pwc-archive/papers-with-abstracts huggingface dataset:
CacheQuant: Comprehensively Accelerated Diffusion Models
Authors: Xuewen Liu, Zhikai Li, Qingyi Gu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Generation, Quantization

    Diffusion models have gradually gained prominence in the field of image synthesis, showcasing remarkable generative capabilities. Nevertheless, the slow inference and complex networks, resulting from redundancy at both temporal and structural levels, hinder their low-latency applications in real-world scenarios. Current acceleration methods for diffusion models focus separately on temporal and structural levels. However, independent optimization at each level to further push the acceleration limits results in significant performance degradation. On the other hand, integrating optimizations at both levels can compound the acceleration effects. Unfortunately, we find that the optimizations at these two levels are not entirely orthogonal. Performing separate optimizations and then simply integrating them results in unsatisfactory performance. To tackle this issue, we propose CacheQuant, a novel training-free paradigm that comprehensively accelerates diffusion models by jointly optimizing model caching and quantization techniques. Specifically, we employ a dynamic programming approach to determine the optimal cache schedule, in which the properties of caching and quantization are carefully considered to minimize errors. Additionally, we propose decoupled error correction to further mitigate the coupled and accumulated errors step by step. Experimental results show that CacheQuant achieves a 5.18xspeedup and 4xcompression for Stable Diffusion on MS-COCO, with only a 0.02 loss in CLIP score.
D^2iT: Dynamic Diffusion Transformer for Accurate Image Generation
Authors: Weinan Jia, Mengqi Huang, Nan Chen, Lei Zhang, Zhendong Mao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Generation

    Diffusion models are widely recognized for their ability to generate high-fidelity images. Despite the excellent performance and scalability of the Diffusion Transformer (DiT) architecture, it applies fixed compression across different image regions during the diffusion process, disregarding the naturally varying information densities present in these regions. However, large compression leads to limited local realism, while small compression increases computational complexity and compromises global consistency, ultimately impacting the quality of generated images. To address these limitations, we propose dynamically compressing different image regions by recognizing the importance of different regions, and introduce a novel two-stage framework designed to enhance the effectiveness and efficiency of image generation: (1) Dynamic VAE (DVAE) at first stage employs a hierarchical encoder to encode different image regions at different downsampling rates, tailored to their specific information densities, thereby providing more accurate and natural latent codes for the diffusion process. (2) Dynamic Diffusion Transformer (D^2iT) at second stage generates images by predicting multi-grained noise, consisting of coarse-grained (less latent code in smooth regions) and fine-grained (more latent codes in detailed regions), through an novel combination of the Dynamic Grain Transformer and the Dynamic Content Transformer. The strategy of combining rough prediction of noise with detailed regions correction achieves a unification of global consistency and local realism. Comprehensive experiments on various generation tasks validate the effectiveness of our approach. Code will be released at https://github.com/jiawn-creator/Dynamic-DiT.
SoMA: Singular Value Decomposed Minor Components Adaptation for Domain Generalizable Representation Learning
Authors: Seokju Yun, Seunghye Chae, Dongheon Lee, Youngmin Ro
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Domain Generalization, parameter-efficient fine-tuning, Representation Learning, Semantic Segmentation

    Domain generalization (DG) aims to adapt a model using one or multiple source domains to ensure robust performance in unseen target domains. Recently, Parameter-Efficient Fine-Tuning (PEFT) of foundation models has shown promising results in the context of DG problem. Nevertheless, existing PEFT methods still struggle to strike a balance between preserving generalizable components of the pre-trained model and learning task-specific features. To gain insights into the distribution of generalizable components, we begin by analyzing the pre-trained weights  through the lens of singular value decomposition. Building on these insights, we introduce Singular Value Decomposed Minor Components Adaptation (SoMA), an approach that selectively tunes minor singular components while keeping the residual parts frozen. SoMA effectively retains the generalization ability of the pre-trained model while efficiently acquiring task-specific skills. Moreover, we freeze domain-generalizable blocks and employ an annealing weight decay strategy, thereby achieving an optimal balance in the delicate trade-off between generalizability and discriminability. SoMA attains state-of-the-art results on multiple benchmarks that span both domain generalized semantic segmentation to domain generalized object detection. In addition, our methods introduce no additional inference overhead or regularization loss, maintain compatibility with any backbone or head, and are designed to be versatile, allowing easy integration into a wide range of tasks.
Generalizable Object Keypoint Localization from Generative Priors
Authors: Dongkai Wang, Jiang Duan, Liangjian Wen, Shiyu Xuan, Hao Chen, Shiliang Zhang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Cross-Domain Few-Shot, Image Generation, Object

