pwc-archive/papers-with-abstracts huggingface dataset:
Attraction Diminishing and Distributing for Few-Shot Class-Incremental Learning
Authors: Li-Jun Zhao, Zhen-Duo Chen, Yongxin Wang, Xin Luo, Xin-Shun Xu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: class-incremental learning, Class Incremental Learning, Few-Shot Class-Incremental Learning, Incremental Learning

    Few-Shot Class-Incremental Learning (FSCIL) aims to continuously learn novel classes with limited samples after pre-training on a set of base classes. To avoid catastrophic forgetting and overfitting, most FSCIL methods first train the model on the base classes and then freeze the feature extractor in the incremental sessions. However, the reliance on nearest neighbor classification makes FSCIL prone to the hubness phenomenon, which negatively impacts performance in this dynamic and open scenario. While recent methods attempt to adapt to the dynamic and open nature of FSCIL, they are often limited to biased optimizations to the feature space. In this paper, we pioneer the theoretical analysis of the inherent hubness in FSCIL. To mitigate the negative effects of hubness, we propose a novel Attraction Diminishing and Distributing (D2A) method from the essential perspectives of distance metric and feature space. Extensive experimental results demonstrate that our method can broadly and significantly improve the performance of existing methods.
CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor Segmentation
Authors: Zhenhui Ding, Guilian Chen, Qin Zhang, Huisi Wu, Jing Qin
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Segmentation, Lesion Segmentation, Semantic Segmentation, Tumor Segmentation

    Accurate automatic breast ultrasound (BUS) image segmentation is essential for early breast cancer screening and diagnosis. However, it remains challenging owing to (1) breast lesions of various scale and shape, (2) ambiguous boundaries caused by speckle noise and artifacts, and (3) the scarcity of high-quality annotations. Most existing semi-supervised methods employ the mean-teacher architecture, which merely learns semantic information within a single image and heavily relies on the performance of the teacher model. Therefore, we present a novel cross-image semantic correlation semi-supervised framework, named CSC-PA, to improve the performance of BUS image segmentation. CSC-PA is trained based on a single network, which integrates a foreground prototype attention (FPA) and an edge prototype attention (EPA). Specifically, FPA transfers complementary foreground information for more stable and complete lesion segmentation. On the other hand, EPA enhances edge features of lesions by using edge prototype, where an adaptive edge container is proposed to store global edge features and generate the edge prototype. Additionally, we introduce a pixel affinity loss (PAL) to exploit previously ignored contextual correlation in supervision, which further improves performance on edges. Extensive experiments on two benchmark BUS datasets demonstrate that our model outperforms other state-of-the-art methods under different partition protocols. Codes are available at https://github.com/shdkdh/CSC-PA.
FIFA: Fine-grained Inter-frame Attention for Driver's Video Gaze Estimation
Authors: Daosong Hu, Mingyue Cui, Kai Huang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Gaze Estimation

    Gaze direction serves as a pivotal indicator for assessing the level of driver attention. While image-based gaze estimation has been extensively researched, there has been a recent shift towards capturing gaze direction from video sequences. This approach encounters notable challenges, including the comprehension of the dynamic pupil evolution across frames and the extraction of head pose information from a relatively static background. To surmount these challenges, we introduce a dual-stream deep learning framework that explicitly models the displacement changes of the pupil through a fine-grained inter-frame attention mechanism and generates weights to adjust gaze embeddings. This technique transforms the face into a set of distinct patches and employs cross-attention to ascertain the correlation between pixel displacements in various patches and adjacent frames, thereby tracking spatial dynamics within the sequence. Our method is validated using two publicly available driver gaze datasets, and the results indicate that it achieves state-of-the-art performance or is on par with the best outcomes while reducing the parameters.
Keep the Balance: A Parameter-Efficient Symmetrical Framework for RGB+X Semantic Segmentation
Authors: Jiaxin Cai, Jingze Su, Qi Li, Wenjie Yang, Shu Wang, Tiesong Zhao, Shengfeng He, Wenxi Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: parameter-efficient fine-tuning, Segmentation, Semantic Segmentation

    Multimodal semantic segmentation is a critical challenge in computer vision, with early methods suffering from high computational costs and limited transferability due to full fine-tuning of RGB-based pre-trained parameters. Recent studies, while leveraging additional modalities as supplementary prompts to RGB, still predominantly rely on RGB, which restricts the full potential of other modalities. To address these issues, we propose a novel symmetric parameter-efficient fine-tuning framework for multimodal segmentation, featuring with a modality-aware prompting and adaptation scheme, to simultaneously adapt the capabilities of a powerful pre-trained model to both RGB and X modalities. Furthermore, prevalent approaches use the global cross-modality correlations of attention mechanism for modality fusion, which inadvertently introduces noise across modalities. To mitigate this noise, we propose a dynamic sparse cross-modality fusion module to facilitate effective and efficient cross-modality fusion. To further strengthen the above two modules, we propose a training strategy that leverages accurately predicted dual-modality results to self-teach the single-modality outcomes. In comprehensive experiments, we demonstrate that our method outperforms previous state-of-the-art approaches across six multimodal segmentation scenarios with minimal computation cost.
EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation
Authors: Daikun Liu, Lei Cheng, Teng Wang, Changyin Sun
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Event-based Optical Flow, Optical Flow Estimation

    Recent learning-based methods for event-based optical flow estimation utilize cost volumes for pixel matching but suffer from redundant computations and limited scalability to higher resolutions for flow refinement. In this work, we take advantage of the complementarity between temporally dense feature differences of adjacent event frames and cost volume and present a lightweight event-based optical flow network (EDCFlow) to achieve high-quality flow estimation at a higher resolution. Specifically, an attention-based multi-scale temporal feature difference layer is developed to capture diverse motion patterns at high resolution in a computation-efficient manner. An adaptive fusion of high-resolution difference motion features and low-resolution correlation motion features is performed to enhance motion representation and model generalization. Notably, EDCFlow can serve as a plug-and-play refinement module for RAFT-like event-based methods to enhance flow details. Extensive experiments demonstrate that EDCFlow achieves better performance with lower complexity compared to existing methods, offering superior generalization. Codes and models will be available at here.
Symbolic Representation for Any-to-Any Generative Tasks
Authors: Jiaqi Chen, Xiaoye Zhu, Yue Wang, Tianyang Liu, Xinhui Chen, Ying Chen, Chak Tou Leong, Yifei Ke, Joseph Liu, Yiwen Yuan, Julian McAuley, Li-Jia Li
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Language Modeling, Language Modelling

    We propose a symbolic generative task description language and a corresponding inference engine that can represent arbitrary multimodal tasks as structured symbolic flows. Unlike conventional generative models, which rely on large-scale training and implicit neural representations to learn cross-modal mappings--often with high computational costs and limited flexibility--our framework introduces an explicit symbolic representation composed of three core primitives: functions, parameters, and topological logic. Using a pre-trained language model, our inference engine maps natural language instructions directly to symbolic workflows in a training-free manner. Our framework successfully performs over 12 diverse multimodal generative tasks, demonstrating strong performance and flexibility without requiring task-specific tuning. Experiments show that our method not only matches or outperforms existing state-of-the-art unified models in content quality but also offers greater efficiency, editability, and interruptibility. We believe symbolic task representations provide a cost-effective and extensible foundation for advancing the capabilities of generative AI.
The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generationf
Authors: Yanis Benidir, Nicolas Gonthier, Clement Mallet
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Change Detection, Earth Observation

    Bi-temporal change detection at scale based on Very High Resolution (VHR) images is crucial for Earth monitoring. Such task remains poorly addressed even in the deep learning era: it either requires large volumes of annotated data - in the semantic case - or is limited to restricted datasets for binary set-ups. Most approaches do not exhibit the versatility required for temporal and spatial adaptation: simplicity in architecture design and pretraining on realistic and comprehensive datasets. Synthetic datasets is the key solution but still fails handling complex and diverse scenes. In this paper, we present HySCDG a generative pipeline for creating a large hybrid semantic change detection dataset that contains both real VHR images and inpainted ones, along with land cover semantic map at both dates and the change map. Being semantically and spatially guided, HySCDG generates realistic images, leading to a comprehensive and hybrid transfer-proof dataset FSC-180k. We evaluate FSC-180k on five change detection cases (binary and semantic), from zero-shot to mixed and sequential training, and also under low data regime training. Experiments demonstrate that pretraining on our hybrid dataset leads to a significant performance boost, outperforming SyntheWorld, a fully synthetic dataset, in every configuration. All codes, models, and data will be made available.
Shape and Texture: What Influences Reliable Optical Flow Estimation?
Authors: Libo Long, Xiao Hu, Jochen Lang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Autonomous Driving, Optical Flow Estimation

    Recent methods have made significant progress in optical flow estimation. However, the evaluation of these methods focuses mainly on improved accuracy in benchmarks and often overlooks the analysis of network robustness, which may be important in safety-critical scenarios such as autonomous driving. In this paper, we propose a novel method for robustness evaluation by modifying data from original benchmarks. Unlike previous benchmarks that focus on complex scenes, we propose to modify shape and texture of objects from the original images in order to analyze the sensitivity to these changes observed in the output. Our aim is to identify common failure cases of state-of-the-art (SOTA) methods to evaluate their robustness and understand their behaviors. We show that: Optical flow methods are more sensitive to shape changes than to texture changes; and optical flow methods tend to "remember" objects seen during training and may "ignore" the motion of unseen objects. Our experimental results and findings provide a more in-depth understanding of the behavior of recent optical flow methods.
GO-N3RDet: Geometry Optimized NeRF-enhanced 3D Object Detector
Authors: Zechuan Li, Hongshan Yu, Yihao Ding, Jinhao Qiao, Basim Azam, Naveed Akhtar
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Object Detection, NeRF, Object, object-detection, Object Detection

