GenAI4ELab/papercli-papers huggingface dataset:
Privacy-Preserving V2X Collaborative Perception Integrating Unknown Collaborators
Authors: Bin Lu; Xinyu Xiao; Changzhou Zhang; Yang Zhou; Zhiyu Xiang; Hangguan Shan; Eryun Liu
Venue: AAAI 2025 (main)

Abstract: Vehicle-to-everything (V2X) collaborative perception has recently gained increasing attention in autonomous driving due to its ability to enhance scene understanding by integrating information from other collaborators, e.g. vehicles or infrastructure. Existing algorithms usually share deep features to achieve a trade-off between accuracy and bandwidth. However, most of these methods require joint training of all agents, which results in privacy leakage and is impractical and unacceptable in the real world. Sharing prediction results seems to be a direct solution, but its performance is suboptimal and sensitive to localization noise and communication delay. In this paper, we propose a privacy-preserving collaborative perception framework, where each agent is separately trained with its own dataset and the ego vehicle needs to integrate with completely unknown collaborators. Specifically, we propose MSD, a multi-scale feature fusion method combined with deformable attention, to better fuse features of different agents. We also propose a plug-in domain adapter to align the features from unknown collaborators to ego-domain. Extensive experiments on the challenging DAIR-V2X and V2V4Real demonstrate that: 1) MSD achieves remarkable performance, outperforming others by at least 2.8% and 6.7% in AP0.7 on DAIR-V2X and V2V4Real, respectively; 2) After domain adaptation, it significantly outperforms the No Fusion, Late Fusion scenarios and can approach or even surpass the performance of joint training. We truly achieves privacy-preserving collaboration, providing a new paradigm for the study of collaborative perception, which is crucial for practical applications.
Towards Generalizable Multi-Camera 3D Object Detection via Perspective Rendering
Authors: Hao Lu; Yunpeng Zhang; Guoqing Wang; Qing Lian; Dalong Du; Ying-Cong Chen
Venue: AAAI 2025 (main)

Abstract: Detecting and localizing objects in 3D space using multiple cameras, known as Multi-Camera 3D Object Detection (MC3D-Det), has gained prominence with the advent of bird's-eye view (BEV) approaches. However, these methods often struggle with the serious domain gaps caused by various viewpoints and environments between the training and testing domains. To address this challenge, we propose a novel framework that aligns 3D detection with 2D camera plane results by perspective rendering, thus achieving consistent and accurate results when facing serious domain shifts. Our approach consists of two main steps in both source and target domains: 1) rendering diverse view maps from BEV features by leveraging implicit foreground volumes and 2) rectifying the perspective bias of these maps. This design promotes the learning of perspective- and context-independent features, crucial for accurate object detection across varying viewpoints, camera parameters, and environmental conditions. Notably, our model-agnostic approach preserves the original network structure without incurring additional inference costs, facilitating seamless integration across various models and simplifying deployment. Worth noting is that our approach achieves satisfactory results in real data when trained only with virtual datasets, eliminating the need for real scene annotations. Experimental results on both Domain Generalization (DG) and Unsupervised Domain Adaptation (UDA) demonstrate its effectiveness.
GenesisTex2: Stable, Consistent and High-Quality Text-to-Texture Generation
Authors: Jiawei Lu; YingPeng Zhang; Zengjun Zhao; He Wang; Kun Zhou; Tianjia Shao
Venue: AAAI 2025 (main)

