United States

The white balance methods for sRGB images (sRGB-WB) aim to directly remove their color temperature shifts. Despite achieving promising white balance (WB) performance, the existing methods suffer from WB instability, i.e., their results are inconsistent for images with different color temperatures. We propose a stable white balance network (SWBNet) to alleviate this problem. It learns the color temperature-insensitive features to generate white-balanced images, resulting in consistent WB results. Speciﬁcally, the color temperature-insensitive features are learned by implicitly suppressing low-frequency information sensitive to color temperatures. Then, a color temperature contrastive loss is introduced to facilitate the most information shared among features of the same scene and different color temperatures. This way, features from the same scene are more insensitive to color temperatures regardless of the inputs. We also present a color temperature sensitivity-oriented transformer that globally perceives multiple color temperature shifts within an image and corrects them by different weights. It helps to improve the accuracy of stabilized SWBNet, especially for multi-illumination sRGB images. Experiments indicate that our SWBNet achieves stable and remarkable WB performance.

AAAI 2023

SWBNet: A Stable White Balance Network for sRGB Images

low level & physics-based vision

applications

computational photography

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.

poster

Pre-trained language models (LMs) store knowledge in their parameters and can generate informative responses when used in conversational systems.
However, LMs suffer from the problem of "hallucination:" they may generate plausible-looking statements that are irrelevant or factually incorrect.
To address this problem, we propose a contrastive learning scheme, namely MixCL.
A novel mixed contrastive objective is proposed to explicitly optimize the implicit knowledge elicitation process of LMs, and thus reduce their hallucination in conversations.
We also examine negative sampling strategies of retrieved hard negatives and model-generated negatives.
We conduct experiments on Wizard-of-Wikipedia, a public open-domain knowledge-grounded dialogue benchmark, and assess the effectiveness of MixCL.
MixCL effectively reduces the hallucination of LMs in conversations and achieves the highest performance among LM-based dialogue agents in terms of relevancy and factuality.
We show that MixCL achieves comparable performance to the state-of-the-art KB-based approaches while enjoying notable advantages in terms of efficiency and scalability.

Contrastive Learning Reduces Hallucination in Conversations

We study competition among contests in a general model that allows for an arbitrary and heterogeneous space of contest design, where the goal of the contest designers is to maximize the contestants' sum of efforts. Our main result shows that optimal contests in the monopolistic setting (i.e., those that maximize the sum of efforts in a model with a single contest) form an equilibrium in the model with competition among contests. Under a very natural assumption these contests are in fact dominant, and the equilibria that they form are unique. Moreover, equilibria with the optimal contests are Pareto-optimal even in cases where other equilibria emerge. In many natural cases, they also maximize the social welfare.

From Monopoly to Competition: Optimal Contests Prevail

Web information extraction (WIE) is a long-standing problem in web document understanding, playing an important role in knowledge bases, retrieval systems, question answering and self-driving web browsers. Besides semantic information and structured DOM tree, visual information is important for WIE tasks because nodes containing information are often endowed with special visual styles, i.e. large font size or bright color to distinguish them from other nodes. However, concerning expensive cost to render visual information of web page, previous works mainly focus on Document Object Model (DOM) tree where nearly no visual information is characterized. To take advantage of visual information efficiently, we leverage render tree as model input, which is the combination of DOM tree and Cascading Style Sheets Object Model (CSSOM) tree, containing not only content and layout information but also rich visual information with a little additional acquisition cost compared to DOM tree. In this paper, we propose WIERT, an effective method which substantially utilizes render tree of web page based on a pre-trained language model. We conduct extensive experiments on the Klarna product page dataset, a manually labeled renderable e-commercial web page dataset. Experiments results show that our proposed method outperforms state-of-the-art methods which demonstrates the effectiveness and robustness of our method.

WIERT: Web Information Extraction via Render Tree

Translating imagined speech from human brain activity into voice is a challenging and absorbing research issue that can provide new means of human communication via brain signals. Endeavors toward reconstructing speech from brain activity have shown their potential using invasive measures of spoken speech data, however, have faced challenges in reconstructing imagined speech. In this paper, we propose NeuroTalk, which converts non-invasive brain signals of imagined speech into the user's own voice. Our model was trained with spoken speech EEG which was generalized to adapt to the domain of imagined speech, thus allowing natural correspondence between the imagined speech and the voice as a ground truth. In our framework, automatic speech recognition decoder contributed to decomposing the phonemes of generated speech, thereby displaying the potential of voice reconstruction from unseen words. Our results imply the potential of speech synthesis from human EEG signals, not only from spoken speech but also from the brain signals of imagined speech.

Towards Voice Reconstruction from EEG during Imagined Speech

Although machine learning on hypergraphs has attracted considerable attention, most of the works have focused on (semi-)supervised learning, which may cause heavy labeling costs and poor generalization. Recently, contrastive learning has emerged as a successful unsupervised representation learning method. Despite the prosperous development of contrastive learning in other domains, contrastive learning on hypergraphs remains little explored. In this paper, we propose TriCL (Tri-directional Contrastive Learning), a general framework for contrastive learning on hypergraphs. Its main idea is tri-directional contrast, and specifically, it aims to maximize in two augmented views the agreement (a) between the same node, (b) between the same group of nodes, and (c) between each group and its members. Together with simple but surprisingly effective data augmentation and negative sampling schemes, these three forms of contrast enable TriCL to capture both node- and group-level structural information in node embeddings. Our extensive experiments using 14 baseline approaches, 10 datasets, and two tasks demonstrate the effectiveness of TriCL, and most noticeably, TriCL almost consistently outperforms not just unsupervised competitors but also (semi-)supervised competitors mostly by significant margins for node classification. The code and datasets are available at https://github.com/wooner49/TriCL.

