GenAI4ELab/papercli-papers huggingface dataset:
EMHI: A Multimodal Egocentric Human Motion Dataset with HMD and Body-Worn IMUs
Authors: Zhen Fan; Peng Dai; Zhuo Su; Xu Gao; Zheng Lv; Jiarui Zhang; Tianyuan Du; Guidong Wang; Yang Zhang
Venue: AAAI 2025 (main)

Abstract: Egocentric human pose estimation (HPE) using wearable sensors is essential for VR/AR applications. Most methods rely solely on either egocentric-view images or sparse Inertial Measurement Unit (IMU) signals, leading to inaccuracies due to self-occlusion in images or the sparseness and drift of inertial sensors. Most importantly, the lack of real-world datasets containing both modalities is a major obstacle to progress in this field. To overcome the barrier, we propose EMHI, a multimodal Egocentric human Motion dataset with Head-Mounted Display (HMD) and body-worn IMUs, with all data collected under the real VR product suite. Specifically, EMHI provides synchronized stereo images from downward-sloping cameras on the headset and IMU data from body-worn sensors, along with pose annotations in SMPL format. This dataset consists of 885 sequences captured by 58 subjects performing 39 actions, totaling about 28.5 hours of recording. We evaluate the annotations by comparing them with optical marker-based SMPL fitting results. To substantiate the reliability of our dataset, we introduce MEPoser, a new baseline method for multimodal egocentric HPE,  which employs a multimodal fusion encoder, temporal feature encoder, and MLP-based regression heads. The experiments on EMHI show that MEPoser outperforms existing single-modal methods and demonstrates the value of our dataset in solving the problem of egocentric HPE. We believe the release of EMHI and the method could advance the research of egocentric HPE and expedite the practical implementation of this technology in VR/AR products.
CoSDA: Enhancing the Robustness of Inversion-based Generative Image Watermarking Framework
Authors: Han Fang; Kejiang Chen; Zijin Yang; Bosen Cui; Weiming Zhang; Ee-Chien Chang
Venue: AAAI 2025 (main)

Abstract: Generative image watermarking inserts secret watermarks into generated images and plays an important role in tracing the usages of generative models. For watermarking of diffusion models, inversion-based framework emerges as an effective approach. 
Such framework employs a robust mechanism to embed the watermark into the starting latent before ``forward sampling'', thereby generating images with the implicit watermark. During watermark detection, inversion techniques are employed to reverse the process and obtain the watermarked latent, followed by further extraction.  The robustness of this technique hinges primarily on the embedding mechanism and inversion accuracy. Previous methods predominantly focused on enhancing the robustness of the embedding mechanism but overlooked the reduction of the inversion errors. However, our results show that inversion error will significantly affect the overall robustness. Therefore, in this paper, we delve into the inversion error aspect and propose CoSDA, a compensation sampling and drift alignment-based approach. The inversion error primarily accumulated during two stages: the internal error incurred by the algorithm, and the inevitable external noise. We observe that the main source of internal error comes from the mismatch in conditions (e.g. prompt, guidance scale) between forward and backward sampling processes.  Therefore, we propose a compensation-based forward sampling, compensating for certain mismatch conditions and reducing the inversion error caused by the mismatch. Addressing external error caused by inevitable image distortions (e.g. JPEG compression), we introduce a drift-alignment approach, where a neural network is trained adversarially to restore the original watermarked latent from the distorted counterpart. Experimental results show that CoSDA effectively enhances watermark robustness while maintaining the visual quality of generated images.
SSUN-Net: Spatial-Spectral Prior-Aware Unfolding Network for Pan-Sharpening
Authors: Shijie Fang; Hongping Gan
Venue: AAAI 2025 (main)

