pwc-archive/papers-with-abstracts huggingface dataset:
Sensitivity-Aware Efficient Fine-Tuning via Compact Dynamic-Rank Adaptation
Authors: Tianran Chen, Jiarui Chen, Baoquan Zhang, Zhehao Yu, Shidong Chen, Rui Ye, Xutao Li, Yunming Ye
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: parameter-efficient fine-tuning, Sensitivity

    Parameter-Efficient Fine-Tuning (PEFT) is a fundamental research problem in computer vision, which aims to tune a few of parameters for efficient storage and adaptation of pre-trained vision models. Recently, sensitivity-aware parameter efficient fine-tuning method (SPT) addresses this problem by identifying sensitive parameters and then leveraging its sparse characteristic to combine unstructured and structured tuning for PEFT. However, existing methods only focus on sparse characteristic of sensitive parameters but overlook its distribution characteristic, which results in additional storage burden and limited performance improvement. In this paper, we find that the distribution of sensitive parameters is not chaotic, but concentrates in a small number of rows or columns in each parameter matrix. Inspired by this fact, we propose a Compact Dynamic-Rank Adaptation-based tuning method for Sensitivity-aware Parameter efficient fine-Tuning, called CDRA-SPT. Specifically, we first identify the sensitive parameters that require tuning for each downstream task. Then, we reorganize the sensitive parameters by following its row and column into a compact sub-parameter matrix. Finally, a dynamic-rank adaptation is designed and applied at sub-parameter matrix level for PEFT. Its advantage is that the dynamic-rank characteristic of sub-parameter matrix can be fully exploited for PEFT. Extensive experiments show that our method achieves superior performance over previous state-of-the-art methods.
Chat-based Person Retrieval via Dialogue-Refined Cross-Modal Alignment
Authors: Yang Bai, Yucheng Ji, Min Cao, Jinqiao Wang, Mang Ye
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute, cross-modal alignment, Data Augmentation, Person Retrieval, Question Generation, Question-Generation, Retrieval, Text based Person Retrieval, Text-based Person Retrieval

    Traditional text-based person retrieval (TPR) relies on a single-shot text as query to retrieve the target person, assuming that the query completely captures the user's search intent. However, in real-world scenarios, it can be challenging to ensure the information completeness of such single-shot text. To address this limitation, we propose chat-based person retrieval (ChatPR), a new paradigm that takes an interactive dialogue as query to perform the person retrieval, engaging the user in conversational context to progressively refine the query for accurate person retrieval. The primary challenge in ChatPR is the lack of available dialogue-image paired data. To overcome this challenge, we establish ChatPedes, the first dataset designed for ChatPR, which is constructed by leveraging large language models to automate the question generation and simulate user responses. Additionally, to bridge the modality gap between dialogues and images, we propose a dialogue-refined cross-modal alignment (DiaNA) framework, which leverages two adaptive attribute refiners to bottleneck the conversational and visual information for fine-grained cross-modal alignment. Moreover, we propose a dialogue-specific data augmentation strategy, random round retaining, to further enhance the model's generalization ability across varying dialogue lengths. Extensive experiments demonstrate that DiaNA significantly outperforms existing TPR approaches, highlighting the effectiveness of conversational interactions for person retrieval.
Z-Magic: Zero-shot Multiple Attributes Guided Image Creator
Authors: Yingying Deng, Xiangyu He, Fan Tang, WeiMing Dong
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute, Image Generation, Multi-Task Learning

    The customization of multiple attributes has gained increasing popularity with the rising demand for personalized content creation. Despite promising empirical results, the contextual coherence between different attributes has been largely overlooked. In this paper, we argue that subsequent attributes should follow the multivariable conditional distribution introduced by former attributes creation. In light of this, we reformulate multi-attribute creation from a conditional probability theory perspective and tackle the challenging zero-shot setting. By explicitly modeling the dependencies between attributes, we further enhance the coherence of generated images across diverse attribute combinations. Furthermore, we identify connections between multi-attribute customization and multi-task learning, effectively addressing the high computing cost encountered in multi-attribute synthesis. Extensive experiments demonstrate that Z-Magic outperforms existing models in zero-shot image generation, with broad implications for AI-driven design and creative applications.
Taxonomy-Aware Evaluation of Vision-Language Models
Authors: Vésteinn Snæbjarnarson, Kevin Du, Niklas Stoehr, Serge Belongie, Ryan Cotterell, Nico Lang, Stella Frank
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Fine-Grained Image Classification, Language Modeling, Language Modelling, Specificity, text similarity

