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High Dynamic Range (HDR) video reconstruction is a technology designed to restore the real-world dynamic range as accurately as possible, and it is widely used in photography, video games, and virtual reality. Existing methods typically process one or a few adjacent frames together, which can lead to inconsistencies in brightness across frames because they do not treat the video as a whole. Additionally, the methods under supervised learning hold data bias from collected training data and degrade their effectiveness when the unseen test cases have a domain gap with the training ones. To address these issues, we propose an event-guided HDR video reconstruction method based on 3D Gaussian Splatting.  We use 3D Gaussians to represent the entire video, ensuring consistent brightness in the HDR output.  Since existing 3D Gaussians are limited to representing the 0-255 range, we have designed an HDR 3D Gaussian that can represent both HDR and LDR simultaneously.  Additionally, we employ learnable parameters to model the relationship between events and LDR images.  Our method is self-supervised, eliminating data bias present in existing event-guided HDR video reconstruction approaches. The proposed method demonstrates state-of-the-art performance both quantitatively and qualitatively for synthetic and real-world data.

AAAI 2025

EvHDR-GS: Event-guided HDR Video Reconstruction with 3D Gaussian Splatting

synthesis

computational photography

video

image

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.



Multi-view classification based on evidence theory aims to enhance result reliability by effectively quantifying prediction uncertainty at the evidence level, particularly when dealing with low-quality views. However, these methods face limitations in real-world applications due to the sensitivity of estimated uncertainty to view distribution, leading to two main issues: 1) difficulty in making clear judgments about whether to trust predictions based on vague uncertainty scores, and 2) the potential negative impact of integrating information from low-quality views on multi-view classification performance. Both limitations compromise the reliability of multi-view decisions. To address these challenges, we introduce an adaptive rejection mechanism based on estimated uncertainty, which is free of data distribution constraints. By integrating this adaptive rejection mechanism into the fusion of multiple views, our method not only indicates whether predictions should be adopted or rejected at the view level but also enhances classification performance by minimizing the impact of unreliable information. The effectiveness of our method is demonstrated through comprehensive theoretical analysis and empirical experiments on various multi-view datasets, establishing its superiority in enhancing the reliability of multi-view classification.

Enhancing Multi-View Classification Reliability with Adaptive Rejection

Models trained on real-world data often mirror and exacerbate existing social biases. Traditional methods for mitigating these biases typically require prior knowledge of the specific biases to be addressed, and the social groups associated with each instance. In this paper, we introduce a novel adversarial training strategy that operates withour relying on prior bias-type knowledge (e.g., gender or racial bias) and protected attribute labels. Our approach dynamically identifies biases during model training by utilizing auxiliary bias detector. These detected biases are simultaneously mitigated through adversarial training. Crucially, we implement these bias detectors at various levels of the feature maps of the main model, enabling the detection of a broader and more nuanced range of bias features. Through experiments on racial and gender biases in sentiment and occupation classification tasks, our method effectively reduces social biases without the need for demographic annotations. Moreover, our approach not only matches but often surpasses the efficacy of methods that require detailed demographic insights, marking a significant advancement in bias mitigation techniques.

MABR: Multilayer Adversarial Bias Removal Without Prior Bias Knowledge

Domain adaptive object detection (DAOD) aims to generalize an object detector trained on labeled source-domain data to a target domain without annotations, the core principle of which is source-target feature alignment. Typically, existing approaches employ adversarial learning to align the distributions of the source and target domains as a whole, barely considering the varying significance of distinct regions, say instances under different circumstances and foreground vs background areas, during feature alignment. To overcome the shortcoming, this work investigates a differential feature alignment strategy. Specifically, a prediction-discrepancy feedback instance alignment module (dubbed PDFA) is designed to adaptively assign higher weights to instances of higher teacher-student detection discrepancy, effectively handling heavier domain-specific information. On the other hand, an uncertainty-based foreground-oriented image alignment module (UFOA) is proposed to explicitly guide the model to focus more on regions of interest. Extensive experiments on widely-used DAOD datasets together with ablation studies are conducted to demonstrate the efficacy of our proposed method and reveal its superiority over other SOTA alternatives. Our code will be made publicly available.

