pwc-archive/papers-with-abstracts huggingface dataset:
Mono3DVLT: Monocular-Video-Based 3D Visual Language Tracking
Authors: Hongkai Wei, Yang Yang, ShiJie Sun, Mingtao Feng, XiangYu Song, Qi Lei, Hongli Hu, Rong Wang, HuanSheng Song, Naveed Akhtar, Ajmal Saeed Mian
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Object Tracking

    Visual-Language Tracking (VLT) is emerging as a promising paradigm to bridge the human-machine performance gap. For single objects, VLT broadens the problem scope to text-driven video comprehension. Yet, this direction is still confined to 2D spatial extents, currently lacking the ability to deal with 3D tracking in the confines of monocular video. Unfortunately, advances in 3D tracking mainly rely on expensive sensor inputs, e.g., point clouds, depth measurements, radar. Absence of language counterpart for the outputs of these mildly democratized sensors in the literature also hinders VLT expansion to 3D tracking. Addressing that, we make the first attempt towards extending VLT to 3D tracking based on monocular video. We present a comprehensive framework, introducing (i) the Monocular-Video-based 3D Visual Language Tracking (Mono3DVLT) task, (ii) a large-scale dataset  for the task, called Mono3DVLT-V2X, and (iii) a customized neural model for the task. Our dataset is carefully curated, leveraging a Large Langauge Model (LLM) followed by human verification, composing natural language descriptions for 79,158 video sequences aiming at single object tracking, providing 2D and 3D bounding box annotations. Our neural model, termed Mono3DVLT-MT, is the first targeted  approach for the Mono3DVLT task. Comprising the pipeline of multi-modal feature extractor, visual-language encoder, tracking decoder and a tracking head, our model sets a strong baseline for the task on Mono3DVLT-V2X. Experimental results show that our method significantly outperforms existing techniques on the Mono3DVLT-V2X dataset. Our dataset and code are available in https://github.com/hongkai-wei/Mono3DVLT.
Just Dance with pi! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection
Authors: Snehashis Majhi, Giacomo D'Amicantonio, Antitza Dantcheva, Quan Kong, Lorenzo Garattoni, Gianpiero Francesca, Egor Bondarev, Francois Bremond
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Anomaly Detection, Optical Flow Estimation, Video Anomaly Detection, Weakly-supervised Video Anomaly Detection

    Weakly-supervised methods for video anomaly detection (VAD) are conventionally based merely on RGB spatio-temporal features, which continues to limit their reliability in real-world scenarios. This is due to the fact that RGB-features are not sufficiently distinctive in setting apart categories such as shoplifting from visually similar events. Therefore, towards robust complex real-world VAD, it is essential to augment RGB spatio-temporal features by additional modalities. Motivated by this, we introduce the Poly-modal Induced framework for VAD: PI-VAD (or \pi-VAD), a novel approach that augments RGB representations by five additional modalities. Specifically, the modalities include sensitivity to fine-grained motion (Pose), three dimensional scene and entity representation (Depth), surrounding objects (Panoptic masks), global motion (optical flow), as well as language cues (VLM). Each modality represents an axis of a polygon, streamlined to add salient cues to RGB. \pi-VAD includes two plug-in modules, namely Pseudo-modality Generation module and Cross Modal Induction module, which generate modality-specific prototypical representation and, thereby, induce multi-modal information into RGB cues. These modules operate by performing anomaly-aware auxiliary tasks and necessitate five modality backbones -- only during training. Notably, \pi-VAD achieves state-of-the-art accuracy on three prominent VAD datasets encompassing real-world scenarios, without requiring the computational overhead of five modality backbones at inference.
LatentHOI: On the Generalizable Hand Object Motion Generation with Latent Hand Diffusion.
Authors: Muchen Li, Sammy Christen, Chengde Wan, Yujun Cai, Renjie Liao, Leonid Sigal, Shugao Ma
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Motion Generation, Object

