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Transferable black-box adversarial attacks against classifiers by disturbing the intermediate-layer features have been extensively studied in recent years. However, these methods have not yet achieved satisfactory performances when directly applied to object detectors. This is largely because the features of detectors are fundamentally different from that of the classifiers. In this study, we propose a simple but effective method to improve the transferability of adversarial examples for object detectors by leveraging the properties of spatial consistency and limited equivariance of object detectors’ features. Specifically, we combine a novel loss function and deliberately designed data augmentation to distort the backbone features of object detectors by suppressing significant features corresponding to objects and amplifying the surrounding vicinal features corresponding to object boundaries. As such the target object and background area on the generated adversarial samples are more likely to be confused by other detectors. Extensive experimental results show that our proposed method achieves state-of-the-art black-box transferability for untargeted attacks on various models, including one/two-stage, CNN/Transformer-based, and anchor-free/anchor-based detectors.

AAAI 2024

Transferable Adversarial Attacks for Object Detection Using Object-Aware Significant Feature Distortion | VIDEO

cv: adversarial attacks & robustness

cv: object detection & categorization

cv: segmentation

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. 

The Diffusion Probabilistic Model (DPM) has recently gained popularity in the field of computer vision, thanks to its image generation applications, such as Imagen, Latent Diffusion Models, and Stable Diffusion, which have demonstrated impressive capabilities and sparked much discussion within the community. Recent investigations have further unveiled the utility of DPM in the domain of medical image analysis, as underscored by the commendable performance exhibited by the medical image segmentation model across various tasks. Although these models were originally underpinned by a UNet architecture, there exists a potential avenue for enhancing their performance through the integration of vision transformer mechanisms. However, we discovered that simply combining these two models resulted in subpar performance. To effectively integrate these two cutting-edge techniques for the Medical image segmentation, we propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2. We verify its effectiveness on 20 medical image segmentation tasks with different image modalities. Through comprehensive evaluation, our approach demonstrates superiority over prior state-of-the-art (SOTA) methodologies. Code is released at https://github.com/WuJunde/MedSegDiff.

MedSegDiff-V2: Diffusion-based Medical Image Segmentation with Transformer

Real-world decision-making problems are usually accompanied by delayed rewards, which affects the sample efficiency of Reinforcement Learning, especially in the extremely delayed case where the only feedback is the episodic reward obtained at the end of an episode. Episodic return decomposition is a promising way to deal with the episodic-reward setting. Several corresponding algorithms have shown remarkable effectiveness of the learned step-wise proxy rewards from return decomposition. However, these existing methods lack either attribution or representation capacity, leading to inefficient decomposition in the case of long-term episodes. In this paper, we propose a novel episodic return decomposition method called Diaster (Difference of implicitly assigned sub-trajectory reward). Diaster decomposes any episodic reward into credits of two divided sub-trajectories at any cut point, and the step-wise proxy rewards come from differences in expectation. We theoretically and empirically verify that the decomposed proxy reward function can guide the policy to be nearly optimal. Experimental results show that our method outperforms previous state-of-the-art methods in terms of both sample efficiency and performance.

Episodic Return Decomposition by Difference of Implicitly Assigned Sub-trajectory Reward | VIDEO

Evolutionary algorithms (EAs) are widely used for multi-objective optimization due to their population-based nature. Traditional multi-objective EAs (MOEAs) generate a large set of solutions to approximate the Pareto front, leaving a decision maker (DM) with the task of selecting a preferred solution. However, this process can be inefficient and time-consuming, especially when there are many objectives or the DM has subjective preferences. To address this issue, interactive MOEAs (iMOEAs) combine decision making into the optimization process, i.e., update the population with the help of the DM. In contrast to their wide applications, there has existed only two pieces of theoretical works on iMOEAs, which only considered interactive variants of the two simple single-objective algorithms, RLS and (1+1)-EA. This paper provides the first running time analysis (the essential theoretical aspect of EAs) for practical iMOEAs. Specifically, we prove that the expected running time of the well-developed interactive NSGA-II (called R-NSGA-II) for solving the OneMinMax, OneJumpZeroJump problems are $O(n \log n)$ and $O(n^k)$, respectively, which are all asymptotically faster than the traditional NSGA-II. Meanwhile, we present a variant of OneMinMax, and prove that R-NSGA-II can be exponentially slower than NSGA-II. These results provide theoretical justification for the effectiveness of iMOEAs while identifying situations where they may fail. Experiments are also conducted to validate the theoretical results.

Towards Running Time Analysis of Interactive Multi-Objective Evolutionary Algorithms

Recently, text-to-image diffusion models have emerged as a powerful tool for image-to-image translation (I2I), allowing flexible image translation via user-provided text prompts. This paper proposes frequency-controlled diffusion model (FCDiffusion), an end-to-end diffusion-based framework contributing a novel solution to text-guided I2I from a frequency-domain perspective. At the heart of our framework is a feature-space frequency-domain filtering module based on Discrete Cosine Transform, which extracts image features carrying different DCT spectral bands to control the text-to-image generation process of the Latent Diffusion Model, realizing versatile I2I applications including style-guided content creation, image semantic manipulation, image scene translation, and image style translation. Different from related methods, FCDiffusion establishes a unified text-driven I2I framework suiting diverse I2I application scenarios simply by switching among different frequency control branches. The effectiveness and superiority of our method for text-guided I2I are demonstrated with extensive experiments both qualitatively and quantitatively. Our project is publicly available at: https://xianggao1102.github.io/FCDiffusion/.

Frequency-Controlled Diffusion Model for Versatile Text-Guided Image-to-Image Translation

BEV perception is of great importance in the field of autonomous driving, serving as the cornerstone of planning, controlling, and motion prediction. The quality of the BEV feature highly affects the performance of BEV perception. However, taking the noises in camera parameters and LiDAR scans into consideration, we usually obtain BEV representation with harmful noises. Diffusion models naturally have the ability to denoise noisy samples to the ideal data, which motivates us to utilize the diffusion model to get a better BEV representation. In this work, we propose an end-to-end framework, named DiffBEV, to exploit the potential of diffusion model to generate a more comprehensive BEV representation. To the best of our knowledge, we are the first to apply diffusion model to BEV perception. In practice, we design three types of conditions to guide the training of the diffusion model which denoises the coarse samples and refines the semantic feature in a progressive way. What's more, a cross-attention module is leveraged to fuse the context of BEV feature and the semantic content of conditional diffusion model. DiffBEV achieves a 25.9% mIoU on the nuScenes dataset, which is 6.2% higher than the best-performing existing approach. Quantitative and qualitative results on multiple benchmarks demonstrate the effectiveness of DiffBEV in BEV semantic segmentation and 3D object detection tasks. The code will be available soon.

DiffBEV: Conditional Diffusion Model for Bird’s Eye View Perception

Traditional machine learning is generally treated as a black-box optimization problem and does not typically produce interpretable functions that connect inputs and outputs. However, the ability to discover such interpretable functions is desirable. In this work, we propose GINN-LP, an interpretable neural network to discover the form and coefficients of the underlying equation of a dataset, when the equation is assumed to take the form of a multivariate Laurent Polynomial. This is facilitated by a new type of interpretable neural network block, named the “power-term approximator block”, consisting of logarithmic and exponential activation functions. GINN-LP is end-to-end differentiable, making it possible to use backpropagation for training. We propose a neural network growth strategy that will enable finding the suitable number of terms in the Laurent polynomial that represents the data, along with sparsity regularization to promote the discovery of concise equations. To the best of our knowledge, this is the first model that can discover arbitrary multivariate Laurent polynomial terms without any prior information on the order. Our approach is first evaluated on a subset of data used in SRBench, a benchmark for symbolic regression. We first show that GINN-LP outperforms the state-of-the-art symbolic regression methods on datasets generated using 48 real-world equations in the form of multivariate Laurent polynomials. Next, we propose an ensemble method that combines our method with a high-performing symbolic regression method, enabling us to discover non-Laurent polynomial equations. We achieve state-of-the-art results in equation discovery, showing an absolute improvement of 7.1% over the best contender, by applying this ensemble method to 113 datasets within SRBench with known ground-truth equations.

GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations

3D semantic occupancy has garnered considerable attention due to its abundant structural information encompassing the entire scene in autonomous driving.
However, existing 3D occupancy prediction methods contend with the constraint of low-resolution 3D voxel features arising from the limitation of computational memory.
To address this limitation and achieve a more fine-grained representation of 3D scenes, we propose OctOcc, a novel octree-based approach for 3D semantic occupancy prediction. 
OctOcc is conceptually rooted in the observation that the vast majority of 3D space is left unoccupied. 
Capitalizing on this insight, we endeavor to cultivate memory-efficient high-resolution 3D occupancy predictions by mitigating superfluous cross-attentions. 
Specifically, we devise a hierarchical octree structure that selectively generates finer-grained cross-attentions solely in potentially occupied regions.
Extending our inquiry beyond 3D space, we identify analogous redundancies within another side of cross attentions, 2D images.
Consequently, a 2D image feature filtering network is conceived to expunge extraneous regions.
Experimental results demonstrate that the proposed OctOcc significantly outperforms existing methods on nuScenes and SemanticKITTI datasets with limited memory consumption.

OctOcc: High-Resolution 3D Occupancy Prediction with Octree | VIDEO

Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This issue primarily stems from the model being trained on pairs of single-scale images and their corresponding text descriptions. Moreover, direct training on images of unlimited sizes is unfeasible, as it would require an immense number of text-image pairs and entail substantial computational expenses. To overcome these challenges, we propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to efficiently generate well-composed images of any size, while minimizing the need for high-memory GPU resources. Specifically, the initial stage, dubbed Any Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a restricted range of ratios to optimize the text-conditional diffusion model, thereby improving its ability to adjust composition to accommodate diverse image sizes. To support the creation of images at any desired size, we further introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the subsequent stage. This method allows for the rapid enlargement of the ASD output to any high-resolution size, avoiding seaming artifacts or memory overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks demonstrate that ASD can produce well-structured images of arbitrary sizes, cutting down the inference time by 2X compared to the traditional tiled algorithm. The source code is available at https://github.com/ProAirVerse/Any-Size-Diffusion.

Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images

Few-shot learning (FSL) is essential in practical applications.
However, the limited training examples make the models more
vulnerable to label noise, which can lead to poor generaliza-
tion capability. To address this critical challenge, we propose
a curriculum meta-learning model that employs a novel dual-
level class-example sampling strategy to create a robust cur-
riculum for adaptive task distribution formulation and robust
model training. The dual-level framework proposes a heuristic
class sampling criterion that measures pairwise class bound-
ary complexity to form a class curriculum; and uses effective
example sampling through an under-trained proxy model to
form an example curriculum. By utilizing both class-level and
example-level information, our approach is more robust to
handle limited training data and noisy labels that commonly
occur in few-shot learning tasks. Our proposed model has
efficient convergence behavior, which is verified through rig-
orous convergence analysis. Additionally, we establish a novel
error bound through a hierarchical PAC-Bayesian analysis
for curriculum meta-learning under noise. We conduct exten-
sive experiments that demonstrate the effectiveness of our
framework in outperforming existing noisy few-shot learning
methods under various few-shot classification benchmarks.

Dual-Level Curriculum Meta-Learning for Noisy Few-Shot Learning Tasks | VIDEO

Ensuring fairness in Recommendation Systems (RSs) across demographic groups is critical due to the increased integration of RSs in applications such as personalized healthcare, finance, and e-commerce. Graph-based RSs play a crucial role in capturing intricate higher-order interactions among entities. However, integrating these graph models into the Federated Learning (FL) paradigm with fairness constraints poses formidable challenges as this requires access to the entire interaction graph and sensitive user information (such as gender, age, etc.) at the central server. This paper addresses the pervasive issue of inherent bias within RSs for different demographic groups without compromising the privacy of sensitive user attributes in FL environment with the graph-based model. To address the group bias, we propose F2PGNN (Fair Federated Personalized Graph Neural Network), a novel framework that leverages the power of Personalized Graph Neural Network (GNN) coupled with fairness considerations. Additionally, we use differential privacy techniques to fortify privacy protection. Experimental evaluation on three publicly available datasets showcases the efficacy of F2PGNN in mitigating group unfairness by 47% ∼ 99% compared to the state-of-the-art while preserving privacy and maintaining the utility. The results validate the significance of our framework in achieving equitable and personalized recommendations using GNN within the FL landscape. Source code is at:
https://github.com/nimeshagrawal/F2PGNN-AAAI24

Transferable Adversarial Attacks for Object Detection Using Object-Aware Significant Feature Distortion | VIDEO

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MedSegDiff-V2: Diffusion-based Medical Image Segmentation with Transformer

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