United States

Many AI systems integrate sensor inputs, world knowledge, and human-provided information to perform inference. While such systems often treat the human input as flawless, humans are better thought of as hazy oracles whose input may be ambiguous or outside of the AI system&#39;s understanding. In such situations it makes sense for the AI system to defer its inference while it disambiguates the human-provided information by, for example, asking the human to rephrase the query. Though this approach has been considered in the past, current work is typically limited to application-specific methods and non-standardized human experiments. We instead introduce and formalize a general notion of deferred inference. Using this formulation, we then propose a novel evaluation centered around the Deferred Error Volume (DEV) metric, which explicitly considers the tradeoff between error reduction and the additional human effort required to achieve it. We demonstrate this new formalization and an innovative deferred inference method on the disparate tasks of Single-Target Video Object Tracking and Referring Expression Comprehension, ultimately reducing error by up to 48% without any change to the underlying model or its parameters.

AAAI 2023

Evaluating and Improving Interactions with Hazy Oracles

ml: evaluation and analysis

hai: human-machine teams

humans and ai (hai)

computer vision (cv)

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).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/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.

Many AI systems integrate sensor inputs, world knowledge, and human-provided information to perform inference. While such systems often treat the human input as flawless, humans are better thought of as hazy oracles whose input may be ambiguous or outside of the AI system's understanding. In such situations it makes sense for the AI system to defer its inference while it disambiguates the human-provided information by, for example, asking the human to rephrase the query. Though this approach has been considered in the past, current work is typically limited to application-specific methods and non-standardized human experiments. We instead introduce and formalize a general notion of deferred inference. Using this formulation, we then propose a novel evaluation centered around the Deferred Error Volume (DEV) metric, which explicitly considers the tradeoff between error reduction and the additional human effort required to achieve it. We demonstrate this new formalization and an innovative deferred inference method on the disparate tasks of Single-Target Video Object Tracking and Referring Expression Comprehension, ultimately reducing error by up to 48% without any change to the underlying model or its parameters.

poster

Traffic prediction is an important component of the intelligent transportation system. Existing deep learning methods encode temporal information and spatial information separately or iteratively. However, the spatial and temporal information is highly correlated in a traffic network, so existing methods may not learn the complex spatial-temporal dependencies hidden in the traffic network due to the decomposed model design. To overcome this limitation, we propose a new model named Trafformer, which unifies spatial and temporal information in one transformer-style model. Trafformer enables every node at every timestamp interact with every other node in every other timestamp in just one step in the spatial-temporal correlation matrix. This design enables Trafformer to catch complex spatial-temporal dependencies. Following the same design principle, we use the generative style decoder to predict multiple timestamps in only one forward operation instead of the iterative style decoder in Transformer. Furthermore, to reduce the complexity brought about by the huge spatial-temporal self-attention matrix, we also propose two variants of Trafformer to further improve the training and inference speed without losing much effectivity. Extensive experiments on two traffic datasets demonstrate that Trafformer outperforms existing methods and provides a promising future direction for the spatial-temporal traffic prediction problem.

Trafformer: Unify Time and Space in Traffic Prediction

In this paper, we propose a cross-modal distillation method named StereoDistill to narrow the gap between the stereo and LiDAR-based approaches via distilling the stereo detectors from the superior LiDAR model at the response level, which is usually overlooked in 3D object detection distillation.
The key designs of StereoDistill are: the X-component Guided Distillation~(XGD) for regression and the Cross-anchor Logit Distillation~(CLD) for classification. In XGD, instead of empirically adopting a threshold to select the high-quality teacher predictions as soft targets, we decompose the predicted 3D box into 
sub-components and retain the corresponding part for distillation if the teacher component pilot is consistent with ground truth to largely boost the number of positive predictions and alleviate the mimicking difficulty of the student model. For CLD, we aggregate the probability distribution of all anchors at the same position  to encourage the highest probability anchor rather than individually distill the distribution at the anchor level. 
Finally, our StereoDistill achieves state-of-the-art results for stereo-based 3D detection on the KITTI test benchmark and extensive experiments on KITTI and Argoverse Dataset validate the effectiveness. 

StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection

Applying large scale pre-trained image-language model to video-language tasks has recently become a trend, which brings two challenges. One is how to effectively transfer knowledge from static images to dynamic videos, and the other is how to deal with the prohibitive cost of fully fine-tuning due to growing model size. Existing works that attempt to realize parameter-efficient image-language to video-language transfer learning can be categorized into two types: 1) appending a sequence of temporal transformer blocks after the 2D Vision Transformer (ViT), and 2) inserting a temporal block into the ViT architecture. While these two types of methods only require fine-tuning the newly added components, there are still many parameters to update, and they are only validated on a single video-language task. In this work, based on our analysis of the core ideas of different temporal modeling components in existing approaches, we propose a token mixing strategy to enable cross-frame interactions, which enables transferring from the pre-trained image-language model to video-language tasks through selecting and mixing a key set and a value set from the input video samples. As token mixing does not require the addition of any components or modules, we can directly partially fine-tune the pre-trained image-language model to achieve parameter-efficiency. We carry out extensive experiments to compare our proposed token mixing method with other parameter-efficient transfer learning methods. Our token mixing method outperforms other methods on both understanding tasks and generation tasks. Besides, our method achieves new records on multiple video-language tasks. The code is available at https://github.com/yuqi657/video_language_model.

Token Mixing: Parameter-Efficient Transfer Learning from Image-Language to Video-Language

In this paper, we study graph-to-image generation conditioned exclusively on scene graphs, in which we seek to disentangle the veiled semantics between knowledge graphs and images.  While most existing research resorts to laborious auxiliary information such as object layouts or segmentation masks, it is also of interest to unveil the generality of the model with limited supervision, moreover, avoiding extra cross-modal alignments.  To tackle this challenge, we delve into the causality of the adversarial generation process, and reason out a new principle to realize a simultaneous semantic disentanglement with an alignment on target and model distributions.  This principle is named knowledge consensus, which explicitly describes a triangle causal dependency among observed images, graph semantics and hidden visual representations.  The consensus also determines a new graph-to-image generation framework, carried on several adversarial optimization objectives.  Extensive experimental results demonstrate that, even conditioned only on scene graphs, our model surprisingly achieves superior performance on semantics-aware image generation, without losing the competence on manipulating the generation through knowledge graphs.

Scene Graph to Image Synthesis via Knowledge Consensus

Forced alignment refers to a technology that time-aligns a given transcription with a corresponding speech. However, as the forced alignment technologies have developed using speech audio, they might fail in alignment when the input speech audio is noise-corrupted or is not accessible. We focus on that there is another component that the speech can be inferred from, the speech video (i.e., talking face video). Since the drawbacks of audio-based forced alignment can be complemented using the visual information when the audio signal is under poor condition, we try to develop a novel video-based forced alignment method. However, different from audio forced alignment, it is challenging to develop a reliable visual forced alignment technology for the following two reasons: 1) Visual Speech Recognition (VSR) has a much lower performance compared to audio-based Automatic Speech Recognition (ASR), and 2) the translation from text to video is not reliable, so the method typically used for building audio forced alignment cannot be utilized in developing visual forced alignment. In order to alleviate these challenges, in this paper, we propose a new method that is appropriate for visual forced alignment, namely Deep Visual Forced Alignment (DVFA). The proposed DVFA can align the input transcription (i.e., sentence) with the talking face video without accessing the speech audio. Moreover, by augmenting the alignment task with anomaly case detection, DVFA can detect mismatches between the input transcription and the input video while performing the alignment. Therefore, we can robustly align the text with the talking face video even if there exist error words in the text. Through extensive experiments, we show the effectiveness of the proposed DVFA not only in the alignment task but also in interpreting the outputs of VSR models.

Deep Visual Forced Alignment: Learning to Align Transcription with Talking Face Video

This paper introduces a new few-shot learning pipeline that
casts relevance ranking for image retrieval as binary ranking
relation classification. In comparison to image classification,
ranking relation classification is sample efficient and
domain agnostic. Besides, it provides a new perspective on
few-shot learning and is complementary to state-of-the-art
methods. The core component of our deep neural network is
a simple MLP, which takes as input an image triplet encoded
as the difference between two vector-Kronecker products,
and outputs a binary relevance ranking order. The proposed
RankMLP can be built on top of any state-of-the-art feature
extractors, and our entire deep neural network is called
the ranking deep neural network, or RankDNN. Meanwhile,
RankDNN can be flexibly fused with other post-processing
methods. During the meta test, RankDNN ranks support images
according to their similarity with the query samples,
and each query sample is assigned the class label of its
nearest neighbor. Experiments demonstrate that RankDNN
can effectively improve the performance of its baselines
based on a variety of backbones and it outperforms previous
state-of-the-art algorithms on multiple few-shot learning
benchmarks, including miniImageNet, tieredImageNet,
Caltech-UCSD Birds, and CIFAR-FS. Furthermore, experiments
on the cross-domain challenge demonstrate the superior
transferability of RankDNN.The code is available at:
https://github.com/guoqianyu-alberta/RankDNN.

