AAAI 2025 论文列表

Graph contrastive learning (GCL) has become a hot topic in the field of graph representaion learning. In contrast to traditional supervised learning relying on a large number of labels, GCL exploits augmentation techniques to generate multiple views and positive/negative pairs, both of which greatly influence the performance. Unfortunately, commonly used random augmentations may disturb the underlying semantics of graphs. Moreover, traditional GNNs, a type of widely employed encoders in GCL, are inevitably confronted with over-smoothing and over-squashing problems. To address these issues, we propose GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive Learning (GTCA), which inherits the advantages of both GNN and Transformer, incorporating graph topology to obtain comprehensive graph representations. Theoretical analysis verifies the trustworthiness of the proposed method. Extensive experiments on benchmark datasets demonstrate state-of-the-art empirical performance.

Perception and interaction with articulated objects present a unique challenge for service robots. Although recent research has emphasized understanding articulated shapes and affordance proposals, existing methods only address isolated aspects, failing to develop comprehensive strategies for robotic perception and manipulation of articulated objects. To bridge this gap, we propose GMAP, which systematically integrates the entire process from command to perception and manipulation. Specifically, we first perform precise part-level segmentation of the object and identify the geometric and kinematic parameters of articulated joints. Then, by evaluating point-level affordance proposals, we determine the interaction poses for the robot's end-effector. Finally, the robot's execution trajectory is dynamically computed by combining commands with joint parameters and interaction points. Additionally, a key innovation of GMAP is addressing the scarcity of annotated data. We designed a multi-scale point cloud feature extraction module and introduced pre-training and fine-tuning techniques, significantly enhancing the generalization capability of the perception model. Extensive experiments demonstrate that GMAP achieves state-of-the-art (SOTA) performance in both the perception and manipulation of articulated objects and adapts to real-world scenarios.

Learned image lossy compression techniques have surpassed traditional methods in both subjective vision and quantitative evaluation. However, current models are only applicable to three-channel image formats, limiting their practical application due to the diversity and complexity of image formats. We propose a high-performance learned image compression model for general image formats. We first introduce a transfer method to unify any-channel image formats, enhancing the applicability of neural networks. This method's effectiveness is demonstrated through image information entropy and image homomorphism theory. Then, we introduce an adaptive attention residual block into the entropy model to give it better generalization ability. Meanwhile, we propose an evenly grouped cross-channel context module for progressive preview image decoding. Experimental results demonstrate that our method achieves state-of-the-art (SOTA) in the field of learned image compression in terms of PSNR and MS-SSIM. This work extends the applicability of learned image compression techniques to more practical production environments.

Deep learning models with large-scale backbones have been increasingly adopted to tackle complex visual question answering (VQA) problems in real settings. While providing powerful learning capacities to handle the high-dimensional and multimodal VQA data, these models tend to suffer from the memorization effect leading to overconfident predictions. This can significantly limit their applicability in critical domains (e.g., medicine, cyber-security, and public safety), where confidently wrong predictions may lead to severe consequences. In this work, we propose to perform novel low-rank network factorization, resulting in much better-calibrated networks. These low-rank factorized networks are then aggregated into an ensemble guided by a generalized focal loss to further improve the overall performance and calibration. The overall framework, referred to as the Generalized focal Loss Ensemble of low-rank Networks (GLEN), is an important step toward developing well-calibrated VQA models. We theoretically demonstrate that the generalized focal loss provides a more balanced bias-variance trade-off, which guarantees to lower the confidence of the incorrect predictions, resulting in improved calibration. Extensive experimentation conducted on benchmark datasets and comparison on various VQA models shows that GLEN leads to much better calibration over both in-distribution and out-of-distribution data without sacrificing the VQA accuracy.