    We propose GO-N3RDet, a scene-geometry optimized multi-view 3D object detector enhanced by neural radiance fields. The key to accurate 3D object detection is in effective voxel representation. However, due to occlusion and lack of 3D information, constructing 3D features from multi-view 2D images is challenging. Addressing that, we introduce a unique 3D positional information embedded voxel optimization mechanism to fuse multi-view features. To prioritize neural field reconstruction in object regions, we also devise a double importance sampling scheme for the NeRF branch of our detector. We additionally propose an opacity optimization module for precise voxel opacity prediction by enforcing multi-view consistency constraints. Moreover, to further improve voxel density consistency across multiple perspectives, we incorporate ray distance as a weighting factor to minimize cumulative ray errors. Our unique modules synergetically form an end-to-end neural model that establishes new state-of-the-art in NeRF-based multi-view 3D detection, verified with extensive experiments on ScanNet and ARKITScenes. Code will be available at https://github.com/ZechuanLi/GO-N3RDet.
MultiMorph: On-demand Atlas Construction
Authors: S. Mazdak Abulnaga, Andrew Hoopes, Neel Dey, Malte Hoffmann, Bruce Fischl, John Guttag, Adrian Dalca
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    We present MultiMorph, a fast and efficient method for constructing anatomical atlases on the fly. Atlases capture the canonical structure of a collection of images and are essential for quantifying anatomical variability across populations. However, current atlas construction methods often require days to weeks of computation, thereby discouraging rapid experimentation. As a result, many scientific studies rely on suboptimal, precomputed atlases from mismatched populations, negatively impacting downstream analyses. MultiMorph addresses these challenges with a feedforward model that rapidly produces high-quality, population-specific atlases in a single forward pass for any 3D brain dataset, without any fine-tuning or optimization. MultiMorph is based on a linear group-interaction layer that aggregates and shares features within the group of input images. Further, by leveraging auxiliary synthetic data, MultiMorph generalizes to new imaging modalities and population groups at test-time. Experimentally, MultiMorph outperforms state-of-the-art optimization-based and learning-based atlas construction methods in both small and large population settings, with a 100-fold reduction in time. This makes MultiMorph an accessible framework for biomedical researchers without machine learning expertise, enabling rapid, high-quality atlas generation for diverse studies.
BiLoRA: Almost-Orthogonal Parameter Spaces for Continual Learning
Authors: Hao Zhu, Yifei Zhang, Junhao Dong, Piotr Koniusz
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Continual Learning, parameter-efficient fine-tuning

    Continual learning requires models to learn tasks sequentially while maintaining a delicate balance between stability (retaining knowledge of previous tasks) and plasticity (adapting to new tasks). A key challenge is preventing interference between tasks - where learning new tasks degrades performance on previously learned ones. Recent approaches have leveraged parameter-efficient fine-tuning (PEFT) methods, which adapt pre-trained models by injecting a small number of learnable parameters. However, existing PEFT-based continual learning methods like InfLoRA face fundamental limitations: they rely on complex optimization procedures to learn orthogonal task-specific spaces, and finding such spaces becomes increasingly difficult as tasks accumulate. We propose a novel bilinear reformulation that fundamentally reimagines task separation through fixed orthogonal bases. Our key insight is that by expanding the parameter space quadratically through two fixed bases, we can achieve "almost orthogonal" task subspaces probabilistically, eliminating the need for explicit interference elimination procedures. We provide theoretical guarantees that this approach reduces the probability of task interference from \bigO((k/d)^2) to \bigO((k/d^2)^2), ensuring reliable task separation without complex optimization. Through extensive experiments on ImageNet-R, CIFAR100, and DomainNet, we validate our theoretical bounds and demonstrate state-of-the-art performance with reduced parameter count.
Generative Map Priors for Collaborative BEV Semantic Segmentation
Authors: Jiahui Fu, Yue Gong, Luting Wang, Shifeng Zhang, Xu Zhou, Si Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Feature Compression, object-detection, Object Detection, Semantic Segmentation

