Canada

Optical flow estimation is a challenging task consisting of predicting per-pixel motion vectors between images. Recent methods have employed larger and more complex models to improve the estimation accuracy. However, this impacts the widespread adoption of optical flow methods and makes it harder to train more general models since the optical flow data is hard to obtain. This paper proposes a small and efficient model for optical flow estimation. We design a new spatial recurrent encoder that extracts discriminative features at a significantly reduced size. Unlike standard recurrent units, we utilize Partial Kernel Convolution (PKConv) layers to produce variable multi-scale features with a single shared block. We also design efficient Separable Large Kernels (SLK) to capture large context information with low computational cost. Experiments on public benchmarks show that we achieve state-of-the-art generalization performance while requiring significantly fewer parameters and memory than competing methods. Our model ranks first in the Spring benchmark without finetuning, improving the results by over 10% while requiring an order of magnitude fewer FLOPs and over four times less memory than the following published method without finetuning. The code is available at github.com/hmorimitsu/ptlflow/tree/main/ptlflow/models/rpknet.

AAAI 2024

Recurrent Partial Kernel Network for Efficient Optical Flow Estimation | VIDEO

partial kernel convolution

recurrent network

optical flow

technical paper

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

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. 

In recent advancements concerning Domain Adaptive Object Detection (DAOD), unsupervised domain adaptation techniques have proven instrumental. These methods enable enhanced detection capabilities within unlabeled target domains by mitigating distribution differences between source and target domains. A subset of DAOD methods employs disentangled learning to segregate Domain-Specific Representations (DSR) and Domain-Invariant Representations (DIR), with ultimate predictions relying on the latter. Current practices in disentanglement, however, often lead to DIR containing residual domain-specific information. To address this, we introduce the Multi-level Disentanglement Module (MDM) that progressively disentangles DIR, enhancing comprehensive disentanglement. Additionally, our proposed Cyclic Disentanglement Module (CDM) facilitates DSR separation. To refine the process further, we employ the Categorical Features Disentanglement Module (CFDM) to isolate DIR and DSR, coupled with category alignment across scales for improved source-target domain alignment. Given its practical suitability, our model is constructed upon the foundational framework of the Single Shot MultiBox Detector (SSD), which is a one-stage object detection approach. Experimental validation highlights the effectiveness of our method, demonstrating its state-of-the-art performance across three benchmark datasets.

Triple Feature Disentanglement for One-Stage Adaptive Object Detection | VIDEO

Cross-sensory interaction is a key aspect for multisensory recognition. Without cross-sensory interaction, artificial neural networks show inferior performance in multisensory recognition. On the contrary, the human brain has an inherently remarkable ability in multisensory recognition, which stems from the diverse neurons that exhibit distinct responses to sensory inputs, especially the multisensory neurons with multisensory responses hence enabling cross-sensory interaction. Based on this neuronal diversity, we propose a Neuronal Diversity inspired Multisensory Recognition Model (ND-MRM), which, similar to the brain, comprises unisensory neurons and multisensory neurons. To reflect the different responses characteristics of diverse neurons in the brain, special connection constraints are innovatively designed to regulate the features transmission in the ND-MRM. Leveraging this novel concept of neuronal diversity, our model is biologically plausible, enabling more effective recognition of multisensory information. To validate the performance of the proposed ND-MRM, we employ a multisensory emotion recognition task as a case study. The results demonstrate that our model surpasses state-of-the-art brain-inspired baselines on two datasets, proving the potential of brain-inspired methods for advancing multisensory interaction and recognition.

ND-MRM: Neuronal Diversity Inspired Multisensory Recognition Model

Intrinsic motivation lies at the heart of the exploration of reinforcement learning, which is primarily driven by the agent's inherent satisfaction rather than external feedback from the environment. However, in recent more challenging procedurally-generated environments with high stochasticity and uninformative extrinsic rewards, we identify two significant issues of applying intrinsic motivation. (1) State representation collapse: In existing methods, the learned representations within intrinsic motivation have a high probability to neglect the distinction among different states and be distracted by the task-irrelevant information brought by the stochasticity. (2) Insufficient interrelation among dynamics: Unsuccessful guidance provided by the uninformative extrinsic reward makes the dynamics learning in intrinsic motivation less effective. In light of the above observations, a novel Behavioral metric with Cyclic Dynamics (BCD) is proposed, which considers both cumulative and immediate effects and facilitates the abstraction and exploration of the agent. For the behavioral metric, the successor feature is utilized to reveal the expected future rewards and alleviate the heavy reliance of previous methods on extrinsic rewards. Moreover, the latent variable and vector quantization techniques are employed to enable an accurate measurement of the transition function in a discrete and interpretable manner. In addition, cyclic dynamics is established to capture the interrelations between state and action, thereby providing a thorough awareness of environmental dynamics. Extensive experiments conducted on procedurally-generated environments demonstrate the state-of-the-art performance of our proposed BCD.