    When a vision-language model (VLM) is prompted to identify an entity depicted in an image, it may answer "I see a conifer," rather than the specific label "Norway spruce". This raises two issues for evaluation: Firstly, the unconstrained generated text needs to be mapped to the evaluation label space (i.e., "conifer"). Secondly, a useful classification measure should give partial credit to less specific, but not incorrect, answers ("Norway spruce" being a type of "conifer"). To meet these requirements, we propose a framework for evaluating unconstrained text predictions such as those generated from a vision-language model against a taxonomy. Specifically, we propose the use of hierarchical precision and recall measures to assess the level of correctness and specificity of predictions with regard to a taxonomy. Experimentally, we first show that existing text similarity measures do not capture taxonomic similarity well. We then develop and compare different methods to map textual VLM predictions onto a taxonomy. This allows us to compute hierarchical similarity measures between the generated text and the ground truth labels. Finally, we analyze modern VLMs on fine-grained visual classification tasks based on our proposed taxonomic evaluation scheme.
CryptoFace: End-to-End Encrypted Face Recognition
Authors: Wei Ao, Vishnu Naresh Boddeti
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Face Recognition

    Face recognition is central to many authentication, security, and personalized applications. Yet, it suffers from significant privacy risks, particularly arising from unauthorized access to sensitive biometric data. This paper introduces CryptoFace, the first end-to-end encrypted face recognition system with fully homomorphic encryption (FHE). It enables secure processing of facial data across all stages of a face-recognition process--feature extraction, storage, and matching--without exposing raw images or features. We introduce a mixture of shallow patch convolutional networks to support higher-dimensional tensors via patch-based processing while reducing the multiplicative depth and thus inference latency. Parallel FHE evaluation of these networks ensures near-resolution-independent latency. On standard face recognition benchmarks, CryptoFace significantly accelerates inference and increases verification accuracy compared to the state-of-the-art FHE neural networks adapted for face recognition. CryptoFace will facilitate secure face recognition systems requiring robust and provable security. The code is available at https://github.com/human-analysis/CryptoFace.
Leveraging Temporal Cues for Semi-Supervised Multi-View 3D Object Detection
Authors: Jinhyung Park, Navyata Sanghvi, Hiroki Adachi, Yoshihisa Shibata, Shawn Hunt, Shinya Tanaka, Hironobu Fujiyoshi, Kris Kitani
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Object Detection, 3D Object Tracking, Autonomous Driving, object-detection, Object Detection, Object Tracking, Pseudo Label

    While recent advancements in camera-based 3D object detection demonstrate remarkable performance, they require thousands or even millions of human-annotated frames. This requirement significantly inhibits their deployment in various locations and sensor configurations. To address this gap, we propose a performant semi-supervised framework that leverages unlabeled RGB-only driving sequences - data easily collected with cost-effective RGB cameras - to significantly improve temporal, camera-only 3D detectors. We observe that the standard semi-supervised pseudo-labeling paradigm underperforms in this temporal, camera-only setting due to poor 3D localization of pseudo-labels. To address this, we train a single 3D detector to handle RGB sequences both forward and backward in time, then ensemble both its forwards and backwards pseudo-labels for semi-supervised learning. We further improve the pseudo-label quality by leveraging 3D object tracking to infill missing detections and by eschewing simple confidence thresholding in favor of using the auxiliary 2D detection head to filter 3D predictions. Finally, to enable the backbone to learn directly from the unlabeled data itself, we introduce an object-query conditioned masked reconstruction objective. Our framework demonstrates remarkable performance improvement on large-scale autonomous driving datasets nuScenes and nuPlan.
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation
Authors: Ying Jin, Jinlong Peng, Qingdong He, Teng Hu, Jiafu Wu, Hao Chen, Haoxuan Wang, Wenbing Zhu, Mingmin Chi, Jun Liu, Yabiao Wang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Anomaly Classification, Anomaly Detection, Anomaly Localization, Diversity, Image Generation

    The performance of anomaly inspection in industrial manufacturing is constrained by the scarcity of anomaly data. To overcome this challenge, researchers have started employing anomaly generation approaches to augment the anomaly dataset. However, existing anomaly generation methods suffer from limited diversity in the generated anomalies and struggle to achieve a seamless blending of this anomaly with the original image. Moreover, the generated mask is usually not aligned with the generated anomaly. In this paper, we overcome these challenges from a new perspective, simultaneously generating a pair of the overall image and the corresponding anomaly part. We propose DualAnoDiff, a novel diffusion-based few-shot anomaly image generation model, which can generate diverse and realistic anomaly images by using a dual-interrelated diffusion model, where one of them is employed to generate the whole image while the other one generates the anomaly part. Moreover, we extract background and shape information to mitigate the distortion and blurriness phenomenon in few-shot image generation.  Extensive experiments demonstrate the superiority of our proposed model over state-of-the-art methods in terms of diversity, realism and the accuracy of mask. Overall, our approach significantly improves the performance of downstream anomaly inspection tasks, including anomaly detection, anomaly localization, and anomaly classification tasks. Code will be made available.
GenAssets: Generating in-the-wild 3D Assets in Latent Space
Authors: Ze Yang, Jingkang Wang, Haowei Zhang, Sivabalan Manivasagam, Yun Chen, Raquel Urtasun
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Neural Rendering

