Canada

Deep neural networks are known to be vulnerable to security risks due to the inherent transferable nature of adversarial examples. Despite the success of recent generative model-based attacks demonstrating strong transferability, it still remains a challenge to design an efficient attack strategy in a real-world strict black-box setting, where both the target domain and model architectures are unknown. In this paper, we seek to explore a feature contrastive approach in the frequency domain to generate adversarial examples that are robust in both cross-domain and cross-model settings. With that goal in mind, we propose two modules that are only employed during the training phase: a Frequency-Aware Domain Randomization (FADR) module to randomize domain-variant low- and high-range frequency components and a Frequency-Augmented Contrastive Learning (FACL) module to effectively separate domain-invariant mid-frequency features of clean and perturbed image. We demonstrate strong transferability of our generated adversarial perturbations through extensive cross-domain and cross-model experiments, while keeping the inference time complexity.

AAAI 2024

FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks

transferability

robustness

adversarial attacks

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. 

Document layout analysis is a crucial step for intelligent document understanding. However, many existing methods primarily focus on the visual aspects and overlook the textual features of documents. Although document pre-trained models utilize multi-modal features during the pre-training phase, they tend to operate as a unimodal pipeline when it comes to layout analysis tasks. Furthermore, current multi-modal methods perform worse than unimodal detectors on complex layout analysis datasets. To address these limitations, we propose an effective and pluggable multi-modal fusion approach named M2Doc, which fuses visual and textual features for better layout detection. M2Doc contains two pluggable multi-modal fusion modules, early-fusion and late-fusion, which align and fuse visual and textual features at the pixel level and block level. The early-fusion module leverages a gate-like mechanism to fuse visual and textual features extracted by the backbone, while the late-fusion module uses simple summation to fuse visual and textual features attributed to the candidate bounding boxes. Benefitting from the concision and effectiveness of M2Doc, it can be easily applied to various detectors for better layout detection, including two-stage and end-to-end object detectors. Our experimental results demonstrate significant performance improvements in detectors equipped with M2Doc on datasets such as DocLayNet (+11.3\% mAP) and M$^6$Doc (+1.9\% mAP). Furthermore, through the integration of the DINO detector with M2Doc, we achieve state-of-the-art results on DocLayNet (89.0\% mAP), M$^6$Doc (69.9\% mAP), and PubLayNet (95.5\% mAP). The code will be publicly released at https://github.com/johnning2333/M2Doc.

M2Doc: A Multi-Modal Fusion Approach for Document Layout Analysis

Recent advances in semi-supervised learning (SSL) have relied on the optimistic assumption that labeled and unlabeled data share the same class distribution. However, this assumption is often violated in real-world scenarios, where unlabeled data may contain out-of-class samples. SSL with such uncurated unlabeled data leads training models to be corrupted. In this paper, we propose a robust SSL method for learning from uncurated real-world data within the context of open-set semi-supervised learning (OSSL). Unlike previous works that rely on feature similarity distance, our method exploits uncertainty in logits. By leveraging task-dependent predictions of logits, our method is capable of robust learning even in the presence of highly correlated outliers. Our key contribution is to present an unknown-aware graph regularization (UAG), a novel technique that enhances the performance of uncertainty-based OSSL frameworks. The technique addresses not only the conflict between training objectives for inliers and outliers but also the limitation of applying the same training rule for all outlier classes, which are existed on previous uncertainty-based approaches. Extensive experiments demonstrate that UAG surpasses state-of-the-art OSSL methods by a large margin across various protocols. Codes are available at https://github.com/heejokong/UAGreg.

Unknown-Aware Graph Regularization for Robust Semi-supervised Learning from Uncurated Data

Data valuation is concerned with determining a fair valuation of data from data sources to compensate them or to identify training examples that are the most or least useful for predictions. With the rising interest in personal data ownership and data protection regulations, model owners will likely have to fulfil more data deletion requests. This raises issues that have not been addressed by existing works: Are the data valuation scores still fair with deletions? Must the scores be expensively recomputed? The answer is no. To avoid recomputations, we propose using our data valuation framework DeRDaVa upfront for valuing each data source's contribution to preserving robust model performance after anticipated data deletions. DeRDaVa can be efficiently approximated and will assign higher values to data that are more useful or less likely to be deleted. We further generalize DeRDaVa to Risk-DeRDaVa to cater to risk-averse/seeking model owners who are concerned with the worst/best-cases model utility. We also empirically demonstrate the practicality of our solutions.

DeRDaVa: Deletion-Robust Data Valuation for Machine Learning

Partial label learning (PLL) expands the applicability of supervised machine learning models by enabling effective learning from weakly annotated overcomplete labels. Existing PLL methods however focus on the standard centralized learning scenarios. In this paper, we expand PLL into the distributed computation setting by formalizing a new learning scenario named as federated partial label learning (FedPLL), where the training data with partial labels are distributed across multiple local clients with privacy constraints. To address this challenging problem, we propose a novel Federated PLL method with Local-Adaptive Augmentation and Regularization (FedPLL-LAAR). In addition to alleviating the partial label noise with moving-average label disambiguation, the proposed method performs MixUp-based local-adaptive data augmentation to mitigate the challenge posed by insufficient and imprecisely annotated local data, and dynamically incorporates the guidance of global model to minimize client drift through adaptive gradient alignment regularization between the global and local models. Extensive experiments conducted on multiple datasets under the FedPLL setting demonstrate the effectiveness of the proposed FedPLL-LAAR method for federated partial label learning.

Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization

Trained with an unprecedented scale of data, large language models (LLMs) like ChatGPT and GPT-4 exhibit the emergence of significant reasoning abilities from model scaling. Such a trend underscored the potential of training LLMs with unlimited language data, advancing the development of a universal embodied agent. In this work, we introduce the NavGPT, a purely LLM-based instruction-following navigation agent, to reveal the reasoning capability of GPT models in complex embodied scenes by performing zero-shot sequential action prediction for vision-and-language navigation (VLN). At each step, NavGPT takes the textual descriptions of visual observations, navigation history, and future explorable directions as inputs to reason the agent's current status, and makes the decision to approach the target. Through comprehensive experiments, we demonstrate NavGPT can explicitly perform high-level planning for navigation, including decomposing instruction into sub-goals, integrating commonsense knowledge relevant to navigation task resolution, identifying landmarks from observed scenes, tracking navigation progress, and adapting to exceptions with plan adjustment. Furthermore, we show that LLMs is capable of generating high-quality navigational instructions from observations and actions along a path, as well as drawing accurate top-down metric trajectory given the agent's navigation history. Despite the performance of using NavGPT to zero-shot R2R tasks still falling short of trained models, we suggest adapting multi-modality inputs for LLMs to use as visual navigation agents and applying the explicit reasoning of LLMs to benefit learning-based models. Code is available at: https://github.com/GengzeZhou/NavGPT.

NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models

Ensemble pruning that combines a subset of individual learners generated in parallel to make predictions is an important topic in ensemble learning. Past decades have developed a lot of pruning algorithms that focus on the external behavior of learners on samples, which may lead to over-fitting. In this paper, we conjecture that the generalization performance of an ensemble is not only related to its external behavior on samples but also dependent on the internal structure of individual learners. We propose the general MEPSI approach based on Kolmogorov complexity and the Minimum Description Length (MDL) principle, which formulates the ensemble pruning task as the two-objective optimization problem that comprises the empirical error and structural information among individual learners. We also provide a concrete implementation of MEPSI on decision trees. The theoretical results provide generalization bounds for both the general MEPSI approach and tree-based implementation. The comparative experiments conducted on multiple real-world data sets demonstrate the effectiveness of our proposed method.