Abstract: Deep Unfolding Networks (DUNs), with their outstanding performance and partial interpretability, have revitalized the field of pan-sharpening. However, the current DUNs for pan-sharpening rely entirely on implicit deep priors, ignoring the intrinsic physical prior knowledge of multispectral image (MS) and panchromatic image (PAN) to guide the reconstruction process. Moreover, these methods often depend on single-scale prior features, failing to adequately capture multiscale information, resulting in spatial and spectral distortions in detail. In this paper, we introduce a spatial-spectral prior-aware framework for pan-sharpening, called SSPF, which formulates a constrained minimization problem integrating MS and PAN prior knowledge based on spatial and spectral domains. We further develop SSPF into a lightweight deep unfolding network, called SSUN-Net, which provides more efficient prior feature extraction and requires less computational cost. Additionally, we augment SSUN-Net's capabilities by integrating a customized multi-scale prior structure (MPS). MPS imposes constraints on the solution space at various scales through regularization, which markedly enhances the reconstruction of intricate details. Extensive experiments demonstrate the significant advantages of our proposed SSUN-Net over the current SOTA methods.
Guided Real Image Dehazing Using YCbCr Color Space
Authors: Wenxuan Fang; Junkai Fan; Yu Zheng; Jiangwei Weng; Ying Tai; Jun Li
Venue: AAAI 2025 (main)

Abstract: Image dehazing, particularly with learning-based methods, has gained significant attention due to its importance in real-world applications. However, relying solely on the RGB color space often fall short, frequently leaving residual haze. This arises from two main issues: the difficulty in obtaining clear textural features from hazy RGB images and the complexity of acquiring real haze/clean image pairs outside controlled environments like smoke-filled scenes. To address these issues, we first propose a novel Structure Guided Dehazing Network (SGDN) that leverages the superior structural properties of YCbCr features over RGB. It comprises two key modules: Bi-Color Guidance Bridge (BGB) and Color Enhancement Module (CEM). BGB integrates a phase integration module and an interactive attention module, utilizing the rich texture features of the YCbCr space to guide the RGB space, thereby recovering clearer features in both frequency and spatial domains. To maintain tonal consistency, CEM further enhances the color perception of RGB features by aggregating YCbCr channel information. Furthermore, for effective supervised learning, we introduce a Real-World Well-Aligned Haze dataset, which includes a diverse range of scenes from various geographical regions and climate conditions. Experimental results demonstrate that our method surpasses existing state-of-the-art methods across multiple real-world smoke/haze datasets.
Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network
Authors: Xiang Fang; Wanlong Fang; Changshuo Wang; Daizong Liu; Keke Tang; Jianfeng Dong; Pan Zhou; Beibei Li
Venue: AAAI 2025 (main)

Abstract: Given some video-query pairs with untrimmed videos and sentence queries, temporal sentence grounding (TSG) aims to locate query-relevant segments in these videos. Although previous respectable TSG methods have achieved remarkable success, they train each video-query pair separately and ignore the relationship between different pairs. To this end, in this paper, we pose a brand-new setting: Multi-Pair TSG, which aims to co-train these pairs. We propose a novel video-query co-training approach, Multi-Thread Knowledge Transfer Network, to locate a variety of video-query pairs effectively and efficiently. Firstly, we mine the spatial and temporal semantics across different queries to cooperate with each other. To learn intra- and inter-modal representations simultaneously, we design a cross-modal contrast module to explore the semantic consistency by a self-supervised strategy. To fully align visual and textual representations between different pairs, we design a prototype alignment strategy to 1) match object prototypes and phrase prototypes for spatial alignment, and 2) align activity prototypes and sentence prototypes for temporal alignment. Finally, we develop an adaptive negative selection module to adaptively generate a threshold for cross-modal matching. Extensive experiments show the effectiveness and efficiency of our proposed method.
AE-NeRF: Augmenting Event-Based Neural Radiance Fields for Non-ideal Conditions and Larger Scenes
Authors: Chaoran Feng; Wangbo Yu; Xinhua Cheng; Zhenyu Tang; Junwu Zhang; Li Yuan; Yonghong Tian
Venue: AAAI 2025 (main)

Abstract: Compared to frame-based methods, computational neuromorphic imaging using event cameras offers significant advantages, such as minimal motion blur, enhanced temporal resolution, and high dynamic range. The multi-view consistency of Neural Radiance Fields combined with the unique benefits of event cameras, has spurred recent research into reconstructing NeRF from data captured by moving event cameras. While showing impressive performance, existing methods rely on ideal conditions with the availability of uniform and high-quality event sequences and accurate camera poses, and mainly focus on the object level reconstruction, thus limiting their practical applications. In this work, we propose AE-NeRF to address the challenges of learning event-based NeRF from non-ideal conditions, including non-uniform event sequences, noisy poses, and various scales of scenes. Our method exploits the density of event streams and jointly learn a pose correction module with an event-based NeRF (e-NeRF) framework for robust 3D reconstruction from inaccurate camera poses. To generalize to larger scenes, we propose hierarchical event distillation with a proposal e-NeRF network and a vanilla e-NeRF network to resample and refine the reconstruction process. We further propose an event reconstruction loss and a temporal loss to improve the view consistency of the reconstructed scene. We established a comprehensive benchmark that includes large-scale scenes to simulate practical non-ideal conditions, incorporating both synthetic and challenging real-world event datasets. The experimental results show that our method achieves a new state-of-the-art in event-based 3D reconstruction.
PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
Authors: Chen Feng; Ziquan Liu; Zhuo Zhi; Ilija Bogunovic; Carsten Gerner-Beuerle; Miguel Rodrigues
Venue: AAAI 2025 (main)

Abstract: It is widely known that state-of-the-art machine learning models, including vision and language models, can be seriously compromised by adversarial perturbations. It is therefore increasingly relevant to develop capabilities to certify their performance in the presence of the most effective adversarial attacks. Our paper offers a new approach to certify the performance of machine learning models in the presence of adversarial attacks with population level risk guarantees. In particular, we introduce the notion of (α,ζ)-safe machine learning model. We propose a hypothesis testing procedure, based on the availability of a calibration set, to derive statistical guarantees providing that the probability of declaring that the adversarial (population) risk of a machine learning model is less than α (i.e. the model is safe), while the model is in fact unsafe (i.e. the model adversarial population risk is higher than α), is less than ζ. We also propose Bayesian optimization algorithms to determine efficiently whether a machine learning model is (α,ζ)-safe in the presence of an adversarial attack, along with statistical guarantees. We apply our framework to a range of machine learning models - including various sizes of vision Transformer (ViT) and ResNet models - impaired by a variety of adversarial attacks, such as PGDAttack, MomentumAttack, GenAttack and BanditAttack, to illustrate the operation of our approach. Importantly, we show that ViT's are generally more robust to adversarial attacks than ResNets, and large models are generally more robust than smaller models. Our approach goes beyond existing empirical adversarial risk-based certification guarantees. It formulates rigorous (and provable) performance guarantees that can be used to satisfy regulatory requirements mandating the use of state-of-the-art technical tools.
VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering
Authors: Chun-Mei Feng; Yang Bai; Tao Luo; Zhen Li; Salman Khan; Wangmeng Zuo; Rick Siow Mong Goh; Yong Liu
Venue: AAAI 2025 (main)

Abstract: Albeit progress has been made in Composed Image Retrieval (CIR), we empirically find that a certain percentage of failure retrieval results are not consistent with their relative captions. To address this issue, this work provides a Visual Question Answering (VQA) perspective to boost the performance of CIR. The resulting VQA4CIR is a post-processing approach and can be directly plugged into existing CIR methods. Given the top-C retrieved images by a CIR method, VQA4CIR aims to decrease the adverse effect of the failure retrieval results being inconsistent with the relative caption. To find the retrieved images inconsistent with the relative caption, we resort to the "QA generation → VQA" self-verification pipeline. For QA generation, we suggest fine-tuning LLM (e.g., LLaMA) to generate several pairs of questions and answers from each relative caption. We then fine-tune LVLM (e.g., LLaVA) to obtain the VQA model. By feeding the retrieved image and question to the VQA model, one can find the images inconsistent with relative caption when the answer by VQA is inconsistent with the answer in the QA pair. Consequently, the CIR performance can be boosted by modifying the ranks of inconsistently retrieved images. Experimental results show that our proposed method outperforms state-of-the-art CIR methods on the CIRR and Fashion-IQ datasets.
PoseLLaVA: Pose Centric Multimodal LLM for Fine-Grained 3D Pose Manipulation
Authors: Dong Feng; Ping Guo; Encheng Peng; Mingmin Zhu; Wenhao Yu; Peng Wang
Venue: AAAI 2025 (main)

