pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Video Super-Resolution for Real-time Rendering with Decoupled G-buffer Guidance
Authors: Mingjun Zheng, Long Sun, Jiangxin Dong, Jinshan Pan
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: GPU, Super-Resolution, Video Super-Resolution

    Latency is a key driver for real-time rendering applications, making super-resolution techniques increasingly popular to accelerate rendering processes. In contrast to existing methods that directly concatenate low-resolution frames and G-buffers as input without discrimination, we develop an asymmetric UNet-based super-resolution network with decoupled G-buffer guidance, dubbed RDG, to facilitate the spatial and temporal feature exploration for minimizing performance overheads and latency.We first propose a dynamic feature modulator (DFM) to selectively encode the spatial information to capture precise structural information.We then incorporate auxiliary G-buffer information to guide the decoder to generate detail-rich, temporally stable results.Specifically, we adopt a high-frequency feature booster (HFB) to adaptively transfer the high-frequency information from the normal and bidirectional reflectance distribution function (BRDF) components of the G-buffer, enhancing the details of the generated results.To further enhance the temporal stability, we design a cross-frame temporal refiner (CTR) with depth and motion vector constraints to aggregate the previous and current frames.Extensive experimental results reveal that our proposed method is capable of generating high-quality and temporally stable results in real-time rendering.The proposed RDG-s produces 1080P rendering results on a RTX 3090 GPU with a speed of 126 FPS.
EventPSR: Surface Normal and Reflectance Estimation from Photometric Stereo Using an Event Camera
Authors: Bohan Yu, Jin Han, Boxin Shi, Imari Sato
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Simultaneously acquisition of the surface normal and reflectance parameters is a crucial but challenging technique in the field of computer vision and graphics. It requires capturing multiple high dynamic range (HDR) images in existing methods using frame-based cameras. In this paper, we propose EventPSR, the first work to recover surface normal and reflectance parameters (e.g., metallic and roughness) simultaneously using an event camera. Compared with the existing methods based on photometric stereo or neural radiance fields, EventPSR is a robust and efficient approach that works consistently with different materials. Thanks to the extremely high temporal resolution and high dynamic range coverage of event cameras, EventPSR can recover accurate surface normal and reflectance of objects with various materials in 10 seconds. Extensive experiments on both synthetic data and real objects show that compared with existing methods using more than 100 HDR images, EventPSR recovers comparable surface normal and reflectance parameters with only about 30% of the data rate.
STPro: Spatial and Temporal Progressive Learning for Weakly Supervised Spatio-Temporal Grounding
Authors: Aaryan Garg, Akash Kumar, Yogesh S Rawat
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Action Understanding, Spatio-Temporal Video Grounding, Video Grounding

    In this work, we study Weakly Supervised Spatio-Temporal Video Grounding (WSTVG), a challenging task of localizing subjects spatio-temporally in videos using only textual queries and no bounding box supervision. Inspired by recent advances in vision-language foundation models, we investigate their utility for WSTVG, leveraging their zero-shot grounding capabilities. However, we find that a simple adaptation lacks essential spatio-temporal grounding abilities. To bridge this gap, we introduce Tubelet Referral Grounding (TRG), which connects textual queries to tubelets to enable spatio-temporal predictions. Despite its promise, TRG struggles with compositional action understanding and dense scene scenarios. To address these limitations, we propose STPro, a progressive learning framework with two key modules: Sub-Action Temporal Curriculum Learning (SA-TCL), which incrementally builds compositional action understanding, and Congestion-Guided Spatial Curriculum Learning (CG-SCL), which adapts the model to complex scenes by spatially increasing task difficulty. STPro achieves state-of-the-art results on three benchmark datasets, with improvements of 1.0% on VidSTG-Declarative and 3.0% on HCSTVG-v1.
Animate and Sound an Image
Authors: Xihua Wang, Ruihua Song, Chongxuan Li, Xin Cheng, Boyuan Li, Yihan Wu, Yuyue Wang, Hongteng Xu, Yunfeng Wang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Audio Generation

    This paper addresses a promising yet underexplored task, Image-to-Sounding-Video (I2SV) generation, which animates a static image and generates synchronized sound simultaneously. Despite advances in video and audio generation models, challenges remain to develop a unified model for generating naturally sounding videos. In this work, we propose a novel approach that leverages two separate pretrained diffusion models and makes vision and audio influence each other during generation based on the Diffusion Transformer (DiT) architecture. First, the individual video and audio pretrained generation models are decomposed into input, output, and expert sub-modules. We propose using a unified joint DiT block to integrate the expert sub-modules to effectively model the interaction between the two modalities, resulting in high-quality I2SV generation. Then, we introduce a joint classifier-free guidance technique to boost the performance during joint generation. Finally, we conduct extensive experiments on three popular benchmark datasets, and in both objective and subjective evaluation our method surpass all the baseline methods in almost all metrics. Case studies show our generated sounding videos are high quality and synchronized between video and audio.
Domain Generalization in CLIP via Learning with Diverse Text Prompts
Authors: Changsong Wen, Zelin Peng, Yu Huang, Xiaokang Yang, Wei Shen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Domain Generalization