MEPSI: An MDL-Based Ensemble Pruning Approach with Structural Information

Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge. Recently, data-driven approaches such as Neural Ordinary Differential Equations (NODE) have shown promising results by leveraging the expressive power of neural networks to model unknown dynamics. However, these approaches often suffer from limited labeled training data, leading to poor generalization and suboptimal predictions. On the other hand, semi-supervised algorithms can utilize abundant unlabeled data and have demonstrated  good performance in classification and regression tasks.
We propose TS-NODE, the first semi-supervised approach to modeling dynamical systems with NODE. TS-NODE explores cheaply generated synthetic pseudo rollouts to broaden exploration in the state space and to tackle the challenges brought by lack of ground-truth system data under a teacher-student model.  TS-NODE employs an unified optimization framework that corrects the teacher model based on the student's feedback while mitigating the potential false system dynamics present in pseudo rollouts.
TS-NODE demonstrates significant performance improvements over a baseline Neural ODE model on multiple dynamical system modeling tasks.

Semi-supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach

Neural radiance fields (NeRFs) are promising 3D representations for scenes, objects, and humans. However, most existing methods require multi-view inputs and per-scene training, which limits their real-life applications. Moreover, current methods focus on single-subject cases, leaving scenes of interacting hands that involve severe inter-hand occlusions and challenging view variations remain unsolved. To tackle these issues, this paper proposes a generalizable visibility-aware NeRF (VA-NeRF) framework for interacting hands. Specifically, given an image of interacting hands as input, our VA-NeRF first obtains a mesh-based representation of hands and extracts their corresponding geometric and textural features. Subsequently, a feature fusion module that exploits the visibility of query points and mesh vertices is introduced to adaptively merge features of both hands, enabling the recovery of features in unseen areas. Additionally, our VA-NeRF is optimized together with a novel discriminator within an adversarial learning paradigm. In contrast to conventional discriminators that predict a single real/fake label for the synthesized image, the proposed discriminator generates a pixel-wise visibility map, providing fine-grained supervision for unseen areas and encouraging the VA-NeRF to improve the visual quality of synthesized images. Experiments on the Interhand2.6M dataset demonstrate that our proposed VA-NeRF outperforms conventional NeRFs significantly. Project Page: https://github.com/XuanHuang0/VANeRF.

3D Visibility-Aware Generalizable Neural Radiance Fields for Interacting Hands

We consider the sparse contextual bandit problem where arm feature affects reward through the inner product of sparse parameters. Recent studies have developed sparsity-agnostic algorithms based on the greedy arm selection policy. However, the analysis of these algorithms requires strong assumptions on the arm feature distribution to ensure that the greedily selected samples are sufficiently diverse; One of the most common assumptions, relaxed symmetry, imposes approximate origin-symmetry on the distribution, which cannot allow distributions that has origin-asymmetric support. In this paper, we show that the greedy algorithm is applicable to a wider range of the arm feature distributions from two aspects. Firstly, we show that a mixture distribution that has a greedy-applicable component is also greedy-applicable. Second, we propose new distribution classes, related to Gaussian mixture, discrete, and radial distribution, for which the sample diversity is guaranteed. The proposed classes can describe distributions with origin-asymmetric support and, in conjunction with the first claim, provide theoretical guarantees of the greedy policy for a very wide range of the arm feature distributions.

New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem

Neoteric works have shown that modern deep learning models can exhibit a sparse double descent phenomenon. Indeed, as the sparsity of the model increases, the test performance first worsens since the model is overfitting the training data; then, the overfitting reduces, leading to an improvement in performance, and finally, the model begins to forget critical information, resulting in underfitting. Such a behavior prevents using traditional early stop criteria.

In this work, we have three key contributions. First, we propose a learning framework that avoids such a phenomenon and improves generalization. Second, we introduce an entropy measure providing more insights into the insurgence of this phenomenon and enabling the use of traditional stop criteria. Third, we provide a comprehensive quantitative analysis of contingent factors such as re-initialization methods, model width and depth, and dataset noise. The contributions are supported by empirical evidence in typical setups. Our code is available at https://github.com/VGCQ/DSD2.

FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks

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M2Doc: A Multi-Modal Fusion Approach for Document Layout Analysis

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FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks

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Next from AAAI 2024

M2Doc: A Multi-Modal Fusion Approach for Document Layout Analysis

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads