GenAI4ELab/papercli-papers huggingface dataset:
Empowering Vector Graphics with Consistently Arbitrary Viewing and View-dependent Visibility
Authors: Yidi Li; Jun Xiao; Zhengda Lu; Yiqun Wang; Haiyong Jiang
Venue: CVPR 2025 (main)

Abstract: This work presents a novel text-to-vector graphics generation approach, Dream3DVG, allowing for arbitrary viewpoint viewing, progressive detail optimization, and view-dependent occlusion awareness.  Our approach is a dual-branch optimization framework, consisting of an auxiliary 3D Gaussian Splatting optimization branch and a 3D vector graphics optimization branch.  The introduced 3DGS branch can bridge the domain gaps between text prompts and vector graphics with more consistent guidance. Moreover, 3DGS allows for progressive detail control by scheduling classifier-free guidance, facilitating guiding vector graphics with coarse shapes at the initial stages and finer details at later stages.  We also improve the view-dependent occlusions by devising a visibility-awareness rendering module.  Extensive results on 3D sketches and 3D iconographies, demonstrate the superiority of the method on different abstraction levels of details, cross-view consistency, and occlusion-aware stroke culling.
Semantic and Expressive Variations in Image Captions Across Languages
Authors: Andre Ye; Sebastin Santy; Jena D. Hwang; Amy X. Zhang; Ranjay Krishna
Venue: CVPR 2025 (main)

Abstract: Most vision-language models today are primarily trained on English image-text pairs, with non-English pairs often filtered out. Evidence from cross-cultural psychology suggests that this approach will bias models against perceptual modes exhibited by people who speak other (non-English) languages. We investigate semantic and expressive variation in image captions across different languages; we analyze both human-annotated datasets and model-produced captions. By analyzing captions across seven languages (English, French, German, Russian, Chinese, Japanese, Korean) in high-quality image captioning datasets (Crossmodal and Visual Genome), we find that multilingual caption sets tend to provide richer visual descriptions than monolingual (including English-only) ones; multilingual sets contain 46.0% more objects, 66.1% more relationships, and 66.8% more attributes. We observe the same results with multilingual captions produced by LLaVA and the Google Vertex API: for example, compared to monolingual captions, they cover 21.9% more objects,18.8% more relations, and 20.1% more attributes. These suggest that, across a large number of samples, different languages bias people and models to focus on different visual concepts. Finally, we show that models trained on image-text data in one language perform distinctly better on that language's test set. Our work points towards the potential value of training vision models on multilingual data sources to widen the range/variation of descriptive information those models are exposed to.
ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models
Authors: Xubing Ye; Yukang Gan; Yixiao Ge; Xiao-Ping Zhang; Yansong Tang
Venue: CVPR 2025 (main)

Abstract: Large Vision Language Models (LVLMs) have achieved significant success across multi-modal tasks. However, the computational cost of processing long visual tokens can be prohibitively expensive on resource-limited devices. Previous methods have identified redundancy in visual tokens within the Large Language Model (LLM) decoder layers and have mitigated this by pruning tokens using a pre-defined or fixed ratio, thereby reducing computational overhead. Nonetheless, we observe that the impact of pruning ratio varies across different LLM layers and instances (image-prompt pairs). Therefore, it is essential to develop a layer-wise and instance-wise vision token pruning strategy to balance computational cost and model performance effectively. We propose ATP-LLaVA, a novel approach that adaptively determines instance-specific token pruning ratios for each LLM layer. Specifically, we introduce an Adaptive Token Pruning (ATP) module, which computes the importance score and pruning threshold based on input instance adaptively. The ATP module can be seamlessly integrated between any two LLM layers with negligible computational overhead. Additionally, we develop a Spatial Augmented Pruning (SAP) strategy that prunes visual tokens with both token redundancy and spatial modeling perspectives. Our approach reduces the average token count by 75% while maintaining performance, with only a minimal 1.9% degradation across seven widely used benchmarks.
ADD: Attribution-Driven Data Augmentation Framework for Boosting Image Super-Resolution
Authors: Ze-Yu Mi; Yu-Bin Yang
Venue: CVPR 2025 (main)