Talking face generation (TFG) allows for producing lifelike talking videos of any character using only facial images and accompanying text. Abuse of this technology could pose significant risks to society, creating the urgent need for research into corresponding detection methods. However, research in this field has been hindered by the lack of public datasets. In this paper, we construct the first large-scale multi-scenario talking face dataset (MSTF), which contains 22 audio and video forgery techniques, filling the gap of datasets in this field. The dataset covers 11 generation scenarios and more than 20 semantic scenarios, closer to the practical application scenario of TFG. Besides, we also propose a TFG detection framework, which leverages the analysis of both global and local coherence in the multimodal content of TFG videos. Therefore, a region-focused smoothness detection module (RSFDM) and a discrepancy capture-time frame aggregation module (DCTAM) are introduced to evaluate the global temporal coherence of TFG videos, aggregating multi-grained spatial information. Additionally, a visual-audio fusion module (V-AFM) is designed to evaluate audiovisual coherence within a localized temporal perspective. Comprehensive experiments demonstrate the reasonableness and challenges of our datasets, while also indicating the superiority of our proposed method compared to the state-of-the-art deepfake detection approaches.

Mimicking the real interaction trajectory in the inference of the world model has been shown to improve the sample efficiency of model-based reinforcement learning (MBRL) algorithms. Many methods directly use known state sequences for reasoning. However, this approach fails to enhance the quality of reasoning by capturing the subtle variation between states. Much like how humans infer trends in event development from this variation, in this work, we introduce Global-Local variation Awareness Mamba-based world model (GLAM) that improves reasoning quality by perceiving and predicting variation between states. GLAM comprises two Mamba-based parallel reasoning modules, GMamba and LMamba, which focus on perceiving variation from global and local perspectives, respectively, during the reasoning process. GMamba focuses on identifying patterns of variation between states in the input sequence and leverages these patterns to enhance the prediction of future state variation. LMamba emphasizes reasoning about unknown information, such as rewards, termination signals, and visual representations, by perceiving variation in adjacent states. By integrating the strengths of the two modules, GLAM accounts for higher-value variation in environmental changes, providing the agent with more efficient imagination-based training. We demonstrate that our method outperforms existing methods in normalized human scores on the Atari 100k benchmark.

Learning graph generative models over latent spaces has received less attention compared to models that operate on the original data space and has so far demonstrated lacklustre performance. We present GLAD a latent space graph generative model. Unlike most previous latent space graph generative models, GLAD operates on a discrete latent space that preserves to a significant extent the discrete nature of the graph structures making no unnatural assumptions such as latent space continuity. We learn the prior of our discrete latent space by adapting diffusion bridges to its structure. By operating over an appropriately constructed latent space we avoid relying on decompositions that are often used in models that operate in the original data space. We present experiments on a series of graph benchmark datasets that demonstrates GLAD as the first equivariant latent graph generative method achieves competitive performance with the state of the art baselines.

Illegal, Unreported, and Unregulated (IUU) fishing aggravates the global crisis caused by overfishing, threatening the sustainability of marine ecosystems and fisheries worldwide. Distinctive operational characteristics of fishing vessels result in unique footprints on marine environments and socio-economic structures, depending on their fishing method and gear type such as trawlers with non-selective gear that disrupts the seabed, purse seiners using Fish Aggregating Devices (FADs), and longliners notorious for high bycatch rates. As these vessels play an essential role in commercial fishing and the industry, effective monitoring, regulation, and enforcement are critical to mitigate the devastating consequences of overfishing and promote sustainable fishing practices. To this end, this paper introduces a novel multi-stage method for Gear type Identification by Spatiotemporal trajectory Transformation (GIST). This method proposes a data-centric approach that employs domain knowledge to facilitate the deployment of an efficient and accurate analysis of operational patterns of fishing vessels derived from Automatic Identification System (AIS) data. Our method first extracts fishing patterns from vessel trajectories to refine data integrity and isolate only the most relevant activities, thereby ensuring a more accurate result. Next, it encapsulates the distributional insights of fishing activities into fixed-sized "images" as actionable input for a multi-class CNN-based classifier. Utilizing GIST bypasses complicated linear analyses of time series data for rendering lengthy trajectories, advancing an efficient gear type identification with 97% accuracy. To the best of our knowledge, GIST is the first to use a multi-stage method to distinguish three principal gear types widely used globally. Our experiments confirm GIST's practicability and effectiveness, marking a significant advancement towards stricter enforcement of regulations in the fight against IUU fishing.

The extraordinary ability of generative models emerges as a new trend in image editing and generating realistic images, posing a serious threat to the trustworthiness of multimedia data and driving the research of image manipulation detection and location (IMDL). However, the lack of a large-scale data foundation makes the IMDL task unattainable. In this paper, we build a local manipulation data generation pipeline that integrates the powerful capabilities of SAM, LLM, and generative models. Upon this basis, we propose the GIM dataset, which has the following advantages: 1) Large scale, GIM includes over one million pairs of AI-manipulated images and real images. 2) Rich image content, GIM encompasses a broad range of image classes. 3) Diverse generative manipulation, the images are manipulated images with state-of-the-art generators and various manipulation tasks. The aforementioned advantages allow for a more comprehensive evaluation of IMDL methods, extending their applicability to diverse images. We introduce the GIM benchmark with two settings to evaluate existing IMDL methods. In addition, we propose a novel IMDL framework, termed GIMFormer, which consists of a ShadowTracer, Frequency-Spatial block (FSB), and a Multi-Window Anomalous Modeling (MWAM) module. Extensive experiments on the GIM demonstrate that GIMFormer surpasses the previous state-of-the-art approach on two different benchmarks.

Evaluations of large-scale recognition methods typically focus on overall performance. While this approach is common, it often fails to provide insights into performance across individual classes, which can lead to fairness issues and misrepresentation. Addressing these gaps is crucial for accurately assessing how well methods handle novel or unseen classes and ensuring a fair evaluation. To address fairness in Open-Set Recognition (OSR), we demonstrate that per-class performance can vary dramatically. We introduce Gaussian Hypothesis Open Set Technique (GHOST), a novel hyperparameter-free algorithm that models deep features using class-wise multivariate Gaussian distributions with diagonal covariance matrices. We apply Z-score normalization to logits to mitigate the impact of feature magnitudes that deviate from the model’s expectations, thereby reducing the likelihood of the network assigning a high score to an unknown sample. We evaluate GHOST across multiple ImageNet-1K pre-trained deep networks and test it with four different unknown datasets. Using standard metrics such as AUOSCR, AUROC and FPR95, we achieve statistically significant improvements, advancing the state-of-the-art in large-scale OSR. Source code is provided online.

We propose GGS, a Generalizable Gaussian Splatting method for Autonomous Driving that can achieve realistic rendering under large viewpoint changes. Previous generalizable 3D gaussian splatting methods are limited to rendering novel views that are very close to the original pair of images, which cannot handle large difference in viewpoint. Especially in autonomous driving scenarios, images are typically collected from a single lane. The limited training perspective makes rendering images of a different lane very challenging. To further improve the rendering capability of GGS under large viewpoint changes, we introduce a novel virtual lane generation module into GSS method to enable high-quality lane switching even without a multi-lane dataset. Besides, we design a diffusion loss to supervise the generation of virtual lane images to further address the problem of data lacking in the virtual lanes. Finally, we also propose a depth refinement module to optimize depth estimation in the GSS model. Extensive validation of our method, compared to existing approaches, demonstrates state-of-the-art performance.

Recovering 4D world from monocular video is a crucial yet challenging task. Conventional methods usually rely on the assumptions of multi-view videos, known camera parameters, or static scenes. In this paper, we relax all these constraints and tackle a highly ambitious but practical task: With only one monocular video without camera parameters, we aim to recover the dynamic 3D world alongside the camera poses. To solve this, we introduce GFlow, a new framework that utilizes only 2D priors (depth and optical flow) to lift a video to a 4D scene, as a flow of 3D Gaussians through space and time. GFlow starts by segmenting the video into still and moving parts, then alternates between optimizing camera poses and the dynamics of the 3D Gaussian points. This method ensures consistency among adjacent points and smooth transitions between frames. Since dynamic scenes always continually introduce new visual content, we present prior-driven initialization and pixel-wise densification strategy for Gaussian points to integrate new content. By combining all those techniques, GFlow transcends the boundaries of 4D recovery from causal videos; it naturally enables tracking of points and segmentation of moving objects across frames. Additionally, GFlow estimates the camera poses for each frame, enabling novel view synthesis by changing camera pose. This capability facilitates extensive scene-level or object-level editing, highlighting GFlow's versatility and effectiveness.

