GenAI4ELab/papercli-papers huggingface dataset:
Disentangled Motion Modeling for Video Frame Interpolation
Authors: Jaihyun Lew; Jooyoung Choi; Chaehun Shin; Dahuin Jung; Sungroh Yoon
Venue: AAAI 2025 (main)

Abstract: Video Frame Interpolation (VFI) aims to synthesize intermediate frames between existing frames to enhance visual smoothness and quality. Beyond the conventional methods based on the reconstruction loss, recent works have employed generative models for improved perceptual quality. However, they require complex training and large computational costs for pixel space modeling. In this paper, we introduce disentangled Motion Modeling (MoMo), a diffusion-based approach for VFI that enhances visual quality by focusing on intermediate motion modeling. We propose a disentangled two-stage training process. In the initial stage, frame synthesis and flow models are trained to generate accurate frames and flows optimal for synthesis. In the subsequent stage, we introduce a motion diffusion model, which incorporates our novel U-Net architecture specifically designed for optical flow, to generate bi-directional flows between frames. By learning the simpler low-frequency representation of motions, MoMo achieves superior perceptual quality with reduced computational demands compared to the generative modeling methods on the pixel space. MoMo surpasses state-of-the-art methods in perceptual metrics across various benchmarks, demonstrating its efficacy and efficiency in VFI.
Tri-Ergon: Fine-Grained Video-to-Audio Generation with Multi-Modal Conditions and LUFS Control
Authors: Bingliang Li; Fengyu Yang; Yuxin Mao; Qingwen Ye; Hongkai Chen; Yiran Zhong
Venue: AAAI 2025 (main)

Abstract: Video-to-audio (V2A) generation utilizes visual-only video features to produce realistic sounds that correspond to the scene. However, current V2A models often lack fine-grained control over the generated audio, especially in terms of loudness variation and the incorporation of multi-modal conditions. To overcome these limitations, we introduce Tri-Ergon, a diffusion-based V2A model that incorporates textual, auditory, and pixel-level visual prompts to enable detailed and semantically rich audio synthesis. Additionally, we introduce Loudness Units relative to Full Scale (LUFS) embedding, which allows for precise manual control of the loudness changes over time for individual audio channels, enabling our model to effectively address the intricate correlation of video and audio in real-world Foley workflows. Tri-Ergon is capable of creating 44.1 kHz high-fidelity stereo audio clips of varying lengths up to 60 seconds, which significantly outperforms existing state-of-the-art V2A methods that typically generate mono audio for a fixed duration.
StyO: Stylize Your Face in Only One-Shot
Authors: Bonan Li; Zicheng Zhang; Xuecheng Nie; Congying Han; Yinhan Hu; Xinmin Qiu; Tiande Guo
Venue: AAAI 2025 (main)

Abstract: This paper focuses on face stylization with a single artistic target. Existing works for this task often fail to retain the source content while achieving geometry variation. Here, we present a novel StyO model, i.e., Stylize the face in only One-shot, to solve the above problem. In particular, StyO exploits a disentanglement and recombination strategy. It first disentangles the content and style of source and target images into identifiers, which are then recombined in a cross manner to derive the stylized face image. In this way, StyO decomposes complex images into independent and specific attributes, and simplifies one-shot face stylization as the combination of different attributes from input images, thus producing results better matching face geometry of target image and content of source one. StyO is implemented with latent diffusion models (LDM) and composed of two key modules: 1) Identifier Disentanglement Learner (IDL) for disentanglement phase. It represents identifiers as contrastive text prompts, i.e. positive and negative descriptions. And it introduces a novel triple reconstruction loss to fine-tune the pre-trained LDM for encoding style and content into corresponding identifiers; 2) Fine-graind Content Controller (FCC) for recombination phase. It recombines disentangled identifiers from IDL to form an augmented text prompt for generating stylized faces. In addition, FCC also constrains the cross-attention maps of latent and text features to preserve source face details in results. The extensive evaluation shows that StyO produces high-quality images on numerous paintings of various styles and outperforms the current state-of-the-art.
FEAST-Mamba: FEAture and SpaTial Aware Mamba Network with Bidirectional Orthogonal Fusion for Cross-Modal Point Cloud Segmentation
Authors: Chade Li; Pengju Zhang; Bo Liu; Hao Wei; Yihong Wu
Venue: AAAI 2025 (main)

