GenAI4ELab/papercli-papers huggingface dataset:
Auto-Regressive Diffusion for Generating 3D Human-Object Interactions
Authors: Zichen Geng; Zeeshan Hayder; Wei Liu; Ajmal Saeed Mian
Venue: AAAI 2025 (main)

Abstract: Text-driven Human-Object Interaction (Text-to-HOI) generation is an emerging field with applications in animation, video games, virtual reality, and robotics. A key challenge in HOI generation is maintaining interaction consistency in long sequences. Existing Text-to-Motion-based approaches, such as discrete motion tokenization, cannot be directly applied to HOI generation due to limited data in this domain and the complexity of the modality. To address the problem of interaction consistency in long sequences, we propose an autoregressive diffusion model (ARDHOI) that predicts the next continuous token. Specifically, we introduce a Contrastive Variational Autoencoder (cVAE) to learn a physically plausible space of continuous HOI tokens, thereby ensuring that generated human-object motions are realistic and natural. For generating sequences autoregressively, we develop a Mamba-based context encoder to capture and maintain consistent sequential actions. Additionally, we implement an MLP-based denoiser to generate the subsequent token conditioned on the encoded context. Our model has been evaluated on the OMOMO and BEHAVE datasets, where it outperforms existing state-of-the-art methods in terms of both performance and inference speed. This makes ARDHOI a robust and efficient solution for text-driven HOI tasks.
Domain Generalized Medical Landmark Detection via Robust Boundary-Aware Pre-Training
Authors: Haifan Gong; Yu Lu; Xiang Wan; Haofeng Li
Venue: AAAI 2025 (main)

Abstract: In recent years, deep learning has revenue in automated medical landmark detection. Nonetheless, prevailing research in this field predominantly addresses single-center scenarios or domain adaptation settings. In practical environments, the acquisition of multi-center data faces privacy concerns, coupled with the time-intensive and costly nature of data collection and annotation. These challenges substantially impede the broader application of deep learning-based medical landmark detection. To mitigate these issues, we propose a novel domain-generalized medical landmark detection framework that relies solely on single-center data for training. Considering the availability of numerous public medical segmentation datasets, we design a simple yet effective method that utilizes single-center segmentation to enhance the domain generalization capabilities of the landmark detection task. Specifically, we introduce a novel boundary-aware pre-training approach to focus the model on regions pertinent to landmarks. To further enhance the robustness and generalization capabilities during pre-training, we have derived a mixing loss term and proved its effectiveness in theory and practice. Extensive experiments conducted on our new domain generalization benchmark for medical landmark detection demonstrate the superiority of our approach.
Rethinking Masked Data Reconstruction Pretraining for Strong 3D Action Representation Learning
Authors: Tao Gong; Qi Chu; Bin Liu; Nenghai Yu
Venue: AAAI 2025 (main)

Abstract: In 3D human action recognition, limited supervised data makes it challenging to fully tap into the modeling potential of powerful networks such as transformers. As a result, researchers have been actively investigating effective self-supervised pre-training strategies. For example, MAMP shows that instead of following the prevalent masked joint reconstruction, explicit masked motion reconstruction is key to the success of learning effective feature representation for 3D action recognition. However, we find that if we make a simple and effective change to the reconstructed target of masked joint reconstruction, masked joint reconstruction can achieve the same results as masked motion reconstruction. The devil is in the special characteristic of 3D skeleton data and the normalization process of training targets. We need to dig for all effective information of targets during normalization. Besides, considering that mask data reconstruction focuses more on learning local relations in input data for fulfilling the reconstruction task, instead of modeling the relation among samples, we further employ contrastive learning to learn more discriminative 3D action representations. We show that contrastive learning can consistently boost the performance of model pre-trained by masked joint prediction under various settings, especially in the semi-supervised setting that has a very limited number of labeled samples. Extensive experiments on NTU-60, NTU-120, and PKU-MMD datasets show that the proposed pre-training strategy achieves state-of-the-art results without bells and whistles.
Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification
Authors: Jiaxiang Gou; Luping Ji; Pei Liu; Mao Ye
Venue: AAAI 2025 (main)

Abstract: Whole Slide Image (WSI) classification has very significant applications in clinical pathology, e.g., tumor identification and cancer diagnosis. Currently, most research attention is focused on Multiple Instance Learning (MIL) using static datasets. One of the most obvious weaknesses of these methods is that they cannot efficiently preserve and utilize previously learned knowledge. With any new data arriving, classification models are required to be re-trained on both previous and current new data. To overcome this shortcoming and break through traditional vision modality, this paper proposes the first Vision-Language-based framework with Queryable Prototype Multiple Instance Learning (QPMIL-VL) specially designed for incremental WSI classification. This framework mainly consists of two information processing branches: one is for generating bag-level features by prototype-guided aggregation of instance features, while the other is for enhancing class features through a combination of class ensemble, tunable vector and class similarity loss. The experiments on four public WSI datasets demonstrate that our QPMIL-VL framework is effective for incremental WSI classification and often significantly outperforms other compared methods, achieving state-of-the-art (SOTA) performance.
Efficient Connectivity-Preserving Instance Segmentation with Supervoxel-Based Loss Function
Authors: Anna Grim; Jayaram Chandrashekar; Uygar Sümbül
Venue: AAAI 2025 (main)

Abstract: Reconstructing the intricate local morphology of neurons and their long-range projecting axons can address many connectivity related questions in neuroscience. The main bottleneck in connectomics pipelines is correcting topological errors, as multiple entangled neuronal arbors is a challenging instance segmentation problem. More broadly, segmentation of curvilinear, filamentous structures continues to pose significant challenges. To address this problem, we extend the notion of simple points from digital topology to connected sets of voxels (i.e. supervoxels) and propose a topology-aware neural network segmentation method with minimal computational overhead. We demonstrate its effectiveness on a new public dataset of 3-d light microscopy images of mouse brains, along with the benchmark datasets DRIVE, ISBI12, and CrackTree.
MaintaAvatar: A Maintainable Avatar Based on Neural Radiance Fields by Continual Learning
Authors: Shengbo Gu; Yu-Kun Qiu; Yu-Ming Tang; Ancong Wu; Wei-Shi Zheng
Venue: AAAI 2025 (main)

Abstract: The generation of a virtual digital avatar is a crucial research topic in the field of computer vision. Many existing works utilize Neural Radiance Fields (NeRF) to address this issue and have achieved impressive results. However, previous works assume the images of the training person are available and fixed while the appearances and poses of a subject could constantly change and increase in real-world scenarios. How to update the human avatar but also maintain the ability to render the old appearance of the person is a practical challenge. One trivial solution is to combine the existing virtual avatar models based on NeRF with continual learning methods. However, there are some critical issues in this approach: learning new appearances and poses can cause the model to forget past information, which in turn leads to a degradation in the rendering quality of past appearances, especially color bleeding issues, and incorrect human body poses. In this work, we propose a maintainable avatar (MaintaAvatar) based on neural radiance fields by continual learning, which resolves the issues by utilizing a Global-Local Joint Storage Module and a Pose Distillation Module. Overall, our model requires only limited data collection to quickly fine-tune the model while avoiding catastrophic forgetting, thus achieving a maintainable virtual avatar. The experimental results validate the effectiveness of our MaintaAvatar model.
HGSFusion: Radar-Camera Fusion with Hybrid Generation and Synchronization for 3D Object Detection
Authors: Zijian Gu; Jianwei Ma; Yan Huang; Honghao Wei; Zhanye Chen; Hui Zhang; Wei Hong
Venue: AAAI 2025 (main)

Abstract: Millimeter-wave radar plays a vital role in 3D object detection for autonomous driving due to its all-weather and all-lighting-condition capabilities for perception. However, radar point clouds suffer from pronounced sparsity and unavoidable angle estimation errors. To address these limitations, incorporating a camera may partially help mitigate the shortcomings. Nevertheless, the direct fusion of radar and camera data can lead to negative or even opposite effects due to the lack of depth information in images and low-quality image features under adverse lighting conditions. Hence, in this paper, we present the radar-camera fusion network with Hybrid Generation and Synchronization (HGSFusion), designed to better fuse radar potentials and image features for 3D object detection. Specifically, we propose the Radar Hybrid Generation Module (RHGM), which fully considers the Direction-Of-Arrival (DOA) estimation errors in radar signal processing. This module generates denser radar points through different Probability Density Functions (PDFs) with the assistance of semantic information. Meanwhile, we introduce the Dual Sync Module (DSM), comprising spatial sync and modality sync, to enhance image features with radar positional information and facilitate the fusion of distinct characteristics in different modalities. Extensive experiments demonstrate the effectiveness of our approach, outperforming the state-of-the-art methods in the VoD and TJ4DRadSet datasets by 6.53% and 2.03% in RoI AP and BEV AP, respectively.
OT-StainNet: Optimal Transport Driven Semantic Matching for Weakly Paired H&E-to-IHC Stain Transfer
Authors: Xianchao Guan; Yifeng Wang; Ye Zhang; Zheng Zhang; Yongbing Zhang
Venue: AAAI 2025 (main)

Abstract: Immunohistochemistry (IHC) examination is essential for characterizing tumor subtypes, providing prognostic information, and developing personalized treatment plans. However, IHC staining preparation is more complex and expensive compared to Hematoxylin and Eosin (H&E) staining, limiting its widespread clinical application. Transforming H&E images into IHC images presents a promising solution. In this paper, we propose OT-StainNet, a novel virtual IHC staining method. OT-StainNet employs a pre-trained diffusion model with richer prior knowledge as the generator and fine-tunes it with LoRA adapters through adversarial training. Given that adjacent images of the same tissue stained with H&E and IHC are not precisely aligned at the pixel level, existing methods struggle to fully utilize the supervisory information from weakly paired IHC images. To address this issue, we propose an optimal transport-driven semantic matching (OTSM) mechanism, establishing accurate semantic correspondences between H&E-IHC image pairs. By leveraging the real IHC features obtained through the OTSM mechanism, we design a semantic consistency constraint (SCC) to ensure that the correlations among virtual IHC features remain consistent with those among real IHC features, thereby preserving valuable correlation information during stain transfer. We validate OT-StainNet using four types of IHC staining across two datasets. Extensive experiments demonstrate the effectiveness of our method compared to state-of-the-art approaches.
DepthFM: Fast Generative Monocular Depth Estimation with Flow Matching
Authors: Ming Gui; Johannes Schusterbauer; Ulrich Prestel; Pingchuan Ma; Dmytro Kotovenko; Olga Grebenkova; Stefan Andreas Baumann; Vincent Tao Hu; Björn Ommer
Venue: AAAI 2025 (main)

Abstract: Current discriminative depth estimation methods often produce blurry artifacts, while generative approaches suffer from slow sampling due to curvatures in the noise-to-depth transport. Our method addresses these challenges by framing depth estimation as a direct transport between image and depth distributions. We are the first to explore flow matching in this field, and we demonstrate that its interpolation trajectories enhance both training and sampling efficiency while preserving high performance. While generative models typically require extensive training data, we mitigate this dependency by integrating external knowledge from a pre-trained image diffusion model, enabling effective transfer even across differing objectives. To further boost our model performance, we employ synthetic data and utilize image-depth pairs generated by a discriminative model on an in-the-wild image dataset. As a generative model, our model can reliably estimate depth confidence, which provides an additional advantage. Our approach achieves competitive zero-shot performance on standard benchmarks of complex natural scenes while improving sampling efficiency and only requiring minimal synthetic data for training.
You Should Learn to Stop Denoising on Point Clouds in Advance
Authors: Chuchen Guo; Weijie Zhou; Zheng Liu; Ying He
Venue: AAAI 2025 (main)

Abstract: Point clouds have become the preferred data format for a variety of tasks in 3D vision and graphics. However, raw point clouds often contain significant noise. This paper introduces the Adaptive Stop Denoising Network (ASDN), a novel approach aimed at restoring high-quality point clouds from noisy data. Our method is built upon a pivotal observation: during the denoising phase, high-noise points draw more focus from the network, which may suppress the points that have already been effectively denoised. This observation has led us to develop an adaptive strategy that ceases denoising already cleaned points to prevent over-denoising, while continuing to refine points that remain noisy. We employ a U-Net architecture complemented by an adaptive classifier, which utilizes a recoverability factor to assess the completion of denoising and make dynamic decisions about when to halt the process. Our method not only demonstrates superior noise removal efficiency but also preserves geometric details more effectively, reducing over- or under-denoising artifacts. Extensive experiments and evaluations demonstrate that our method outperforms the state-of-the-art both qualitatively and quantitatively.
Surgical Workflow Recognition and Blocking Effectiveness Detection in Laparoscopic Liver Resection with Pringle Maneuver
Authors: Diandian Guo; Weixin Si; Zhixi Li; Jialun Pei; Pheng-Ann Heng
Venue: AAAI 2025 (main)

Abstract: Pringle maneuver (PM) in laparoscopic liver resection aims to reduce blood loss and provide a clear surgical view by intermittently blocking blood inflow of the liver, whereas prolonged PM may cause ischemic injury. To comprehensively monitor this surgical procedure and provide timely warnings of ineffective and prolonged blocking, we suggest two complementary AI-assisted surgical monitoring tasks: workflow recognition and blocking effectiveness detection in liver resections. The former presents challenges in real-time capturing of short-term PM, while the latter involves the intraoperative discrimination of long-term liver ischemia states. To address these challenges, we meticulously collect a novel dataset, called PmLR50, consisting of 25,037 video frames covering various surgical phases from 50 laparoscopic liver resection procedures. Additionally, we develop an online baseline for PmLR50, termed PmNet. This model embraces Masked Temporal Encoding (MTE) and Compressed Sequence Modeling (CSM) for efficient short-term and long-term temporal information modeling, and embeds Contrastive Prototype Separation (CPS) to enhance action discrimination between similar intraoperative operations. Experimental results demonstrate that PmNet outperforms existing state-of-the-art surgical workflow recognition methods on the PmLR50 benchmark. Our research offers potential clinical applications for the laparoscopic liver surgery community.
Cross-Spectral Gaussian Splatting with Spatial Occupancy Consistency
Authors: Haipeng Guo; Huanyu Liu; Jiazheng Wen; Junbao Li
Venue: AAAI 2025 (main)

Abstract: Using images captured by cameras with different light spectrum sensitivities, training a unified model for cross-spectral scene representation is challenging. Recent advances have shown the possibility of jointly optimizing cross-spectral relative poses and neural radiance fields using normalized cross-device coordinates. However, such method suffers from cross-spectral misalignment when collecting data asynchronously from devices and lacks the capability to render in real-time or handle large scenes. We address these issues by proposing cross-spectral Gaussian Splatting with spatial occupancy consistency, strictly aligns cross-spectral scene representation by sharing explicit Gaussian surfaces across spectra and separately optimizing each view's extrinsic using a matching-optimizing pose estimation method. Additionally, to address field-of-view differences in cross-spectral cameras, we improve the adaptive densify controller to fill non-overlapping areas. Comprehensive experiments demonstrate that SOC-GS achieves superior performance in novel view synthesis and real-time cross-spectral rendering.
Enhancing Low-Rank Adaptation with Recoverability-Based Reinforcement Pruning for Object Counting
Authors: Haojie Guo; Junyu Gao; Yuan Yuan
Venue: AAAI 2025 (main)

Abstract: Object counting is crucial for understanding the distribution of objects in different scenarios. Recently, many object counting networks have been designed to be more complex to achieve marginal improvements, leading to excessive time spent on model design. With the development of large models (LMs), various visual tasks can be accomplished by transferring pre-trained weights from LMs and fine-tuning them. However, tens of millions of training data make the pre-training parameters of LMs not entirely necessary. Moreover, if unnecessary parameters in the large model are not removed, it may lead to decreased performance on the tasks to be transferred. Motivated by this, this paper proposes an Enhancing low-Rank adaptation with Recoverability-based Reinforcement Pruning (E3RP) method to balance the complexity of large model and the accuracy of counting tasks. Firstly, we design a new reward mechanism based on the feature similarity of large model before and after globally unstructured pruning of specific parameters. Additionally, we propose a Patch Query Flip Attention (PQFA) mechanism to align multi-scale features through bidirectional interaction of features. Finally, the parameters of large model are pruned utilizing the pruning rate autonomously determined by the reinforcement learning network, and the large model is fine-tuned to counting tasks by a simple decoding head. Extensive experiments on four cross-scenario datasets demonstrate that the proposed method can remove redundant network parameters while ensuring network performance, with a maximum reduction of up to 63%.
Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model Using 3D Whole-Body CT Scans
Authors: Heng Guo; Jianfeng Zhang; Jiaxing Huang; Tony C. W. Mok; Dazhou Guo; Ke Yan; Le Lu; Dakai Jin; Minfeng Xu
Venue: AAAI 2025 (main)

Abstract: Segment anything model (SAM) demonstrates strong generalization ability on natural image segmentation. However, its direct adaptation in medical image segmentation tasks shows significant performance drops. It also requires an excessive number of prompt points to obtain a reasonable accuracy. Although quite a few studies explore adapting SAM into medical image volumes, the efficiency of 2D adaptation methods is unsatisfactory and 3D adaptation methods are only capable of segmenting specific organs/tumors. In this work, we propose a comprehensive and scalable 3D SAM model for whole-body CT segmentation, named CT-SAM3D. Instead of adapting SAM, we propose a 3D promptable segmentation model using a (nearly) fully labeled CT dataset. To train CT-SAM3D effectively, ensuring the model's accurate responses to  higher-dimensional spatial prompts is crucial, and 3D patch-wise training is required due to GPU memory constraints. Therefore, we propose two key technical developments: 1) a progressively and spatially aligned prompt encoding method to effectively encode click prompts in local 3D space; and 2) a cross-patch prompt scheme to capture more 3D spatial context, which is beneficial for reducing the editing workloads when interactively prompting on large organs. CT-SAM3D is trained using a curated dataset of 1204 CT scans containing 107 whole-body anatomies and extensively validated using five datasets, achieving significantly better results against all previous SAM-derived models.
MetaNeRV: Meta Neural Representations for Videos with Spatial-Temporal Guidance
Authors: Jialong Guo; Ke Liu; Jiangchao Yao; Zhihua Wang; Jiajun Bu; Haishuai Wang
Venue: AAAI 2025 (main)

Abstract: Neural Representations for Videos (NeRV) has emerged as a promising implicit neural representation (INR) approach for video analysis, which represents videos as neural networks with frame indexes as inputs.  
However, NeRV-based methods are time-consuming when adapting to a large number of diverse videos, as each video requires a separate NeRV model to be trained from scratch. In addition, NeRV-based methods spatially require generating a high-dimension signal (i.e., an entire image) from the input of a low-dimension timestamp, and a video typically consists of tens of frames temporally that have a minor change between adjacent frames.
To improve the efficiency of video representation, we propose Meta Neural Representations for Videos, named MetaNeRV, a novel framework for fast NeRV representation for unseen videos. 
MetaNeRV leverages a meta-learning framework to learn an optimal parameter initialization, which serves as a good starting point for adapting to new videos.
To address the unique spatial and temporal characteristics of video modality, we further introduce spatial-temporal guidance to improve the representation capabilities of MetaNeRV. Specifically, the spatial guidance with a multi-resolution loss aims to capture the information from different resolution stages, and the temporal guidance with an effective progressive learning strategy could gradually refine the number of fitted frames during the meta-learning process. Extensive experiments conducted on multiple datasets demonstrate the superiority of MetaNeRV for video representations and video compression.