Abstract: Large-scale text-guided image diffusion models have demonstrated remarkable results in text-to-image (T2I) generation. However, applying these models to synthesize textures for 3D geometries remains challenging due to the domain gap between 2D images and textures on a 3D surface. Early works that used a projecting-inpainting approach managed to preserve generation diversity, but often resulted in noticeable artifacts and style inconsistencies. While recent methods have attempted to address these inconsistencies, they often introduce other issues, such as blurring, over-saturation, or over-smoothing. To overcome these challenges, we propose a novel text-to-texture synthesis framework that takes advantage of pre-trained diffusion models. We introduce a local attention reweighing mechanism in the self-attention layers to guide the model in focusing on spatial-correlated patches across different views, thereby enhancing local details while preserving cross-view consistency. Additionally, we propose a novel latent space merge pipeline, which further ensures consistency across different viewpoints without sacrificing too much diversity. Our method significantly outperforms existing state-of-the-art techniques in terms of texture consistency and visual quality, while delivering results much faster than distillation-based methods. Importantly, our framework does not require additional training or fine-tuning, making it highly adaptable to a wide range of models available on public platforms.
DeMo: Deep Motion Field Consensus with Learnable Kernels for Two-view Correspondence Learning
Authors: Yifan Lu; Jiajun Le; Zizhuo Li; Yixuan Yuan; Jiayi Ma
Venue: AAAI 2025 (main)

Abstract: As a long-range prior, motion consensus essentially forces the overall spatial transformation between a pair of images to be smooth and consistent, which is naturally well-suited for two-view correspondence learning. However, such precious property remains under-explored by most existing studies due to the modeling challenges posed by the sparsity and uneven distributions of putative correspondences. In this paper, we propose DeMo, a novel and cutting-edge network for outlier rejection, which possesses the capacity to fully capture global motion consensus clues by way of consensus interpolation over the entire high-dimensional motion field generated by putative correspondences. Specifically, through incorporating regularization techniques into a Reproducing Kernel Hilbert Space (RKHS), a concise interpolation formula can be derived for the high-dimensional motion field, which inherently allows a closed-form solution. Subsequently, learnable deep kernels are collaboratively used to flexibly and efficiently capture the relationships between global inputs, thus maintaining the entire motion field consensus. In addition, to remedy the cubic computational overhead of explicit interpolation, a scene-adaptive sampling strategy is introduced, which implicitly selects the more scene-representative motions, reducing the computational complexity of motion consensus interpolation to be approximately linear while maintaining the accuracy. Moreover, to deal with underlying depth discontinuities caused by complicated scene variations, a local consensus complementation block is designed, which maintains local bilateral consensus across both feature and spatial channels. Without bells and whistles, DeMo achieves superior performance in various geometric tasks, including relative pose estimation, homography estimation, and visual localization.
Can LVLMs Obtain a Driver’s License? A Benchmark Towards Reliable AGI for Autonomous Driving
Authors: Yuhang Lu; Yichen Yao; Jiadong Tu; Jiangnan Shao; Yuexin Ma; Xinge Zhu
Venue: AAAI 2025 (main)

Abstract: Large Vision-Language Models (LVLMs) have recently garnered significant attention, with many efforts aimed at harnessing their general knowledge to enhance the interpretability and robustness of autonomous driving models. However, LVLMs typically rely on large, general-purpose datasets and lack the specialized expertise required for professional and safe driving. Existing vision-language driving datasets focus primarily on scene understanding and decision-making, without providing explicit guidance on traffic rules and driving skills, which are critical aspects directly related to driving safety. To bridge this gap, we propose IDKB, a large-scale dataset containing over one million data items collected from various countries, including driving handbooks, theory test data, and simulated road test data. Much like the process of obtaining a driver's license, IDKB encompasses nearly all the explicit knowledge needed for driving from theory to practice. In particular, we conducted comprehensive tests on 15 LVLMs using IDKB to assess their reliability in the context of autonomous driving and provided extensive analysis. We also fine-tuned popular models, achieving notable performance improvements, which further validate the significance of our dataset.
Generative Video Diffusion for Unseen Novel Semantic Video Moment Retrieval
Authors: Dezhao Luo; Shaogang Gong; Jiabo Huang; Hailin Jin; Yang Liu
Venue: AAAI 2025 (main)

Abstract: Video moment retrieval (VMR) aims to locate the most likely video moment(s) corresponding to a text query in untrimmed videos. Training of existing methods is limited by the lack of diverse and generalisable VMR datasets, hindering their ability to generalise moment-text associations to queries containing novel semantic concepts (unseen both visually and textually in a training source domain). For model generalisation to novel semantics, existing methods rely heavily on assuming to have access to both video and text sentence pairs from a   target domain in addition to the source domain pair-wise training data. This is neither practical nor scalable. In this work, we introduce a more generalisable approach by assuming only text sentences describing new semantics are available in model training without having seen any videos from a target domain. To that end, we propose a Fine-grained Video Editing framework, termed FVE, that explores generative video diffusion to facilitate fine-grained video editing from the seen  source concepts to the unseen target sentences consisting of new concepts. This enables generative hypotheses of unseen video moments corresponding to the novel concepts in the target domain. This fine-grained generative video diffusion retains the original video structure and subject specifics from the source domain while introducing semantic distinctions of unseen novel vocabularies in the target domain. A critical challenge is how to enable this generative fine-grained diffusion process to be meaningful in optimising VMR, more than just synthesising visually pleasing videos. We solve this problem by introducing a hybrid selection mechanism that integrates three quantitative metrics to selectively incorporate synthetic video 
 moments (novel video hypotheses) as enlarged additions to the original source training data, whilst minimising potential detrimental noise or unnecessary repetitions in the novel synthetic videos harmful to VMR learning. Experiments on three datasets demonstrate
the effectiveness of FVE to unseen novel semantic video moment retrieval tasks
Diff-Shadow: Global-guided Diffusion Model for Shadow Removal
Authors: Jinting Luo; Ru Li; Chengzhi Jiang; Xiaoming Zhang; Mingyan Han; Ting Jiang; Haoqiang Fan; Shuaicheng Liu
Venue: AAAI 2025 (main)

Abstract: We propose Diff-Shadow, a global-guided diffusion model for high-quality shadow removal. Previous transformer-based approaches can utilize global information to relate shadow and non-shadow regions but are limited in their synthesis ability and recover images with obvious boundaries. In contrast, diffusion-based methods can generate better content but they are not exempt from issues related to inconsistent illumination. In this work, we combine the advantages of diffusion models and global guidance to realize shadow-free restoration. Specifically, we propose a parallel UNets architecture: 1) the local branch performs the patch-based noise estimation in the diffusion process, and 2) the global branch recovers the low-resolution shadow-free images. A Reweight Cross Attention (RCA) module is designed to integrate global contextual information of non-shadow regions into the local branch. We further design a Global-guided Sampling Strategy (GSS) that mitigates patch boundary issues and ensures consistent illumination across shaded and unshaded regions in the recovered image. Comprehensive experiments on three publicly standard datasets ISTD, ISTD+, and SRD have demonstrated the effectiveness of Diff-Shadow. Compared to state-of-the-art methods, our method achieves a significant improvement in terms of PSNR, increasing from 32.33dB to 33.69dB on the ISTD dataset.
Beyond Pixel and Object: Part Feature as Reference for Few-Shot Video Object Segmentation
Authors: Naisong Luo; Guoxin Xiong; Tianzhu Zhang
Venue: AAAI 2025 (main)

Abstract: Few-Shot Video Object Segmentation (FSVOS) aims to achieve accurate segmentation of video sequences supported by limited annotated images. In this work, we analyze the deficiencies inherent in the use of object prototypes and pixel features as references in previous methods. Then we shed light on that part features, with the ability to adapt to appearance variations and resist noise, are advantageous as representative reference features for aligning support images and query videos. Therefore, we propose a Part Agent Learning Network (PALN) to leverage part features from two aspects. First, we elaborately employ Optimal Transport algorithm with equal partition constraint to make part agents capable of dividing support objects into diverse parts in an adaptive manner. Second, we design a dedicated cache mechanism to learn temporal part agents as lightweight historic target representation to exploit temporal consistency. With the aid of these learned part agents, our PALN can effectively achieve support-query alignment and temporal alignment for accurate segmentation of query videos. Extensive experimental results on two challenging benchmarks demonstrate that our method performs favorably against state-of-the-art FSVOS methods.
Rethinking U-Net: Task-Adaptive Mixture of Skip Connections for Enhanced Medical Image Segmentation
Authors: Zichen Luo; Xinshan Zhu; Lan Zhang; Biao Sun
Venue: AAAI 2025 (main)

