mtybilly/Instruct2DS huggingface dataset:
Grounding Image Matching in 3D with MASt3R
Authors: Vincent Leroy*, Yohann Cabon, Jerome Revaud
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Image Matching is a core component of all best-performing algorithms and pipelines in 3D vision. Yet despite matching being fundamentally a 3D problem, intrinsically linked to camera pose and scene geometry, it is typically treated as a 2D problem. This makes sense as the goal of matching is to establish correspondences between 2D pixel fields, but also seems like a potentially hazardous choice. In this work, we take a different stance and propose to cast matching as a 3D task with , a recent and powerful 3D reconstruction framework based on Transformers. Based on pointmaps regression, this method displayed impressive robustness in matching views with extreme viewpoint changes, yet with limited accuracy. We aim here to improve the matching capabilities of such an approach while preserving its robustness. We thus propose to augment the network with a new head that outputs dense local features, trained with an additional matching loss. We further address the issue of quadratic complexity of dense matching, which becomes prohibitively slow for downstream applications if not treated carefully. We introduce a fast reciprocal matching scheme that not only accelerates matching by orders of magnitude, but also comes with theoretical guarantees and, lastly, yields improved results. Extensive experiments show that our approach, coined , significantly outperforms the state of the art on multiple matching tasks. In particular, it largely outperforms the best published methods on the challenging Map-free localization dataset."
TP2O: Creative Text Pair-to-Object Generation using Balance Swap-Sampling
Authors: Jun Li*, Zedong Zhang, Jian Yang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Generating creative combinatorial objects from two seemingly unrelated object texts is a challenging task in text-to-image synthesis, often hindered by a focus on emulating existing data distributions. In this paper, we develop a straightforward yet highly effective method, called balance swap-sampling. First, we propose a swapping mechanism that generates a novel combinatorial object image set by randomly exchanging intrinsic elements of two text embeddings through a cutting-edge diffusion model. Second, we introduce a balance swapping region to efficiently sample a small subset from the newly generated image set by balancing CLIP distances between the new images and their original generations, increasing the likelihood of accepting the high-quality combinations. Last, we employ a segmentation method to compare CLIP distances among the segmented components, ultimately selecting the most promising object from the sampled subset. Extensive experiments demonstrate that our approach outperforms recent SOTA T2I methods. Surprisingly, our results even rival those of human artists, such as frog-broccoli in Figure ??. Project"
RoDUS: Robust Decomposition of Static and Dynamic Elements in Urban Scenes
Authors: Thang-Anh-Quan Nguyen*, Luis G Roldao Jimenez*, Nathan Piasco*, Moussab Bennehar*, Dzmitry Tsishkou*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The task of separating dynamic objects from static environments using NeRFs has been widely studied in recent years. However, capturing large-scale scenes still poses a challenge due to their complex geometric structures and unconstrained dynamics. Without the help of 3D motion cues, previous methods often require simplified setups with slow camera motion and only a few/single dynamic actors, leading to suboptimal solutions in most urban setups. To overcome such limitations, we present RoDUS, a pipeline for decomposing static and dynamic elements in urban scenes, with thoughtfully separated NeRF models for moving and non-moving components. Our approach utilizes a robust kernel-based initialization coupled with 4D semantic information to selectively guide the learning process. This strategy enables accurate capturing of the dynamics in the scene, resulting in reduced floating artifacts in the reconstructed background, all by using self-supervision. Notably, experimental evaluations on KITTI-360 and Pandaset datasets demonstrate the effectiveness of our method in decomposing challenging urban scenes into precise static and dynamic components."