Abstract and Explore: A Novel Behavioral Metric with Cyclic Dynamics in Reinforcement Learning

Neural network sparsity has attracted many research interests due to its similarity to biological schemes and high energy efficiency. However, existing methods depend on long-time training or fine-tuning, which prevents large-scale applications. Recently, some works focusing on post-training sparsity (PTS) have emerged. They get rid of the high training cost but usually suffer from distinct accuracy degradation due to neglect of the reasonable sparsity rate at each layer. Previous methods for finding sparsity rates mainly focus on the training-aware scenario, which usually fails to converge stably under the PTS setting with limited data and much less training cost. In this paper, we propose a fast and controllable post-training sparsity (FCPTS) framework. By incorporating a differentiable bridge function and a controllable optimization objective, our method allows for rapid and accurate sparsity allocation learning in minutes, with the added assurance of convergence to a predetermined global sparsity rate. Equipped with these techniques, we can surpass the state-of-the-art methods by a large margin, e.g., over 30\% improvement for ResNet-50 on ImageNet under the sparsity rate of 80\%. Our plug-and-play code and supplementary materials are open-sourced at \url{https://github.com/ModelTC/FCPTS}.

Fast and Controllable Post-training Sparsity: Learning Optimal Sparsity Allocation with Global Constraint in Minutes

Mobile edge computing (MEC) is a promising paradigm for real-time applications with intensive computational needs (e.g., autonomous driving), as it can reduce the processing delay. In this work, we focus on the timeliness of computational-intensive updates, measured by Age-of-Information (AoI), and study how to jointly optimize the task updating and offloading  policies for AoI with fractional form. Specifically, we consider edge load dynamics and formulate a task scheduling problem to minimize the expected time-average AoI. The uncertain edge load dynamics, the nature of the fractional objective, and hybrid continuous-discrete action space (due to the joint optimization) make this problem challenging and existing approaches not directly applicable. To this end, we propose a fractional reinforcement learning (RL) framework and prove its convergence. We further design a model-free fractional deep RL (DRL) algorithm, where each device makes scheduling decisions with the hybrid action space without knowing the system dynamics and decisions of other devices. Experimental results show that our proposed algorithms reduce the average AoI by up to $57.6\%$ compared with several non-fractional benchmarks.

Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing | VIDEO

Current weakly-supervised semantic segmentation (WSSS) techniques concentrate on enhancing class activation maps (CAMs) with image-level annotations. Yet, the emphasis on producing these pseudo-labels often overshadows the pivotal role of training the segmentation model itself. This paper underscores the significant influence of noisy pseudo-labels on segmentation network performance, particularly in boundary region. To address above issues, we introduce a novel paradigm: Weak to Partial Supervision (W2P). At its core, W2P categorizes the pseudo-labels from WSSS into two unique supervisions: trustworthy clean labels and uncertain noisy labels. Next, our proposed partially-supervised framework adeptly employs these clean labels to rectify the noisy ones, thereby promoting the continuous enhancement of the segmentation model. To further optimize boundary segmentation, we incorporate a noise detection mechanism that specifically preserves boundary regions while eliminating noise. During the noise refinement phase, we adopt a boundary-conscious noise correction technique to extract comprehensive boundaries from noisy areas. Furthermore, we devise a boundary generation approach that assists in predicting intricate boundary zones. Evaluations on the PASCAL VOC 2012 and MS COCO 2014 datasets confirm our method's impressive segmentation capabilities across various pseudo-labels.

W2P: Switching from Weak Supervision to Partial Supervision for Semantic Segmentation