Abstract: Point cloud segmentation has a wide range of applications in autonomous driving, augmented reality and virtual reality. Multi-modal fusion strategies have received increasing attention in point cloud segmentation recently. Despite the success, existing methods usually generate unnecessary information loss or redundancy. In this paper, we propose FEAST-Mamba, a novel FEAture and SpaTial aware Mamba network to tackle multi-modal point cloud segmentation. To exploit the complementarity between different modals, we propose a bidirectional orthogonal attention module, where features are first bidirectionally interacted with each other through cross-modal attention, and then orthogonal fusion is used to reduce feature redundancy. Furthermore, a reordering strategy is proposed for the Mamba architecture that takes into account both spatial and semantic information during cross-modal feature ordering. Experiments on indoor datasets, S3DIS and ScanNet, and outdoor datasets, nuScenes and SemanticKITTI, show that the proposed method achieves state-of-the-art performances.
RemDet: Rethinking Efficient Model Design for UAV Object Detection
Authors: Chen Li; Rui Zhao; Zeyu Wang; Huiying Xu; Xinzhong Zhu
Venue: AAAI 2025 (main)

Abstract: Object detection in Unmanned Aerial Vehicle (UAV) images has emerged as a focal area of research, which presents two significant challenges: i) objects are typically small and dense within vast images; ii) computational resource constraints render most models unsuitable for real-time deployment. Current real-time object detectors are not optimized for UAV images, and complex methods designed for small object detection often lack real-time capabilities. To address these challenges, we propose a novel detector, RemDet (Reparameter efficient multiplication Detector). Our contributions are as follows: 1) Rethinking the challenges of existing detectors for small and dense UAV images, and proposing information loss as a design guideline for efficient models. 2) We introduce the ChannelC2f module to enhance small object detection performance, demonstrating that high-dimensional representations can effectively mitigate information loss. 3) We design the GatedFFN module to provide not only strong performance but also low latency, effectively addressing the challenges of real-time detection. Our research reveals that GatedFFN, through the use of multiplication, is more cost-effective than feed-forward networks for high-dimensional representation. 4) We propose the CED module, which combines the advantages of ViT and CNN downsampling to effectively reduce information loss. It specifically enhances context information for small and dense objects. Extensive experiments on large UAV datasets, Visdrone and UAVDT, validate the real-time efficiency and superior performance of our methods. On the challenging UAV dataset VisDrone, our methods not only provided state-of-the-art results, improving detection by more than 3.4%, but also achieve 110 FPS on a single 4090.
U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation
Authors: Chenxin Li; Xinyu Liu; Wuyang Li; Cheng Wang; Hengyu Liu; Yifan Liu; Zhen Chen; Yixuan Yuan
Venue: AAAI 2025 (main)

Abstract: U-Net has become a cornerstone in various visual applications such as image segmentation and diffusion probability models. While numerous innovative designs and improvements have been introduced by incorporating transformers or MLPs, the networks are still limited to linearly modeling patterns as well as the deficient interpretability. To address these challenges, our intuition is inspired by the impressive results of the Kolmogorov-Arnold Networks (KANs) in terms of accuracy and interpretability, which reshape the neural network learning via the stack of non-linear learnable activation functions derived from the Kolmogorov-Anold representation theorem. Specifically, in this paper, we explore the untapped potential of KANs in improving backbones for vision tasks. We investigate, modify and re-design the established U-Net pipeline by integrating the dedicated KAN layers on the tokenized intermediate representation, termed U-KAN. Rigorous medical image segmentation benchmarks verify the superiority of UKAN by higher accuracy even with less computation cost. We further delved into the potential of U-KAN as an alternative U-Net noise predictor in diffusion models, demonstrating its applicability in generating task-oriented model architectures.
Consistency of Compositional Generalization Across Multiple Levels
Authors: Chuanhao Li; Zhen Li; Chenchen Jing; Xiaomeng Fan; Wenbo Ye; Yuwei Wu; Yunde Jia
Venue: AAAI 2025 (main)