Lane detection plays a crucial role in autonomous driving systems, enabling vehicles to navigate safely and efficiently in complex environment. Despite significant advancements in recent years, accurate lane detection remains a challenging task, particularly in scenarios with occlusions, ambiguous lane markings, and diverse lighting conditions. In this paper, we propose the Global Enhancement and Optimization Network (GEONet) for lane detection, which is designed to refine both feature extraction and global feature transmission. Traditional approaches typically depend on deep convolutional layer stacks for global feature extraction, a process that often compromises inference speed and the precision of global feature representation. In contrast, GEONet introduces a novel and more effective methodology. We present the Global Feature Extraction Module (GFEM), which is specifically engineered to capture comprehensive global features with higher accuracy. Additionally, we introduce the Top-Tier Supplementary Module (TTSM), which enhances these features through a bottom-up approach, improving overall lane detection accuracy. To further bolster our framework, we incorporate Whitening Batch Normalization (WBN) and Whitening Contrastive Learning (WCL), which enhance feature robustness and ensure better generalization. In addition to our novel network design, we propose two new loss functions to enhance lane detection accuracy. The Generalized Rectangular Intersection over Union (GRIoU) Loss extends the predicted points into rectangles, optimizing overlap and smoothness of lane predictions.The Angle Loss accounts for angular differences between predicted and ground truth lanes, improving alignment and continuity. Experimental results demonstrate that our proposed method significantly outperforms current state-of-the-art lane detection techniques.

Developing intelligent negotiation dialogue systems that resolve conflicts and promote equitable, inclusive, and sustainable outcomes is at the forefront of advancing automated negotiation technology for social good. Negotiation involves balancing cooperation and competition to maximize value without causing offense. Using polite language fosters mutual understanding and creates a respectful and collaborative environment essential for successful negotiations in various domains. Considering this, in this paper, we propose a polite negotiation dialogue system, GENTEEL-NEGOTIATOR for social good applications to boost the overall quality of negotiation outcomes. We focus on developing a negotiation dialogue system for two key application areas, namely tourism and e-commerce. We begin by curating a unique negotiation dialogue dataset, NEGOCHAT for tourism. We further enrich the NEGOCHAT and Integrative Negotiation Dataset (IND) for e-commerce with various negotiation strategies. These datasets are then used to develop the GENTEEL-NEGOTIATOR, leveraging the Large Language Model (LLM) and mixture-of-expert (MoE)-based reinforcement learning approach. The proposed MoE-based method employs heuristic experts dedicated to negotiation, politeness, and dialogue coherence to facilitate the learning of diverse semantics by analyzing the dialogue context. A novel reward function with negotiation strategy congruence, politeness, dialogue coherence, and engagingness rewards is designed to guide the policy’s learning for generating responses. Automatic and human evaluations on NEGOCHAT and IND datasets validate the effectiveness of GENTEEL-NEGOTIATOR in generating polite responses during negotiation while maintaining conversation goals, including coherence and engagingness.

Retrosynthesis prediction focuses on identifying reactants capable of synthesizing a target product. Typically, the retrosynthesis prediction involves two phases: Reaction Center Identification and Reactant Generation. However, we argue that most existing methods suffer from two limitations in the two phases: (i) Existing models do not adequately capture the ``face'' information in molecular graphs for the reaction center identification. (ii) Current approaches for the reactant generation predominantly use sequence generation in a 2D space, which lacks versatility in generating reasonable distributions for completed reactive groups and overlooks molecules' inherent 3D properties. To overcome the above limitations, we propose GDiffRetro. For the reaction center identification, GDiffRetro uniquely integrates the original graph with its corresponding dual graph to represent molecular structures, which helps guide the model to focus more on the faces in the graph. For the reactant generation, GDiffRetro employs a conditional diffusion model in 3D to further transform the obtained synthon into a complete reactant. Our experimental findings reveal that GDiffRetro outperforms state-of-the-art semi-template models across various evaluative metrics.

