GenAI4ELab/papercli-papers huggingface dataset:
Explore In-Context Segmentation via Latent Diffusion Models
Authors: Chaoyang Wang; Xiangtai Li; Henghui Ding; Lu Qi; Jiangning Zhang; Yunhai Tong; Chen Change Loy; Shuicheng Yan
Venue: AAAI 2025 (main)

Abstract: In-context segmentation has drawn increasing attention with the advent of vision foundation models. Its goal is to segment objects using given reference images. Most existing approaches adopt metric learning or masked image modeling to build the correlation between visual prompts and input image queries. This work approaches the problem from a fresh perspective - unlocking the capability of the latent diffusion model (LDM) for in-context segmentation and investigating different design choices. Specifically, we examine the problem from three angles: instruction extraction, output alignment, and meta-architectures. We design a two-stage masking strategy to prevent interfering information from leaking into the instructions. In addition, we propose an augmented pseudo-masking target to ensure the model predicts without forgetting the original images. Moreover, we build a new and fair in-context segmentation benchmark that covers both image and video datasets. Experiments validate the effectiveness of our approach, demonstrating comparable or even stronger results than previous specialist or visual foundation models. We hope our work inspires others to rethink the unification of segmentation and generation.
Geometry-Aware 3D Salient Object Detection Network
Authors: Chen Wang; Liyuan Zhang; Le Hui; Qi Liu; Yuchao Dai
Venue: AAAI 2025 (main)

Abstract: Point cloud salient object detection has attracted the attention of researchers in recent years. Since existing works do not fully utilize the geometry context of 3D objects, blurry boundaries are generated when segmenting objects with complex backgrounds. In this paper, we propose a geometry-aware 3D salient object detection network that explicitly clusters points into superpoints to enhance the geometric boundaries of objects, thereby segmenting complete objects with clear boundaries. Specifically, we first propose a simple yet effective superpoint partition module to cluster points into superpoints. In order to improve the quality of superpoints, we present a point cloud class-agnostic loss to learn discriminative point features for clustering superpoints from the object. After obtaining superpoints, we then propose a geometry enhancement module that utilizes superpoint-point attention to aggregate geometric information into point features for predicting the salient map of the object with clear boundaries. Extensive experiments show that our method achieves new state-of-the-art performance on the PCSOD dataset.
QCS:Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition
Authors: Chengpeng Wang; Li Chen; Lili Wang; Zhaofan Li; Xuebin Lv
Venue: AAAI 2025 (main)

Abstract: Facial expression recognition faces challenges where labeled significant features in datasets are mixed with unlabeled redundant ones. In this paper, we introduce Cross Similarity Attention (CSA) to mine richer intrinsic information from image pairs, overcoming a limitation when the Scaled Dot-Product Attention of ViT is directly applied to calculate the similarity between two different images. Based on CSA, we simultaneously minimize intra-class differences and maximize inter-class differences at the fine-grained feature level through interactions among multiple branches. Contrastive residual distillation is utilized to transfer the information learned in the cross module back to the base network. We ingeniously design a four-branch centrally symmetric network, named Quadruplet Cross Similarity (QCS), which alleviates gradient conflicts arising from the cross module and achieves balanced and stable training. It can adaptively extract discriminative features while isolating redundant ones. The cross-attention modules exist during training, and only one base branch is retained during inference, resulting in no increase in inference time. Extensive experiments show that our proposed method  achieves state-of-the-art performance on several FER datasets.
RHanDS: Refining Malformed Hands for Generated Images with Decoupled Structure and Style Guidance
Authors: Chengrui Wang; Pengfei Liu; Min Zhou; Ming Zeng; Xubin Li; Tiezheng Ge; Bo Zheng
Venue: AAAI 2025 (main)

Abstract: Although diffusion models can generate high-quality human images, their applications are limited by the instability in generating hands with correct structures. In this paper, we introduce RHanDS, a conditional diffusion-based framework designed to refine malformed hands by utilizing decoupled structure and style guidance. The hand mesh reconstructed from the malformed hand offers structure guidance for correcting the structure of the hand, while the malformed hand itself provides style guidance for preserving the style of the hand. To alleviate the mutual interference between style and structure guidance, we introduce a two-stage training strategy and build a series of multi-style hand datasets. In the first stage, we use paired hand images for training to ensure stylistic consistency in hand refining. In the second stage, various hand images generated based on human meshes are used for training, enabling the model to gain control over the hand structure. Experimental results demonstrate that RHanDS can effectively refine hand structure while preserving consistency in hand style.
Multi-clue Consistency Learning to Bridge Gaps Between General and Oriented Object in Semi-supervised Detection
Authors: Chenxu Wang; Chunyan Xu; Xiang Li; YuXuan Li; Xu Guo; Ziqi Gu; Zhen Cui
Venue: AAAI 2025 (main)

Abstract: While existing semi-supervised object detection (SSOD) methods perform well in general scenes, they encounter challenges in handling oriented objects in aerial images. 
We experimentally find three gaps between general and oriented object detection in semi-supervised learning: 
1) Sampling inconsistency: the common center sampling is not suitable for oriented objects with larger aspect ratios when selecting positive labels from labeled data. 
2) Assignment inconsistency: balancing the precision and localization quality of oriented pseudo-boxes poses greater challenges which introduces more noise when selecting positive labels from unlabeled data. 
3) Confidence inconsistency: there exists more mismatch between the predicted classification and localization qualities when considering oriented objects, affecting the selection of pseudo-labels. 
Therefore, we propose a Multi-clue Consistency Learning (MCL) framework to bridge gaps between general and oriented objects in semi-supervised detection. 
Specifically, considering various shapes of rotated objects, the Gaussian Center Assignment is specially designed to select the pixel-level positive labels from labeled data. 
We then introduce the Scale-aware Label Assignment to select pixel-level pseudo-labels instead of unreliable pseudo-boxes, which is a divide-and-rule strategy suited for objects with various scales. 
The Consistent Confidence Soft Label is adopted to further boost the detector by maintaining the alignment of the predicted results. 
Comprehensive experiments on DOTA-v1.5 and DOTA-v1.0 benchmarks demonstrate that our proposed MCL can achieve state-of-the-art performance in the semi-supervised oriented object detection task.
RA-GAR: A Richly Annotated Benchmark for Gait Attribute Recognition
Authors: Chenye Wang; Saihui Hou; Aoqi Li; Qingyuan Cai; Yongzhen Huang
Venue: AAAI 2025 (main)

Abstract: Gait attracts growing interest from researchers due to its advantages as a non-invasive and non-cooperative biometric feature. Current gait-based attribute recognition methods primarily focus on estimating attributes such as gender, age, and emotions. However, there is insufficient attention to diverse gait attributes in various covariate scenarios. In this paper, we design and collect a Richly Annotated benchmark for 15 gait attributes, named RA-GAR, comprising data from 533 individuals with over 120,000 sequences. To our knowledge, RA-GAR represents the largest and most diverse benchmark of gait attributes currently available. Furthermore, to fully leverage the semantic information and enhance attribute-specific local perception, we propose a two-stage CLIP-based method for Gait Attribute Recognition, named CLIP-GAR. Experiments on the RA-GAR and MA-Gait datasets demonstrate the effectiveness of CLIP-GAR, showing significant improvements in mean accuracy and F1 score.
Towards Efficient Object Re-Identification with a Novel Cloud-Edge Collaborative Framework
Authors: Chuanming Wang; Yuxin Yang; Mengshi Qi; Huanhuan Zhang; Huadong Ma
Venue: AAAI 2025 (main)