Abstract: Compositional generalization is the capability of a model to understand novel compositions composed of seen concepts. There are multiple levels of novel compositions including phrase-phrase level, phrase-word level, and word-word level. Existing methods achieve promising compositional generalization, but the consistency of compositional generalization across multiple levels of novel compositions remains unexplored. The consistency refers to that a model should generalize to a phrase-phrase level novel composition, and phrase-word/word-word level novel compositions that can be derived from it simultaneously. In this paper, we propose a meta-learning based framework, for achieving consistent compositional generalization across multiple levels. The basic idea is to progressively learn compositions from simple to complex for consistency. Specifically, we divide the original training set into multiple validation sets based on compositional complexity, and introduce multiple meta-weight-nets to generate sample weights for samples in different validation sets. To fit the validation sets in order of increasing compositional complexity, we optimize the parameters of each meta-weight-net independently and sequentially in a multilevel optimization manner. We build a GQA-CCG dataset to quantitatively evaluate the consistency.  Experimental results on visual question answering and temporal video grounding, demonstrate the effectiveness of the proposed framework.
An Efficient Framework for Enhancing Discriminative Models via Diffusion Techniques
Authors: Chunxiao Li; Xiaoxiao Wang; Boming Miao; Chuanlong Xie; Zizhe Wang; Yao Zhu
Venue: AAAI 2025 (main)

Abstract: Image classification serves as the cornerstone of computer vision, traditionally achieved through discriminative models based on deep neural networks. Recent advancements have introduced classification methods derived from generative models, which offer the advantage of zero-shot classification. However, these methods suffer from two main drawbacks: high computational overhead and inferior performance compared to discriminative models. Inspired by the coordinated cognitive processes of rapid-slow pathway interactions in the human brain during visual signal recognition, we propose the Diffusion-Based Discriminative Model Enhancement Framework (DBMEF). This framework seamlessly integrates discriminative and generative models in a training-free manner, leveraging discriminative models for initial predictions and endowing deep neural networks with rethinking capabilities via diffusion models. Consequently, DBMEF can effectively enhance the classification accuracy and generalization capability of discriminative models in a plug-and-play manner. We have conducted extensive experiments across 17 prevalent deep model architectures with different training methods, including both CNN-based models such as ResNet and Transformer-based models like ViT, to demonstrate the effectiveness of the proposed DBMEF.Specifically, the framework yields a 1.51% performance improvement for ResNet-50 on the ImageNet dataset and 3.02% on the ImageNet-A dataset. In conclusion, our research introduces a novel paradigm for image classification, demonstrating stable improvements across different datasets and neural networks.
CALLIC: Content Adaptive Learning for Lossless Image Compression
Authors: Daxin Li; Yuanchao Bai; Kai Wang; Junjun Jiang; Xianming Liu; Wen Gao
Venue: AAAI 2025 (main)

Abstract: Learned lossless image compression has achieved significant advancements in recent years. However, existing methods often rely on training amortized generative models on massive datasets, resulting in sub-optimal probability distribution estimation for specific testing images during encoding process.  To address this challenge, we explore the connection between the Minimum Description Length (MDL) principle and Parameter-Efficient Transfer Learning (PETL), leading to the development of a novel content-adaptive approach for learned lossless image compression, dubbed CALLIC. Specifically, we first propose a content-aware autoregressive self-attention mechanism by leveraging convolutional gating operations, termed Masked Gated ConvFormer (MGCF), and pretrain MGCF on training dataset. Cache then Crop Inference (CCI) is proposed to accelerate the coding process. During encoding, we decompose pretrained layers, including depth-wise convolutions, using low-rank matrices and then adapt the incremental weights on testing image by Rate-guided Progressive Fine-Tuning (RPFT). RPFT fine-tunes with gradually  increasing patches that are sorted in descending order by estimated entropy, optimizing learning process and reducing adaptation time. Extensive experiments across diverse datasets demonstrate that CALLIC sets a new state-of-the-art (SOTA) for learned lossless image compression.
Cascaded Diffusion Models for Virtual Try-On: Improving Control and Resolution
Authors: Guangyuan Li; Yongkang Wang; Junsheng Luan; Lei Zhao; Wei Xing; Huaizhong Lin; Binkai Ou
Venue: AAAI 2025 (main)

Abstract: Previous virtual try-on methods have employed ControlNet architecture in exemplar-based inpainting diffusion models to guide the generation of try-on images, preserving the garment's features and enhancing the realism of the generated images. While these methods have maintained the identity of the garment and improved the naturalness of the generated images, they still face the following limitations: (1) For garments with complex features, such as intricate text, patterns, and uncommon styles, they struggle to retain these detailed features in the generated try-on images. (2) They are limited to generating try-on images at a maximum resolution of 1K, which may not meet the demands of real-world scenarios, where higher resolutions might be required. To address the aforementioned issues, in this paper, we propose a Cascaded Diffusion Model for virtual try-on to enhance both image controllability and resolution. We call it CDM-VTON. Specifically, we design two diffusion models: the Multi-Conditioned Diffusion Model (MC-DM) and the Super-Resolution Diffusion Model (SR-DM). The former generates low-resolution try-on images while preserving the garment's complex features, and the latter enhances the resolution of these images. Additionally, we incorporate a multi-control integration module in the MC-DM, which injects multiple control conditions into a frozen denoising U-Net to ensure that the generated try-on images retain complex garment features. Our experimental results demonstrate that our method outperforms previous approaches in preserving garment details and generating authentic virtual try-on images, both qualitatively and quantitatively.
HomeDiffusion: Zero-Shot Object Customization with Multi-View Representation Learning for Indoor Scenes
Authors: Guoqiu Li; Jin Song; Yiyun Fei
Venue: AAAI 2025 (main)

Abstract: Recently, zero-shot object customization generation methods have rapidly developed and shown tremendous potential for applications. For instance, in the e-commerce domain, consumers can observe the visual effect of furniture placed within their personal living spaces or clothes worn on their own bodies.
Many existing approaches perform object customization generation based on diffusion models and extracted  reference object features. However, the generated object significantly diverges from the original reference object in details such as patterns and curves. Particularly for asymmetrical reference objects, the absence of comprehensive multi-viewpoint information prevents the generation of object poses that harmonize with the background scene.
To address these shortcomings, we have constructed a novel dataset comprising multi-angle images of  furniture and indoor scenes. Based on diffusion models, we introduce HomeDiffusion, which can leverage multi-viewpoint images of the same reference object to accurately generate visually harmonious object poses within specified areas of the background scene.
During the diffusion process, we further extract high-fidelity details of the reference object and perform cross-attention with the noise latents in the latent space, thereby ensuring the preservation of details in the customized object generation.
Extensive qualitative and quantitative experiments demonstrate that our method achieves superior performance over other existing zero-shot as well as few-shot object customization approaches.
VEGAS: Towards Visually Explainable and Grounded Artificial Social Intelligence
Authors: Hao Li; Hao Fei; Zechao Hu; Zhengwei Yang; Zheng Wang
Venue: AAAI 2025 (main)

Abstract: Social Intelligence Queries (Social-IQ) serve as the primary multimodal benchmark for evaluating a  model’s social intelligence level. While impressive multiple-choice question (MCQ) accuracy is  achieved by current solutions, increasing evidence shows that they are largely, and in some cases entirely, dependent on language modality, overlooking visual context. Additionally, the closed-set nature further prevents the exploration of whether and to what extent the reasoning path behind selection is correct. To address these limitations, we propose the Visually Explainable and Grounded Artificial Social Intelligence (VEGAS) model. As a generative multimodal model, VEGAS leverages open-ended answering to provide explainable responses, which enhances the clarity and evaluation of reasoning paths. To enable visually grounded answering, we propose a novel sampling strategy to provide the model with more relevant visual frames. We then enhance the model’s interpretation of these frames through Generalist Instruction Fine-Tuning (GIFT), which aims to: i) learn multimodal language transformations for fundamental emotional social traits, and ii) establish multimodal joint reasoning capabilities. Extensive experiments, comprising modality ablation, open-ended assessments, and supervised MCQ evaluations, consistently show that VEGAS effectively utilizes visual information in reasoning to produce correct and also credible answers. We expect this work to offer a new perspective on Social-IQ and advance the development of human-like social AI.
AIF-SFDA: Autonomous Information Filter Driven Source-Free Domain Adaptation for Medical Image Segmentation
Authors: Haojin Li; Heng Li; Jianyu Chen; Rihan Zhong; Ke Niu; Huazhu Fu; Jiang Liu
Venue: AAAI 2025 (main)

