GenAI4ELab/papercli-papers huggingface dataset:
Deconfound Semantic Shift and Incompleteness in Incremental Few-shot Semantic Segmentation
Authors: Yirui Wu; Yuhang Xia; Hao Li; Lixin Yuan; Junyang Chen; Jun Liu; Tong Lu; Shaohua Wan
Venue: AAAI 2025 (main)

Abstract: Incremental few-shot semantic segmentation (IFSS) expands segmentation capacity of the trained model to segment new-class images with few samples. However, semantic meanings may shift from background to object class or vice versa during incremental learning. Moreover, new-class samples often lack representative attribute features when the new class greatly differs from the pre-learned old class. In this paper, we propose a causal framework to discuss the cause of semantic shift and incompleteness in IFSS, and we deconfound the revealed causal effects from two aspects. First, we propose a Causal Intervention Module (CIM) to resist semantic shift. CIM progressively and adaptively updates prototypes of old class, and removes the confounder in an intervention manner. Second, a Prototype Refinement Module (PRM) is proposed to complete the missing semantics. In PRM, knowledge gained from the episode learning scheme assists in fusing features of new-class and old-class prototypes. Experiments on both PASCAL-VOC 2012 and ADE20k benchmarks demonstrate the outstanding performance of our method.
Video Repurposing from User Generated Content: A Large-scale Dataset and Benchmark
Authors: Yongliang Wu; Wenbo Zhu; Jiawang Cao; Yi Lu; Bozheng Li; Weiheng Chi; Zihan Qiu; Lirian Su; Haolin Zheng; Jay Wu; Xu Yang
Venue: AAAI 2025 (main)

Abstract: The demand for producing short-form videos for sharing on social media platforms has experienced significant growth in recent times. Despite notable advancements in the fields of video summarization and highlight detection, which can create partially usable short films from raw videos, these approaches are often domain-specific and require an in-depth understanding of real-world video content. To tackle this predicament, we propose Repurpose-10K, an extensive dataset comprising over 10,000 videos with more than 120,000 annotated clips aimed at resolving the video long-to-short task. Recognizing the inherent constraints posed by untrained human annotators, which can result in inaccurate annotations for repurposed videos, we propose a two-stage solution to obtain annotations from real-world user-generated content. Furthermore, we offer a baseline model to address this challenging task by integrating audio, visual, and caption aspects through a cross-modal fusion and alignment framework. We aspire for our work to ignite groundbreaking research in the lesser-explored realms of video repurposing.
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
Authors: Yongliang Wu; Shiji Zhou; Mingzhuo Yang; Lianzhe Wang; Heng Chang; Wenbo Zhu; Xinting Hu; Xiao Zhou; Xu Yang
Venue: AAAI 2025 (main)

Abstract: Text-to-image diffusion models have achieved remarkable success in generating photorealistic images. However, the inclusion of sensitive information during pre-training poses significant risks. Machine Unlearning (MU) offers a promising solution to eliminate sensitive concepts from these models. Despite its potential, existing MU methods face two main challenges: 1) limited generalization, where concept erasure is effective only within the unlearned set, failing to prevent sensitive concept generation from out-of-set prompts; and 2) utility degradation, where removing target concepts significantly impacts the model's overall performance. To address these issues, we propose a novel concept domain correction framework named \textbf{DoCo} (\textbf{Do}main \textbf{Co}rrection). By aligning the output domains of sensitive and anchor concepts through adversarial training, our approach ensures comprehensive unlearning of target concepts. Additionally, we introduce a concept-preserving gradient surgery technique that mitigates conflicting gradient components, thereby preserving the model's utility while unlearning specific concepts. Extensive experiments across various instances, styles, and offensive concepts demonstrate the effectiveness of our method in unlearning targeted concepts with minimal impact on related concepts, outperforming previous approaches even for out-of-distribution prompts.
MUCD: Unsupervised Point Cloud Change Detection via Masked Consistency
Authors: Yue Wu; Zhipeng Wang; Yongzhe Yuan; Maoguo Gong; Hao Li; Mingyang Zhang; Wenping Ma; Qiguang Miao
Venue: AAAI 2025 (main)

Abstract: 3D Change Detection (3DCD) has gradually become another research hotspot after image change detection. Recent works focus on using artificial labels for supervised or weakly-supervised training of siamese networks to segment changed points. However, labeling every points of multi-temporal point clouds is very expensive and time-consuming. In addition, these works lack effective self-supervised signals, and existing self-supervised signals often fail to capture sufficiently rich change information. To solve this problem, we assume that the powerful representation of 3D objects should model the consistency information of unchanged regions and distinguish different objects. Based on this assumption, we propose a new unsupervised framework called MUCD to learn change information of multi-temporal point clouds through bidirectional optimization of change segmentor and feature extractor. The training of network is divided into two stages. We first design a foreknowledge point contrastive loss based on the characteristics of the 3DCD task to initialize the feature extractor, and then propose a masked consistency loss to further learn the shared geometric information of unchanged regions in the multi-temporal point clouds, utilizing it as a free and powerful supervised signal to train a change segmentor. In the inference stage, only the segmentor is used to take multi-temporal point clouds as input and produce change segmentation result. Extensive experiments are conducted on SLPCCD and Urb3DCD, two real-world datasets of streets and urban buildings, to verify that our proposed unsupervised method is highly competitive and even outperforms supervised methods in scenes where semantic information changes occur, exhibiting better performance in generalization ability and robustness.
Sparis: Neural Implicit Surface Reconstruction of Indoor Scenes from Sparse Views
Authors: Yulun Wu; Han Huang; Wenyuan Zhang; Chao Deng; Ge Gao; Ming Gu; Yu-Shen Liu
Venue: AAAI 2025 (main)

Abstract: In recent years, reconstructing indoor scene geometry from multi-view images has achieved encouraging accomplishments. Current methods incorporate monocular priors into neural implicit surface models to achieve high-quality reconstructions. However, these methods require hundreds of images for scene reconstruction. When only a limited number of views are available as input, the performance of monocular priors deteriorates due to scale ambiguity, leading to the collapse of the reconstructed scene geometry. In this paper, we propose a new method, named Sparis, for indoor surface reconstruction from sparse views. Specifically, we investigate the impact of monocular priors on sparse scene reconstruction, introducing a novel prior based on inter-image matching information. Our prior offers more accurate depth information while ensuring cross-view matching consistency. Additionally, we employ an angular filter strategy and an epipolar matching weight function, aiming to reduce errors due to view matching inaccuracies, thereby refining the inter-image prior for improved reconstruction accuracy. The experiments conducted on widely used benchmarks demonstrate superior performance in sparse-view scene reconstruction.
FriendsQA: A New Large-Scale Deep Video Understanding Dataset with Fine-grained Topic Categorization for Story Videos
Authors: Zhengqian Wu; Ruizhe Li; Zijun Xu; Zhongyuan Wang; Chunxia Xiao; Chao Liang
Venue: AAAI 2025 (main)

Abstract: Video question answering (VideoQA) aims to answer natural language questions according to the given videos. Although existing models perform well in the factoid VideoQA task, they still face challenges in deep video understanding (DVU) task, which focuses on story videos. Compared to factoid videos, the most significant feature of story videos is storylines, which are composed of complex interactions and long-range evolvement of core story topics including characters, actions and locations. Understanding these topics requires models to possess DVU capability. However, existing DVU datasets rarely organize questions according to these story topics, making them difficult to comprehensively assess VideoQA models' DVU capability of complex storylines. Additionally, the question quantity and video length of these dataset are limited by high labor costs of handcrafted dataset building method. In this paper, we devise a large language model based multi-agent collaboration framework, StoryMind, to automatically generate a new large-scale DVU dataset. The dataset, FriendsQA, derived from the renowned sitcom Friends with an average episode length of 1,358 seconds, contains 44.6K questions evenly distributed across 14 fine-grained topics. Finally, We conduct comprehensive experiments on 10 state-of-the-art VideoQA models using the FriendsQA dataset.
Hierarchically Controlled Deformable 3D Gaussians for Talking Head Synthesis
Authors: Zhenhua Wu; Linxuan Jiang; Xiang Li; Chaowei Fang; Yipeng Qin; Guanbin Li
Venue: AAAI 2025 (main)

Abstract: Audio-driven talking head synthesis is a critical task in digital human modeling. While recent advances using diffusion models and Neural Radiance Fields (NeRF) have improved visual quality, they often require substantial computational resources, limiting practical deployment. We present a novel framework for audio-driven talking head synthesis, namely it Hierarchically Controlled Deformable 3D Gaussians (HiCoDe), which achieves state-of-the-art performance with significantly reduced computational costs. Our key contribution is a hierarchical control strategy that effectively bridges the gap between sparse audio features and dense 3D Gaussian point clouds. Specifically, this strategy comprises two control levels: i) coarse-level control based on a 3D Morphable Model (3DMM) and ii) fine-level control using facial landmarks. Extensive experiments on the HDTF dataset and additional test sets demonstrate that our method outperforms existing approaches in visual quality, facial landmark accuracy, and audio-visual synchronization while being more computationally efficient in both training and inference.
Relaxed Rotational Equivariance via G-Biases in Vision
Authors: Zhiqiang Wu; Yingjie Liu; Licheng Sun; Jian Yang; Hanlin Dong; Shing-Ho J. Lin; Xuan Tang; Jinpeng Mi; Bo Jin; Xian Wei
Venue: AAAI 2025 (main)

Abstract: Group Equivariant Convolution (GConv) can capture rotational equivariance from original data. It assumes uniform and strict rotational equivariance across all features as the transformations under the specific group. However, the presentation or distribution of real-world data rarely conforms to strict rotational equivariance, commonly referred to as Rotational Symmetry-Breaking (RSB) in the system or dataset, making GConv unable to adapt effectively to this phenomenon. Motivated by this, we propose a simple but highly effective method to address this problem, which utilizes a set of learnable biases called G-Biases under the group order to break strict group constraints and then achieve a Relaxed Rotational Equivariant Convolution (RREConv). To validate the efficiency of RREConv, we conduct extensive ablation experiments on the discrete rotational group Cn. Experiments demonstrate that the proposed RREConv-based methods achieve excellent performance compared to existing GConv-based methods in both classification and 2D object detection tasks on the natural image datasets.
Unified Knowledge Maintenance Pruning and Progressive Recovery with Weight Recalling for Large Vision-Language Models
Authors: Zimeng Wu; Jiaxin Chen; Yunhong Wang
Venue: AAAI 2025 (main)

Abstract: Large Vision-Language Model (LVLM), leveraging Large Language Model (LLM) as the cognitive core, has recently become one of the most representative multimodal model paradigms.
However, with the expansion of unimodal branches, \emph{i.e.} visual encoder and LLM, the storage and computational burdens intensify, posing challenges for deployment.
Structured pruning has proved promising in compressing large models by trimming a large portion of insignificant network structures.
Nevertheless, most of them are predominantly designed for LLMs, either relying on unitary importance metrics that fail to deal with modality-wise imbalances or adopting generic pruning and recovery paradigms that overlook the unique calibration status and capability requirements of large models, leading to substantial performance degradation.
To address these issues, we propose a novel structured pruning approach for LVLMs, dubbed Unified Knowledge Maintenance Pruning and Progressive Recovery with Weight Recalling (UKMP). Specifically, we design a Unified Knowledge Maintenance Importance (UKMI) metric, which simultaneously considers balancing the block-wise and modality-wise importance by adaptive normalization, optimizing the importance estimation by refining gradient-based criteria, and maintaining the knowledge capacity of LVLMs by using the angle distribution information entropy. Moreover, we develop a LoRA-based Progressive Distillation (LPD) method that recalls the pruned weights and performs progressive distillation for comprehensive recovery.
Extensive experimental results across various vision-language tasks demonstrate the effectiveness of our approach, comparing to the state-of-the-art structured pruning methods.
RETRACTED: GEONet: Global Enhancement and Optimization Network for Lane Detection
Authors: Suyang Xi; Yunhao Liu; Hong Ding; Mingshuo Wang; Zhenghan Chen; Xiaoxuan Liang
Venue: AAAI 2025 (main)

Abstract: 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.Editorial NotesThis article, which was published in Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2025), has been retracted by agreement between the authors and the journal.
Enhancing Close-up Novel View Synthesis via Pseudo-labeling
Authors: Jiatong Xia; Libo Sun; Lingqiao Liu
Venue: AAAI 2025 (main)

Abstract: Recent methods, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have demonstrated remarkable capabilities in novel view synthesis. However, despite their success in producing high-quality images for viewpoints similar to those seen during training, they struggle when generating detailed images from viewpoints that significantly deviate from the training set, particularly in close-up views. The primary challenge stems from the lack of specific training data for close-up views, leading to the inability of current methods to render these views accurately. To address this issue, we introduce a novel pseudo-label-based learning strategy. This approach leverages pseudo-labels derived from existing training data to provide targeted supervision across a wide range of close-up viewpoints. Recognizing the absence of benchmarks for this specific challenge, we also present a new dataset designed to assess the effectiveness of both current and future methods in this area. Our extensive experiments demonstrate the efficacy of our approach.
Multi-Shape Matching with Cycle Consistency Basis via Functional Maps
Authors: Yifan Xia; Tianwei Ye; Huabing Zhou; Zhongyuan Wang; Jiayi Ma
Venue: AAAI 2025 (main)

Abstract: Multi-shape matching is a central problem in various applications of computer vision and graphics, where cycle consistency constraints play a pivotal role. For this issue, we propose a novel and efficient approach that models multi-shapes as directed graphs for two-stage optimization, i.e., optimizing pairwise correspondence accuracy using landmarks, and refining matching consistency through cycle consistency basis. Specifically, we utilize local mapping distortion to identify landmarks and extract the dimension of the functional space, which is then used to upsample in the spectral domain, thereby producing smoother results. Next, to optimize the consistency of correspondences, we introduce the cycle consistency basis, which succinctly describes all consistent cycles in the collection. We then propose cycle consistency refinement, which resolves inconsistencies in cycles efficiently via the alternating direction method of multipliers. Our approach simultaneously balances the accuracy and consistency of multi-shape matching, achieving lower correspondence errors. Extensive experiments on several public datasets demonstrate the superiority of our approach over current state-of-the-art methods.
PlaNet: Learning to Mitigate Atmospheric Turbulence in Planetary Images
Authors: Yifei Xia; Chu Zhou; Chengxuan Zhu; Chao Xu; Boxin Shi
Venue: AAAI 2025 (main)

Abstract: Obtaining planetary images with good visual quality is not an easy task since they are usually degenerated by atmospheric turbulence during the imaging procedure. Existing atmospheric turbulence mitigation methods designed for conventional images cannot be applied to planetary images, since the objects on the Earth have totally different degeneration patterns to planets. Besides, in planetary imaging, photographers often capture as many frames as possible to reduce the noise level of planetary images, which requires the method designed for planetary images to support an arbitrary number of input frames. In this paper, we propose a vertical distance-aware turbulence simulation pipeline to synthesize realistic planetary images in accordance with their unique degeneration patterns at a large scale with affordable computational cost, and design a neural network to mitigate the turbulence with flexible input frames by adopting an edge-based supervision strategy to handle the background scarcity issue. Experimental results show that our method achieves state-of-the-art performance on both synthetic and real-world images.
CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing
Authors: Xiaole Xian; Xilin He; Zenghao Niu; Junliang Zhang; Weicheng Xie; Siyang Song; Zitong Yu; Linlin Shen
Venue: AAAI 2025 (main)

Abstract: For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional fine-tuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. Our codes will be made publicly available.
POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position Search
Authors: Chong-Yang Xiang; Jun-Yan He; Zhi-Qi Cheng; Xiao Wu; Xian-Sheng Hua
Venue: AAAI 2025 (main)

Abstract: Achieving a balance between accuracy and efficiency is a critical challenge in facial landmark detection (FLD). This paper introduces Parallel Optimal Position Search (POPoS), a high-precision encoding-decoding framework designed to address the limitations of traditional FLD methods. POPoS employs three key contributions: (1) Pseudo-range multilateration is utilized to correct heatmap errors, improving landmark localization accuracy. By integrating multiple anchor points, it reduces the impact of individual heatmap inaccuracies, leading to robust overall positioning. (2) To enhance the pseudo-range accuracy of selected anchor points, a new loss function, named multilateration anchor loss, is proposed. This loss function enhances the accuracy of the distance map, mitigates the risk of local optima, and ensures optimal solutions. (3) A single-step parallel computation algorithm is introduced, boosting computational efficiency and reducing processing time. Extensive evaluations across five benchmark datasets demonstrate that POPoS consistently outperforms existing methods, particularly excelling in low-resolution heatmaps scenarios with minimal computational overhead. These advantages make POPoS as a highly efficient and accurate tool for FLD, with broad applicability in real-world scenarios.