Abstract: Object re-identification (ReID) is committed to searching for objects of the same identity across cameras, and its real-world deployment is gradually increasing. Current ReID methods assume that the deployed system follows the centralized processing paradigm, i.e., all computations are conducted in the cloud server and edge devices are only used to capture images. As the number of videos experiences a rapid escalation, this paradigm has become impractical due to the finite computational resources in the cloud server. Therefore, the ReID system should be converted to fit in the cloud-edge collaborative processing paradigm, which is crucial to boost its scalability and practicality. However, current works lack relevant research on this important specific issue, making it difficult to adapt them into a cloud-edge framework effectively. In this paper, we propose a cloud-edge collaborative inference framework for ReID systems, aiming to expedite the return of the desired image captured by the camera to the cloud server by learning the spatial-temporal correlations among objects. In the system, a Distribution-aware Correlation Modeling network (DaCM) is particularly proposed to embed the spatial-temporal correlations of the camera network implicitly into a graph structure, and it can be applied 1) in the cloud to regulate the size of the upload window and 2) on the edge device to adjust the sequence of images, respectively. Notably, the proposed DaCM can be seamlessly combined with traditional ReID methods, enabling their application within our proposed edge-cloud collaborative framework. Extensive experiments demonstrate that our method obviously reduces transmission overhead and significantly improves performance.
Intra and Inter Parser-Prompted Transformers for Effective Image Restoration
Authors: Cong Wang; Jinshan Pan; Liyan Wang; Wei Wang
Venue: AAAI 2025 (main)

Abstract: We propose Intra and Inter Parser-Prompted Transformers (PPTformer) that explore useful features from visual foundation models for image restoration. Specifically, PPTformer contains two parts: an Image Restoration Network (IRNet) for restoring images from degraded observations and a Parser-Prompted Feature Generation Network (PPFGNet) for providing IRNet with reliable parser information to boost restoration. To enhance the integration of the parser within IRNet, we propose Intra Parser-Prompted Attention (IntraPPA) and Inter Parser-Prompted Attention (InterPPA) to implicitly and explicitly learn useful parser features to facilitate restoration. The IntraPPA re-considers cross attention between parser and restoration features, enabling implicit perception of the parser from a long-range and intra-layer perspective. Conversely, the InterPPA initially fuses restoration features with those of the parser, followed by formulating these fused features within an attention mechanism to explicitly perceive parser information. Further, we propose a parser-prompted feed-forward network to guide restoration within pixel-wise gating modulation. Experimental results show that PPTformer achieves state-of-the-art performance on image deraining, defocus deblurring, desnowing, and low-light enhancement.
Target-Driven Distillation: Consistency Distillation with Target Timestep Selection and Decoupled Guidance
Authors: Cunzheng Wang; Ziyuan Guo; Yuxuan Duan; Huaxia Li; Nemo Chen; Xu Tang; Yao Hu
Venue: AAAI 2025 (main)

Abstract: Consistency distillation methods have demonstrated significant success in accelerating generative tasks of diffusion models. However, since previous consistency distillation methods use simple and straightforward strategies in selecting target timesteps, they usually struggle with blurs and detail losses in generated images. To address these limitations, we introduce Target-Driven Distillation (TDD), which (1) adopts a delicate selection strategy of target timesteps, increasing the training efficiency; (2) utilizes decoupled guidances during training, making TDD open to post-tuning on guidance scale during inference periods; (3) can be optionally equipped with non-equidistant sampling and x0 clipping, enabling a more flexible and accurate way for image sampling. Experiments verify that TDD achieves state-of-the-art performance in few-step generation, offering a better choice among consistency distillation models.
ADBA: Approximation Decision Boundary Approach for Black-Box Adversarial Attacks
Authors: Feiyang Wang; Xingquan Zuo; Hai Huang; Gang Chen
Venue: AAAI 2025 (main)

