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The Diffusion Transformers Models (DiTs) have transitioned the network architecture from traditional UNets to transformers, demonstrating exceptional capabilities in image generation. Although DiTs have been widely applied to high-definition video generation tasks, their large parameter size hinders inference on edge devices. Vector quantization (VQ) can decompose weights into a codebook and assignments, allowing extreme weight quantization and significantly reducing memory usage. In this paper, we propose VQ4DiT, a fast post-training vector quantization method for DiTs. We found that traditional VQ methods calibrate only the codebook without calibrating the assignments, leading to weight sub-vectors incorrectly assigned to the same assignment providing inconsistent gradients to the codebook, resulting in a suboptimal result. To address this challenge, VQ4DiT calculates the candidate assignment set for each weight sub-vector based on Euclidean distance. Then, using the zero-data and block-wise calibration method, the optimal assignment from the set is efficiently selected while calibrating the codebook. Depending on the different quantization settings, VQ4DiT quantizes a DiT XL/2 model on a single A100 GPU within 20 minutes to 5 hours. Experiments show that VQ4DiT establishes a new state-of-the-art in model size and performance trade-offs, quantizing weights to 2-bit precision while retaining acceptable image generation quality.

AAAI 2025

VQ4DiT: Efficient Post-Training Vector Quantization for Diffusion Transformers

learning on the edge

model compression

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

Please register!

EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

poster

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/)

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.



Sexism affects both women and men, yet research often overlooks misandry and suffers from overly broad annotations that limit AI applications. To address this, we introduce BeyondGender, a dataset meticulously annotated according to the latest definitions of misogyny and misandry. It features innovative multifaceted labels encompassing aspects of sexism, gender, phrasing, misogyny, and misandry. The dataset includes 6K English and 1.7K Chinese sexism instances, alongside 13K non-sexism examples.
Our evaluations of masked language models and large language models reveal that they detect misogyny in English and misandry in Chinese more effectively, with F1-scores of 0.87 and 0.62, respectively. However, they frequently misclassify hostile and mild comments, underscoring the complexity of sexism detection. Parallel corpus experiments suggest promising data augmentation strategies to enhance AI systems for nuanced sexism detection, and our dataset can be leveraged to improve value alignment in large language models.

BeyondGender: A Multifaceted Bilingual Dataset for Practical Sexism Detection

Large Language Models (LLMs) have gained significant attention for their exceptional performance across various domains. Despite their advancements, concerns persist regarding their implicit bias, which often leads to negative social impacts. Therefore, it is essential to identify the implicit bias in LLMs and investigate the potential threat posed by it. Our study focused on a specific type of implicit bias, termed the ''Yes-No'' implicit bias, which refers to LLMs' inherent tendency to favor ''Yes'' or ''No'' responses to a single instruction. By comparing the probability of LLMs generating a series of ''Yes'' versus ''No'' responses, we observed different inherent response tendencies exhibited by LLMs when faced with different instructions. To further investigate the impact of such bias, we developed an attack method called Implicit Bias In-Context Manipulation, attempting to manipulate LLMs' behavior. Specifically, we explored whether the ''Yes'' implicit bias could manipulate ''No'' responses into ''Yes'' in LLMs' responses to malicious instructions, leading to harmful outputs. Our findings revealed that the ''Yes'' implicit bias brings a significant security threat, comparable to that of carefully designed attack methods. Moreover, we offered a comprehensive analysis from multiple perspectives to deepen the understanding of this security threat, emphasizing the need for ongoing improvement in LLMs' security.

Investigating the Security Threat Arising from “Yes-No” Implicit Bias in Large Language Models