Abstract: Data augmentation (DA) stands out as a powerful technique to enhance the generalization capabilities of deep neural networks across diverse tasks. However, in low-level vision tasks, DA remains rudimentary (i.e., vanilla DA), facing a critical bottleneck due to information loss. In this paper, we introduce a novel Calibrated Attribution Maps (CAM) to generate saliency masks, followed by two saliency-based DA methods-- Attribution-Driven Data augmentation (ADD) and ADD+--designed to address this issue. CAM leverages integrated gradients and incorporates two key innovations: a global feature detector and calibrated integrated gradients. Based on CAM and the proposed methods, we have two new insights for low-level vision tasks: (1) increasing pixel diversity, as seen in vanilla DA, can improve performance, and (2) focusing on salient features while minimizing the impact of irrelevant pixels, as seen in saliency-based DA, more effectively enhances model performance. Additionally, we find and highlight the key guiding principle for designing saliency-based DA: a wider spectrum of degradation patterns. Extensive experiments demonstrate the compatibility and consistency of our method, as well as the significant performance improvement across various SR tasks and networks.
DIFFER: Disentangling Identity Features via Semantic Cues for Clothes-Changing Person Re-ID
Authors: Xin Liang; Yogesh S Rawat
Venue: CVPR 2025 (main)

Abstract: Clothes-changing person re-identification (CC-ReID) aims to recognize individuals under different clothing scenarios. Current CC-ReID approaches either concentrate on modeling body shape using additional modalities including silhouette, pose, and body mesh, potentially causing the model to overlook other critical biometric traits such as gender, age, and style, or they incorporate supervision through additional labels that the model tries to disregard or emphasize, such as clothing or personal attributes. However, these annotations are discrete in nature and do not capture comprehensive descriptions. In this work, we propose DIFFER: Disentangle Identity Features From Entangled Representations, a novel adversarial learning method that leverages textual descriptions to disentangle identity features. Recognizing that image features inherently mix inseparable information, DIFFER introduces NBDetach, a mechanism designed for feature disentanglement by leveraging the separable nature of text descriptions as supervision. It partitions the feature space into distinct subspaces and, through gradient reversal layers, effectively separates identity-related features from non-biometric features. We evaluate DIFFER on 4 different benchmark datasets (LTCC, PRCC, CelebreID-Light, and CCVID) to demonstrate its effectiveness and provide state-of-the-art performance across all the benchmarks. DIFFER consistently outperforms the baseline method, with improvements in top-1 accuracy of 3.6% on LTCC, 3.4% on PRCC, 2.5% on CelebReID-Light, and 1% on CCVID. Our code can be found at https://github.com/xliangp/DIFFER.git.
HyperPose: Hypernetwork-Infused Camera Pose Localization and an Extended Cambridge Landmarks Dataset
Authors: Ron Ferens; Yosi Keller
Venue: CVPR 2025 (main)

Abstract: In this work, we propose HyperPose, which utilizes hypernetworks in absolute camera pose regressors. The inherent appearance variations in natural scenes, attributable to environmental conditions, perspective, and lighting, induce a significant domain disparity between the training and test datasets. This disparity degrades the precision of contemporary localization networks. To mitigate this, we advocate for incorporating hypernetworks into single-scene and multiscene camera pose regression models. During inference, the hypernetwork dynamically computes adaptive weights for the localization regression heads based on the particular input image, effectively narrowing the domain gap. Using indoor and outdoor datasets, we evaluate the HyperPose methodology across multiple established absolute pose regression architectures. In particular, we introduce and share the Extended Cambridge Landmarks (ECL), which is a novel localization dataset, based on the Cambridge Landmarks dataset, showing it in multiple seasons with significantly varying appearance conditions. Our empirical experiments demonstrate that HyperPose yields notable performance enhancements for both single- and multi-scene architectures. We have made our source code, pre-trained models, and ECL dataset openly available.
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning
Authors: Di Zhang; Jingdi Lei; Junxian Li; Xunzhi Wang; Yujie Liu; Zonglin Yang; Jiatong Li; Weida Wang; Suorong Yang; Jianbo Wu; Peng Ye; Wanli Ouyang; Dongzhan Zhou
Venue: CVPR 2025 (main)

Abstract: Vision-language models (VLMs) have shown remarkable advancements in multimodal reasoning tasks. However, they still often generate inaccurate or irrelevant responses due to issues like hallucinated image understandings or unrefined reasoning paths. To address these challenges, we introduce Critic-V, a novel framework inspired by the Actor-Critic paradigm to boost the reasoning capability of VLMs. This framework decouples the reasoning process and critic process by integrating two independent components: the Reasoner, which generates reasoning paths based on visual and textual inputs, and the Critic, which provides constructive critique to refine these paths. In this approach, the Reasoner generates reasoning responses according to text prompts, which can evolve iteratively as a policy based on feedback from the Critic. This interaction process was theoretically driven by a reinforcement learning framework where the Critic offers natural language critiques instead of scalar rewards, enabling more nuanced feedback to boost the Reasoner's capability on complex reasoning tasks. The Critic model is trained using Direct Preference Optimization (DPO), leveraging a preference dataset of critiques ranked by Rule-based Reward (RBR) to enhance its critic capabilities. Evaluation results show that the Critic-V framework significantly outperforms existing methods, including GPT-4V, on 5 out of 8 benchmarks, especially regarding reasoning accuracy and efficiency. Combining a dynamic text-based policy for the Reasoner and constructive feedback from the preference-optimized Critic enables a more reliable and context-sensitive multimodal reasoning process. Our approach provides a promising solution to enhance the reliability of VLMs, improving their performance in real-world reasoning-heavy multimodal applications such as autonomous driving and embodied intelligence.
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
Venue: CVPR 2025 (main)

Abstract: 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.
Towards Universal Dataset Distillation via Task-Driven Diffusion
Authors: Ding Qi; Jian Li; Junyao Gao; Shuguang Dou; Ying Tai; Jianlong Hu; Bo Zhao; Yabiao Wang; Chengjie Wang; Cairong Zhao
Venue: CVPR 2025 (main)

Abstract: Dataset distillation (DD) condenses key information from large-scale datasets into smaller synthetic datasets, reducing storage and computational costs for training networks. However, recent research has primarily focused on image classification tasks, with limited expansion to detection and segmentation. Two key challenges remain: (i) Task Optimization Heterogeneity, where existing methods focus on class-level information and fail to address the diverse needs of detection and segmentation and (ii) Inflexible Image Generation, where current generation methods rely on global updates for single-class targets and lack localized optimization for specific object regions.To address these challenges, we propose a universal dataset distillation framework, named UniDD, a task-driven diffusion model for diverse DD tasks, as illustrated in Fig.1. Our approach operates in two stages: Universal Task Knowledge Mining, which captures task-relevant information through task-specific proxy model training, and Universal Task-Driven Diffusion, where these proxies guide the diffusion process to generate task-specific synthetic images.Extensive experiments across ImageNet-1K, Pascal VOC, and MS COCO demonstrate that UniDD consistently outperforms state-of-the-art methods. In particular, on ImageNet-1K with IPC-10, UniDD surpasses previous diffusion-based methods by 6.1%, while also reducing deployment costs.
Parametric Point Cloud Completion for Polygonal Surface Reconstruction
Authors: Zhaiyu Chen; Yuqing Wang; Liangliang Nan; Xiao Xiang Zhu
Venue: CVPR 2025 (main)

Abstract: Existing polygonal surface reconstruction methods heavily depend on input completeness and struggle with incomplete point clouds. We argue that while current point cloud completion techniques may recover missing points, they are not optimized for polygonal surface reconstruction, where the parametric representation of underlying surfaces remains overlooked. To address this gap, we introduce parametric completion, a novel paradigm for point cloud completion, which recovers parametric primitives instead of individual points to convey high-level geometric structures. Our presented approach, PaCo, enables high-quality polygonal surface reconstruction by leveraging plane proxies that encapsulate both plane parameters and inlier points, proving particularly effective in challenging scenarios with highly incomplete data. Comprehensive evaluations of our approach on the ABC dataset establish its effectiveness with superior performance and set a new standard for polygonal surface reconstruction from incomplete data. Project page: https://parametric-completion.github.io.
SyncSDE: A Probabilistic Framework for Diffusion Synchronization
Authors: Hyunjun Lee; Hyunsoo Lee; Sookwan Han
Venue: CVPR 2025 (main)

Abstract: There have been many attempts to leverage multiple diffusion models for collaborative generation, extending beyond the original domain. A prominent approach involves synchronizing multiple diffusion trajectories by mixing the estimated scores to artificially correlate the generation processes. However, existing methods rely on naive heuristics, such as averaging, without considering task specificity. These approaches do not clarify why such methods work and often fail when a heuristic suitable for one task is blindly applied to others. In this paper, we present a probabilistic framework for analyzing why diffusion synchronization works and reveal where heuristics should be focused--modeling correlations between multiple trajectories and adapting them to each specific task. We further identify optimal correlation models per task, achieving better results than previous approaches that apply a single heuristic across all tasks without justification.
MaRI: Material Retrieval Integration across Domains
Authors: Jianhui Wang; Zhifei Yang; Yangfan He; Huixiong Zhang; Yuxuan Chen; Jingwei Huang
Venue: CVPR 2025 (main)

Abstract: Accurate material retrieval is critical for creating realistic 3D assets. Existing methods rely on datasets that capture shape-invariant and lighting-varied representations of materials, which are scarce and face challenges due to limited diversity and inadequate real-world generalization. Most current approaches adopt traditional image search techniques. They fall short in capturing the unique properties of material spaces, leading to suboptimal performance in retrieval tasks. Addressing these challenges, we introduce MaRI, a framework designed to bridge the feature space gap between synthetic and real-world materials. MaRI constructs a shared embedding space that harmonizes visual and material attributes through a contrastive learning strategy by jointly training a image and a material encoder, bringing similar materials and images closer while separating dissimilar pairs within the feature space. To support this, we construct a comprehensive dataset comprising high-quality synthetic materials rendered with controlled shape variations and diverse lighting conditions, along with real-world materials processed and standardized using material transfer techniques. Extensive experiments demonstrate the superior performance, accuracy, and generalization capabilities of MaRI across diverse and complex material retrieval tasks, outperforming existing methods.
MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation
Authors: Mingcheng Li; Xiaolu Hou; Ziyang Liu; Dingkang Yang; Ziyun Qian; Jiawei Chen; Jinjie Wei; Yue Jiang; Qingyao Xu; Lihua Zhang
Venue: CVPR 2025 (main)

Abstract: Diffusion models have shown excellent performance in text-to-image generation. However, existing methods often suffer from performance bottlenecks when dealing with complex prompts involving multiple objects, characteristics, and relations.  Therefore, we propose a Multi-agent Collaboration-based Compositional Diffusion (MCCD) for text-to-image generation for complex scenes. Specifically, we design a multi-agent collaboration based scene parsing module that generates an agent system containing multiple agents with different tasks using MLLMs to adequately extract multiple scene elements. In addition, Hierarchical Compositional diffusion utilizes Gaussian mask and filtering to achieve the refinement of bounding box regions and highlights objects through region enhancement for accurate and high-fidelity generation of complex scenes. Comprehensive experiments demonstrate that our MCCD significantly improves the performance of the baseline models in a training-free manner, which has a large advantage in complex scene generation. The code will be open-source on github.
Dual Semantic Guidance for Open Vocabulary Semantic Segmentation
Authors: Zhengyang Wang; Tingliang Feng; Fan Lyu; Fanhua Shang; Wei Feng; Liang Wan
Venue: CVPR 2025 (main)

Abstract: Open-vocabulary semantic segmentation aims to enable models to segment arbitrary categories. Currently, though pre-trained Vision-Language Models (VLMs) like CLIP have established a robust foundation for this task by learning to match text and image representations from large-scale data, their lack of pixel-level recognition necessitates further fine-tuning. Most existing methods leverage text as a guide to achieve pixel-level recognition. However, the inherent biases in text semantic descriptions and the lack of pixel-level supervisory information make it challenging to fine-tune CLIP-based models effectively. This paper considers leveraging image-text data to simultaneously capture the semantic information contained in both image and text, thereby constructing Dual Semantic Guidance and corresponding pixel-level pseudo annotations. Particularly, the visual semantic guidance is enhanced via explicitly exploring foreground regions and minimizing the influence of background. The dual semantic guidance is then jointly utilized to fine-tune CLIP-based segmentation models, achieving decent fine-grained recognition capabilities. As the comprehensive evaluation shows, our method outperforms state-of-art results with large margins, on eight commonly used datasets with/without background.
CroCoDL: Cross-device Collaborative Dataset for Localization
Authors: Hermann Blum; Alessandro Mercurio; Joshua O'Reilly; Tim Engelbracht; Mihai Dusmanu; Marc Pollefeys; Zuria Bauer
Venue: CVPR 2025 (main)

Abstract: Accurate localization plays a pivotal role in the autonomy of systems operating in unfamiliar environments, particularly when interaction with humans is expected. High-accuracy visual localization systems encompass various components, such as image retrievers, feature extractors, matchers, reconstruction and pose estimation methods. This complexity translates to the necessity of robust evaluation settings and pipelines. However, existing datasets and benchmarks primarily focus on single-agent scenarios, overlooking the critical issue of cross-device localization. Different agents with different sensors will show their own specific strengths and weaknesses, and the data they have available varies substantially. This work addresses this gap by enhancing an existing augmented reality visual localization benchmark with data from legged robots, and evaluating human-robot, cross-device mapping and localization. Our contributions extend beyond device diversity and include high environment variability, spanning ten distinct locations ranging from disaster sites to art exhibitions. Each scene in our dataset features recordings from robot agents, hand-held and head-mounted devices, and high-accuracy ground truth LiDAR scanners, resulting in a comprehensive multi-agent dataset and benchmark. This work represents a significant advancement in the field of visual localization benchmarking, with key insights into the performance of cross-device localization methods across diverse settings.