Abstract: Many machine learning models are susceptible to adversarial attacks, with decision-based black-box attacks representing the most critical threat in real-world applications. These attacks are extremely stealthy, generating adversarial examples using hard labels obtained from the target machine learning model. This is typically realized by optimizing perturbation directions, guided by decision boundaries identified through query-intensive exact search, significantly limiting the attack success rate. This paper introduces a novel approach using the Approximation Decision Boundary (ADB) to efficiently and accurately compare perturbation directions without precisely determining decision boundaries. The effectiveness of our ADB approach (ADBA) hinges on promptly identifying suitable ADB, ensuring reliable differentiation of all perturbation directions. For this purpose, we analyze the probability distribution of decision boundaries, confirming that using the distribution's median value as ADB can effectively distinguish different perturbation directions, giving rise to the development of the ADBA-md algorithm. ADBA-md only requires four queries on average to differentiate any pair of perturbation directions, which is highly query-efficient. Extensive experiments on six well-known image classifiers clearly demonstrate the superiority of ADBA and ADBA-md over multiple state-of-the-art black-box attacks.
A Black-Box Evaluation Framework for Semantic Robustness in Bird’s Eye View Detection
Authors: Fu Wang; Yanghao Zhang; Xiangyu Yin; Guangliang Cheng; Zeyu Fu; Xiaowei Huang; Wenjie Ruan
Venue: AAAI 2025 (main)

Abstract: Camera-based Bird's Eye View (BEV) perception models receive increasing attention for their crucial role in autonomous driving, a domain where concerns about the robustness and reliability of deep learning have been raised. While only a few works have investigated the effects of randomly generated semantic perturbations, aka natural corruptions, on the multi-view BEV detection task, we develop a black-box robustness evaluation framework that adversarially optimises three common semantic perturbations: geometric transformation, colour shifting, and motion blur, to deceive BEV models, serving as the first approach in this emerging field. To address the challenge posed by optimising the semantic perturbation, we design a smoothed, distance-based surrogate function to replace the mAP metric and introduce SimpleDIRECT, a deterministic optimisation algorithm that utilises observed slopes to guide the optimisation process. By comparing with randomised perturbation and two optimisation baselines, we demonstrate the effectiveness of the proposed framework. Additionally, we provide a benchmark on the semantic robustness of ten recent BEV models. The results reveal that PolarFormer, which emphasises geometric information from multi-view images, exhibits the highest robustness, whereas BEVDet is fully compromised, with its precision reduced to zero.
Scene Graph-Grounded Image Generation
Authors: Fuyun Wang; Tong Zhang; Yuanzhi Wang; Xiaoya Zhang; Xin Liu; Zhen Cui
Venue: AAAI 2025 (main)

Abstract: With the beneft of explicit object-oriented reasoning capabilities of scene graphs, scene graph-to-image generation has  made remarkable advancements in comprehending object coherence and interactive relations. Recent state-of-the-arts typically predict the scene layouts as an intermediate representation of a scene graph before synthesizing the image. Nevertheless, transforming a scene graph into an exact layout may  restrict its representation capabilities, leading to discrepancies  in interactive relationships (such as standing on, wearing, or  covering) between the generated image and the input scene  graph. In this paper, we propose a Scene Graph-Grounded Image Generation (SGG-IG) method to mitigate the above  issues. Specifcally, to enhance the scene graph representation, we design a masked auto-encoder module and a relation  embedding learning module to integrate structural knowledge  and contextual information of the scene graph with a mask  self-supervised manner. Subsequently, to bridge the scene  graph with visual content, we introduce a spatial constraint and  image-scene alignment constraint to capture the fne-grained  visual correlation between the scene graph symbol representation and the corresponding image representation, thereby  generating semantically consistent and high-quality images. Extensive experiments demonstrate the effectiveness of the  method both quantitatively and qualitatively.
S³-Mamba: Small-Size-Sensitive Mamba for Lesion Segmentation
Authors: Gui Wang; Yuexiang Li; Wenting Chen; Meidan Ding; Wooi Ping Cheah; Rong Qu; Jianfeng Ren; Linlin Shen
Venue: AAAI 2025 (main)