    Domain generalization (DG) aims to train a model on source domains that can generalize well to unseen domains. Recent advances in Vision-Language Models (VLMs), such as CLIP, exhibit remarkable generalization capabilities across a wide range of data distributions, benefiting tasks like DG. However, CLIP is pre-trained by aligning images with their descriptions, which inevitably captures domain-specific details. Moreover, adapting CLIP to source domains with limited feature diversity introduces bias. These limitations hinder the model's ability to generalize across domains. In this paper, we propose a new DG approach by learning with diverse text prompts. These text prompts incorporate varied contexts to imitate different domains, enabling DG model to learn domain-invariant features. The text prompts guide DG model learning in three aspects: feature suppression, which uses these prompts to identify domain-sensitive features and suppress them; feature consistency, which ensures the model's features are robust to domain variations imitated by the diverse prompts; and feature diversification, which diversifies features based on the prompts to mitigate bias. Experimental results show that our approach improves domain generalization performance on five datasets on the DomainBed benchmark, achieving state-of-the-art results.
VLMs-Guided Representation Distillation for Efficient Vision-Based Reinforcement Learning
Authors: Haoran Xu, Peixi Peng, Guang Tan, Yiqian Chang, Luntong Li, Yonghong Tian
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Self-Supervised Learning

    Vision-based Reinforcement Learning (VRL) attempts to establish associations between visual inputs and optimal actions through interactions with the environment. Given the high-dimensional and complex nature of visual data, it becomes essential to learn policy upon high-quality state representation. To this end, existing VRL methods primarily rely on interaction-collected data, combined with self-supervised auxiliary tasks. However, two key challenges remain: limited data samples and a lack of task-relevant semantic constraints. To tackle this, we propose DGC, a method that distills guidance from Visual Language Models (VLMs) alongside self-supervised learning into a compact VRL agent. Notably, we leverage the state representation capabilities of VLMs, rather than their decision-making abilities. Within DGC, a novel prompting-reasoning pipeline is designed to convert historical observations and actions into usable supervision signals, enabling semantic understanding within the compact visual encoder. By leveraging these distilled semantic representations, the VRL agent achieves significant improvements in the sample efficiency. Extensive experiments on the Carla benchmark demonstrate our state-of-the-art performance.
Beyond Image Classification: A Video Benchmark and Dual-Branch Hybrid Discrimination Framework for Compositional Zero-Shot Learning
Authors: Dongyao Jiang, Haodong Jing, Yongqiang Ma, Nanning Zheng
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Action Recognition, Attribute, Compositional Zero-Shot Learning, image-classification, Image Classification, Zero-Shot Learning

    Human reasoning naturally combines concepts to identify unseen compositions, a capability that Compositional Zero-Shot Learning (CZSL) aims to replicate in machine learning models. However, we observe that focusing solely on typical image classification tasks in CZSL may limit models' compositional generalization potential. To address this, we introduce C-EgoExo, a video-based benchmark, along with a compositional action recognition task to enable more comprehensive evaluations. Inspired by human reasoning processes, we propose a Dual-branch Hybrid Discrimination (DHD) framework, featuring two branches that decode visual inputs in distinct observation sequences. Through a cross-attention mechanism and a contextual dependency encoder, DHD effectively mitigates challenges posed by conditional variance. We further design a Copula-based orthogonal decoding loss to counteract contextual interference in primitive decoding. Our approach demonstrates outstanding performance across diverse CZSL tasks, excelling in both image-based and video-based modalities and in attribute-object and action-object compositions, setting a new benchmark for CZSL evaluation.
Dual Energy-Based Model with Open-World Uncertainty Estimation for Out-of-distribution Detection
Authors: Qi Chen, Hu Ding
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

    Out-of-distribution (OOD) detection is crucial for machine learning models deployed in open-world environments. However, existing methods often struggle with model over-confidence or rely heavily on empirical energy value estimation, limiting their scalability and generalizability. This paper introduces DEBO (Dual Energy-Based Model for Out-of-distribution Detection), a novel approach that addresses these limitations through an innovative dual classifier architecture and a unified energy-based objective function. DEBO enhances the standard classification framework by integrating a dual-purpose output space within a single classifier. The primary component classifies in-distribution (ID) data conventionally, while the secondary component captures open-world information and estimates uncertainty. Our method overcomes the dependence of traditional energy model-based OOD detection methods on empirical energy estimation while maintaining theoretical guarantees. Theoretical analysis demonstrates that DEBO promotes low energy and high confidence for ID data, while simultaneously inducing higher energy and decreased confidence for OOD samples. Extensive experiments conducted on benchmark datasets reveal that DEBO achieves state-of-the-art OOD detection performance while maintaining comparable classification accuracy on ID data.
Boosting Adversarial Transferability through Augmentation in Hypothesis Space
Authors: Yu Guo, Weiquan Liu, Qingshan Xu, Shijun Zheng, Shujun Huang, Yu Zang, Siqi Shen, Chenglu Wen, Cheng Wang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Stochastic Optimization

    Adversarial examples can mislead deep neural networks with subtle perturbations, causing them to make incorrect predictions. Notably, adversarial examples crafted for one model can also deceive other models, a phenomenon known as the transferability of adversarial examples. To improve transferability, existing studies have designed increasingly complex mechanisms, but the improvements achieved remain relatively limited and are often difficult to adapt to other modalities, further restricting the scalability of these methods. In this work, we observe a mirroring relationship between model generalization and adversarial example transferability. Motivated by this observation, we propose an augmentation-based attack, called OPS (Operator-Perturbation-based Stochastic optimization), which constructs a stochastic optimization problem by input transformation operators and random perturbations, and solves this problem to generate adversarial examples with better transferability. Extensive experiments on both images and 3D point clouds demonstrate that OPS significantly outperforms existing state-of-the-art methods in terms of both performance and cost, showcasing the universality and superiority of our approach. The code is available at https://github.com/the-full/OPS.
Medusa: A Multi-Scale High-order Contrastive Dual-Diffusion Approach for Multi-View Clustering
Authors: Liang Chen, Zhe Xue, Yawen Li, Meiyu Liang, Yan Wang, Anton Van Den Hengel, Yuankai Qi
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Clustering

    Deep multi-view clustering methods utilize information from multiple views to achieve enhanced clustering results and have gained increasing popularity in recent years. Most existing methods typically focus on either inter-view or intra-view relationships, aiming to align information across views or analyze structural patterns within individual views. However, they often incorporate inter-view complementary information in a simplistic manner, while overlooking the complex, high-order relationships within multi-view data and the interactions among samples, resulting in an incomplete utilization of the rich information available. Instead, we propose a multi-scale approach that exploits all of the available information. We first introduce a dual graph diffusion module guided by a consensus graph. This module leverages inter-view information to enhance the representation of both nodes and edges within each view. Secondly, we propose a novel contrastive loss function based on hypergraphs to more effectively model and leverage complex intra-view data relationships. Finally, we propose to adaptively learn fusion weights at the sample level, which enables a more flexible and dynamic aggregation of multi-view information. Extensive experiments on eight datasets show favorable performance of the proposed method compared to state-of-the-art approaches, demonstrating its effectiveness across diverse scenarios.
V2V3D: View-to-View Denoised 3D Reconstruction for Light Field Microscopy
Authors: Jiayin Zhao, Zhenqi Fu, Tao Yu, Hui Qiao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Reconstruction, Computational Efficiency, Denoising, Image Denoising

    Light field microscopy (LFM) has gained significant attention due to its ability to capture snapshot-based, large-scale 3D fluorescence images. However, existing LFM reconstruction algorithms are highly sensitive to sensor noise or require hard-to-get ground-truth annotated data for training. To address these challenges, this paper introduces V2V3D, an unsupervised view2view-based framework that establishes a new paradigm for joint optimization of image denoising and 3D reconstruction in a unified architecture. We assume that the LF images are derived from a consistent 3D signal, with the noise in each view being independent. This enables V2V3D to incorporate the principle of noise2noise for effective denoising. To enhance the recovery of high-frequency details, we propose a novel wave-optics-based feature alignment technique, which transforms the point spread function, used for forward propagation in wave optics, into convolution kernels specifically designed for feature alignment. Moreover, we introduce an LFM dataset containing LF images and their corresponding 3D intensity volumes. Extensive experiments demonstrate that our approach achieves high computational efficiency and outperforms the other state-of-the-art methods. These advancements position V2V3D as a promising solution for 3D imaging under challenging conditions. Our code and dataset will be publicly accessible at https://joey1998hub.github.io/V2V3D/.
Argus: A Compact and Versatile Foundation Model for Vision
Authors: Weiming Zhuang, Chen Chen, Zhizhong Li, Sina Sajadmanesh, Jingtao Li, Jiabo Huang, Vikash Sehwag, Vivek Sharma, Hirotaka Shinozaki, Felan Carlo Garcia, Yihao Zhan, Naohiro Adachi, Ryoji Eki, Michael Spranger, Peter Stone, Lingjuan Lyu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    While existing vision and multi-modal foundation models can handle multiple computer vision tasks, they often suffer from significant limitations, including huge demand for data and computational resources during training and inconsistent performance across vision tasks at deployment time. To address these challenges, we introduce Argus (The name comes from Argus Panoptes -- a hundred-eyed giant with "all-seeing" capability in Greek mythology), a compact and versatile vision foundation model designed to support a wide range of vision tasks through a unified multitask architecture. Argus employs a two-stage training strategy: (i) multitask pretraining over core vision tasks with a shared backbone that includes a lightweight adapter to inject task-specific inductive biases, and (ii) scalable and efficient adaptation to new tasks by fine-tuning only the task-specific decoders. Extensive evaluations demonstrate that Argus, despite its relatively compact and training-efficient design of merely 100M backbone parameters (only 13.6% of which are trained using 1.6M images), competes with and even surpasses much larger models. Compared to state-of-the-art foundation models, Argus not only covers a broader set of vision tasks but also matches or outperforms the models with similar sizes on 12 tasks. We expect that Argus will accelerate the real-world adoption of vision foundation models in resource-constrained scenarios.
Pseudo Visible Feature Fine-Grained Fusion for Thermal Object Detection
Authors: Ting Li, Mao Ye, Tianwen Wu, Nianxin Li, Shuaifeng Li, Song Tang, Luping Ji
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Autonomous Driving, Mamba, object-detection, Object Detection

    Thermal object detection is a critical task in various fields, such as surveillance and autonomous driving. Current state-of-the-art (SOTA) models always leverage a prior Thermal-To-Visible (T2V) translation model to obtain visible spectrum information, followed by a cross-modality aggregation module to fuse information from both modalities. However, this fusion approach does not fully exploit the complementary visible spectrum information beneficial for thermal detection. To address this issue, we propose a novel cross-modal fusion method called Pseudo Visible Feature Fine-Grained Fusion (PFGF). Specifically, a graph is constructed with nodes generated from multi-level thermal features and pseudo-visual latent features produced by the T2V model. Each level of features corresponds to a subgraph. An Inter-Mamba block is proposed to perform cross-modality fusion between nodes at the lowest level; while a Cascade Knowledge Integration (CKI) strategy is used to fuse low-level fused information to high-level subgraphs in a cascade manner. After several iterations of graph node updating, each subgraph outputs an aggregated feature to the detection head respectively. Unlike previous cross-modal fusion methods, our approach explicitly models high-level relationships between cross-modal data, effectively fusing different granularity information. Experimental results demonstrate that our method achieves SOTA detection performance. Code is available at https://github.com/liting1018/PFGF.
Shape Abstraction via Marching Differentiable Support Functions
Authors: Sunkyung Park, Jeongmin Lee, Dongjun Lee
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Shape abstraction, simplifying shape representation into a set of primitives, is a fundamental topic in computer vision. The choice of primitives shapes the structure of world understanding, yet achieving both high abstraction accuracy and versatility remains challenging. In this paper, we introduce a novel framework for shape abstraction utilizing a differentiable support function (DSF), which offers unique advantages in representing a wide range of convex shapes with fewer parameters, providing smooth surface approximation and enabling differentiable contact features (gap, point, normal) essential for downstream applications involving contact-related problems. To tackle the associated optimization and combinatorial challenges, we introduce two techniques: differentiable shape parameterization and hyperplane-based marching to enhance accuracy and reduce DSF requirements. We validate our method through experiments demonstrating superior accuracy and efficiency, and showcase its applicability in tasks requiring differentiable contact information.
BWFormer: Building Wireframe Reconstruction from Airborne LiDAR Point Cloud with Transformer
Authors: Yuzhou Liu, Lingjie Zhu, Hanqiao Ye, Shangfeng Huang, Xiang Gao, Xianwei Zheng, Shuhan Shen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Data Augmentation

    In this paper, we present BWFormer, a novel Transformer-based model for building wireframe reconstruction from airborne LiDAR point cloud.  The problem is solved in a ground-up manner here by detecting the building corners in 2D, lifting and connecting them in 3D space afterwards with additional data augmentation.Due to the 2.5D  characteristic of the airborne LiDAR point cloud, we simplify the problem by projecting the points on the ground plane to produce a 2D height map. With the height map, a heat map is first generated with pixel-wise corner likelihood to predict the possible 2D corners.Then, 3D corners are predicted by a Transformer-based network with extra height embedding initialization. This 2D-to-3D corner detection strategy reduces the search space significantly. To recover the topological connections among the corners, edges are finally predicted from the height map with the proposed edge attention mechanism, which extracts holistic features and preserves local details simultaneously. In addition, due to the limited datasets in the field and the irregularity of the point clouds, a conditional latent diffusion model for LiDAR scanning simulation is utilized for data augmentation. BWFormer surpasses other state-of-the-art methods, especially in reconstruction completeness.  Our code is available at: https://github.com/3dv-casia/BWformer/.