Differential Alignment for Domain Adaptive Object Detection

Deep hashing models have achieved great success in retrieval tasks due to their powerful representation ability and strong information compression capabilities. However, they inherit the vulnerability of deep neural networks to adversarial perturbations. Attackers can severely impact the retrieval capability of hashing models by adding small, carefully crafted adversarial perturbations to benign images, transforming them into adversarial images. Most existing adversarial attacks target image classification models, with few focusing on hashing retrieval models. We propose HUANG, a targeted adversarial attack algorithm for hashing retrieval in black-box scenarios. To the best of our knowledge, this is the first algorithm that leverages a diffusion model to achieve adversarial attacks on retrieval models. In our approach, adversarial denoising uses adversarial perturbations and residual images to guide the shift from benign to adversarial distribution. Extensive experiments demonstrate the superiority of HUANG across different datasets, achieving state-of-the-art performance in black-box targeted attacks. Additionally, the dynamic interplay between denoising and adding adversarial perturbations in adversarial denoising endows HUANG with exceptional robustness and transferability.

HUANG: A Robust Diffusion Model-based Targeted Adversarial Attack Against Deep Hashing Retrieval

Diffusion models (DMs) have attracted attention in generative modeling due to their ability to produce high-quality, diverse outputs by progressively adding noise to data and then denoising it. However, DMs are computationally intensive due to their iterative nature, requiring numerous forward passes and high-precision operations, making them less efficient for resource-constrained environments. Recent efforts to reduce these computational demands using quantization show promise by converting high-precision parameters to lower precision, but they face challenges unique to DMs, particularly in addressing cross-timestep error propagation in the iterative process. In this paper, we analyze cross-timestep error propagation in quantized DMs, revealing that preivous methods focusing only on reducing noise estimation discrepancies are insufficient. Instead, we introduce ***Cross-Timestep Error Correction*** (CTEC), where the quantized model not only approximates the full-precision model but also corrects errors from the previous timestep. A distillation method is applied to effectively learn this correction process.

Optimizing Quantized Diffusion Models via Distillation with Cross-Timestep Error Correction

Probability calibration transforms raw output of a classification model into empirically interpretable probability. When the model is purposed to detect rare event and only a small expensive data source has clean labels, it becomes extraordinarily challenging to obtain accurate probability calibration. Utilizing an additional large cheap data source is very helpful, however, such data sources oftentimes suffer from biased labels. To this end, we introduce an approximate expectation-maximization (EM) algorithm to extract useful information from the large data sources. For a family of calibration methods based on the logistic likelihood, we derive closed-form updates and call the resulting iterative algorithm CalEM. We shpw that CalEM inherits convergence guarantees from the approximate EM algorithm. We test the proposed model in simulation and on the marketing uplift dataset, where it shows significant performance increases.

Improving Model Probability Calibration by Integration of Large Data Sources with Biased Labels

A recent endeavor in one class of video anomaly detection is to leverage diffusion models and posit the task as a generation problem, where the diffusion model is trained to recover normal patterns exclusively, thus reporting abnormal patterns as outliers. Yet, existing attempts neglect the various formations of anomaly and predict normal samples at the feature level regardless that abnormal objects in surveillance videos are often relatively small.
To address this, a novel patch-based diffusion model is proposed, specifically engineered to capture fine-grained local information. We further observe that anomalies in videos manifest themselves as deviations in both appearance and motion. Therefore, we argue that a comprehensive solution must consider both of these aspects simultaneously to achieve accurate frame prediction. To address this, we introduce innovative motion and appearance conditions that are seamlessly integrated into our patch diffusion model. These conditions are designed to guide the model in generating coherent and contextually appropriate predictions for both semantic content and motion relations. Experimental results on four challenging video anomaly detection datasets empirically substantiate the efficacy of our proposed approach, demonstrating that it consistently outperforms most existing methods in detecting abnormal behaviors.

Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model

Noisy labels can negatively impact deep neural network performance. One common solution is label refurbishment, which involves reconstructing noisy labels through predictions and distributions. However, these methods may introduce problematic semantic associations, a phenomenon we identify as Semantic Contamination. Through an analysis of Robust LR, a representative label refurbishment method, we found that utilizing the logits of views for refurbishment does not adequately balance the semantic information of individual classes. Conversely, using the logits of models fails to maintain consistent semantic relationships across models, which explains why label refurbishment methods frequently encounter issues related to Semantic Contamination. To address this issue, we propose a novel method called Collaborative Cross Learning, which utilizes semi-supervised learning on refurbished labels to extract appropriate semantic associations from embeddings across views and models. Experimental results show that our method outperforms existing approaches on both synthetic and real-world noisy datasets, effectively mitigating the impact of label noise and Semantic Contamination.

Combating Semantic Contamination in Learning with Label Noise

Current multimodal sentiment analysis (MSA) and emotion recognition in conversations (ERC) methods based on pre-trained language models exhibit two primary limitations:
1) Once trained for MSA and ERC tasks, these pre-trained language models lose their original generalized capabilities. 2) They demand considerable computational resources.  As the size of pre-trained language models continues to grow, training larger multimodal sentiment analysis models using previous approaches could result in unnecessary computational cost.  In response to this challenge, we propose Multimodal Sentiment Analysis and Emotion Recognition Adapter (MSE-Adapter), a lightweight and adaptable plugin. This plugin enables a large language model (LLM) to carry out MSA or ERC tasks with minimal computational overhead (only introduces approximately 2.6M to 2.8M trainable parameters upon the 6/7B models),  while preserving the intrinsic capabilities of the LLM. In the MSE-Adapter, the Text-Guide-Mixer (TGM) module is introduced to establish explicit connections between non-textual and textual modalities through the Hadamard product. This allows non-textual modalities to better align with textual modalities at the feature level, promoting the generation of higher-quality pseudo tokens. Extensive experiments were conducted on four public English and Chinese datasets using consumer-grade GPUs and open-source LLMs (Qwen-1.8B, ChatGLM3-6B-base, and LLaMA2-7B) as the backbone. The results demonstrate the effectiveness of the proposed plugin. The code will be released on GitHub after a blind review.

MSE-Adapter: A Lightweight Plugin Endowing LLMs with the Capability to Perform Multimodal Sentiment Analysis and Emotion Recognition

The strength of multimodal learning lies in its ability to integrate information from various sources, providing rich and comprehensive insights. However, in real-world scenarios, multi-modal systems often face the challenge of dynamic modality contributions, the dominance of different modalities may change with the environments, leading to suboptimal performance in multimodal learning. Current methods mainly enhance weak modalities to balance multimodal representation bias, which inevitably optimizes from a partial modality perspective, easily leading to performance descending for dominant modalities. To address this problem, we propose an Asymmetric Reinforcing method against Multimodal Representation bias (ARMR). Our ARMR dynamically reinforces on the weak modalities while maintaining the representation ability of dominant modalities through condition mutual information. Moreover, we first provide an in-depth analysis that optimizing certain modalities could cause information loss and prevent leveraging the full advantages of multimodal data. By exploring the dominance and narrowing the contribution gaps between modalities, we have significantly improved the performance of multimodal learning, making notable progress in mitigating imbalanced multimodal cooperation.

EvHDR-GS: Event-guided HDR Video Reconstruction with 3D Gaussian Splatting

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Enhancing Multi-View Classification Reliability with Adaptive Rejection

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EvHDR-GS: Event-guided HDR Video Reconstruction with 3D Gaussian Splatting

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Next from AAAI 2025

Enhancing Multi-View Classification Reliability with Adaptive Rejection

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads