Unnamed: 0 int64 0 2.72k | title stringlengths 14 153 | Arxiv link stringlengths 1 31 | authors stringlengths 5 1.5k | arxiv_id float64 2k 2.41k ⌀ | abstract stringlengths 435 2.86k | Model stringclasses 1
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2,700 | Finding Lottery Tickets in Vision Models via Data-driven Spectral Foresight Pruning | Leonardo Iurada, Marco Ciccone, Tatiana Tommasi | null | Recent advances in neural network pruning have shown how it is possible to reduce the computational costs and memory demands of deep learning models before training. We focus on this framework and propose a new pruning at initialization algorithm that leverages the Neural Tangent Kernel (NTK) theory to align the traini... | [] | [] | [] | [] | 2,700 | |
2,701 | InNeRF360: Text-Guided 3D-Consistent Object Inpainting on 360-degree Neural Radiance Fields | http://arxiv.org/abs/2305.15094 | Dongqing Wang, Tong Zhang, Alaa Abboud, Sabine Süsstrunk | 2,305.15094 | We propose InNeRF360 an automatic system that accurately removes text-specified objects from 360-degree Neural Radiance Fields (NeRF). The challenge is to effectively remove objects while inpainting perceptually consistent content for the missing regions which is particularly demanding for existing NeRF models due to t... | [] | [] | [] | [] | 2,701 |
2,702 | Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects | http://arxiv.org/abs/2404.01440 | Yijia Weng, Bowen Wen, Jonathan Tremblay, Valts Blukis, Dieter Fox, Leonidas Guibas, Stan Birchfield | 2,404.0144 | We address the problem of building digital twins of unknown articulated objects from two RGBD scans of the object at different articulation states. We decompose the problem into two stages each addressing distinct aspects. Our method first reconstructs object-level shape at each state then recovers the underlying artic... | [] | [] | [] | [] | 2,702 |
2,703 | Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning | http://arxiv.org/abs/2404.07713 | Shiming Chen, Wenjin Hou, Salman Khan, Fahad Shahbaz Khan | 2,404.07713 | Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones supported by semantic information (e.g. attributes). However existing ZSL methods simply extract visual features using a pre-trained network backbone (i.e. CN... | [] | [] | [] | [] | 2,703 |
2,704 | IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection | Junbo Yin, Jianbing Shen, Runnan Chen, Wei Li, Ruigang Yang, Pascal Frossard, Wenguan Wang | null | Bird's eye view (BEV) representation has emerged as a dominant solution for describing 3D space in autonomous driving scenarios. However objects in the BEV representation typically exhibit small sizes and the associated point cloud context is inherently sparse which leads to great challenges for reliable 3D perception.... | [] | [] | [] | [] | 2,704 | |
2,705 | Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model | http://arxiv.org/abs/2403.17460 | Runmin Dong, Shuai Yuan, Bin Luo, Mengxuan Chen, Jinxiao Zhang, Lixian Zhang, Weijia Li, Juepeng Zheng, Haohuan Fu | 2,403.1746 | Reference-based super-resolution (RefSR) has the potential to build bridges across spatial and temporal resolutions of remote sensing images. However existing RefSR methods are limited by the faithfulness of content reconstruction and the effectiveness of texture transfer in large scaling factors. Conditional diffusion... | [] | [] | [] | [] | 2,705 |
2,706 | Vanishing-Point-Guided Video Semantic Segmentation of Driving Scenes | http://arxiv.org/abs/2401.15261 | Diandian Guo, Deng-Ping Fan, Tongyu Lu, Christos Sakaridis, Luc Van Gool | 2,401.15261 | The estimation of implicit cross-frame correspondences and the high computational cost have long been major challenges in video semantic segmentation (VSS) for driving scenes. Prior works utilize keyframes feature propagation or cross-frame attention to address these issues. By contrast we are the first to harness vani... | [] | [] | [] | [] | 2,706 |
2,707 | Enhancing Intrinsic Features for Debiasing via Investigating Class-Discerning Common Attributes in Bias-Contrastive Pair | http://arxiv.org/abs/2404.19250 | Jeonghoon Park, Chaeyeon Chung, Jaegul Choo | 2,404.1925 | In the image classification task deep neural networks frequently rely on bias attributes that are spuriously correlated with a target class in the presence of dataset bias resulting in degraded performance when applied to data without bias attributes. The task of debiasing aims to compel classifiers to learn intrinsic ... | [] | [] | [] | [] | 2,707 |
2,708 | LAMP: Learn A Motion Pattern for Few-Shot Video Generation | Ruiqi Wu, Liangyu Chen, Tong Yang, Chunle Guo, Chongyi Li, Xiangyu Zhang | null | In this paper we present a few-shot text-to-video framework LAMP which enables a text-to-image diffusion model to Learn A specific Motion Pattern with 8 16 videos on a single GPU. Unlike existing methods which require a large number of training resources or learn motions that are precisely aligned with template videos ... | [] | [] | [] | [] | 2,708 | |
2,709 | Compositional Chain-of-Thought Prompting for Large Multimodal Models | http://arxiv.org/abs/2311.17076 | Chancharik Mitra, Brandon Huang, Trevor Darrell, Roei Herzig | 2,311.17076 | The combination of strong visual backbones and Large Language Model (LLM) reasoning has led to Large Multimodal Models (LMMs) becoming the current standard for a wide range of vision and language (VL) tasks. However recent research has shown that even the most advanced LMMs still struggle to capture aspects of composit... | [] | [] | [] | [] | 2,709 |
2,710 | Diffusion Time-step Curriculum for One Image to 3D Generation | http://arxiv.org/abs/2404.04562 | Xuanyu Yi, Zike Wu, Qingshan Xu, Pan Zhou, Joo-Hwee Lim, Hanwang Zhang | 2,404.04562 | Score distillation sampling (SDS) has been widely adopted to overcome the absence of unseen views in reconstructing 3D objects from a single image. It leverages pre-trained 2D diffusion models as teacher to guide the reconstruction of student 3D models. Despite their remarkable success SDS-based methods often encounter... | [] | [] | [] | [] | 2,710 |
2,711 | Language-driven Object Fusion into Neural Radiance Fields with Pose-Conditioned Dataset Updates | http://arxiv.org/abs/2309.11281 | Ka Chun Shum, Jaeyeon Kim, Binh-Son Hua, Duc Thanh Nguyen, Sai-Kit Yeung | 2,309.11281 | Neural radiance field (NeRF) is an emerging technique for 3D scene reconstruction and modeling. However current NeRF-based methods are limited in the capabilities of adding or removing objects. This paper fills the aforementioned gap by proposing a new language-driven method for object manipulation in NeRFs through dat... | [] | [] | [] | [] | 2,711 |
2,712 | Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning | Fan Qi, Shuai Li | null | In Federated Learning (FL) the issue of statistical data heterogeneity has been a significant challenge to the field's ongoing development. This problem is further exacerbated when clients' data vary in modalities. In response to these issues of statistical heterogeneity and modality incompatibility we propose the Adap... | [] | [] | [] | [] | 2,712 | |
2,713 | SPIN: Simultaneous Perception Interaction and Navigation | http://arxiv.org/abs/2405.07991 | Shagun Uppal, Ananye Agarwal, Haoyu Xiong, Kenneth Shaw, Deepak Pathak | 2,405.07991 | While there has been remarkable progress recently in the fields of manipulation and locomotion mobile manipulation remains a long-standing challenge. Compared to locomotion or static manipulation a mobile system must make a diverse range of long-horizon tasks feasible in unstructured and dynamic environments. While the... | [] | [] | [] | [] | 2,713 |
2,714 | DREAM: Diffusion Rectification and Estimation-Adaptive Models | http://arxiv.org/abs/2312.00210 | Jinxin Zhou, Tianyu Ding, Tianyi Chen, Jiachen Jiang, Ilya Zharkov, Zhihui Zhu, Luming Liang | 2,312.0021 | We present DREAM a novel training framework representing Diffusion Rectification and Estimation-Adaptive Models requiring minimal code changes (just three lines) yet significantly enhancing the alignment of training with sampling in diffusion models. DREAM features two components: diffusion rectification which adjusts ... | [] | [] | [] | [] | 2,714 |
2,715 | Exploring the Potential of Large Foundation Models for Open-Vocabulary HOI Detection | http://arxiv.org/abs/2404.06194 | Ting Lei, Shaofeng Yin, Yang Liu | 2,404.06194 | Open-vocabulary human-object interaction (HOI) detection which is concerned with the problem of detecting novel HOIs guided by natural language is crucial for understanding human-centric scenes. However prior zero-shot HOI detectors often employ the same levels of feature maps to model HOIs with varying distances leadi... | [] | [] | [] | [] | 2,715 |
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