ECCV
Collection
Accepted papers for ECCV (European Conference on Computer Vision), one dataset per year. • 4 items • Updated
paper_id uint32 | title string | authors list | ecva_url string | pdf_url string | supp_url string | doi string | arxiv_id string | arxiv_id_source string | abstract large_string | embedding list |
|---|---|---|---|---|---|---|---|---|---|---|
0 | Is Retain Set All You Need in Machine Unlearning? Restoring Performance of Unlearned Models with Out-Of-Distribution Images | [
"Jacopo Bonato",
"Marco Cotogni",
"Luigi Sabetta"
] | https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/4_ECCV_2024_paper.php | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00004.pdf | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00004-supp.pdf | 10.1007/978-3-031-73232-4_1 | 2404.12922 | title_snapshot | In this paper, we introduce Selective-distillation for Class and Architecture-agnostic unleaRning (SCAR), a novel approximate unlearning method. SCAR efficiently eliminates specific information while preserving the model’s test accuracy without using a retain set, which is a key component in state-of-the-art approximat... | [
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1 | Octopus: Embodied Vision-Language Programmer from Environmental Feedback | [
"Jingkang Yang",
"Yuhao Dong",
"Shuai Liu",
"Bo Li",
"Ziyue Wang",
"ChenCheng Jiang",
"Haoran Tan",
"Jiamu Kang",
"Yuanhan Zhang",
"Kaiyang Zhou",
"Ziwei Liu"
] | https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/6_ECCV_2024_paper.php | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00006.pdf | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00006-supp.pdf | 10.1007/978-3-031-73232-4_2 | 2310.08588 | title_snapshot | Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. When integrated into an embodied agent, existing embodied VLM works either output detailed action sequences at the manipulation level or only provide plans at an abstract level, leaving a gap between high-leve... | [
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2 | FunQA: Towards Surprising Video Comprehension | [
"Binzhu Xie",
"Sicheng Zhang",
"Zitang Zhou",
"Bo Li",
"Yuanhan Zhang",
"Jack Hessel",
"Jingkang Yang",
"Ziwei Liu"
] | https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/10_ECCV_2024_paper.php | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00010.pdf | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00010-supp.pdf | 10.1007/978-3-031-73232-4_3 | 2306.14899 | title_snapshot | Surprising videos, e.g., funny clips, creative performances, or visual illusions, attract significant attention. Enjoyment of these videos is not simply a response to visual stimuli; rather, it hinges on the human capacity to understand (and appreciate) commonsense violations depicted in these videos. We introduce FunQ... | [
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3 | 4D Contrastive Superflows are Dense 3D Representation Learners | [
"Xiang Xu",
"Lingdong Kong",
"Hui Shuai",
"Wenwei Zhang",
"Liang Pan",
"Kai Chen",
"Ziwei Liu",
"Qingshan Liu"
] | https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/19_ECCV_2024_paper.php | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00019.pdf | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00019-supp.pdf | 10.1007/978-3-031-73232-4_4 | 2407.06190 | title_snapshot | In the realm of autonomous driving, accurate 3D perception is the foundation. However, developing such models relies on extensive human annotations – a process that is both costly and labor-intensive. To address this challenge from a data representation learning perspective, we introduce SuperFlow, a novel framework de... | [
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4 | ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation | [
"Yuyuan Liu",
"Yuanhong Chen",
"Hu Wang",
"Vasileios Belagiannis",
"Ian Reid",
"Gustavo Carneiro"
] | https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/22_ECCV_2024_paper.php | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00022.pdf | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00022-supp.pdf | 10.1007/978-3-031-73232-4_5 | 2407.07171 | title_snapshot | The costly and time-consuming annotation process to produce large training sets for modelling semantic LiDAR segmentation methods has motivated the development of semi-supervised learning (SSL) methods. However, such SSL approaches often concentrate on employing consistency learning only for individual LiDAR representa... | [
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5 | Ponymation: Learning Articulated 3D Animal Motions from Unlabeled Online Videos | [
"Keqiang Sun",
"Dor Litvak",
"Yunzhi Zhang",
"Hongsheng Li",
"Jiajun Wu",
"Shangzhe Wu"
] | https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/29_ECCV_2024_paper.php | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00029.pdf | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00029-supp.pdf | 10.1007/978-3-031-73232-4_6 | 2312.13604 | title_snapshot | We introduce a new method for learning a generative model of articulated 3D animal motions from raw, unlabeled online videos. Unlike existing approaches for 3D motion synthesis, our model requires no pose annotations or parametric shape models for training; it learns purely from a collection of unlabeled web video clip... | [
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6 | Robust Fitting on a Gate Quantum Computer | [
"Frances F Yang",
"Michele Sasdelli",
"Tat-Jun Chin"
] | https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/37_ECCV_2024_paper.php | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00037.pdf | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00037-supp.pdf | 10.1007/978-3-031-73232-4_7 | 2409.02006 | title_snapshot | Gate quantum computers generate significant interest due to their potential to solve certain difficult problems such as prime factorization in polynomial time. Computer vision researchers have long been attracted to the power of quantum computers. Robust fitting, which is fundamentally important to many computer vision... | [
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7 | H-V2X: A Large Scale Highway Dataset for BEV Perception | [
"Chang Liu",
"MingXu zhu",
"Cong Ma"
] | https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/41_ECCV_2024_paper.php | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00041.pdf | null | 10.1007/978-3-031-73232-4_8 | null | null | Vehicle-to-everything (V2X) technology has become an area of interest in research due to the availability of roadside infrastructure perception datasets. However, these datasets primarily focus on urban intersections and lack data on highway scenarios. Additionally, the perception tasks in the datasets are mainly MONO ... | [
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8 | Learning Camouflaged Object Detection from Noisy Pseudo Label | [
"Jin Zhang",
"Ruiheng Zhang",
"Yanjiao Shi",
"Zhe Cao",
"Nian Liu",
"Fahad Shahbaz Khan"
] | https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/51_ECCV_2024_paper.php | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00051.pdf | null | 10.1007/978-3-031-73232-4_9 | 2407.13157 | title_snapshot | Existing Camouflaged Object Detection (COD) methods rely heavily on large-scale pixel-annotated training sets, which are both time-consuming and labor-intensive. Although weakly supervised methods offer higher annotation efficiency, their performance is far behind due to the unclear visual demarcations between foregrou... | [
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9 | Weakly Supervised 3D Object Detection via Multi-Level Visual Guidance | [
"Kuan-Chih Huang",
"Yi-Hsuan Tsai",
"Ming-Hsuan Yang"
] | https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/55_ECCV_2024_paper.php | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00055.pdf | null | 10.1007/978-3-031-73232-4_10 | 2312.07530 | title_snapshot | Weakly supervised 3D object detection aims to learn a 3D detector with lower annotation cost, e.g., 2D labels. Unlike prior work which still relies on few accurate 3D annotations, we propose a framework to study how to leverage constraints between 2D and 3D domains without requiring any 3D labels. Specifically, we empl... | [
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