I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on Hypergraphs

With the increasing use of deep neural network (DNN) in time series classification (TSC), recent work reveals the threat of adversarial attack, where the adversary can construct adversarial examples to cause model mistakes. However, existing researches on the adversarial attack of TSC typically adopt an unrealistic white-box setting with model details transparent to the adversary. In this work, we study a more rigorous black-box setting with attack detection applied, which restricts gradient access and requires the adversarial example to be also stealthy. Theoretical analyses reveal that the key lies in: estimating black-box gradient with diversity and non-convexity of TSC models resolved, and restricting the l0 norm of the perturbation to construct adversarial samples. Towards this end, we propose a new framework named BlackTreeS, which solves the hard optimization issue for adversarial example construction with two simple yet effective modules. In particular, we propose a tree search strategy to find influential positions in a sequence, and independently estimate the black-box gradients for these positions. Extensive experiments on three real-world TSC datasets and five DNN based models validate the effectiveness of BlackTreeS, e.g., it improves the attack success rate from 19.3% to 27.3%, and decreases the detection success rate from 90.9% to 6.8% for LSTM on the UWave dataset.

Black-box Adversarial Attack on Time Series Classification

Contrastive learning (CL), which can extract the information shared between different contrastive views, has become a popular paradigm for vision representation learning. Inspired by the success in computer vision, recent work introduces CL into graph modeling, dubbed as graph contrastive learning (GCL). However, generating contrastive views in graphs is more challenging than that in images, since we have little prior knowledge on how to significantly augment a graph without changing its labels. We argue that typical data augmentation techniques (e.g., edge dropping) in GCL cannot generate diverse enough contrastive views to filter out noises. Moreover, previous GCL methods employ two view encoders with exactly the same neural architecture and tied parameters, which further harms the diversity of augmented views. To address this limitation, we propose a novel paradigm named model augmented GCL (MA-GCL), which will focus on manipulating the architectures of view encoders instead of perturbing graph inputs. Specifically, we present three easy-to-implement model augmentation tricks for GCL, namely asymmetric, random and shuffling, which can respectively help alleviate high-frequency noises, enrich training instances and bring safer augmentations. All three tricks are compatible with typical data augmentations. Experimental results show that MA-GCL can achieve state-of-the-art performance on node classification benchmarks by applying the three tricks on a simple base model. Extensive studies also validate our motivation and the effectiveness of each trick.

MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning

Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detection, little work has been done in this line of research. There are numerous domain adaptation methods in the literature, but it is difficult to adapt them for GAD due to the unknown distributions of the anomalies and the complex node relations embedded in graph data. To this end, we introduce a novel domain adaptation approach, namely Anomaly-aware Contrastive alignmenT (ACT), for GAD. ACT is designed to jointly optimise: (i) unsupervised contrastive learning of normal representations of nodes in the target graph, and (ii) anomaly-aware one-class alignment that aligns these contrastive node representations and the representations of labelled normal nodes in the source graph, while enforcing significant deviation of the representations of the normal nodes from the labelled anomalous nodes in the source graph. In doing so, ACT effectively transfers anomaly-informed knowledge from the source graph to learn the complex node relations of the normal class for GAD on the target graph without any specification of the anomaly distributions. Extensive experiments on eight CD-GAD settings demonstrate that our approach ACT achieves substantially improved detection performance over 10 state-of-the-art GAD methods. Code is available at https://github.com/QZ-WANG/ACT.

Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment

Although large-scale video-language pre-training models, which usually build a global alignment between the video and the text, have achieved remarkable progress on various downstream tasks, the idea of adopting fine-grained information during the pre-training stage is not well explored. In this work, we propose STOA-VLP, a pre-training framework that jointly models object and action information across spatial and temporal dimensions. More specifically, the model regards object trajectories across frames and multiple action features from the video as fine-grained features. Besides, We design two auxiliary tasks to better incorporate both kinds of information into the pre-training process of the video-language model. The first is the dynamic object-text alignment task, which builds a better connection between object trajectories and the relevant noun tokens. The second is the spatial-temporal action set prediction, which guides the model to generate consistent action features by predicting actions found in the text. Extensive experiments on three downstream tasks (video captioning, text-video retrieval, and video question answering) demonstrate the effectiveness of our proposed STOA-VLP (e.g. 3.7 Rouge-L improvements on MSR-VTT video captioning benchmark, 2.9% accuracy improvements on MSVD video question answering benchmark, compared to previous approaches).

STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-training

Recent research has shown that integrating domain knowledge in deep learning architectures is effective -- it helps reduce the amount of required data, supports reasoning over complex tasks, thus improving the quality of the models' output, and improves the interpretability of models. However, the research community is missing a convened benchmark for systematically evaluating knowledge integration methods.
In this work, we create a collection of benchmarks that includes ten tasks in the domains of natural language processing and computer vision. In all cases, we model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
We report the results of these models using a new set of extended evaluation criteria in addition to the task performances for a more in-depth analysis. This effort provides a framework for a more comprehensive and systematic comparison of constraint integration techniques and for identifying the related research challenges. It will facilitate further research for alleviating some problems of state-of-the-art neural models.

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SWBNet: A Stable White Balance Network for sRGB Images

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