RankDNN: Learning to Rank for Few-shot Learning

This work addresses fair generative models. Dataset biases have been a major cause of unfairness in deep generative models. Previous work had proposed to augment large, biased datasets with small, unbiased reference datasets. Under this setup, a weakly-supervised approach has been proposed, which achieves state-of-the-art quality and fairness in generated samples. In our work, based on this setup, we propose a simple yet effective approach. Specifically, first, we propose fairTL, a transfer learning approach to learn fair generative models. Under fairTL, we pre-train the generative model with the available large, biased datasets and subsequently adapt the model using the small, unbiased reference dataset. We find that our fairTL can learn expressive sample generation during pre-training, thanks to the large (biased) dataset. This knowledge is then transferred to the target model during adaptation, which also learns to capture the underlying fair distribution of the small reference dataset. Second, we propose fairTL++, where we introduce two additional innovations to improve upon fairTL: (i) multiple feedback and (ii) Linear-Probing followed by Fine-Tuning (LP-FT). Taking one step further, we consider an alternative, challenging setup when only a pre-trained (potentially biased) model is available but the dataset that was used to pre-train the model is inaccessible. We demonstrate that our proposed fairTL and fairTL++ remain very effective under this setup. We note that previous work requires access to the large, biased datasets and is incapable of handling this more challenging setup. Extensive experiments show that fairTL and fairTL++ achieve state-of-the-art in both quality and fairness of generated samples. The code and additional resources can be found at bearwithchris.github.io/fairTL/.

Fair Generative Models via Transfer Learning

Learning programs with numerical values is fundamental to many AI applications, including bio-informatics and drug design. However, current program synthesis approaches struggle to learn programs with numerical values.
An especially difficult problem is learning continuous values from multiple examples, such as intervals. To overcome this limitation, we introduce an inductive logic programming approach which combines relational learning with numerical reasoning. Our approach, which we call NumSynth, uses satisfiability modulo theories solvers to efficiently learn programs with numerical values. Our approach can identify numerical values in linear arithmetic fragments, such as real difference logic, and from infinite domains, such as real numbers or integers. Our experiments on four diverse domains, including game playing and program synthesis, show that our approach can (i) learn programs with numerical values from linear arithmetical reasoning, and (ii) outperform existing approaches in terms of predictive accuracies and learning times.

Relational program synthesis with numerical reasoning

Neural machine translation (NMT) has got a great success in generating high-quality translations. However, the current NMT systems are not reliable enough to have stable outputs. The results of NMT are generally affected by lexical or syntactic changes in inputs, resulting in a large variance in quality, which limits the practicability and trustworthiness of NMT. One reason is that the \textit{one-to-one} training paradigm cannot handle the \textit{source diversity} phenomenon, in which inputs with the same meaning can be expressed differently. In this work, we treat this problem as a bilevel optimization problem and propose a consistency-aware meta-learning (CAML) framework derived from the model-agnostic meta-learning (MAML) algorithm to address it. Specifically, the NMT model with CAML first learns the consistent meta representation of semantically equivalent sentences in the outer loop. Subsequently, a mapping from the meta representation to the output sentence is learned in the inner loop. That is, the NMT model can translate semantically equivalent input sentences to the same output sentence.  We conduct experiments on the NIST Chinese to English task, TED M2O task and three WMT translation tasks. Experimental results demonstrate that the NMT model with our approach effectively improves the overall translation quality and is reliable in coping with diverse inputs.

Towards Reliable Neural Machine Translation with Consistency-aware Meta-learning

This paper proposes a Semi-Attention Partition (SAP) method to learn well-aligned part features for occluded person re-identification (re-ID). Currently, the mainstream methods employ either external semantic partition or attention-based partition, and the latter manner is usually better than the former one. Under this background, this paper explores a potential that the weak semantic partition can be a good teacher for the strong attention-based partition. In other words, the attention-based student can substantially surpass its noisy semantic-based teacher, contradicting the common sense that the student usually achieves inferior (or comparable) accuracy. A key to this effect is: the proposed SAP encourages the attention-based partition of the (transformer) student to be partially consistent with the semantic-based teacher partition through knowledge distillation, yielding the so-called semi-attention. Such partial consistency allows the student to have both consistency and reasonable conflict with the noisy teacher. More specifically, on the one hand, the attention is guided by the semantic partition from the teacher. On the other hand, the attention mechanism itself still has some degree of freedom to comply with the inherent similarity between different patches, thus gaining resistance against noisy supervision. Moreover, we integrate a battery of well-engineered designs into SAP to reinforce their cooperation (e.g., multiple forms of teacher-student consistency), as well as to promote reasonable conflict (e.g., mutual absorbing partition refinement and a supervision signal dropout strategy). Experimental results confirm that the transformer student achieves substantial improvement after this semi-attention learning scheme, and produces new state-of-the-art accuracy on several standard re-ID benchmarks.

Evaluating and Improving Interactions with Hazy Oracles

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