    Current research on generating 3D hand-object interaction motion primarily focuses on in-domain objects. Generalization to unseen objects is essential for practical applications, yet it remains both challenging and largely unexplored.In this paper, we propose LatentHOI, a novel approach designed to tackle the challenges of generalizing hand-object interaction to unseen objects.Our main insight lies in decoupling high-level temporal motion from fine-grained spatial hand-object interactions with a latent diffusion model coupled with a Grasping Variational Autoencoder (GraspVAE). This configuration not only enhances the conditional dependency between spatial grasp and temporal motion but also improves data utilization and reduces overfitting through regularization in the latent space. We conducted extensive experiments in an unseen-object setting on both single-hand grasping and bi-manual motion datasets, including GRAB, DexYCB, and OakInk.Quantitative and qualitative evaluations demonstrate that our method significantly enhances the realism and physical plausibility of generated motions for unseen objects, both in single and bimanual manipulations, compared to the state-of-the-art.
VideoGLaMM : A Large Multimodal Model for Pixel-Level Visual Grounding in Videos
Authors: Shehan Munasinghe, Hanan Gani, Wenqi Zhu, Jiale Cao, Eric Xing, Fahad Shahbaz Khan, Salman Khan
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Large Language Model, Video Segmentation, Video Semantic Segmentation, Visual Grounding

    Fine-grained alignment between videos and text is challenging due to complex spatial and temporal dynamics in videos. Existing video-based Large Multimodal Models (LMMs) handle basic conversations but struggle with precise pixel-level grounding in videos. To address this, we introduce VideoGLaMM, a LMM designed for fine-grained pixel-level grounding in videos based on user-provided textual inputs. Our design seamlessly connects three key components: a Large Language Model, a dual vision encoder that emphasizes both spatial and temporal details, and a spatio-temporal decoder for accurate mask generation. This connection is facilitated via tunable V-L and L-V adapters that enable close Vision-Language (VL) alignment. The architecture is trained to synchronize both spatial and temporal elements of video content with textual instructions. To enable fine-grained grounding, we curate a multimodal dataset featuring detailed visually-grounded conversations using a semiautomatic annotation pipeline, resulting in a diverse set of 38k video-QA triplets along with 83k objects and 671k masks. We evaluate VideoGLaMM on three challenging tasks: Grounded Conversation Generation, Visual Grounding, and Referring Video Segmentation. Experimental results show that our model consistently outperforms existing approaches across all three tasks.
LoRA Recycle: Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs
Authors: Zixuan Hu, Yongxian Wei, Li Shen, Chun Yuan, DaCheng Tao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: In-Context Learning, Meta-Learning

    Large Language Models (LLMs) such as ChatGPT demonstrate strong few-shot adaptability without requiring fine-tuning, positioning them ideal for data-limited and real-time applications. However, this adaptability has not yet been replicated in current Visual Foundation Models (VFMs), which require explicit fine-tuning with sufficient tuning data. Besides, the pretraining-finetuning paradigm has led to the surge of numerous task-specific modular components, such as Low-Rank Adaptation (LoRA). For the first time, we explore the potential of reusing diverse pre-tuned LoRAs without accessing their original training data, to achieve tuning-free few-shot adaptation in VFMs. Our framework, LoRA Recycle, distills a meta-LoRA from diverse pre-tuned LoRAs with a meta-learning objective, using synthetic data inversely generated from pre-tuned LoRAs themselves. The VFM, once equipped with the meta-LoRA, is empowered to solve new few-shot tasks in a single forward pass, akin to the in-context learning of LLMs. Additionally, we incorporate a double-efficient mechanism, accelerating the data-generation and meta-training process while maintaining or even improving performance. Extensive experiments across various few-shot classification benchmarks across both in- and cross-domain scenarios demonstrate the superiority of our framework. Code is available at https://github.com/Egg-Hu/LoRA-Recycle.
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning
Authors: Zichen Tian, Yaoyao Liu, Qianru Sun
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Meta-Learning, parameter-efficient fine-tuning, Transfer Learning

    Training large foundation models from scratch for domain-specific applications is almost impossible due to data limits and long-tailed distributions -- taking remote sensing (RS) as an example. Fine-tuning natural image pre-trained models on RS images is a straightforward solution. To reduce computational costs and improve performance on tail classes, existing methods apply parameter-efficient fine-tuning (PEFT) techniques, such as LoRA and AdaptFormer. However, we observe that fixed hyperparameters -- such as intra-layer positions, layer depth, and scaling factors, can considerably hinder PEFT performance, as fine-tuning on RS images proves highly sensitive to these settings. To address this, we propose MetaPEFT, a method incorporating adaptive scalers that dynamically adjust module influence during fine-tuning. MetaPEFT dynamically adjusts three key factors of PEFT on RS images: module insertion, layer selection, and module-wise learning rates, which collectively control the influence of PEFT modules across the network. We conduct extensive experiments on three transfer-learning scenarios and five datasets in both RS and natural image domains. The results show that MetaPEFT achieves state-of-the-art performance in cross-spectral adaptation, requiring only a small amount of trainable parameters and improving tail-class accuracy significantly.
Towards More General Video-based Deepfake Detection through Facial Component Guided Adaptation for Foundation Model
Authors: Yue-Hua Han, Tai-Ming Huang, Kai-Lung Hua, Jun-Cheng Chen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Decoder, DeepFake Detection, Face Swapping

    The current deep generative models have enabled the creation of synthetic facial images with remarkable photorealism, raising significant societal concerns over their potential misuse. Despite rapid advancements in the field of deepfake detection, developing an efficient and effective approach for the generalized deepfake detection of unseen forgery samples remains challenging. To address this challenge, we leverage the rich semantic priors of foundation models and propose a novel side-network-based decoder that extracts spatial and temporal cues using the CLIP image encoder for generalized video-based Deepfake detection. Additionally, we introduce Facial Component Guidance (FCG) to enhance spatial learning generalizability by encouraging the model to focus on key facial regions. By leveraging the generic features of a vision-language foundation model, our approach demonstrates promising generalizability on challenging Deepfake datasets while also exhibiting superiority in training data efficiency, parameter efficiency, and model robustness. The source code is available at: https://github.com/aiiu-lab/DFD-FCG.
MAGE : Single Image to Material-Aware 3D via the Multi-View G-Buffer Estimation Model
Authors: Haoyuan Wang, Zhenwei Wang, Xiaoxiao Long, Cheng Lin, Gerhard Hancke, Rynson W.H. Lau
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D geometry, 3D Reconstruction, Single-View 3D Reconstruction

    With advances in deep learning models and the availability of large-scale 3D datasets, we have recently witnessed significant progress in single-view 3D reconstruction. However, existing methods often fail to reconstruct physically based material properties given a single image, limiting their applicability in complicated scenarios. This paper presents a novel approach (named MAGE) for generating 3D geometry with realistic decomposed material properties given a single image as input. Our method leverages inspiration from traditional computer graphics deferred rendering pipelines to introduce a multi-view G-buffer estimation model. The proposed model estimates G-buffers for various views as multi-domain images, including XYZ coordinates, normals, albedo, roughness, and metallic properties from a single-view RGB image. To address the inherent ambiguity and inconsistency in generating G-buffers simultaneously, we also formulate a deterministic network from the pretrained diffusion models and propose a lighting response loss that enforces consistency across these domains using PBR principles. Finally, we propose a large-scale synthetic dataset rich in material diversity for our model training. Experimental results demonstrate the effectiveness of our method in producing high-quality 3D meshes with rich material properties. Our code and dataset can be found at https://www.whyy.site/paper/mage.
Can Machines Understand Composition? Dataset and Benchmark for Photographic Image Composition Embedding and Understanding
Authors: Zhaoran Zhao, Peng Lu, Anran Zhang, Peipei Li, Xia Li, Xuannan Liu, Yang Hu, Shiyi Chen, LiWei Wang, Wenhao Guo
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    With the rapid growth of social media and digital photography, visually appealing images have become essential for effective communication and emotional engagement. Among the factors influencing aesthetic appeal, composition--the arrangement of visual elements within a frame--plays a crucial role. In recent years, specialized models for photographic composition have achieved impressive results across various aesthetic tasks. Meanwhile, rapidly advancing multimodal large language models (MLLMs) have excelled in several visual perception tasks. However, their ability to embed and understand compositional information remains underexplored, primarily due to the lack of suitable evaluation datasets. To address this gap, we introduce the Photographic Image Composition Dataset (PICD), a large-scale dataset consisting of 36,857 images categorized into 24 composition categories across 355 diverse scenes. We demonstrate the advantages of PICD over existing datasets in terms of data scale, composition category, label quality, and scene diversity. Building on PICD, we establish benchmarks to evaluate the composition embedding capabilities of specialized models and the compositional understanding ability of MLLMs. To enable efficient and effective evaluation, we propose a novel Composition Discrimination Accuracy (CDA) metric. Our evaluation highlights the limitations of current models and provides insights into directions for improving their ability to embed and understand composition.
Person De-reidentification: A Variation-guided Identity Shift Modeling
Authors: Yi-Xing Peng, Yu-Ming Tang, Kun-Yu Lin, Qize Yang, Jingke Meng, Xihan Wei, Wei-Shi Zheng
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Face Recognition, Machine Unlearning, Person Re-Identification

    Person re-identification (ReID) is to associate images of individuals from different camera views against cross-view variations. Like other surveillance technologies, Re-ID faces serious privacy challenges, particularly the potential for unauthorized tracking. Although various tasks (e.g., face recognition) have developed machine unlearning techniques to address privacy concerns, such methods have not yet been explored within the Re-ID field. In this work, we pioneer the exploration of the person de-reidentification (De-ReID) problem and present its inherent challenges. In the context of ReID, De-ReID is to unlearn the knowledge about accurately matching specific persons so that these "unlearned persons" cannot be re-identified across cameras for privacy guarantee. The primary challenge is to achieve the unlearning without degrading the identity-discriminative feature embeddings to ensure the model's utility. To address this, we formulate a De-ReID framework that utilizes a labeled dataset of unlearned persons for unlearning and an unlabeled dataset of accessible persons for knowledge preservation. Instead of unlearning based on (pseudo) identity labels, we introduce a variation-guided identity shift mechanism that unlearns the specific persons by fitting the variations in their images while preserving ReID ability on other persons by overcoming the variations in images of accessible persons. As a result, the model shifts the unlearned persons to a feature space that is vulnerable to cross-view variations. Extensive experiments on benchmarks demonstrate the superiority of our method.
Shift the Lens: Environment-Aware Unsupervised Camouflaged Object Detection
Authors: Ji Du, Fangwei Hao, Mingyang Yu, Desheng Kong, Jiesheng Wu, Bin Wang, Jing Xu, Ping Li
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Density Estimation, object-detection, Object Detection, Retrieval

    Camouflaged Object Detection (COD) seeks to distinguish objects from their highly similar backgrounds. Existing work has essentially focused on isolating camouflaged objects from the environment, demonstrating ever-improving performance but at the cost of extensive annotations and complex optimizations. In this paper, we diverge from this paradigm and shift the lens to isolating the salient environment from the camouflaged object. We introduce EASE, an Environment-Aware unSupErvised COD framework that identifies the environment by referencing an environment prototype library and detects camouflaged objects by inverting the retrieved environmental features. Specifically, our approach (DiffPro) uses large multimodal models, diffusion models, and vision-foundation models to construct the environment prototype library. To retrieve environments from the library and refrain from confusing foreground and background, we incorporate three retrieval schemes: Kernel Density Estimation-based Adaptive Threshold (KDE-AT), Global-to-Local pixel-level retrieval (G2L), and Self-Retrieval (SR). Our experiments demonstrate significant improvements over current unsupervised methods, with EASE achieving an average gain of over 10% on the COD10K dataset. When integrated with SAM, EASE surpasses prompt-based segmentation approaches and performs competitively with state-of-the-art fully-supervised methods. Code is available at https://github.com/xiaohainku/EASE.
Dataset Distillation with Neural Characteristic Function: A Minmax Perspective
Authors: Shaobo Wang, Yicun Yang, Zhiyuan Liu, Chenghao Sun, Xuming Hu, Conghui He, Linfeng Zhang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Computational Efficiency, Dataset Distillation, GPU

    Dataset distillation has emerged as a powerful approach for reducing data requirements in deep learning. Among various methods, distribution matching-based approaches stand out for their balance of computational efficiency and strong performance. However, existing distance metrics used in distribution matching often fail to accurately capture distributional differences, leading to unreliable measures of discrepancy. In this paper, we reformulate dataset distillation as a minmax optimization problem and introduce Neural Characteristic Function Discrepancy (NCFD), a comprehensive and theoretically grounded metric for measuring distributional differences. NCFD leverages the Characteristic Function (CF) to encapsulate full distributional information, employing a neural network to optimize the sampling strategy for the CF's frequency arguments, thereby maximizing the discrepancy to enhance distance estimation. Simultaneously, we minimize the difference between real and synthetic data under this optimized NCFD measure. Our approach, termed Neural Characteristic Function Matching (NCFM), inherently aligns the phase and amplitude of neural features in the complex plane for both real and synthetic data, achieving a balance between realism and diversity in synthetic samples. Experiments demonstrate that our method achieves significant performance gains over state-of-the-art methods on both low- and high-resolution datasets. Notably, we achieve a 22.9% accuracy boost on ImageSquawk. Our method also reduces GPU memory usage by over 300x and achieves 20x faster processing speeds compared to state-of-the-art methods. To the best of our knowledge, this is the first work to achieve lossless compression of CIFAR-100 on a single NVIDIA 2080 Ti GPU using only 2.3 GB of memory. The code for this work is publicly available at: https://github.com/gszfwsb/NCFM.
Hierarchical Adaptive Filtering Network for Text Image Specular Highlight Removal
Authors: Zhi Jiang, Jingbo Hu, Ling Zhang, Gang Fu, Chunxia Xiao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: highlight removal, Image Reconstruction

    Despite significant advances in the field of specular highlight removal in recent years, existing methods predominantly focus on natural images, where highlights typically appear on raised or edged surfaces of objects. These highlights are often small and sparsely distributed. However, for text images such as cards and posters, the flat surfaces reflect light uniformly, resulting in large areas of highlights. Current methods struggle with these large-area highlights in text images, often producing severe visual artifacts or noticeable discrepancies between filled pixels and the original image in the central high-intensity highlight areas. To address these challenges, we propose the Hierarchical Adaptive Filtering Network (HAFNet). Our approach performs filtering at both the downsampled deep feature layer and the upsampled image reconstruction layer.  By designing and applying the Adaptive Comprehensive Filtering Module (ACFM) and Adaptive Dilated Filtering Module (ADFM) at different layers, our method effectively restores semantic information in large-area specular highlight regions and recovers detail loss at various scales. The required filtering kernels are pre-generated by a prediction network, allowing them to adaptively adjust according to different images and their semantic content, enabling robust performance across diverse scenarios.  Additionally, we utilize Unity3D to construct a comprehensive large-area highlight dataset featuring images with rich texts and complex textures. Experimental results on various datasets demonstrate that our method outperforms state-of-the-art approaches.
Towards Optimizing Large-Scale Multi-Graph Matching in Bioimaging
Authors: Max Kahl, Sebastian Stricker, Lisa Hutschenreiter, Florian Bernard, Carsten Rother, Bogdan Savchynskyy
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Graph Matching

    Multi-graph matching is an important problem in computer vision. Our task comes from bioimaging, where a set of 100 3D-microscopic images of worms have to be brought into correspondence. Surprisingly, virtually all existing methods are not applicable to this large-scale, real-world problem since they either assume a complete or dense problem setting, and they have so far only been applied to small-scale, toy or synthetic problems. Despite claims in literature that methods addressing complete multi-graph matching are applicable in an incomplete setting, our first contribution is to prove that their runtime would be excessive and impractical.  Our second contribution is a new method for incomplete multi-graph matching that applies to real-world, larger-scale problems. We experimentally show that for our bioimaging application we are able to attain results in less than two minutes, whereas the only competing approach requires at least half an hour while producing far worse results. Furthermore, even for small-scale, dense or complete problem instances we achieve results that are at least on par with the leading methods, but an order of magnitude faster.