Spiking neural networks (SNNs), inspired by the inherent spiking computation paradigm of the biological neural systems, have exhibited superior energy efficiency in 2D classification tasks over traditional artificial neural networks (ANNs). However, the regression potential of SNNs has not been well explored, especially in 3D point cloud processing.
In this paper, we propose noise-injected spiking graph convolutional networks to leverage the full regression potential of SNNs in 3D point cloud denoising. 
Specifically, we first emulate the noise-injected neuronal dynamics to build noise-injected spiking neurons.
On this basis, we design noise-injected spiking graph convolution for promoting disturbance-aware spiking representation learning on 3D points.
Starting from the spiking graph convolution, we build two SNN-based denoising networks.
One is a purely spiking graph convolutional network, which achieves low accuracy loss compared with some ANN-based alternatives, while resulting in significantly reduced energy consumption on two benchmark datasets, PU-Net and PC-Net.
The other is a hybrid architecture, which integrates some ANN-based learning operations and exhibits a high performance-efficiency trade-off with only a few time steps.
Our work lights up SNN’s potential for 3D point cloud denoising, 
injecting new perspectives of exploring the deployment on neuromorphic chips while paving the way for developing energy-efficient 3D data acquisition devices.
Our source code has been submitted and will be made publicly available.

Noise-Injected Spiking Graph Convolution for Energy-Efficient 3D Point Cloud Denoising

Semi-supervised learning (SSL) is a fundamental task in machine learning, empowering models to extract valuable insights from datasets with limited labeled samples and a large amount of unlabeled data. Although pseudo-labeling is a widely used approach for SSL that generates pseudo-labels for unlabeled data and leverages them as ground truth labels for training, traditional pseudo-labeling techniques often face challenges that significantly decrease the quality of pseudo-labels and hence the overall model performance. In this paper, we propose a novel Bi-level Optimization method for Pseudo-label Learning (BOPL) to boost semi-supervised training. It treats pseudo-labels as latent variables, and optimizes the model parameters and pseudo-labels jointly within a bi-level optimization framework. By enabling direct optimization over the pseudo-labels towards maximizing the prediction model performance, the method is expected to produce high-quality pseudo-labels. To evaluate the effectiveness of the proposed approach, we conduct extensive experiments on multiple SSL benchmarks. The experimental results show the proposed BOPL outperforms the state-of-the-art SSL techniques.

Bi-Level Optimization for Semi-Supervised Learning with Pseudo-Labeling

Diabetic retinopathy (DR), with its large patient population, has become a formidable threat to human visual health. In the clinical diagnosis of DR, multi-view fundus images are considered to be more suitable for DR diagnosis because of the wide coverage of the field of view. Therefore, different from most of the previous single-view DR grading methods, we design a dynamic selection-driven multi-view DR grading method to fit clinical scenarios better. Since lesion information plays a key role in DR diagnosis, previous methods usually boost the model performance by enhancing the lesion feature. However, during the actual diagnosis, ophthalmologists not only focus on the crucial parts, but also exclude irrelevant features to ensure that do not compromise the accuracy of their judgment. To this end, we introduce the idea of dynamic selection and design a series of selection mechanisms from fine granularity to coarse granularity. In this work, we first introduce an Ophthalmic Image Reader (OIR) agent to provide the model with pixel-level prompts of suspected lesion areas. Moreover, a Multi-View Token Selection Module (MVTSM) is designed to prune redundant feature tokens and realize dynamic selection of key information. In the final decision stage, we dynamically fuse multi-view features through the novel Multi-View Mixture of Experts Module (MVMoEM), to enhance key views and reduce the impact of conflicting views. Extensive experiments on a large multi-view fundus image dataset with 34,452 images demonstrate that our method performs favorably against state-of-the-art models. The public code can be available in the attachment.

Like an Ophthalmologist: Dynamic Selection Driven Multi-View Learning for Diabetic Retinopathy Grading

With the advancement of graph representation learning, self-supervised graph contrastive learning (GCL) has emerged as a key technique in the field. In GCL, positive and negative samples are generated through data augmentation. While recent works have introduced model-based methods to enhance positive graph augmentations, they often overlook the importance of negative samples, relying instead on rule-based methods that can fail to capture meaningful graph patterns. To address this issue, we propose a novel model-based adversarial contrastive graph augmentation (ACGA) method that automatically generates both positive graph samples with minimal sufficient information and hard negative graph samples. Additionally, we provide a theoretical framework to analyze the process of positive and negative graph augmentation in self-supervised GCL. We evaluate our ACGA method through extensive experiments on representative benchmark datasets, and the results demonstrate that ACGA outperforms state-of-the-art baselines.