Abstract: Decoupling domain-variant information (DVI) from domain-invariant information (DII) serves as a prominent strategy for mitigating domain shifts in the practical implementation of deep learning algorithms. However, in medical settings, concerns surrounding data collection and privacy often restrict access to both training and test data, hindering the empirical decoupling of information by existing methods. To tackle this issue, we propose an Adaptive Information Filter-driven Source-free Domain Adaptation (AIF-SFDA) algorithm, which leverages a frequency-based learnable information filter to autonomously decouple DVI and DII. Information Bottleneck (IB) and Self-supervision (SS) are incorporated to optimize the learnable frequency filter. The IB governs the information flow within the filter to diminish redundant DVI, while SS preserves DII in alignment with the specific task and image modality. Thus, the adaptive information filter can overcome domain shifts relying solely on target data. A series of experiments covering various medical image modalities and segmentation tasks were conducted to demonstrate the benefits of AIF-SFDA through comparisons with leading algorithms and ablation studies.
BSAFusion: A Bidirectional Stepwise Feature Alignment Network for Unaligned Medical Image Fusion
Authors: Huafeng Li; Dayong Su; Qing Cai; Yafei Zhang
Venue: AAAI 2025 (main)

Abstract: If unaligned multimodal medical images can be simultaneously aligned and fused using a single-stage approach within a unified processing framework, it will not only achieve mutual promotion of dual tasks but also help reduce the complexity of the model. However, the design of this model faces the challenge of incompatible requirements for feature fusion and alignment. To address this challenge, this paper proposes an unaligned medical image fusion method called Bidirectional Stepwise Feature Alignment and Fusion (BSFA-F) strategy. To reduce the negative impact of modality differences on cross-modal feature matching, we incorporate the Modal Discrepancy-Free Feature Representation (MDF-FR) method into BSFA-F. MDF-FR utilizes a Modality Feature Representation Head (MFRH) to integrate the global information of the input image. By injecting the information contained in MFRH of the current image into other modality images, it effectively reduces the impact of modality differences on feature alignment while preserving the complementary information carried by different images. In terms of feature alignment, BSFA-F employs a bidirectional stepwise alignment deformation field prediction strategy based on the path independence of vector displacement between two points. This strategy solves the problem of large spans and inaccurate deformation field prediction in single-step alignment. Finally, Multi-Modal Feature Fusion block achieves the fusion of aligned features. The experimental results across multiple datasets demonstrate the effectiveness of our method.
Prompt-SID: Learning Structural Representation Prompt via Latent Diffusion for Single Image Denoising
Authors: Huaqiu Li; Wang Zhang; Xiaowan Hu; Tao Jiang; Zikang Chen; Haoqian Wang
Venue: AAAI 2025 (main)

Abstract: Many studies have concentrated on constructing supervised models utilizing paired datasets for image denoising, which proves to be expensive and time-consuming. Current self-supervised and unsupervised approaches typically rely on blind-spot networks or sub-image pairs sampling, resulting in pixel information loss and destruction of detailed structural information, thereby significantly constraining the efficacy of such methods. In this paper, we introduce Prompt-SID, a prompt-learning-based single image denoising framework that emphasizes the preservation of structural details. This approach is trained in a self-supervised manner using downsampled image pairs. It captures original-scale image information through structural encoding and integrates this prompt into the denoiser. To achieve this, we propose a structural representation generation model based on the latent diffusion process and design a structural attention module within the transformer-based denoiser architecture to decode the prompt. Additionally, we introduce a scale replay training mechanism, which effectively mitigates the scale gap from images of different resolutions. We conduct comprehensive experiments on synthetic, real-world, and fluorescence imaging datasets, showcasing the remarkable effectiveness of Prompt-SID.