    Collaborative perception aims to address the constraint of single-agent perception by exchanging information among multiple agents. Previous works primarily focus on collaborative object detection, exploring compressed transmission and fusion prediction tailored to sparse object features. However, these strategies are not well-suited for dense features in collaborative BEV semantic segmentation. Therefore, we propose CoGMP, a novel Collaborative framework that leverages Generative Map Priors to achieve efficient compression and robust fusion. CoGMP introduces two key innovations: Element Format Feature Compression (EFFC) and Structure Guided Feature Fusion (SGFF). Specifically, EFFC leverages map element priors from codebook to encode BEV features as discrete element indices for transmitted information compression. Meanwhile, SGFF utilizes a diffusion model with structural priors to coherently integrate multi-agent features, thereby achieving consistent fusion predictions. Evaluations on the OPV2V dataset show that CoGMP achieves a 6.89/7.64 Road/Lane IoU improvement and a 32-fold reduction in communication volume.
FlashSloth : Lightning Multimodal Large Language Models via Embedded Visual Compression
Authors: Bo Tong, Bokai Lai, Yiyi Zhou, Gen Luo, Yunhang Shen, Ke Li, Xiaoshuai Sun, Rongrong Ji
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Descriptive

    Despite a big leap forward in capability, multimodal large language models (MLLMs) tend to behave like a sloth in practical use, i.e., slow response and large latency. Recent efforts are devoted to building tiny MLLMs for better efficiency, but the plethora of visual tokens still used limit their actual speedup. In this paper, we propose a powerful and fast tiny MLLM called FlashSloth. Different from previous efforts, FlashSloth focuses on improving the descriptive power of visual tokens in the process of compressing their redundant semantics. In particular, FlashSloth introduces embedded visual compression designs to capture both visually salient and instruction-related image information, so as to achieving superior multimodal performance with fewer visual tokens. Extensive experiments are conducted to validate the proposed FlashSloth, and a bunch of tiny but strong MLLMs are also comprehensively compared, e.g., InternVL-2, MiniCPM-V2 and Qwen2-VL. The experimental results show that compared with these advanced tiny MLLMs, our FlashSloth can greatly reduce the number of visual tokens, training memory and computation complexity while retaining high performance on various VL tasks. Our code is released at: https://github.com/codefanw/FlashSloth.
D^3-Human: Dynamic Disentangled Digital Human from Monocular Video
Authors: Honghu Chen, Bo Peng, Yunfan Tao, Juyong Zhang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    We introduce \text D ^3\text -Human , a method for reconstructing Dynamic Disentangled Digital Human geometry from monocular videos. Past monocular video human reconstruction primarily focuses on reconstructing undecoupled clothed human bodies or only reconstructing clothing, making it difficult to apply directly in applications such as animation production. The challenge in reconstructing decoupled clothing and body lies in the occlusion caused by clothing over the body. To this end, the details of the visible area and the plausibility of the invisible area must be ensured during the reconstruction process. Our proposed method combines explicit and implicit representations to model the decoupled clothed human body, leveraging the robustness of explicit representations and the flexibility of implicit representations. Specifically, we reconstruct the visible region as SDF and propose a novel human manifold signed distance field (hmSDF) to segment the visible clothing and visible body, and then merge the visible and invisible body. Extensive experimental results demonstrate that, compared with existing reconstruction schemes, \text D ^3\text -Human  can achieve high-quality decoupled reconstruction of the human body wearing different clothing, and can be directly applied to clothing transfer and animation production.
Diffusion Model is Effectively Its Own Teacher
Authors: Xinyin Ma, Runpeng Yu, Songhua Liu, Gongfan Fang, Xinchao Wang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: model

    In this paper, we introduce a novel self-distillation paradigm for improving the performance of diffusion models. Previous studies have shown that introducing a teacher to distill the diffusion model can enhance its sampling efficiency. We raise an intriguing question: can the diffusion model itself serve as its teacher to further improve the performance of itself? To this end, we propose a new paradigm called Self Step-Distillation (SSD). The core idea of SSD is to integrate the predictions or the intermediate activations of the diffusion model at each timestep with its preceding timestep through a fusion mechanism. We propose two forms, explicit SSD and implicit SSD (iSSD), to perform N-step to N-step distillation from the diffusion model itself to achieve improved image quality. We further elucidate the relationship between SSD and high-order solver, highlighting their underlying relationship. The effectiveness of SSD is validated through extensive experiments on diffusion transformers of various sizes and across different sampling steps. Our results show that this novel self-distillation paradigm can significantly enhance performance. Additionally, our method is compatible with the distillation method designed for few-step inference. Notably, with iSSD trained less than one epoch, we obtain a 32-step DiT-XL/2 achieving an FID of 1.99, outperforming the original 250-step DiT-XL/2 with an FID of 2.26. We further validate the effectiveness of our method on text-to-image diffusion models, such as Stable Diffusion, and also observe notable improvement in image quality.