constraint. Second, under the new formulation, we convert the original NP-hard combinatorial NF architectural optimization problem to an unconstrained continuous relaxation admitting the discrete optimal architectural solution, circumventing the loss of optimality due to binarization in architectural optimization.   We evaluate AutoNF with various density estimation datasets and show its superior performance-cost trade-offs over a set of existing hand-crafted baselines.

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