Canada

Personalized federated learning is a new paradigm to address heterogeneous problems ({e.g.}~issues with {non-i.i.d.~}data) in federated learning. However, existing personalized federated learning methods lack standards for how personalized and shared parts of the models are designed. Sometimes, manual design can even lead to worse performance than non-personalization. As a result, we propose a new algorithm for personalized federated neural architecture search, called PerFedRLNAS, to automatically personalize the architectures and weights of models on each client. With such an algorithm, we can solve the issues of low efficiency as well as failure to adapt to new search spaces in previous federated neural architecture search work. We further show that with automatically assigning different client architectures can solve heterogeneity of data distribution, efficiency and memory in federated learning.  In our experiments, we empirically show that our framework shows much better performance with respect to personalized accuracy and overall time compared to state-of-the-art methods. Furthermore, PerFedRLNAS has a good generalization ability to new clients, and is easy to be deployed in practice.

AAAI 2024

PerFedRLNAS: One-for-All Personalized Federated Neural Architecture Search

ml: auto ml and hyperparameter tuning; ml: distributed machine learning & federated learning; ml: reinforcement learning algorithms

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

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-25 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.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

AAAI 2025

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-25 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.



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.

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. 

Perceptual quality assessment of user generated content (UGC) videos is challenging due to the requirement of large scale human annotated videos for training. In this work, we address this challenge by first designing a self-supervised Spatio-Temporal Visual Quality Representation Learning (ST-VQRL) framework to generate robust quality aware features for videos. Then, we propose a dual-model based Semi Supervised Learning (SSL) method specifically designed for the Video Quality Assessment (SSL-VQA) task, through a novel knowledge transfer of quality predictions between the two models. Our SSL-VQA method uses the ST-VQRL backbone to produce robust performances across various VQA datasets including cross-database settings, despite being learned with limited human annotated videos. Our model improves the state-of-the-art performance when trained only with limited data by around 10%, and by around 15% when unlabelled data is also used in SSL. Source codes and checkpoints are available at https://github.com/Shankhanil006/SSL-VQA.

Knowledge Guided Semi-supervised Learning for Quality Assessment of User Generated Videos

Recently the prompt-based models have become popular across various language and vision tasks. In this work, we perform few-shot keypoint detection (FSKD) by detecting any keypoints in a query image, given the prompts formed by support images and keypoints. FSKD can be applied to detecting keypoints and poses of diverse animal species. In order to maintain flexibility of detecting varying number of keypoints, existing FSKD approaches modulate query feature map per support keypoint, then detect the corresponding keypoint from each modulated feature via a detection head. Such a modulation-detection separate design would scale up model into heavy yet slow one when the number of keypoints increases. To overcome this issue, we design a novel light-weight detector which combines modulation and detection into one step, with the goal of reducing the computational cost without the drop of performance. Moreover, to bridge the large domain shift of keypoints between seen and unseen species, we propose mean feature based contrastive learning that aligns/separates mean features of the same/different type, resulting in better keypoint representations. Compared to the state of the art, our light-weight detector reduces the number of parameters by 50\%, training/test time by 50\%, and achieves 5.62\% accuracy gain on 1-shot novel keypoint detection in the Animal pose dataset. Our model is also robust to the number of keypoints and saves much memory when evaluating a large number of keypoints (\eg, 1000) per episode.

Detect Any Keypoints: An Efficient Light-Weight Few-Shot Keypoint Detector

Multi-object tracking (MOT) is a challenging vision task that aims to detect individual objects within a single frame and associate them across multiple frames. Recent MOT approaches can be categorized into two-stage tracking-by-detection (TBD) methods and one-stage joint detection and tracking (JDT) methods. Despite the success of these approaches, they also suffer from common problems, such as harmful global or local inconsistency, poor trade-off between robustness and model complexity, and lack of flexibility in different scenes within the same video. In this paper we propose a simple but robust framework that formulates object detection and association jointly as a consistent denoising diffusion process from paired noise boxes to paired ground-truth boxes. This novel progressive denoising diffusion strategy substantially augments the tracker's effectiveness, enabling it to discriminate between various objects. During the training stage, paired object boxes diffuse from paired ground-truth boxes to random distribution, and the model learns detection and tracking simultaneously by reversing this noising process. In inference, the model refines a set of paired randomly generated boxes to the detection and tracking results in a flexible one-step or multi-step denoising diffusion process. Extensive experiments on three widely used MOT benchmarks, including MOT17, MOT20, and DanceTrack, demonstrate that our approach achieves competitive performance compared to the current state-of-the-art methods. Code is available at https://github.com/RainBowLuoCS/DiffusionTrack.