Abstract: Small lesions play a critical role in early disease diagnosis and intervention of severe infections. Popular models often face challenges in segmenting small lesions, as it occupies only a minor portion of an image, while down-sampling operations may inevitably lose focus on local features of small lesions. To tackle the challenges, we propose a Small-Size-Sensitive Mamba (S³-Mamba), which promotes the sensitivity to small lesions across three dimensions: channel, spatial, and training strategy. Specifically, an Enhanced Visual State Space block is designed to focus on small lesions through multiple residual connections to preserve local features, and selectively amplify important details while suppressing irrelevant ones through channel-wise attention. A Tensor-based Cross-feature Multi-scale Attention is designed to integrate input image features and intermediate-layer features with edge features and exploit the attentive support of features across multiple scales, thereby retaining spatial details of small lesions at various granularities. Finally, we introduce a novel regularized curriculum learning to automatically assess lesion size and sample difficulty, and gradually focus from easy samples to hard ones like small lesions. Extensive experiments on three medical image segmentation datasets show the superiority of our S³-Mamba, especially in segmenting small lesions.
SSC-VAE: Structured Sparse Coding Based Variational Autoencoder for Detail Preserved Image Reconstruction
Authors: Hao Wang; Lu Wang; Zhongyu Wang; Lixin Ma; Ye Luo
Venue: AAAI 2025 (main)

Abstract: Discrete latent representation techniques, such as Vector Quantization (VQ) and Sparse Coding (SC), have demonstrated superior image reconstruction and generation quality compared to continuous representation methods in Variational Autoencoders (VAEs).
However, existing approaches often treat the latent representations of an image independently in their discrete representation space, neglecting both the inherent structural information within each representation and the correlations among them. This oversight leads to coarse representations and suboptimal generated results. In this paper, we address these limitations by introducing correlations among and within the latent representations of individual images in the latent discrete space of VAEs using sparse coding. We impose two-dimensional structural information through adaptive thresholding, enhancing local structure in image representations while suppressing noise via parsimonious representation with a learned dictionary. 
Empirical studies on three real benchmark datasets, including a clinical Ultrasound dataset, BSDS500, and mini-Imagenet, demonstrate that our proposed model preserves fine-grained details in image reconstruction and significantly outperforms baseline models of SC-VAE and VQ-VAE across objective and subjective image quality metrics. Particularly noteworthy are the substantial performance improvements observed on the ultrasound dataset, where structure information is crucial. Specifically, we observe significant performance improvements of 7.68 % and 17.03 % in SSIM, 3.25 dB and 6.58 dB in PSNR, 0.15 and 0.24 in LPIPS, 45.38 and 84.05 in FID over SC-VAE and VQ-VAE, respectively, indicating the superiority of our method in terms of image reconstruction quality and fidelity.
BLS-GAN: A Deep Layer Separation Framework for Eliminating Bone Overlap in Conventional Radiographs
Authors: Haolin Wang; Yafei Ou; Prasoon Ambalathankandy; Gen Ota; Pengyu Dai; Masayuki Ikebe; Kenji Suzuki; Tamotsu Kamishima
Venue: AAAI 2025 (main)

Abstract: Conventional radiography is the widely used imaging technology in diagnosing, monitoring, and prognosticating musculoskeletal (MSK) diseases because of its easy availability, versatility, and cost-effectiveness. Bone overlaps are prevalent in conventional radiographs, and can impede the accurate assessment of bone characteristics by radiologists or algorithms, posing significant challenges to conventional clinical diagnosis and computer-aided diagnosis. This work initiated the study of a challenging scenario - bone layer separation in conventional radiographs, in which separate overlapped bone regions enable the independent assessment of the bone characteristics of each bone layer and lay the groundwork for MSK disease diagnosis and its automation. This work proposed a Bone Layer Separation GAN (BLS-GAN) framework that can produce high-quality bone layer images with reasonable bone characteristics and texture. This framework introduced a reconstructor based on conventional radiography imaging principles, which achieved efficient reconstruction and mitigates the recurrent calculations and training instability issues caused by soft tissue in the overlapped regions. Additionally, pre-training with synthetic images was implemented to enhance the stability of both the training process and the results. The generated images passed the visual Turing test, and improved performance in downstream tasks. This work affirms the feasibility of extracting bone layer images from conventional radiographs, which holds promise for leveraging layer separation technology to facilitate more comprehensive analytical research in MSK diagnosis, monitoring, and prognosis.