United States

Humans naturally use referring expressions with verbal utterances and nonverbal gestures to refer to objects and events. As these referring expressions can be interpreted differently from the speaker&#39;s or the observer&#39;s perspective, people effectively decide on the perspective in comprehending the expressions. However, existing models do not explicitly learn perspective grounding, which often causes the models to perform poorly in understanding embodied referring expressions. To make it exacerbate, these models are often trained on datasets collected in non-embodied settings without nonverbal gestures and curated from an exocentric perspective. To address these issues, in this paper, we present a perspective-aware multitask learning model, called PATRON, for relation and object grounding tasks in embodied settings by utilizing verbal utterances and nonverbal cues. In PATRON, we have developed a guided fusion approach, where a perspective grounding task guides the relation and object grounding task. Through this approach, PATRON learns disentangled task-specific and task-guidance representations, where task-guidance representations guide the extraction of salient multimodal features to ground the relation and object accurately. Furthermore, we have curated a synthetic dataset of embodied referring expressions with multimodal cues, called CAESAR-PRO. The experimental results suggest that PATRON outperforms the evaluated state-of-the-art visual-language models. Additionally, the results indicate that learning to ground perspective helps machine learning models to improve the performance of the relation and object grounding task. Furthermore, the insights from the extensive experimental results and the proposed dataset will enable researchers to evaluate visual-language models&#39; effectiveness in understanding referring expressions in other embodied settings.

AAAI 2023

PATRON: Perspective-aware Multitask Model for Referring Expression Grounding using Embodied Multimodal Cues

visual language representation learning

multimodal machine learning

multitask learning

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

Humans naturally use referring expressions with verbal utterances and nonverbal gestures to refer to objects and events. As these referring expressions can be interpreted differently from the speaker's or the observer's perspective, people effectively decide on the perspective in comprehending the expressions. However, existing models do not explicitly learn perspective grounding, which often causes the models to perform poorly in understanding embodied referring expressions. To make it exacerbate, these models are often trained on datasets collected in non-embodied settings without nonverbal gestures and curated from an exocentric perspective. To address these issues, in this paper, we present a perspective-aware multitask learning model, called PATRON, for relation and object grounding tasks in embodied settings by utilizing verbal utterances and nonverbal cues. In PATRON, we have developed a guided fusion approach, where a perspective grounding task guides the relation and object grounding task. Through this approach, PATRON learns disentangled task-specific and task-guidance representations, where task-guidance representations guide the extraction of salient multimodal features to ground the relation and object accurately. Furthermore, we have curated a synthetic dataset of embodied referring expressions with multimodal cues, called CAESAR-PRO. The experimental results suggest that PATRON outperforms the evaluated state-of-the-art visual-language models. Additionally, the results indicate that learning to ground perspective helps machine learning models to improve the performance of the relation and object grounding task. Furthermore, the insights from the extensive experimental results and the proposed dataset will enable researchers to evaluate visual-language models' effectiveness in understanding referring expressions in other embodied settings.

poster

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. 
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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.

Exemplar rehearsal-based methods with knowledge distillation (KD) have been widely used in class incremental learning (CIL) scenarios. However, they still suffer from performance degradation because of severely distribution discrepancy between training and test set caused by the limited storage memory on previous classes. In this paper, we mathematically model the data distribution and the discrepancy at the incremental stages with mixture uniform distribution (MUD). Then, we propose the asymmetric mix distillation method to uniformly minimize the error of each class from distribution discrepancy perspective. Specifically, we firstly promote mixup in CIL scenarios with the incremental mix samplers and incremental mix factor to calibrate the raw training data distribution. Next, mix distillation label augmentation is incorporated into the data distribution to inherit the knowledge information from the previous models. Based on the above augmented data distribution, our trained model effectively alleviates the performance degradation and extensive experimental results validate that our method exhibits superior performance on CIL benchmarks. 