DiffusionTrack: Diffusion Model for Multi-Object Tracking

In this paper, we present an approximate distance oracle for fault-tolerant Euclidean spanners. An $f$-fault-tolerant Euclidean $t$-spanner for a point set $V \subset \mathbb{R}^d$ is a graph $G=(V,E)$ where, for any two points $p$ and $q$ in $V$ and a set $F$ of $f$ vertices of $V$, the distance between $p$ and $q$ in $G-F$ is at most $t$ times their Euclidean distance. Given an $f$-fault-tolerant 
Euclidean $t$-spanner $G$ and a constant $\varepsilon$ for constants $t$ and $f$,
we can construct a data structure of size $O(m\log n)$ in $O(m\log^2 n)$ time so that an $(1+\varepsilon)$-approximate distance in $G-F$ between $s$ and $s'$ can be computed in constant time for any two vertices $s$ and $s'$ and a set $F$ of $f$ failed vertices. Additionally, we can construct a data structure of size $O(m\log n\log\log n)$ in $O(m^2\log^2 n)$ so that an $(1+\varepsilon)$-approximate shortest path in $G-F$ between $s$ and $s'$ can be compute in $O(\log^2 n\log\log n)$ time for any two vertices $s$ and $s'$ and a set $F$ of failed vertices.

Approximate Distance Oracle for Fault-Tolerant Geometric Spanners

The ability of model quantization with arbitrary bit-width to dynamically meet diverse bit-width requirements during runtime has attracted significant attention. Recent research has focused on optimizing large-scale training methods to achieve robust bit-width adaptation, which is a time-consuming process requiring hundreds of GPU hours. Furthermore, converting bit-widths requires recalculating statistical parameters of the norm layers, thereby impeding real-time switching of the bit-width. To overcome these challenges, we propose an efficient Post-Training Multi-bit Quantization (PTMQ) scheme that requires only a small amount of calibration data to perform block-wise reconstruction of multi-bit quantization errors. It eliminates the influence of statistical parameters by fusing norm layers, and supports real-time switching bit-widths in uniform quantization and mixed-precision quantization. To improve quantization accuracy and robustness, we propose a Multi-bit Feature Mixer technique (MFM) for fusing features of different bit-widths to enhance robustness across varying bit-widths. Moreover, we introduced the Group-wise Distillation Loss (GD-Loss) to enhance the correlation between different bit-width groups and further improve the overall performance of PTMQ. Extensive experiments demonstrate that PTMQ achieves comparable performance to existing state-of-the-art post-training quantization methods, while optimizing it speeds up by 100$\times$ compared to recent multi-bit quantization works. Code can be available at https://github.com/xuke225/PTMQ.

PTMQ: Post-training Multi-Bit Quantization of Neural Networks

Deep learning models have the ability to extract empirical knowledge from large-scale datasets. However, the sharing of data has become increasingly challenging due to concerns regarding data copyright and privacy. Consequently, this hampers the effective transfer of knowledge from existing data to novel downstream tasks and concepts. Zero-shot learning (ZSL) approaches aim to recognize unseen classes by transferring semantic knowledge learned from seen classes. However, traditional ZSL methods often require access to real images from seen classes and rely on manually annotated attributes, which presents challenges in terms of data restrictions and model scalability. To this end, this paper tackles a challenging and practical problem dubbed as data-free zero-shot learning, where only the CLIP-based seen classes data pre-trained classifier is available for zero-shot classification. Specifically, we propose a generic framework for data-free zero-shot learning, which consists of three main components. Firstly, to recover the virtual features of the seen data, we model the CLIP features of seen class images as samples from a von Mises-Fisher (vMF) distribution based on the pre-trained classifier. Secondly, eliminates the need for manual attribute annotations, we leverage the text features of CLIP as low-cost semantic information and propose
a feature-language prompt tuning method to further align the virtual image features and textual features. Thirdly, we train a conditional generative model using the well-aligned virtual image features and corresponding semantic text features, which enables us to generate features for unseen classes and achieve zero-shot classification. Our framework has been evaluated on five commonly used benchmarks for zero-shot learning, as well as 11 benchmarks for the base-to-new generalization. The results demonstrate the superiority and effectiveness of our approach.