Due to the detrimental impact of noise on the conventional audio speech recognition (ASR) task, audio-visual speech recognition~(AVSR) has been proposed by incorporating both audio and  visual video signals. Although existing methods have demonstrated that aligned visual input of lip movements can enhance the robustness of AVSR systems against noise, the paired videos are not always obtainable during inference, leading to the problem of visual missing modality, which restricts their practicality in real-world scenarios. To tackle this problem, we propose a Discrete Feature based Visual Generative Model (DFVGM) which exploits semantic correspondences between the audio and visual modalities 
during training and generates visual hallucinations in lieu of
real videos during inference. To achieve that, the 
primary challenge is to \textit{generate the visual hallucination 
given the noisy audio while preserving semantic correspondences with the clean speech.} To tackle this challenge, we start with training the audio encoder in the Audio-Only (AO) setting, which generates continuous semamtic features closely associated with the linguistic information. Simultaneously, the visual encoder is trained in the Visual-Only (VO) setting, producing visual features that are phonetically related. Next, we employ k-means to discretize the continuous audio and visual feature spaces. The discretization step 
allows DFVGM to capture high-level semantic structures that are more resilient to  noise and  generate visual hallucinations with high quality. We conduct extensive experiments on two publicly available datasets. The results demonstrate that our method achieves a remarkable 53\% relative reduction (30.5\%$\to$12.9\%) in Word Error Rate (WER) on average compared to the current state-of-the-art Audio-Only (AO) baselines while maintaining comparable results ($<$ 5 difference) under the Audio-Visual (AV) setting without video as input.

Visual Hallucination Elevates Speech Recognition

The grounding accuracy of existing video captioners is still behind the expectation. The majority of existing methods perform grounded video captioning on sparse entity annotations, whereas the captioning accuracy often suffers from degenerated object appearances on the annotated area such as motion blur and video defocus. Moreover, these methods seldom consider the complex interactions among entities. In this paper, we propose a comprehensive visual grounding network to improve video captioning, by explicitly linking the entities and actions to the visual clues across the video frames. Specifically, the network consists of spatial-temporal entity grounding and action grounding. The proposed entity grounding encourages the attention mechanism to focus on informative spatial areas across video frames, albeit the entity is annotated in only one frame of a video. The action grounding dynamically associates the verbs to related subjects and the corresponding context, which keeps fine-grained spatial and temporal details for action prediction. Both entity grounding and action grounding are formulated as a unified task guided by a soft grounding supervision, which brings architecture simplification and improves training efficiency as well. We conduct extensive experiments on two challenging datasets, and demonstrate significant performance improvements of +2.3 CIDEr on ActivityNet-Entities and +2.2 CIDEr on MSR-VTT compared to state-of-the-arts.

Comprehensive Visual Grounding for Video Description

Prediction of drug-target interactions (DTIs) is a crucial step in drug discovery, and deep learning methods have shown great promise in various DTI datasets. However, existing approaches still face several challenges, including limited labeled data, hidden bias issue, and a lack of generalization ability to out-of-domain data. These challenges hinder the model's capacity to learn truly informative interaction features, leading to shortcut learning and inferior predictive performance on novel drug-target pairs. To address these issues, we propose MlanDTI, a semi-supervised domain adaptive multilevel attention network (Mlan) for DTI prediction. We utilize two pre-trained BERT models to acquire bidirectional representations enriched with information from unlabeled data. Additionally, we introduce a Multilevel attention mechanism, enabling the model to learn domain-invariant drug-target interactions at different hierarchical levels. Moreover, we present a simple yet effective semi-supervised pseudo-labeling method to further enhance our model's predictive ability in cross-domain scenarios. Experiments on four datasets show that MlanDTI achieves state-of-the-art performances over other methods in inter-domain settings and outperforms all other approaches in cross-domain settings.

Multilevel Attention Network with Semi-supervised Domain Adaptation for Drug-Target Prediction

Although there has been significant progress in category-level object pose estimation in recent years, there is still considerable room for improvement. In this paper, we propose a novel transformer-based category-level 6D pose estimation method  called CatFormer to enhance the accuracy pose estimation. CatFormer comprises three main parts: a coarse deformation part, a fine deformation part, and a recurrent refinement part. In the coarse and fine deformation sections, we introduce a transformer-based deformation module that performs point cloud deformation and completion in the feature space. Additionally, after each deformation, we incorporate a transformer-based graph module to adjust fused features and establish geometric and topological relationships between points based on these features. Furthermore, we present an end-to-end recurrent refinement module that enables the prior point cloud to deform multiple times according to real scene features. We evaluate CatFormer's performance by training and testing it on CAMERA25 and REAL275 datasets. Experimental results demonstrate that CatFormer surpasses state-of-the-art methods. Moreover, we extend the usage of CatFormer to instance-level object pose estimation on the LINEMOD dataset, as well as object pose estimation in real-world scenarios. The experimental results validate the effectiveness and generalization capabilities of CatFormer. Our code and the supplemental materials are avaliable at https://github.com/BIT-robot-group/CatFormer.

Recurrent Partial Kernel Network for Efficient Optical Flow Estimation | VIDEO

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Recurrent Partial Kernel Network for Efficient Optical Flow Estimation | VIDEO

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Triple Feature Disentanglement for One-Stage Adaptive Object Detection | VIDEO

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