GenAI4ELab/papercli-papers huggingface dataset:
DanceFix: An Exploration in Group Dance Neatness Assessment Through Fixing Abnormal Challenges of Human Pose
Authors: Huangbiao Xu; Xiao Ke; Huanqi Wu; Rui Xu; Yuezhou Li; Peirong Xu; Wenzhong Guo
Venue: AAAI 2025 (main)

Abstract: The fair and objective assessment of performances and competitions is a common pursuit and challenge in human society. The application of computer vision technology offers hope for this purpose, but it still faces obstacles such as occlusion and motion blur. To address these hindrances, our DanceFix proposes a bidirectional spatial-temporal context optical flow correction (BOFC) method. This approach leverages the consistency and complementarity of motion information between two modalities: optical flow, which excels at pixel capture, and lightweight skeleton data. It enables the extraction of pixel-level motion changes and the correction of abnormal skeleton data. Furthermore, we propose a part-level dance dataset (Dancer Parts) and part-level motion feature extraction based on task decoupling (PETD). This aims to decouple complex whole-body parts tracking into fine-grained limb-level motion extraction, enhancing the confidence of temporal information and the accuracy of correction for abnormal data. Finally, we present the DNV dataset, which simulates fully neat group dance scenes and provides reliable labels and validation methods for the newly introduced group dance neatness assessment (GDNA). To the best of our knowledge, this is the first work to develop quantitative criteria for assessing limb and joint neatness in group dance. We conduct experiments on DNV and video-based public JHMDB datasets. Our method effectively corrects abnormal skeleton points, flexibly embeds, and improves the accuracy of existing pose estimation algorithms.
Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model
Authors: Jiahua Xu; Dawei Zhou; Lei Hu; Jianfeng Guo; Feng Yang; Zaiyi Liu; Nannan Wang; Xinbo Gao
Venue: AAAI 2025 (main)

Abstract: Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis. Removing motion artifacts is a straightforward solution and has been extensively studied. However, paired data are still heavily relied on in recent works and the perturbations in k-space (frequency domain) are not well considered, which limits their applications in the clinical field. To address these issues, we propose a novel unsupervised purification method which leverages pixel-frequency information of noisy MRI images to guide a pre-trained diffusion model to recover clean MRI images. Specifically, considering that motion artifacts are mainly concentrated in high-frequency components in k-space, we utilize the low-frequency components as the guide to ensure correct tissue textures. Additionally, given that high-frequency and pixel information are helpful for recovering shape and detail textures, we design alternate complementary masks to simultaneously destroy the artifact structure and exploit useful information. Quantitative experiments are performed on datasets from different tissues and show that our method achieves superior performance on several metrics. Qualitative evaluations with radiologists also show that our method provides better clinical feedback.
OmniSR: Shadow Removal Under Direct and Indirect Lighting
Authors: Jiamin Xu; Zelong Li; Yuxin Zheng; Chenyu Huang; Renshu Gu; Weiwei Xu; Gang Xu
Venue: AAAI 2025 (main)

Abstract: Shadows can originate from occlusions in both direct and indirect illumination. Although most current shadow removal research focuses on shadows caused by direct illumination, shadows from indirect illumination are often just as pervasive, particularly in indoor scenes. A significant challenge in removing shadows from indirect illumination is obtaining shadow-free images to train the shadow removal network. To overcome this challenge, we propose a novel rendering pipeline for generating shadowed and shadow-free images under direct and indirect illumination, and create a comprehensive synthetic dataset that contains over 30,000 image pairs, covering various object types and lighting conditions. We also propose an innovative shadow removal network that explicitly integrates semantic and geometric priors through concatenation and attention mechanisms. The experiments show that our method outperforms state-of-the-art shadow removal techniques and can effectively generalize to indoor and outdoor scenes under various lighting conditions, enhancing the overall effectiveness and applicability of shadow removal methods.
CLIP-driven View-aware Prompt Learning for Unsupervised Vehicle Re-identification
Authors: Jiyang Xu; Qi Wang; Xin Xiong; Di Gai; Ruihua Zhou; Dong Wang
Venue: AAAI 2025 (main)

Abstract: With the emergence of vision-language pre-trained models, such as CLIP, some textual prompts have been gradually introduced recently into re-identification (Re-ID) tasks to obtain considerably robust multimodal information. However, most textual descriptions based on vehicle Re-ID tasks only contain identity index words without specific words to describe vehicle view information, thereby resulting in difficulty to be widely applied in vehicle Re-ID tasks with view variations. This case inspires us to propose a CLIP-driven view-aware prompt learning framework for unsupervised vehicle Re-ID. We first design a learnable textual prompt template called view-aware context optimization (ViewCoOp) based on dynamic multi-view word embeddings, which can fully obtain the proportion and position encoding of each view in the whole vehicle body region. Subsequently, a cross-modal mutual graph is constructed to explore the connections between inter-modal and intra-modal. Each sample is treated as a graph node, which extracts textual features based on ViewCoOp and the visual features of images. Moreover, leveraging the inter-cluster and intra-cluster correlation in the bimodal clustering results in the determination of connectivity between graph node pairs. Lastly, the proposed cross-modal mutual graph method utilizes supervised information from the bimodal gap to directly fine-tune the image encoder of CLIP for downstream unsupervised vehicle Re-ID tasks. Extensive experiments verify that the proposed method is capable of effectively obtaining cross-modal description ability from multiple views.
Physical-aware Neural Radiance Fields for Efficient Exposure Correction
Authors: Kai Xu; Mingwen Shao; Yuanjian Qiao; Yan Wang
Venue: AAAI 2025 (main)

Abstract: Neural Radiance Fields (NeRF) has achieved remarkable success in synthesizing impressive novel views. However, existing methods usually fail to handle scenes with adverse lighting conditions caused by external time variations and different camera settings, leading to poor visual quality. To address this challenge, we propose a physical-aware NeRF for efficient exposure correction, named PHY-NeRF. Specifically, we design Adaptive Lighting Particles inspired by the theory of light scattering and absorption, which can adjust the illumination intensity during volume rendering. Subsequently, we can handle scenes with different lighting conditions by jointly optimizing camera parameters and these lighting particles. Moreover, to promote natural brightness transitions, we devise a global illumination consistency module to control the lighting intensity across views at the feature level while completing more details. Benefiting from the above designs, our PHY-NeRF can tackle arbitrary low-light or overexposed scenes in an unsupervised manner. Extensive experiments show that our PHY-NeRF achieves state-of-the-art results in addressing adverse lighting problems while ensuring high rendering efficiency.
DASK: Distribution Rehearsing via Adaptive Style Kernel Learning for Exemplar-Free Lifelong Person Re-Identification
Authors: Kunlun Xu; Chenghao Jiang; Peixi Xiong; Yuxin Peng; Jiahuan Zhou
Venue: AAAI 2025 (main)

Abstract: Lifelong person re-identification (LReID) is an important but challenging task that suffers from catastrophic forgetting due to significant domain gaps between training steps. Existing LReID approaches typically rely on data replay and knowledge distillation to mitigate this issue. However, data replay methods compromise data privacy by storing historical exemplars, while knowledge distillation methods suffer from limited performance due to the cumulative forgetting of undistilled knowledge. To overcome these challenges, we propose a novel paradigm that models and rehearses the distribution of the old domains to enhance knowledge consolidation during the new data learning, possessing a strong anti-forgetting capacity without storing any exemplars. Specifically, we introduce an exemplar-free LReID method called Distribution Rehearsing via Adaptive Style Kernel Learning (DASK). DASK includes a Distribution Rehearser Learning mechanism that learns to transform arbitrary distribution data into the current data style at each learning step. To enhance the style transfer capacity, an Adaptive Kernel Prediction network is explored to achieve an instance-specific distribution adjustment. Additionally, we design a Distribution Rehearsing-driven LReID Training module, which rehearses old distribution based on the new data via the old AKPNet model, achieving effective knowledge accumulation. Experimental results show our DASK outperforms the existing methods by 3.6%-6.8% and 4.5%-6.5% on seen and unseen domains, respectively.
Multiple Feature Refining Network for Visual Emotion Distribution Learning
Authors: Qinfu Xu; Shaozu Yuan; Yiwei Wei; Jie Wu; Leiquan Wang; Chunlei Wu
Venue: AAAI 2025 (main)

Abstract: The significance of visual emotion distribution learning (VEDL) has surged, particularly with the growing inclination to convey emotions through images. The key of VEDL lies in capturing both low- and high-level features within the same visual content, thus promoting the model for salient and subtle emotion awareness. To learn the distribution of emotions involved in images, most previous works learn coarse semantic knowledge with unbiased filtering. Consequently, they focus on the entire scene and suffer from the redundancy of semantic-irrelevant information, which diminishes the affective coherence, impeding the comprehension of emotional attributes within the treated features. In light of this, we reanalyze from the perspective of information filtering and propose a novel method called Multiple Feature Refining Network (MFRN). To minimize low-level feature redundancy, we design a wavelet-based separated frequency modeling, named Spectral Mixer, to learn invariant representations and enhance emotion saliency in low-level image features. At the higher semantic level, we design a Semantic Graph Prompt Learning for emotional semantic filtering, ensuring the purity of emotional information and providing the model with richer content semantics. Experiments conducted on three commonly used datasets have demonstrated the superiority of our MFRN model over cutting-edge methods.
SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object Detection
Authors: Ruoyu Xu; Zhiyu Xiang; Chenwei Zhang; Hanzhi Zhong; Xijun Zhao; Ruina Dang; Peng Xu; Tianyu Pu; Eryun Liu
Venue: AAAI 2025 (main)

Abstract: 3D object detection is one of the fundamental perception tasks for autonomous vehicles. Fulfilling such a task with a 4D millimeter-wave radar is very attractive since the sensor is able to acquire 3D point clouds similar to Lidar while maintaining robust measurements under adverse weather. However, due to the high sparsity and noise associated with the radar point clouds, the performance of the existing methods is still much lower than expected. In this paper, we propose a novel Semi-supervised Cross-modality Knowledge Distillation (SCKD) method for 4D radar-based 3D object detection. It characterizes the capability of learning the feature from a Lidar-radar-fused teacher network with semi-supervised distillation. We first propose an adaptive fusion module in the teacher network to boost its performance. Then, two feature distillation modules are designed to facilitate the cross-modality knowledge transfer. Finally, a semi-supervised output distillation is proposed to increase the effectiveness and flexibility of the distillation framework. With the same network structure, our radar-only student trained by SCKD boosts the mAP by 10.38% over the baseline and outperforms the state-of-the-art works on the VoD dataset. The experiment on ZJUODset also shows 5.12% mAP improvements on the moderate difficulty level over the baseline when extra unlabeled data are available.
Motion-adaptive Transformer for Event-based Image Deblurring
Authors: Senyan Xu; Zhijing Sun; Mingchen Zhong; Chengzhi Cao; Yidi Liu; Xueyang Fu; Yan Chen
Venue: AAAI 2025 (main)

Abstract: Event cameras, which capture pixel-level brightness changes asynchronously, provide rich motion information that is often missed during traditional frame-based camera exposures, thereby offering fresh perspectives for motion deblurring. Although current approaches incorporate event intensity, they neglect essential spatial motion information. Unlike their CNN architectures, Transformers excel in modeling long-range dependencies but struggle with establishing relevant non-local connections in sparse events and fail to highlight significant interactions in dense images. To address these limitations, we introduce a Motion-Adaptive Transformer network (MAT) that utilizes spatial motion information to forge robust global connections. The core design is an Adaptive Motion Mask Predictor (AMMP) that identifies key motion regions, guiding the Motion-Sparse Attention (MSA) to eliminate irrelevant event tokens and enabling the Motion-Aware Attention (MAA) to focus on relevant ones, thereby enhancing long-range dependency modeling. Additionally, we elaborately design a Cross-Modal Intensity Gating mechanism that efficiently merges intensity data across modalities while minimizing parameter use. The learnable Expansion-Controlled Spatial Gating further optimizes the transmission of event features. Comprehensive testing confirms that our approach sets a new benchmark in image deblurring, surpassing previous methods by up to 0.60dB on the GoPro dataset, 1.04dB on the HS-ERGB dataset, and achieving an average improvement of 0.52dB across two real-world datasets.
LiON: Learning Point-Wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data
Authors: Shaocong Xu; Pengfei Li; Qianpu Sun; Xinyu Liu; Yang Li; Shihui Guo; Zhen Wang; Bo Jiang; Rui Wang; Kehua Sheng; Bo Zhang; Li Jiang; Hao Zhao; Yilun Chen
Venue: AAAI 2025 (main)

Abstract: LiDAR-based semantic scene understanding is an important module in the modern autonomous driving perception stack. However, identifying outlier points in a LiDAR point cloud is challenging as LiDAR point clouds lack semantically-rich information. While former SOTA methods adopt heuristic architectures, we revisit this problem from the perspective of Selective Classification, which introduces a selective function into the standard closed-set classification setup. Our solution is built upon the basic idea of abstaining from choosing any inlier categories but learns a point-wise abstaining penalty with a margin-based loss. Apart from learning paradigms, synthesizing outliers to approximate unlimited real outliers is also critical, so we propose a strong synthesis pipeline that generates outliers originated from various factors: object categories, sampling patterns and sizes. We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance. We benchmark our method on SemanticKITTI and nuScenes and achieve SOTA results.
TB-HSU: Hierarchical 3D Scene Understanding with Contextual Affordances
Authors: Wenting Xu; Viorela Ila; Luping Zhou; Craig T. Jin
Venue: AAAI 2025 (main)

Abstract: The concept of function and affordance is a critical aspect of 3D scene understanding and supports task-oriented objectives. In this work, we develop a model that learns to structure and vary functional affordance across a 3D hierarchical scene graph representing the spatial organization of a scene. The varying functional affordance is designed to integrate with the varying spatial context of the graph. More specifically, we develop an algorithm that learns to construct a 3D hierarchical scene graph (3DHSG) that captures the spatial organization of the scene. Starting from segmented object point clouds and object semantic labels, we develop a 3DHSG with a top node that identifies the room label, child nodes that define local spatial regions inside the room with region-specific affordances, and grand-child nodes indicating object locations and object-specific affordances. To support this work, we create a custom 3DHSG dataset that provides ground truth data for local spatial regions with region-specific affordances and also object-specific affordances for each object. We employ a Transformer Based Hierarchical Scene Understanding (TB-HSU) model to learn the 3DHSG. We use a multi-task learning framework that learns both room classification and learns to define spatial regions within the room with region-specific affordances. Our work improves on the performance of state-of-the-art baseline models and shows one approach for applying transformer models to 3D scene understanding and the generation of 3DHSGs that capture the spatial organization of a room. The code and dataset are publicly available.
PDDM: Pseudo Depth Diffusion Model for RGB-PD Semantic Segmentation Based in Complex Indoor Scenes
Authors: Xinhua Xu; Hong Liu; Jianbing Wu; Jinfu Liu
Venue: AAAI 2025 (main)

Abstract: The integration of RGB and depth modalities significantly enhances the accuracy of segmenting complex indoor scenes, with depth data from RGB-D cameras playing a crucial role in this improvement. However, collecting an RGB-D dataset is more expensive than an RGB dataset due to the need for specialized depth sensors. Aligning depth and RGB images also poses challenges due to sensor positioning and issues like missing data and noise. In contrast, Pseudo Depth (PD) from high-precision depth estimation algorithms can eliminate the dependence on RGB-D sensors and alignment processes, as well as provide effective depth information and show significant potential in semantic segmentation. Therefore, to explore the practicality of utilizing pseudo depth instead of real depth for semantic segmentation, we design an RGB-PD segmentation pipeline to integrate RGB and pseudo depth and propose a Pseudo Depth Aggregation Module (PDAM) for fully exploiting the informative clues provided by the diverse pseudo depth maps. The PDAM aggregates multiple pseudo depth maps into a single modality, making it easily adaptable to other RGB-D segmentation methods. In addition, the pre-trained diffusion model serves as a strong feature extractor for RGB segmentation tasks, but multi-modal diffusion-based segmentation methods remain unexplored. Therefore, we present a Pseudo Depth Diffusion Model (PDDM) that adopts a large-scale text-image diffusion model as a feature extractor and a simple yet effective fusion strategy to integrate pseudo depth. To verify the applicability of pseudo depth and our PDDM, we perform extensive experiments on the NYUv2 and SUNRGB-D datasets. The experimental results demonstrate that pseudo depth can effectively enhance segmentation performance, and our PDDM achieves state-of-the-art performance, outperforming other methods by +6.98 mIoU on NYUv2 and +2.11 mIoU on SUNRGB-D.
Zero-shot Video Moment Retrieval via Off-the-shelf Multimodal Large Language Models
Authors: Yifang Xu; Yunzhuo Sun; Benxiang Zhai; Ming Li; Wenxin Liang; Yang Li; Sidan Du
Venue: AAAI 2025 (main)

Abstract: The target of video moment retrieval (VMR) is predicting temporal spans within a video that semantically match a given linguistic query. Existing VMR methods based on multimodal large language models (MLLMs) overly rely on expensive high-quality datasets and time-consuming fine-tuning. Although some recent studies introduce a zero-shot setting to avoid fine-tuning, they overlook inherent language bias in the query, leading to erroneous localization. To tackle the aforementioned challenges, this paper proposes Moment-GPT, a tuning-free pipeline for zero-shot VMR utilizing frozen MLLMs. Specifically, we first employ LLaMA-3 to correct and rephrase the query to mitigate language bias. Subsequently, we design a span generator combined with MiniGPT-v2 to produce candidate spans adaptively. Finally, to leverage the video comprehension capabilities of MLLMs, we apply Video-ChatGPT and span scorer to select the most appropriate spans. Our proposed method substantially outperforms the state-of-the-art MLLM-based and zero-shot models on several public datasets, including QVHighlights, ActivityNet-Captions, and Charades-STA.
HOIMamba: Efficient Mamba-based Disentangled Progressive Learning for HOI Detection
Authors: Yongchao Xu; Jiawei Liu; Sen Tao; Qiang Zhang; Zheng-Jun Zha
Venue: AAAI 2025 (main)

Abstract: Human-object interaction (HOI) detection aims to detect the spatial positions of human-object pairs and recognize their interactions. Existing single-branch, two-branch, and three-branch methods are challenging to make an appropriate trade-off on efficiency, multi-task decoupling, and collaborative learning, while they fail to identify rare and complex interaction categories effectively as well. In this work, we propose a novel Efficient Mamba-based Disentangled Progressive Learning (HOIMamba) for HOI Detection to absorb the advantages of the existing three approaches and adaptively aggregate multi-level interaction semantics guided by cross-task bidirectional information contexts. Specifically, HOIMamba builds an efficient and effective decoder through cascaded Low-Rank Adaptations (LoRAs), with high efficiency, thorough decoupling of tasks, and good multi-task collaborative learning. Furthermore, to alleviate the recognition problem of interactions in difficult HOI samples, a novel Mamba-based comprehensive progressive learning strategy with Cross-enhance Mamba (CEM) blocks and Detection Context Propagation (DCP) blocks is designed to gradually excavate interaction-related discriminative cues from four levels. CEM blocks automatically aggregate context to generate diverse task-shared semantics and simultaneously realize the cross-task interaction between human and object branches, guiding the interaction branch to extract more expressive HOI representation. DCP blocks further transfer the comprehensive interaction context to human and object branches to achieve rich and effective information exchange, facilitating the model to discover more HOI instances. Extensive experimental results on two standard benchmarks demonstrate the effectiveness of our HOIMamba.
OOTDiffusion: Outfitting Fusion Based Latent Diffusion for Controllable Virtual Try-On
Authors: Yuhao Xu; Tao Gu; Weifeng Chen; Arlene Chen
Venue: AAAI 2025 (main)

Abstract: We present OOTDiffusion, a novel network architecture for realistic and controllable image-based virtual try-on (VTON). We leverage the power of pretrained latent diffusion models, designing an outfitting UNet to learn the detailed garment features. Without a redundant warping process, the garment features are precisely aligned with the target human body via the proposed outfitting fusion in the self-attention layers of the denoising UNet. In order to further enhance the controllability, we introduce outfitting dropout to the training process, which enables us to adjust the strength of the garment features through classifier-free guidance. Our comprehensive experiments on the VITON-HD and Dress Code datasets demonstrate that OOTDiffusion efficiently generates high-quality try-on results for arbitrary human and garment images, which outperforms other VTON methods in both realism and controllability, indicating a breakthrough in virtual try-on.