    Generalizable object keypoint localization is a fundamental computer vision task in understanding the object structure. It is challenging for existing keypoint localization methods because their limited training data cannot provide generalizable shape and semantic cues, leading to inferior performance and generalization capability. Instead of relying on large scale training data, this work tackles this challenge by exploiting the rich priors from large generative models. We propose a data-efficient generalizable localization method named GenLoc. GenLoc extracts the generative priors from a pre-trained image generation model by calculating the correlation map between image latent feature and condition embedding. Those priors are hence optimized with our proposed heatmap expectation loss to perform object keypoint localization. Benefited by the rich knowledge of generative priors in understanding of object semantics and structures, GenLoc achieves superior performance on various object keypoint localization benchmarks. It shows more substantial performance enhancements in cross-domain, few-shot and zero-shot evaluation settings, e.g., getting 20%+ AP enhancement over CLAMP in various zero-shot settings.
SmartCLIP: Modular Vision-language Alignment with Identification Guarantees
Authors: Shaoan Xie, Lingjing Lingjing, Yujia Zheng, Yu Yao, Zeyu Tang, Eric P. Xing, Guangyi Chen, Kun Zhang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Contrastive Learning

    Contrastive Language-Image Pre-training (CLIP) \citep radford2021learning  has emerged as a pivotal model in computer vision and multimodal learning, achieving state-of-the-art performance at aligning visual and textual representations through contrastive learning. However, CLIP struggles with potential information misalignment in many image-text datasets and suffers from entangled representation. On the one hand, short captions for a single image in datasets like MSCOCO may describe disjoint regions in the image, leaving the model uncertain about which visual features to retain or disregard. On the other hand, directly aligning long captions with images can lead to the retention of entangled details, preventing the model from learning disentangled, atomic concepts -- ultimately limiting its generalization on certain downstream tasks involving short prompts. In this paper, we establish theoretical conditions that enable flexible alignment between textual and visual representations across varying levels of granularity. Specifically, our framework ensures that a model can not only preserve cross-modal semantic information in its entirety but also disentangle visual representations to capture fine-grained textual concepts.  Building on this foundation, we introduce \ours, a novel approach that identifies and aligns the most relevant visual and textual representations in a modular manner. Superior performance across various tasks demonstrates its capability to handle information misalignment and supports our identification theory. The code is available at https://github.com/Mid-Push/SmartCLIP.
Classifier-to-Bias: Toward Unsupervised Automatic Bias Detection for Visual Classifiers
Authors: Quentin Guimard, Moreno D'Incà, Massimiliano Mancini, Elisa Ricci
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Bias Detection, Large Language Model

    A person downloading a pre-trained model from the web should be aware of its biases. Existing approaches for bias identification rely on datasets containing labels for the task of interest, something that a non-expert may not have access to, or may not have the necessary resources to collect: this greatly limits the number of tasks where model biases can be identified. In this work, we present Classifier-to-Bias (C2B), the first bias discovery framework that works without access to any labeled data: it only relies on a textual description of the classification task to identify biases in the target classification model. This description is fed to a large language model to generate bias proposals and corresponding captions depicting biases together with task-specific target labels. A retrieval model collects images for those captions, which are then used to assess the accuracy of the model w.r.t. the given biases. C2B is training-free, does not require any annotations, has no constraints on the list of biases, and can be applied to any pre-trained model on any classification task. Experiments on two publicly available datasets show that C2B discovers biases beyond those of the original datasets and outperforms a recent state-of-the-art bias detection baseline that relies on task-specific annotations, being a promising first step toward addressing task-agnostic unsupervised bias detection.
TADFormer: Task-Adaptive Dynamic TransFormer for Efficient Multi-Task Learning
Authors: Seungmin Baek, Soyul Lee, Hayeon Jo, Hyesong Choi, Dongbo Min
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Multi-Task Learning, parameter-efficient fine-tuning, Scene Understanding, Transfer Learning

    Transfer learning paradigm has driven substantial advancements in various vision tasks. However, as state-of-the-art models continue to grow, classical full fine-tuning often becomes computationally impractical, particularly in multi-task learning (MTL) setup where training complexity increases proportional to the number of tasks. Consequently, recent studies have explored Parameter-Efficient Fine-Tuning (PEFT) for MTL architectures. Despite some progress, these approaches still exhibit limitations in capturing fine-grained, task-specific features that are crucial to MTL. In this paper, we introduce Task-Adaptive Dynamic transFormer, termed TADFormer, a novel PEFT framework that performs task-aware feature adaptation in the fine-grained manner by dynamically considering task-specific input contexts. TADFormer proposes the parameter-efficient prompting for task adaptation and the Dynamic Task Filter (DTF) to capture task information conditioned on input contexts. Experiments on the PASCAL-Context benchmark demonstrate that the proposed method achieves higher accuracy in dense scene understanding tasks, while reducing the number of trainable parameters by up to 8.4 times when compared to full fine-tuning of MTL models. TADFormer also demonstrates superior parameter efficiency and accuracy compared to recent PEFT methods. Our code is available at supplementary material.
ATA: Adaptive Transformation Agent for Text-Guided Subject-Position Variable Background Inpainting
Authors: Yizhe Tang, Zhimin Sun, Yuzhen Du, Ran Yi, Guangben Lu, Teng Hu, Luying Li, Lizhuang Ma, Fangyuan Zou
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Inpainting, Position