Mixture Uniform Distribution Modeling and Asymmetric Mix Distillation for Class Incremental Learning

In robust optimization, finding a solution that solely respects the constraints is not enough.
Usually, the uncertainty and unknown parameters of the model are represented by random variables.
In such conditions, a good solution is a solution robust to most-likely assignments of these random variables.
The Confidence constraint allows us to enforce this type of robustness in constraint programming.
In this paper, we generalize the Confidence constraint and introduce the Multi-valued Decision Diagram (MDD) confidence. 
The MDD Confidence constraint is defined over a set of random variables. 
Then, for a given constraint represented as an MDD, and given a threshold, the generalized Confidence constraint ensures that the probability for the constraint to be respected by a sample of the variables is greater than the threshold.
On the one hand, constraints that random variables should respect often depend on decision variables.
On the other hand, the MDD represents the constraint that random variables should respect.
That is why this paper proposes several ways to constrain the MDD using decision variables during search.
These constraints are a generalization of the Confidence constraint recently introduced by Mercier-Aubin et al.
In fact, the existing constraint is restricted to a conjunction of binary inequalities.
The proposed generalization can be applied to any constraint, and filtering algorithms are provided using their MDD representation.
Moreover, the original work was restricted to independent random variables, while our generalization can be used with Markov chains, 
allowing a better expressiveness.
Finally, the experimental section shows the efficiency of the proposed methodology on a chemical deliveries problem in factories 
where the chemicals uses are uncertain.

Generalized Confidence Constraints

The field of emergent communication aims to understand the characteristics of communication as it emerges from artificial agents solving tasks that require information exchange. Communication with discrete messages is considered a desired characteristic, for scientific and applied reasons. However,  training a multi-agent system with discrete communication is not straightforward, requiring either reinforcement learning algorithms or relaxing the discreteness requirement via a continuous approximation such as the Gumbel-softmax. Both these solutions result in poor performance compared to fully continuous communication. In this work, we propose an alternative approach to achieve discrete communication -- quantization of communicated message. Using message quantization allows us to train the model end-to-end, achieving superior performance in multiple setups. Moreover, quantization is a natural framework that runs the gamut from continuous to discrete communication. Thus,  it sets the ground for a broader view of multi-agent communication in the deep learning era. 

Emergent Quantized Communication

This paper considers the problem of cooperative localization of multiple robots under uncertainty, communicating over a partially connected, dynamic communication network and assisted by an agile landmark. Each robot owns an IMU and a relative pose sensing suite, which can get faulty due to system or environmental uncertainty, and therefore exhibit large bias in their estimation output. For the robots to localize accurately under sensor failure and system or environmental uncertainty, a novel Distributed Learning based Decentralized Cooperative Localization (DL-DCL) algorithm is proposed that involves real-time learning of an information fusion strategy by each robot for combining pose estimates from its own sensors as well as from those of its neighboring robots, and utilizing the moving landmark's pose information as a feedback to the learning process. Convergence analysis shows that the learning process converges exponentially under certain reasonable assumptions. Simulations involving sensor failures inducing around 40-60 times increase in the nominal bias show DL-DCL's estimation performance to be approximately 40% better than the well-known covariance-based estimate fusion methods. For the evaluation of DL-DCL's implementability and fault-tolerance capability in practice, a high-fidelity simulation is carried out in Gazebo with ROS2.

Moving-Landmark assisted Distributed Learning based Decentralized Cooperative Localization (DL-DCL) with Fault Tolerance

Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field. To address it, we propose a progressive estimation strategy by converting large-baseline homography into multiple intermediate ones, cumulatively multiplying these intermediate items can reconstruct the initial homography. Meanwhile, a semi-supervised homography identity loss, which consists of two components: a supervised objective and an unsupervised objective, is introduced. The first supervised loss is acting to optimize intermediate homographies, while the second unsupervised one helps to estimate a large-baseline homography without photometric losses. To validate our method, we propose a large-scale dataset that covers regular and challenging scenes. Experiments show that our method achieves state-of-the-art performance in large-baseline scenes while keeping competitive performance in small-baseline scenes.