Abstract: Manipulating human poses based on natural language is an emerging research field that has traditionally focused on coarse commands such as “walking” or “dancing.” However, fine-grained pose manipulation, like instructing “put both hands in front of the stomach,” remains underexplored. In this paper, we introduce PoseLLaVA, a pioneering model that integrates SMPL-based pose representations into the multimodal LLaVA framework. Through a novel pose encoder decoder mechanism, PoseLLaVA achieves precise alignment between pose, textual, and visual modalities, enabling detailed control over pose manipulation tasks. PoseLLaVA excels in three key tasks: pose estimation, generation, and adjustment, all driven by detailed language instructions. We further introduce a fine-grained pose adjustment dataset PosePart, where each sample contains an initial pose and a target pose, along with specific instructions for adjustments, mimicking the guidance a human instructor might provide. Extensive evaluations across these tasks demonstrate significant improvements over existing methods, including metrics such as MPJPE and PA-MPJPE, which measure SMPL reconstruction errors, and Recall rates, which assess feature alignment across modalities. Specifically, PoseLLaVA reduces MPJPE errors by more than 20% compared to state-of-the-art methods in pose adjustment and generation tasks. Additionally, we demonstrate the feasibility of combining PoseLLaVA with generative models, such as diffusion, for pose image editing, highlighting its potential applications in language-controlled pose manipulation.
Residual Diffusion Deblurring Model for Single Image Defocus Deblurring
Authors: Haoxuan Feng; Haohui Zhou; Tian Ye; Sixiang Chen; Lei Zhu
Venue: AAAI 2025 (main)

Abstract: Defocus deblurring is a challenging task due to the spatially varying nature of defocus blur with multiple plausible solutions of a single given image. However, most existing methods falter when faced with extensive and variable defocus blur, either ignoring it or relying on additional loss functions to enhance perceptual quality. This often results in unrealistic reconstructions and compromised generalizability. In this paper, we propose a novel Residual Diffusion Deblurring Model framework for single image defocus deblurring. Our approach integrates a pre-trained defocus map estimator and a lightweight pre-deblur module with a learnable receptive field, providing crucial posterior information to effectively address large-scale and varying shaped defocus blur. In addition, a carefully-design denoising network enables the generation of diverse reconstructions from a single input. This approach not only significantly improves the perceptual quality of defocus deblurring outputs through multi-step residual learning, but also offers a more efficient inference strategy. Experimental results demonstrate that our method achieves competitive performance on real-world defocus deblurring image datasets across both perceptual and distortion evaluation metrics.
DiT4Edit: Diffusion Transformer for Image Editing
Authors: Kunyu Feng; Yue Ma; Bingyuan Wang; Chenyang Qi; Haozhe Chen; Qifeng Chen; Zeyu Wang
Venue: AAAI 2025 (main)

Abstract: Despite recent advances in UNet-based image editing, methods for shape-aware object editing in high-resolution images are still lacking. Compared to UNet, Diffusion Transformers (DiT) demonstrate superior capabilities to effectively capture the long-range dependencies among patches, leading to higher-quality image generation. In this paper, we propose DiT4Edit, the first Diffusion Transformer-based image editing framework. Specifically, DiT4Edit uses the DPM-Solver inversion algorithm to obtain the inverted latents, reducing the number of steps compared to the DDIM inversion algorithm commonly used in UNet-based frameworks. Additionally, we design unified attention control and patch merging, tailored for transformer computation streams. This integration allows our framework to generate higher-quality edited images faster. Our design leverages the advantages of DiT, enabling it to surpass UNet structures in image editing, especially in high-resolution and arbitrary-size images. 
Extensive experiments demonstrate the strong performance of DiT4Edit in various editing scenarios, highlighting the potential of diffusion transformers for image editing.
Semantic Ambiguity Modeling and Propagation for Fine-Grained Visual Cross View Geo-Localization
Authors: Mingtao Feng; Fenghao Tian; Jianqiao Luo; Zijie Wu; Weisheng Dong; Yaonan Wang; Ajmal Saeed Mian
Venue: AAAI 2025 (main)