    Image inpainting aims to fill the missing region of an image.Recently, there has been a surge of interest in foreground-conditioned background inpainting, a sub-task that fills the background of an image while the foreground subject and associated text prompt are provided.Existing background inpainting methods typically strictly preserve the subject's original position from the source image,resulting in inconsistencies between the subject and the generated background.To address this challenge, we propose a new task, the "Text-Guided Subject-Position Variable Background Inpainting", which aims to dynamically adjust the subject position to achieve a harmonious relationship between the subject andthe inpainted background, and propose the Adaptive Transformation Agent (A^\text T A) for this task.Firstly, we design a PosAgent Block that adaptively predicts an appropriate displacement based on given features to achieve variable subject-position.Secondly, we design the Reverse Displacement Transform (RDT) module, which arranges multiple PosAgent blocks in a reverse structure, to transform hierarchical feature maps from deep to shallow based on semantic information.Thirdly, we equip A^\text T A with a Position Switch Embedding to control whether the subject's position in the generated image is adaptively predicted or fixed.Extensive comparative experiments validate the effectiveness of our A^\text T A approach, which not only demonstrates superior inpainting capabilities in subject-position variable inpainting, but also ensures good performance on subject-position fixed inpainting.
CorrBEV: Multi-View 3D Object Detection by Correlation Learning with Multi-modal Prototypes
Authors: Ziteng Xue, Mingzhe Guo, Heng Fan, Shihui Zhang, Zhipeng Zhang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Object Detection, Autonomous Driving, Object, object-detection, Object Detection, Object Tracking

    Camera-only multi-view 3D object detection in autonomous driving has witnessed encouraging developments in recent years, largely attributed to the revolution of fundamental architectures in modeling bird's eye view (BEV). Despite the growing overall average performance, we contend that the exploration of more specific and challenging corner cases hasn't received adequate attention. In this work, we delve into a specific yet critical issue for safe autonomous driving: occlusion. To alleviate this challenge, we draw inspiration from the human amodal perception system, which is proven to have the capacity for mentally reconstructing the complete semantic concept of occluded objects with prior knowledge. More specifically, we introduce auxiliary visual and language prototypes, akin to human prior knowledge, to enhance the diminished object features caused by occlusion. Inspired by Siamese object tracking, we fuse the information from these prototypes with the baseline model through an efficient depth-wise correlation, thereby enhancing the quality of object-related features and guiding the learning of 3D object queries, especially for partially occluded ones. Furthermore, we propose the random pixel drop to mimic occlusion and the multi-modal contrastive loss to align visual features of different occlusion levels to a unified space during training. Our inspiration originates from addressing occlusion, however, we are surprised to find that the proposed framework also enhances robustness in various challenging scenarios that diminish object representation, such as inclement weather conditions. By applying our model to different baselines, i.e., BEVFormer and SparseBEV, we demonstrate consistent improvements.
Yo'Chameleon: Personalized Vision and Language Generation
Authors: Thao Nguyen, Krishna Kumar Singh, Jing Shi, Trung Bui, Yong Jae Lee, Yuheng Li
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Generation, Text Generation

    Large Multimodal Models (e.g., GPT-4, Gemini, Chameleon) have evolved into powerful tools with millions of users. However, they remain generic models and lack personalized knowledge of specific user concepts. Previous work has explored personalization for text generation, yet it remains unclear how these methods can be adapted to new modalities, such as image generation. In this paper, we introduce Yo'Chameleon, the first attempt to study personalization for large multimodal models. Given 3-5 images of a particular concept, Yo'Chameleon leverages soft-prompt tuning to embed subject-specific information to (i) answer questions about the subject and (ii) recreate pixel-level details to produce images of the subject in new contexts. Yo'Chameleon is trained with (i) a self-prompting optimization mechanism to balance performance across multiple modalities, and (ii) a "soft-positive" image generation approach to enhance image quality in a few-shot setting. Our qualitative and quantitative analyses reveal that Yo'Chameleon can learn concepts more efficiently using fewer tokens and effectively encode visual attributes, outperforming prompting baselines.
DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery
Authors: Utkarsh Mall, Cheng Perng Phoo, Mia Chiquier, Bharath Hariharan, Kavita Bala, Carl Vondrick
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Common Sense Reasoning, Density Estimation

    Visual data is used in numerous different scientific workflows ranging from remote sensing to ecology. As the amount of observation data increases, the challenge is not just to make accurate predictions but also to understand the underlying mechanisms for those predictions. Good interpretation is important in scientific workflows, as it allows for better decision-making by providing insights into the data. This paper introduces an automatic way of obtaining such interpretable-by-design models, by learning programs that interleave neural networks. We propose DiSciPLE (Discovering Scientific Programs using LLMs and Evolution) an evolutionary algorithm that leverages common sense and prior knowledge of large language models (LLMs) to create Python programs explaining visual data. Additionally, we propose two improvements: a program critic and a program simplifier to improve our method further to synthesize good programs. On three different real world problems, DiSciPLE learns state-of-the-art programs on novel tasks with no prior literature. For example, we can learn programs with 35% lower error than the closest non-interpretable baseline for population density estimation.
LOGICZSL: Exploring Logic-induced Representation for Compositional Zero-shot Learning
Authors: Peng Wu, Xiankai Lu, Hao Hu, Yongqin Xian, Jianbing Shen, Wenguan Wang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute, Compositional Zero-Shot Learning, Zero-Shot Learning

    Compositional zero-shot learning (CZSL) aims to recognize unseen attribute-object compositions by learning the primitive concepts (*i.e.*, attribute and object) from the training set. While recent works achieve impressive results in CZSL by leveraging large vision-language models like CLIP, they ignore the rich semantic relationships between primitive concepts and their compositions. In this work, we propose LOGICZSL, a novel logic-induced learning framework to explicitly model the semantic relationships. Our logic-induced learning framework formulates the relational knowledge constructed from large language models as a set of logic rules, and grounds them onto the training data. Our logic-induced losses are complementary to the widely used CZSL losses, therefore can be employed to inject the semantic information into any existing CZSL methods. Extensive experimental results show that our method brings significant performance improvements across diverse datasets (*i.e.*, CGQA, UT-Zappos50K, MIT-States) with strong CLIP-based methods and settings (*i.e.*, Close World, Open World).
GliaNet: Adaptive Neural Network Structure Learning with Glia-Driven
Authors: Mengqiao Han, Liyuan Pan, Xiabi Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Neural networks derived from the M-P model have excelled in various visual tasks. However, as a simplified simulation version of the brain neural pathway, their structures are locked during training, causing over-fitting and over-parameterization. Although recent models have begun using the biomimetic concept and empirical pruning, they still result in irrational pruning, potentially affecting the accuracy of the model. In this paper, we introduce the Glia unit, composed of oligodendrocytes (Oli) and astrocytes (Ast), to emulate the exact workflow of the mammalian brain, thereby enhancing the biological plausibility of neural functions. Oli selects neurons involved in signal transmission during neural communication and, together with Ast, adaptively optimizes the neural structure. Specifically, we first construct the artificial Glia-Neuron (G-N) model, which is formulated at the instance, group, and interaction levels with adaptive and collaborative mechanisms. Then, we construct GliaNet based on our G-N model, whose structure and connections can be continuously optimized during training. Experiments show that our GliaNet advances state-of-the-art on multiple tasks while significantly reducing its parameters.
Detection-Friendly Nonuniformity Correction: A Union Framework for Infrared UAV Target Detection
Authors: Houzhang Fang, Xiaolin Wang, Zengyang Li, Lu Wang, Qingshan Li, Yi Chang, Luxin Yan
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: parameter estimation

    Infrared unmanned aerial vehicle (UAV) images captured using thermal detectors are often affected by temperature-dependent low-frequency nonuniformity, which significantly reduces the contrast of the images. Detecting UAV targets under nonuniform conditions is crucial in UAV surveillance applications. Existing methods typically treat infrared nonuniformity correction (NUC) as a preprocessing step for detection, which leads to suboptimal performance. Balancing the two tasks while enhancing detection-beneficial information remains challenging. In this paper, we present a detection-friendly union framework, termed UniCD, that simultaneously addresses both infrared NUC and UAV target detection tasks in an end-to-end manner. We first model NUC as a small number of parameter estimation problem jointly driven by priors and data to generate detection-conducive images. Then, we incorporate a new auxiliary loss with target mask supervision into the backbone of the infrared UAV target detection network to strengthen target features while suppressing the background. To better balance correction and detection, we introduce a detection-guided self-supervised loss to reduce feature discrepancies between the two tasks, thereby enhancing detection robustness to varying nonuniformity levels. Additionally, we construct a new benchmark composed of 50,000 infrared images in various nonuniformity types, multi-scale UAV targets and rich backgrounds with target annotations, called IRBFD. Extensive experiments on IRBFD demonstrate that our UniCD is a robust union framework for NUC and UAV target detection while achieving real-time processing capabilities. Dataset can be available at https://github.com/IVPLaboratory/UniCD.