Adversarial Contrastive Graph Augmentation with Counterfactual Regularization

From image to video understanding, the capabilities of Multi-modal LLMs (MLLMs) are increasingly powerful. However, most existing video understanding benchmarks are relatively short, which makes them inadequate for effectively evaluating the long-sequence modeling capabilities of MLLMs. This highlights the urgent need for a comprehensive and integrated long video understanding benchmark to assess the ability of MLLMs thoroughly. To this end, we propose ALLVB (\textbf{ALL}-in-one long \textbf{V}ideo understanding \textbf{B}enchmark). ALLVB's main contributions include: 1) It integrates 9 major video understanding tasks. These tasks are converted into video Q\&A formats, allowing a single benchmark to evaluate 9 different video understanding capabilities of MLLMs, highlighting the versatility, comprehensiveness, and challenging nature of ALLVB. 2) A fully automated annotation pipeline using GPT-4o is designed, requiring only human quality control, which facilitates the maintenance and expansion of the benchmark. 3) It contains 1,376 videos across 16 categories, averaging nearly 2 hours each, with a total of 252k Q\&As. To the best of our knowledge, it is the largest long video understanding benchmark in terms of the number of videos, average duration, and number of Q\&As. We have tested various mainstream MLLMs on ALLVB, and the results indicate that even the most advanced commercial models have significant room for improvement. This reflects the benchmark's challenging nature and demonstrates the substantial potential for development in long video understanding.

ALLVB: All-in-One Long Video Understanding Benchmark

In recent years, the distributed training of foundation models (FMs) has seen a surge in popularity. In particular, federated learning enables collaborative model training among edge clients while safeguarding the privacy of their data. However, federated training of FMs across resource-constrained and highly heterogeneous edge devices encounters several challenges. These include the difficulty of deploying FMs on clients with limited computational resources and the high computation and communication costs associated with fine-tuning and collaborative training. To address these challenges, we propose FedCAMS, a Cluster-Aware Framework with Knowledge-Aware Model Search. Specifically, FedCAMS incorporates a multi-factor heterogeneity-aware clustering algorithm to group clients into clusters based on data distribution and resource limitations, and selects an appropriate model as the cluster model within each cluster. In consideration of the computational limitations of different client devices, we design knowledge-aware model architecture search (KAMAS), which allows each client to identify the optimal sub-model from the cluster model without any training. After local training, a partial aggregation method is employed for intra-cluster aggregation. Finally, cluster-Aware knowledge transfer facilitates knowledge sharing between clusters and the server, addressing model heterogeneity and reducing communication overhead. Extensive experiments demonstrate that FedCAMS outperforms state-of-the-art baselines by 3-10\% in accuracy.

Cluster Based Heterogeneous Federated Foundation Model Adaptation and Fine-Tuning

Classical Transformer-based line segment detection methods have delivered impressive results. However, we observe that some accurately detected line segments are assigned low confidence scores during prediction, causing them to be ranked lower and potentially suppressed. Additionally, these models often require prolonged training periods to achieve strong performance, largely due to the necessity of bipartite matching. In this paper, we introduce RANK-LETR, a novel Transformer-based line segment detection method. Our approach leverages learnable geometric information to refine the ranking of predicted line segments by enhancing the confidence scores of high-quality predictions in a posterior verification step. We also propose a new line segment proposal method, wherein the feature point nearest to the centroid of the line segment directly predicts the location, significantly improving training efficiency and stability. Moreover, we introduce a line segment ranking loss to stabilize rankings during training, thereby enhancing the generalization capability of the model. Experimental results demonstrate that our method outperforms other Transformer-based and CNN-based approaches in prediction accuracy while requiring fewer training epochs than previous Transformer-based models.

Improving Transformer Based Line Segment Detection with Matched Predicting and Re-ranking

Partially linear models (PLMs) have attracted much attention in the field of statistical machine learning. Specially, the ability of variable selection of PLMs has been studied extensively due to the high requirement of model interpretability. However, few of the existing works concerns the false discovery rate (FDR) controllability of variable selection associated with PLMs. To address this issue, we formuate a new Knockoffs Inference scheme for Linear And Nonlinear Discoverer (called KI-LAND), where FDR is controlled with respect to both linear and nonlinear variables for automatic structure discovery. For the proposed KI-LAND, theoretical guarantees are established for both FDR controllability and power, and experimental evaluations are provided to validate its effectiveness on simulated exmaples and real data.

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