Time series data, prevalent in fields like medical, e-commerce, finance, etc., is used for forecasting, such as predicting next quarter’s product demand based on past trends. However, some problems necessitate causal models to answer questions like “What the product demand would have been without a specific intervention (e.g., products with slower delivery time suppressed from the search results)?” Such questions require causal models to estimate unobserved counterfactual outcome. In this paper, we propose a novel Graph Causal Forecasting (GCF) model, that predicts the unobserved demand leveraging the relationship of a product with other similar products in the marketplace (spatial aspect), along with change in demand over time for each product (temporal aspect). The core idea is to estimate the counterfactual outcome using a synthetic control unaffected by the treatment. Our approach uses RGCN-dilated CNN based network, which leverages domain knowledge to automatically design a synthetic control during training. Using GCF for our demand forecasting problem, we achieve 75.3% lower MAPE compared to baseline. We use the forecasted values to recommend high demand products, in terms of our business metric (discussed later) which tracks the quality of these recommendations, we achieve a significant jump of 61.2%. Moreover, it adds 67.8% more high demand products to the marketplace, compared to existing model in production. Deployment of GCF in 2023, led to +1399 bps improvement in number of products with a view from customers, and +310 bps improvement in number of products with a sale. We also compare GCF with state of the art forecasting methods on a semi-synthetic data, created by simulating a treatment on open source traffic data METR-LA. We achieve 30% lower MSE against TGCN, a time series forecasting approach and 30% lower MSE against CRN and 25% lower MSE against Google Causal Impact model, both of which are causal forecasting approaches.

Incremental object detection (IOD) is a challenging task that requires detection models to continuously learn from newly arriving data. This work focuses on incremental learning for vision-language detectors (VLDs), an under explored domain. Existing research typically adopts a local alignment paradigm to avoid label conflicts, where different tasks are learned separately without interaction. However, we reveal that this practice fails to effectively preserve the semantic structure. Specifically, aligned relationships between objects and texts would collapse when handling novel categories, ultimately leading to catastrophic forgetting. Though knowledge distillation (KD) is a common approach for tackling this, traditional KD performs poorly when directly applied to VLDs, as for different phases, a natural knowledge gap exists in both encoding and decoding processes. To address above issues, we propose a novel method called Global alignment and Correspondence Distillation (GCD). Differently, we first integrate knowledge across phases within the same embedding space to construct global semantic structure. We then enable effective knowledge distillation in VLDs through a semantic correspondence mechanism, ensuring consistent proposal generation and decoding. On the top of that, we distill teacher model’s informative predictions and topological relationships to maintain stable local semantic structure. Extensive experiments on COCO 2017 demonstrate that our method significantly outperforms existing approaches, achieving new state-of-the-art in various IOD scenarios.

Sampling strategy (e.g., fixed farthest point sampling) of point cloud has been an essential step for developing practical solutions in 3D computer vision tasks. Previous fixed sampling is simple, but suffer from suboptimal performance for downstream tasks. To adapt to target networks properly, adaptive sampling methods with trainable parameters have been recently developed to enhance the performance. However, existing adaptive sampling methods still suffer from the over-coupling problem of target network, and thus become model-specific, which limits their practical applications. To address this issue, we propose a novel general cross-scale decoupled sampling method (GCD-sampling) for point cloud, which consists of original feature cache, cross-scale feature fusion and convex combination learning for better feature extraction. To reduce the coupling relationship with the target task network, our method only utilizes the point cloud coordinates as the input and output of itself. Besides, we introduce an arbitrary scale structure to enable parameter sharing across multi-scale sampling in point cloud networks. Extensive experiments on different architectures demonstrate the effectiveness of our method over other existing adaptive sampling methods.

Multivariate time series anomaly detection has numerous real-world applications and is being extensively studied. Modeling pairwise correlations between variables is crucial. Existing methods employ learnable graph structures and graph neural networks to explicitly model the spatial dependencies between variables. However, these methods are primarily based on prediction or reconstruction tasks, which can only learn similarity relationships between sequence embeddings and lack interpretability in how graph structures affect time series evolution. In this paper, we designed a framework that models spatial dependencies using interpretable causal relationships and detects anomalies through changes in causal patterns. Specifically, we propose a method to dynamically discover Granger causality using gradients in nonlinear deep predictors and employ a simple sparsification strategy to obtain a Granger causality graph, detecting anomalies from a causal perspective. Experiments on real-world datasets demonstrate that the proposed model achieves more accurate anomaly detection compared to baseline methods.