Abstract: Visual cross view geo-localization is generally approached within a joint retrieval-and-calibration framework. However, existing methods overlook semantic ambiguities arising from query and reference images characterized by low overlap, dynamic foregrounds, viewpoint changes, and perceptual aliasing. This makes it challenging to automatically control the relative importance of the two tasks, potentially compromising the retrieval task in favor of the offset regression. Consequently, the model may encounter conflicting dominating gradients during joint training. To address this, we propose to model the semantic ambiguity during the offset regression process by integrating associated uncertainty scores, represented as 2D Gaussian distributions, to mitigate negative transfer effects within the joint tasks. We further introduce an uncertainty-aware similarity metric to enhance similarity assessment between query and reference images, accounting for their semantic ambiguities. This metric propagates uncertainty scores into the retrieval task, focusing on certain samples and learning discriminative feature embeddings, allowing the model to adaptively handle conflicting dominating gradients during joint training. Extensive experiments demonstrate that our method improves the overall performance of the joint tasks, achieving state-of-the-art results on the VIGOR and CVACT datasets.
Weakly Supervised Gland Segmentation with Class Semantic Consistency and Purified Labels Filtration
Authors: Siyang Feng; Huadeng Wang; Chu Han; Zhenbing Liu; Hualong Zhang; Rushi Lan; Xipeng Pan
Venue: AAAI 2025 (main)

Abstract: Image-level weakly supervised semantic segmentation (WSSS) reduces the dependence on high-quality data annotation, which plays a crucial role in computational pathology. Benefit from the ability to localize the objects with only binary labels, Class Activation Map (CAM) is a widely used method to initial pseudo masks. However, due to the low contrast among different tissues in histopathological images, most existing CAM-based methods perform poorly in gland segmentation. We retrospect this process and find that class consistency and semantic consistency can guide the network to effectively distinguish confusing pixels and generate fine-grained pseudo masks. Specifically, for class consistency, we propose Consistency Correlation Attention (CCA) to encourage the network to focus on the contribution of class features to semantic dependencies. For semantic consistency, we propose Multi-scale Pyramid Fusion Pooling (MPFP) to aggregate coarse-to-fine global semantic information from CAMs at multiple spatial resolutions, thus identifying class localization. Additionally, we introduce a Purified Labels Filtration (PLF) strategy during the segmentation phase to mitigate the noisy supervision signal and improve the segmentation quality of the model. Extensive experiments show that the our method achieves new state-of-the-art results on three publicly available gland datasets. Furthermore, our method demonstrates impressive domain adaptation capability, achieving satisfactory results with only a small portion of samples when faced with unseen domain data.
HDLayout: Hierarchical and Directional Layout Planning for Arbitrary Shaped Visual Text Generation
Authors: Tonghui Feng; Chunsheng Yan; Qianru Wang; Jiangtao Cui; Xiaotian Qiao
Venue: AAAI 2025 (main)

Abstract: Visual text generation, which aims to generate photo-realistic images with coherent and well-formed scene text being rendered, has attracted widespread attention. Although recent works have achieved promising performance, the limited flexibility and controllability hinder their practical applications. We observe that different from natural objects, visual text in real scenes often has an arbitrarily shaped structure with different granularities (i.e., character, word, or line). In this paper, we consider the modality gap between image and text, and propose a new separation and composition pipeline for flexible and controllable visual text generation from only text prompts. At the core of our framework is a novel Hierarchical and Directional Layout representation, i.e., HDLayout, which can model the sequential and multi-granularity nature of the visual text. Under this formulation, we are able to generate arbitrarily shaped visual text automatically. Extensive experiments demonstrate that our method outperforms several strong baselines in a variety of scenarios both qualitatively and quantitatively, yielding state-of-the-art performances on arbitrarily shaped visual text generation.