Semi-supervised Deep Large-baseline Homography Estimation with Progressive Equivalence Constraint

In this work, we propose a novel unsupervised contrastive learning framework to improve state-of-the-art sentence embeddings. First, we train a set of contrastive submodels which take multilingual round-trip translation as data augmentation. The round-trip translation naturally changes the length of the same sentence and replaces Synonyms at the same time. Then we incorporate them into a single model through knowledge distillation. Specifically, it takes an input sentence and predicts the ensemble output of all submodels via a contrastive objective. Thus we preserve nearly the same semantic expressiveness as the ensemble model without increasing the test cost. We evaluate our framework on standard semantic textual similarity (STS) tasks. Experimental results show the advantage of our framework that we achieve an average of 79.27% Spearman's correlation, a 3.02% improvement compared to the previous best results using BERT-base.

An Ensemble Distillation Framework for Sentence Embeddings with Multilingual Round-trip Translation

Autoregressive models have achieved impressive results over a wide range of domains in terms of generation quality and downstream task performance. In the continuous domain, a key factor behind this success is the usage of quantized latent spaces (e.g., obtained via VQ-VAE autoencoders), which allow for dimensionality reduction and faster inference times. However, using existing pre-trained models to perform new non-trivial tasks is difficult since it requires additional fine-tuning or extensive training to elicit prompting. This paper introduces LASS as a way to perform vector-quantized Latent Autoregressive Source Separation (i.e., de-mixing an input signal into its constituent sources) without requiring additional gradient-based optimization or modifications of existing models. Our separation method relies on the Bayesian formulation in which the autoregressive models are the priors, and a discrete (non-parametric) likelihood function is constructed by performing frequency counts over latent sums of addend tokens. We test our method on images and audio with several sampling strategies (e.g., ancestral, beam search) showing competitive results with existing approaches in terms of separation quality while offering at the same time significant speedups in terms of inference time and scalability to high dimensional data.

Latent Autoregressive Source Separation

This paper focuses on contrastive learning for gait-based emotion recognition. The existing contrastive learning approaches are rarely suitable for learning skeleton-based gait representations, which suffer from limited gait diversity and inconsistent semantics. In this paper, we propose a Cross-coordinate contrastive learning framework utilizing Ambiguity samples for self-supervised Gait-based Emotion representation (CAGE). First, we propose ambiguity transform to push positive samples into ambiguous semantic space. By learning similarities between ambiguity samples and positive samples, our model can learn higher-level semantics of the gait sequences and maintain semantic diversity. Second, to encourage learning the semantic invariance, we uniquely propose cross-coordinate contrastive learning between the Cartesian coordinate and the Spherical coordinate, which brings rich supervisory signals to learn the intrinsic semantic consistency information. Exhaustive experiments show that CAGE improves existing self-supervised methods by 5%–10% accuracy, and it achieves comparable or even superior performance to supervised methods.

See Your Emotion from Gait Using Unlabeled Skeleton Data

In many real-world applications of reinforcement learning (RL), performing actions requires consuming certain types of resources that are non-replenishable in each episode. Typical applications include robotic control with limited energy and video games with consumable items. In tasks with non-replenishable resources, we observe that popular RL methods such as soft actor critic suffer from poor sample efficiency. The major reason is that, they tend to exhaust resources fast and thus the subsequent exploration is severely restricted due to the absence of resources. To address this challenge, we first formalize the aforementioned problem as a resource-restricted reinforcement learning, and then propose a novel resource-aware exploration bonus (RAEB) to make reasonable usage of resources. An appealing feature of RAEB is that, it can significantly reduce unnecessary resource-consuming trials while effectively encouraging the agent to explore unvisited states. Experiments demonstrate that the proposed RAEB significantly outperforms state-of-the-art exploration strategies in resource-restricted reinforcement learning environments, improving the sample efficiency by up to an order of magnitude.

Efficient Exploration in Resource-Restricted Reinforcement Learning

Concept bottleneck models (CBMs) (Koh et al. 2020) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions. We extend CBMs to interactive prediction settings where the model can query a human collaborator for the label to some concepts. We develop an interaction policy that, at prediction time, chooses which concepts to request a label for so as to maximally improve the final prediction. We demonstrate that a simple policy combining concept prediction uncertainty and influence of the concept on the final prediction achieves strong performance and outperforms a static approach proposed in Koh et al. (2020) as well as active feature acquisition methods proposed in the literature. We show that the interactive CBM can achieve accuracy gains of 5-10% with only 5 interactions over competitive baselines on the Caltech UCSD Birds, CheXpert and OAI datasets.

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