    High-quality 3D assets for traffic participants are critical for multi-sensor simulation, which is essential for the safe end-to-end development of autonomy. Building assets from in-the-wild data is key for diversity and realism, but existing neural-rendering based reconstruction methods are slow and generate assets that render well only from viewpoints close to the original observations, limiting their usefulness in simulation. Recent diffusion-based generative models build complete and diverse assets, but perform poorly on in-the-wild driving scenes, where observed actors are captured under sparse and limited fields of view, and are partially occluded. In this work, we propose a 3D latent diffusion model that learns on in-the-wild LiDAR and camera data captured by a sensor platform and generates high-quality 3D assets with complete geometry and appearance. Key to our method is a "reconstruct-then-generate" approach that first leverages occlusion-aware neural rendering trained over multiple scenes to build a high-quality latent space for objects, and then trains a diffusion model that operates on the latent space. We show our method outperforms existing reconstruction and generation based methods, unlocking diverse and scalable content creation for simulation. Please visit https://waabi.ai/genassets for more details.
Generalized Zero-Shot Classification via Semantics-Free Inter-Class Feature Generation
Authors: Libiao Chen, Dong Nie, Junjun Pan, Jing Yan, Zhenyu Tang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Classification, cross-modal alignment, Generalized Zero-Shot Learning, zero-shot-classification, Zero-Shot Learning

    Generalized Zero-Shot Learning (GZSL) addresses the challenge of classifying unseen classes in the presence of seen classes by leveraging semantic attributes to bridge the gap for unseen classes. However, in image based disease classification, such as glioma sub-typing, distinguishing between classes using image semantic attributes can be challenging. To address this challenge, we introduce a novel GZSL method that eliminates the dependency on semantic information. Specifically, we propose that the primary of most classification in clinic is risk stratification, and classes are inherently ordered rather than purely categorical. Based on this insight, we present an inter-class feature augmentation (IFA) module, where distributions of different classes are ordered by their risk levels in a learned feature space using pre-defined joint conditional Gaussian distribution model. This ordering enables the generation of unseen class features through feature mixing of adjacent seen classes, effectively transforming the zero-shot learning problem into a supervised learning task. Our method eliminates the need for explicit semantic information, avoiding the cross-modal alignment between visual and semantic features. Moreover, the IFA module for GZSL requires no structural modifications to the existing classification models. In the experiment, both in-house and public datasets are used to evaluate our method across different tasks, including glioma subtyping, Alzheimer's disease (AD) classification and diabetic retinopathy classification. Experimental results demonstrate that our method outperforms the state-of-the-art GZSL methods with statistical significance.
Boosting the Dual-Stream Architecture in Ultra-High Resolution Segmentation with Resolution-Biased Uncertainty Estimation
Authors: Rong Qin, Xingyu Liu, Jinglei Shi, Liang Lin, Jufeng Yang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Semantic Segmentation

    Over the last decade, significant efforts have been dedicated to designing efficient models for the challenge of ultra-high resolution (UHR) semantic segmentation. These models mainly follow the dual-stream architecture and generally fall into three subcategories according to the improvement objectives, i.e., dual-stream ensemble, selective zoom, and complementary learning. However, most of them overly concentrate on crafting complex pipelines to pursue one of the above objectives separately, limiting the model performance in both accuracy and inference consumption. In this paper, we suggest simultaneously achieving these objectives by estimating resolution-biased uncertainties in low resolution stream. Here, the resolution-biased uncertainty refers to the degree of prediction unreliability primarily caused by resolution loss from down-sampling operations. Specifically, we propose a dual-stream UHR segmentation framework, where an estimator is used to assess resolution-biased uncertainties through the entropy map and high-frequency feature residual. The framework also includes a selector, an ensembler, and a complementer to boost the model with obtained estimations. They share the uncertainty estimations as the weights to choose difficult regions as the inputs for UHR stream, perform weighted fusion between distinct streams, and enhance the learning for important pixels, respectively. Experiment results demonstrate that our method achieves a satisfactory balance between accuracy and inference consumption against other state-of-the-art (SOTA) methods. The code is available in the https://github.com/Qinrong-NKU/RUE.
Joint Optimization of Neural Radiance Fields and Continuous Camera Motion from a Monocular Video
Authors: Hoang Chuong Nguyen, Wei Mao, Jose M. Alvarez, Miaomiao Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D geometry, Depth Estimation, NeRF, Novel View Synthesis