Data-Free Generalized Zero-Shot Learning | VIDEO

Recently, many carefully designed graph representation learning methods have achieved impressive performance on either strong heterophilic or homophilic graphs, but not both. Therefore, they are incapable of generalizing well across real-world graphs with different levels of homophily. This is attributed to their neglect of homophily in heterophilic graphs, and vice versa. In this paper, we propose a two-fold filtering mechanism to mine homophily in heterophilic graphs, and vice versa. In particular, we extend the graph heat equation to perform heterophilic aggregation of global information from a long distance. The resultant filter can be exactly approximated by the Possion-Charlier (PC) polynomials. To further exploit information at multiple orders, we introduce a powerful graph convolution PC-Conv and its instantiation PCNet for the node classification task. 
Compared to the state-of-the-art GNNs, PCNet shows competitive performance on well-known homophilic and heterophilic graphs.

PC-Conv: Unifying Homophily and Heterophily with Two-Fold Filtering

The goal of 3D mesh watermarking is to embed the message in 3D meshes that can withstand various attacks imperceptibly and reconstruct the message accurately from watermarked meshes. The watermarking algorithm is supposed to withstand multiple attacks, and the complexity should not grow significantly with the mesh size. Unfortunately, previous methods are less robust against attacks and lack of adaptability. In this paper, we propose a robust and adaptable deep 3D mesh watermarking Deep3DMark that leverages attention-based convolutions in watermarking tasks to embed binary messages in vertex distributions without texture assistance. Furthermore, our Deep3DMark exploits the property that simplified meshes inherit similar relations from the original ones, where the relation is the offset vector directed from one vertex to its neighbor. By doing so, our method can be trained on simplified meshes but remains effective on large size meshes (size adaptable) and unseen categories of meshes (geometry adaptable). Extensive experiments demonstrate our method remains efficient and effective even if the mesh size is 190× increased. Under mesh attacks, Deep3DMark achieves 10%∼50% higher accuracy than traditional methods, and 2× higher SNR and 8% higher accuracy than previous DNN-based methods.

Rethinking Mesh Watermark: Towards Highly Robust and Adaptable Deep 3D Mesh Watermarking

Image registration plays a crucial role in histological image analysis, encompassing tasks like multi-modality fusion and disease grading. 
Traditional registration methods optimize objective functions for each image pair, yielding reliable accuracy but demanding heavy inference burdens.
Recently, learning-based registration methods utilize networks to learn the optimization process during training and apply a one-step forward process during testing. 
While these methods offer promising registration performance with reduced inference time, they remain sensitive to appearance variances and local structure changes commonly encountered in histological image registration scenarios.
In this paper, for the first time, we propose a novel test-time adaptation method for histological image registration, aiming to improve the generalization ability of learning-based methods. 
Specifically, we design two operations, style guidance and shape guidance, for the test-time adaptation process. 
The former leverages style representations encoded by feature statistics to address the issue of appearance variances, while the latter incorporates shape representations encoded by HOG features to improve registration accuracy in regions with structural changes.
Furthermore, we consider the continuity of the model during the test-time adaptation process.
Different from the previous methods initialized by a given trained model, we introduce a smoothing strategy to leverage historical models for better generalization. 
We conduct experiments with several representative learning-based backbones on the public histological dataset, demonstrating the superior registration performance of our test-time adaptation method.

Test-Time Adaptation via Style and Structure Guidance for Histological Image Registration

In recent years, following the success of text guided image generation, text guided 3D generation has gained increasing attention among researchers. Dreamfusion is a notable approach that enhances generation quality by utilizing 2D text guided diffusion models and introducing SDS loss, a technique for distilling 2D diffusion model information to train 3D models. However, the SDS loss has two major limitations that hinder its effectiveness. Firstly, when given a text prompt, the SDS loss struggles to produce diverse content. Secondly, during training, SDS loss may cause the generated content to overfit and collapse, limiting the model's ability to learn intricate texture details. To overcome these challenges, we propose a novel approach called Noise Recalibration algorithm. By incorporating this technique, we can generate 3D content with significantly greater diversity and stunning details. Our approach offers a promising solution to the limitations of SDS loss.

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