Abstract: U-Net is a widely used model for medical image segmentation, renowned for its strong feature extraction capabilities and U-shaped design, which incorporates skip connections to preserve critical information. However, its decoders exhibit information-specific preferences for the supplementary content provided by skip connections, instead of adhering to a strict one-to-one correspondence, which limits its flexibility across diverse tasks. To address this limitation, we propose the Task-Adaptive Mixture of Skip Connections (TA-MoSC) module, inspired by the Mixture of Experts (MoE) framework. TA-MoSC innovatively reinterprets skip connections as a task allocation problem, employing a routing mechanism to adaptively select expert combinations at different decoding stages. By introducing MoE, our approach enhances the sparsity of the model, and lightweight convolutional experts are shared across all skip connection stages, with a Balanced Expert Utilization (BEU) strategy ensuring that all experts are effectively trained, maintaining training balance and preserving computational efficiency. Our approach introduces minimal additional parameters to the original U-Net but significantly enhances its performance and stability. Experiments on GlaS, MoNuSeg, Synapse, and ISIC16 datasets demonstrate state-of-the-art accuracy and better generalization across diverse tasks. Moreover, while this work focuses on medical image segmentation, the proposed method can be seamlessly extended to other segmentation tasks, offering a flexible and efficient solution for diverse applications.
Privacy-Preserving Low-Rank Adaptation Against Membership Inference Attacks for Latent Diffusion Models
Authors: Zihao Luo; Xilie Xu; Feng Liu; Yun Sing Koh; Di Wang; Jingfeng Zhang
Venue: AAAI 2025 (main)

Abstract: Low-rank adaptation (LoRA) is an efficient strategy for adapting latent diffusion models (LDMs) on a private dataset to generate specific images by minimizing the adaptation loss. However, the LoRA-adapted LDMs are vulnerable to membership inference (MI) attacks that can judge whether a particular data point belongs to the private dataset, thus leading to the privacy leakage. To defend against MI attacks, we first propose a straightforward solution: Membership-Privacy-preserving LoRA (MP-LoRA). MP-LoRA is formulated as a min-max optimization problem where a proxy attack model is trained by maximizing its MI gain while the LDM is adapted by minimizing the sum of the adaptation loss and the MI gain of the proxy attack model. However, we empirically find that MP-LoRA has the issue of unstable optimization, and theoretically analyze that the potential reason is the unconstrained local smoothness, which impedes the privacy-preserving adaptation. To mitigate this issue, we further propose a Stable Membership-Privacy-preserving LoRA (SMP-LoRA) that adapts the LDM by minimizing the ratio of the adaptation loss to the MI gain. Besides, we theoretically prove that the local smoothness of SMP-LoRA can be constrained by the gradient norm, leading to improved convergence. Our experimental results corroborate that SMP-LoRA can indeed defend against MI attacks and generate high-quality images.
Revisiting Change Captioning from Self-supervised Global-Part Alignment
Authors: Feixiao Lv; Rui Wang; Lihua Jing
Venue: AAAI 2025 (main)

Abstract: The goal of image change captioning is to capture the content differences between two images and describe them in natural language. The key is how to learn stable content changes from noise such as viewpoint and image structure. However, current work mostly focuses on identifying changes, and the influence of global noise leads to unstable recognition of global features. In order to tackle this problem, we propose a Self-supervised Global-Part Alignment (SSGPA) network and revisit the image change captioning task by enhancing the construction process of overall image global features, enabling the model to integrate global changes such as viewpoint into local changes, and to detect and describe changes in the image through alignment.  Concretely, we first design a Global-Part Transport Alignment mechanism to enhance global features and learn stable content changes through a self-supervised method of optimal transport. Further, we design a Change Fusion Adapter with pre-trained vision-language model to enhance the similar parts features of paired images, thereby enhancing global features, and expanding content changes. Extensive experiments show our method achieves the state-of-the-art results on four datasets.
Infer the Whole from a Glimpse of a Part: Keypoint-Based Knowledge Graph for Vehicle Re-Identification
Authors: Kai Lv; Yunlong Li; Zhuo Chen; Shuo Wang; Sheng Han; Youfang Lin
Venue: AAAI 2025 (main)

Abstract: Vehicle re-identification aims to match vehicles across non-overlapping camera views. Many existing methods extract features from one specific image, and these methods lack view-invariance when comparing vehicles of different orientations. As a result, discriminative parts obscured by viewpoint changes cannot contribute effectively to matching. This work presents a novel keypoint-based framework for vehicle Re-ID. We propose to explicitly model the intrinsic structural relationships between vehicle components via knowledge graph. By establishing connection between keypoints, our approach aims to leverage such prior to match vehicles even when some parts are not directly comparable due to orientation inconsistencies. Specifically, given query and gallery images, we first detect visible keypoints. Then, a transformer-based model infers features for non-overlapped keypoints by conditioning on visible correspondences defined in the knowledge graph. The final representation integrates visible and inferred features. Extensive experiments demonstrate our method outperforms state-of-the-arts on standard benchmarks under cross-view matching scenarios. To our knowledge, this is the first work introducing structural priors via keypoint knowledge graphs for view-invariant vehicle re-identification.
ScaleMatch: Multi-scale Consistency Enhancement for Semi-supervised Semantic Segmentation
Authors: Liang Lv; Lefei Zhang
Venue: AAAI 2025 (main)

Abstract: Semi-supervised learning improves semantic segmentation performance by leveraging unlabeled data, thereby significantly reducing labeling costs. Previous semi-supervised semantic segmentation (S4) methods explored perturbations at the image level but neglected to adequately utilize multi-scale information. When labeled information is insufficient, the scale variation between different objects makes learning instances with extreme scales even more difficult. To address this issue, we propose ScaleMatch, which aims to learn scale-invariant features by obtaining a mixed dual-scale pseudo-label and scale consistency learning. Specifically, the cross-scale interaction fusion (CIF) module enforces interactive information across different scaled-views, allowing for more reliable pseudo-label generation. More importantly, ScaleMatch introduces variable scale branches to utilize scale-invariant supervision. It consists of image-level scale variation consistency (ISVC) and feature-level scale variation consistency (FSVC). Consequently, our ScaleMatch enhances the model's generalization under scale variation, outperforming existing state-of-the-art methods on both the Pascal VOC and Cityscapes datasets under various partition protocols.
Arbitrary Reading Order Scene Text Spotter with Local Semantics Guidance
Authors: Jiahao Lyu; Wei Wang; Dongbao Yang; Jinwen Zhong; Yu Zhou
Venue: AAAI 2025 (main)

Abstract: Scene text spotting has attracted the enthusiasm of relative researchers in recent years. Most existing scene text spotters follow the detection-then-recognition paradigm, where the vanilla detection module hardly determines the reading order and leads to failure recognition. After rethinking the auto-regressive scene text recognition method, we find that a well-trained recognizer can implicitly perceive the local semantics of all characters in a complete word or a sentence without a character-level detection module. Local semantic knowledge not only includes text content but also spatial information in the right reading order. Motivated by the above analysis, we propose the Local Semantics Guided scene text Spotter (LSGSpotter), which auto-regressively decodes the position and content of characters guided by the local semantics. Specifically, two effective modules are proposed in LSGSpotter. On the one hand, we design a Start Point Localization Module (SPLM) for locating text start points to determine the right reading order. On the other hand,  a Multi-scale Adaptive Attention Module (MAAM) is proposed to adaptively aggregate text features in a local area. In conclusion, LSGSpotter achieves the arbitrary reading order spotting task without the limitation of sophisticated detection, while alleviating the cost of computational resources with the grid sampling strategy. Extensive experiment results show LSGSpotter achieves state-of-the-art performance on the InverseText benchmark. Moreover, our spotter demonstrates superior performance on English benchmarks for arbitrary-shaped text, achieving improvements of 0.7% and 2.5% on Total-Text and SCUT-CTW1500, respectively. These results validate our text spotter is effective for scene texts in arbitrary reading order and shape.