    Neural Radiance Fields (NeRF) has demonstrated its superior capability to represent 3D geometry but require accurately precomputed camera poses during training. To mitigate this requirement, existing methods jointly optimize camera poses and NeRF often relying on good pose initialisation or depth priors. However, these approaches struggle in challenging scenarios, such as large rotations, as they map each camera to a world coordinate system. We propose a novel method that eliminates prior dependencies by modeling continuous camera motions as time-dependent angular velocity and velocity. Relative motions between cameras are learned first via velocity integration, while camera poses can be obtained by aggregating such relative motions up to a world coordinate system defined at a single time step within the video. Specifically, accurate continuous camera movements are learned through a time-dependent NeRF, which captures local scene geometry and motion by training from neighboring frames for each time step. The learned motions enable fine-tuning the NeRF to represent the full scene geometry. Experiments on Co3D and Scannet show our approach achieves superior camera pose and depth estimation and comparable novel-view synthesis performance compared to state-of-the-art methods.
A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets
Authors: David Mildenberger, Paul Hager, Daniel Rueckert, Martin J. Menten
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Contrastive Learning, Medical Diagnosis

    Supervised contrastive learning (SupCon) has proven to be a powerful alternative to the standard cross-entropy loss for classification of multi-class balanced datasets. However, it struggles to learn well-conditioned representations of datasets with long-tailed class distributions. This problem is potentially exacerbated for binary imbalanced distributions, which are commonly encountered during many real-world problems such as medical diagnosis. In experiments on seven binary datasets of natural and medical images, we show that the performance of SupCon decreases with increasing class imbalance. To substantiate these findings, we introduce two novel metrics that evaluate the quality of the learned representation space. By measuring the class distribution in local neighborhoods, we are able to uncover structural deficiencies of the representation space that classical metrics cannot detect. Informed by these insights, we propose two new supervised contrastive learning strategies tailored to binary imbalanced datasets that improve the structure of the representation space and increase downstream classification accuracy over standard SupCon by up to 35%.
Exploring Contextual Attribute Density in Referring Expression Counting
Authors: Zhicheng Wang, Zhiyu Pan, Zhan Peng, Jian Cheng, Liwen Xiao, Wei Jiang, Zhiguo Cao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute, Referring Expression

    Referring expression counting (REC) algorithms are for more flexible and interactive counting ability across varied fine-grained text expressions. However, the requirement for fine-grained attribute understanding poses challenges for prior arts, as they struggle to accurately align attribute information with correct visual patterns. Given the proven importance of "visual density", it is presumed that the limitations of current REC approaches stem from an under-exploration of "contextual attribute density" (CAD). In the scope of REC, we define the CAD as the measure of the information intensity of one certain fine-grained attribute in visual regions. To model the the CAD, we propose a U-shape CAD estimator in which referring expression and multi-scale visual features from GroundingDINO can interact with each other. With additional density supervisions, we can effectively encode CAD, which is subsequently decoded via a novel attention procedure with CAD-refined queries. Integrating all these contributions, our framework significantly outperforms state-of-the-art REC methods, achieves 30% error reduction in counting metics and a 10% improvement in localization accuracy. The surprising results shed lights on the significance of contextual attribute density for REC. Code will be at github.com/Xu3XiWang/CAD-GD.
DejaVid: Encoder-Agnostic Learned Temporal Matching for Video Classification
Authors: Darryl Ho, Samuel Madden
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 3
Tasks: Action Classification, Action Recognition, Feature Importance, Time Series Alignment, Video Classification

    In recent years, large transformer-based video encoder models have greatly advanced state-of-the-art performance on video classification tasks. However, these large models typically process videos by averaging embedding outputs from multiple clips over time to produce fixed-length representations. This approach fails to account for a variety of time-related features, such as variable video durations, chronological order of events, and temporal variance in feature significance. While methods for temporal modeling do exist, they often require significant architectural changes and expensive retraining, making them impractical for off-the-shelf, fine-tuned large encoders. To overcome these limitations, we propose DejaVid, an encoder-agnostic method that enhances model performance without the need for retraining or altering the architecture. Our framework converts a video into a variable-length temporal sequence of embeddings (TSE). A TSE naturally preserves temporal order and accommodates variable video durations. We then learn per-timestep, per-feature weights over the encoded TSE frames, allowing us to account for variations in feature importance over time. We introduce a new neural network architecture inspired by traditional time series alignment algorithms for this learning task. Our evaluation demonstrates that DejaVid substantially improves the performance of a state-of-the-art large encoder, achieving leading Top-1 accuracy of 77.2% on Something-Something V2, 89.1% on Kinetics-400, and 88.6% on HMDB51, while adding fewer than 1.8% additional learnable parameters and requiring less than 3 hours of training time. Our code is available at https://github.com/darrylho/DejaVid.