Research Outputs

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Browsing Research Outputs by Author "18th European Conference on Computer Vision (ECCV) (29 Sep 2024 - 4 Oct 2024 : Milan, Italy)"

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    Bayesian Detector Combination for Object Detection with Crowdsourced Annotations
    (Springer Nature, 2025) Tan, Z.Q.; Isupova, O.; Carneiro, G.; Zhu, X.; Li, Y.; 18th European Conference on Computer Vision (ECCV) (29 Sep 2024 - 4 Oct 2024 : Milan, Italy); Leonardis, A.; Ricci, E.; Roth, S.; Russakovsky, O.; Sattler, T.; Varol, G.
    Acquiring fine-grained object detection annotations in unconstrained images is time-consuming, expensive, and prone to noise, especially in crowdsourcing scenarios. Most prior object detection methods assume accurate annotations; A few recent works have studied object detection with noisy crowdsourced annotations, with evaluation on distinct synthetic crowdsourced datasets of varying setups under artificial assumptions. To address these algorithmic limitations and evaluation inconsistency, we first propose a novel Bayesian Detector Combination (BDC) framework to more effectively train object detectors with noisy crowdsourced annotations, with the unique ability of automatically inferring the annotators’ label qualities. Unlike previous approaches, BDC is model-agnostic, requires no prior knowledge of the annotators’ skill level, and seamlessly integrates with existing object detection models. Due to the scarcity of real-world crowdsourced datasets, we introduce large synthetic datasets by simulating varying crowdsourcing scenarios. This allows consistent evaluation of different models at scale. Extensive experiments on both real and synthetic crowdsourced datasets show that BDC outperforms existing state-of-the-art methods, demonstrating its superiority in leveraging crowdsourced data for object detection. Our code and data are available at: https://github.com/zhiqin1998/bdc.
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    External Knowledge Enhanced 3D Scene Generation from Sketch
    (Springer Nature, 2025) Wu, Z.; Feng, M.; Wang, Y.; Xie, H.; Dong, W.; Miao, B.; Mian, A.; 18th European Conference on Computer Vision (ECCV) (29 Sep 2024 - 4 Oct 2024 : Milan, Italy); Leonardis, A.; Ricci, E.; Roth, S.; Russakovsky, O.; Sattler, T.; Varol, G.
    Generating realistic 3D scenes is challenging due to the complexity of room layouts and object geometries. We propose a sketch based knowledge enhanced diffusion architecture (SEK) for generating customized, diverse, and plausible 3D scenes. SEK conditions the denoising process with a hand-drawn sketch of the target scene and cues from an object relationship knowledge base. We first construct an external knowledge base containing object relationships and then leverage knowledge enhanced graph reasoning to assist our model in understanding hand-drawn sketches. A scene is represented as a combination of 3D objects and their relationships, and then incrementally diffused to reach a Gaussian distribution. We propose a 3D denoising scene transformer that learns to reverse the diffusion process, conditioned by a hand-drawn sketch along with knowledge cues, to regressively generate the scene including the 3D object instances as well as their layout. Experiments on the 3D-FRONT dataset show that our model improves FID, CKL by 17.41%, 37.18% in 3D scene generation and FID, KID by 19.12%, 20.06% in 3D scene completion compared to the nearest competitor DiffuScene.
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    VLAD-BuFF: Burst-Aware Fast Feature Aggregation for Visual Place Recognition
    (Springer, 2025) Khaliq, A.; Xu, M.; Hausler, S.; Milford, M.; Garg, S.; 18th European Conference on Computer Vision (ECCV) (29 Sep 2024 - 4 Oct 2024 : Milan, Italy); Leonardis, A.; Ricci, E.; Roth, S.; Russakovsky, O.; Sattler, T.; Varol, G.
    Visual Place Recognition (VPR) is a crucial component of many visual localization pipelines for embodied agents. VPR is often formulated as an image retrieval task aimed at jointly learning local features and an aggregation method. The current state-of-the-art VPR methods rely on VLAD aggregation, which can be trained to learn a weighted contribution of features through their soft assignment to cluster centers. However, this process has two key limitations. Firstly, the feature-to-cluster weighting does not account for over-represented repetitive structures within a cluster, e.g., shadows or window panes; this phenomenon is also referred to as the ‘burstiness’ problem, classically solved by discounting repetitive features before aggregation. Secondly, feature to cluster comparisons are compute-intensive for state-of-the-art image encoders with high-dimensional local features. This paper addresses these limitations by introducing VLAD-BuFF with two novel contributions: i) a self-similarity based feature discounting mechanism to learn Burst-aware features within end-to-end VPR training, and ii) Fast Feature aggregation by reducing local feature dimensions specifically through PCA-initialized learnable pre-projection. We benchmark our method on 9 public datasets, where VLAD-BuFF sets a new state of the art. Our method is able to maintain its high recall even for 12x reduced local feature dimensions, thus enabling fast feature aggregation without compromising on recall. Through additional qualitative studies, we show how our proposed weighting method effectively downweights the non-distinctive features. Source code: https://github.com/Ahmedest61/VLAD-BuFF/.