RecurrentBEV: A Long-term Temporal Fusion Framework for Multi-view 3D Detection
Authors: Ming Chang, Xishan Zhang*, Rui Zhang, Zhipeng Zhao, Guanhua He, Shaoli Liu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Long-term temporal fusion is frequently employed in camera-based Bird’s-Eye-View (BEV) 3D object detection to improve detection of occluded objects. Existing methods can be divided into two categories, parallel fusion and recurrent fusion. Recurrent fusion reduces inference latency and memory consumption but fails to exploit the long-term information as well as parallel fusion. In this paper, we first find two shortcomings of recurrent fusion paradigm: (1) Gradients of previous BEV features cannot directly contribute to the fusion module. (2) Semantic ambiguity are caused by coarse granularity of the BEV grids during aligning BEV features. Then based on the above analysis, we propose RecurrentBEV, a novel recurrent temporal fusion method for BEV based 3D object detector. By adopting RNN-style back-propagation and new-designed inner grid transformation, RecurrentBEV improves the long-term fusion ability while still enjoying efficient inference latency and memory consumption during inference. Extensive experiments on the nuScenes benchmark demonstrate its effectiveness, achieving a new state-of-the-art performance of 57.4% mAP and 65.1% NDS on the test set. The real-time version (25.6 FPS) achieves 44.5% mAP and 54.9% NDS without external dataset, outperforming the previous best method StreamPETR by 1.3% mAP and 0.9% NDS. The code is available at https://github.com/lucifer443/RecurrentBEV."
Efficient Bias Mitigation Without Privileged Information
Authors: Mateo Espinosa Zarlenga*, Swami Sankaranarayanan, Jerone T. A. Andrews, Zohreh Shams, Mateja Jamnik, Alice Xiang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Deep neural networks trained via empirical risk minimization often exhibit significant performance disparities across groups, particularly when group and task labels are spuriously correlated (e.g., “grassy background” and “cows”). Existing bias mitigation methods that aim to address this issue often either rely on group labels for training or validation, or require an extensive hyperparameter search. Such data and computational requirements hinder the practical deployment of these methods, especially when datasets are too large to be group-annotated, computational resources are limited, and models are trained through already complex pipelines. In this paper, we propose Targeted Augmentations for Bias Mitigation (TAB), a simple hyperparameter-free framework that leverages the entire training history of a helper model to identify spurious samples, and generate a group-balanced training set from which a robust model can be trained. We show that TAB improves worst-group performance without any group information or model selection, outperforming existing methods while maintaining overall accuracy."
MC-PanDA: Mask Confidence for Panoptic Domain Adaptation
Authors: Ivan Martinović*, Josip Šarić, Siniša Šegvić
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Domain adaptive panoptic segmentation promises to resolve the long tail of corner cases in natural scene understanding. Previous state of the art addresses this problem with cross-task consistency, careful system-level optimization and heuristic improvement of teacher predictions. In contrast, we propose to build upon remarkable capability of mask transformers to estimate their own prediction uncertainty. Our method avoids noise amplification by leveraging fine-grained confidence of panoptic teacher predictions. In particular, we modulate the loss with mask-wide confidence and discourage back-propagation in pixels with uncertain teacher or confident student. Experimental evaluation on standard benchmarks reveals a substantial contribution of the proposed selection techniques. We report 47.4 PQ on Synthia→Cityscapes, which corresponds to an improvement of 6.2 percentage points over the state of the art. The source code is available at github.com/helen1c/MC-PanDA."
Learning Neural Deformation Representation for 4D Dynamic Shape Generation
Authors: Gyojin Han*, Jiwan Hur, Jaehyun Choi, Junmo Kim*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent developments in 3D shape representation opened new possibilities for generating detailed 3D shapes. Despite these advances, there are few studies dealing with the generation of 4D dynamic shapes that have the form of 3D objects deforming over time. To bridge this gap, we focus on generating 4D dynamic shapes with an emphasis on both generation quality and efficiency in this paper. HyperDiffusion, a previous work on 4D generation, proposed a method of directly generating the weight parameters of 4D occupancy fields but suffered from low temporal consistency and slow rendering speed due to motion representation that is not separated from the shape representation of 4D occupancy fields. Therefore, we propose a new neural deformation representation and combine it with conditional neural signed distance fields to design a 4D representation architecture in which the motion latent space is disentangled from the shape latent space. The proposed deformation representation, which works by predicting skinning weights and rigid transformations for multiple parts, also has advantages over the deformation modules of existing 4D representations in understanding the structure of shapes. In addition, we design a training process of a diffusion model that utilizes the shape and motion features that are extracted by our 4D representation as data points. The results of unconditional generation, conditional generation, and motion retargeting experiments demonstrate that our method not only shows better performance than previous works in 4D dynamic shape generation but also has various potential applications."
Dynamic Guidance Adversarial Distillation with Enhanced Teacher Knowledge
Authors: Hyejin Park, Dongbo Min*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In the realm of Adversarial Distillation (AD), strategic and precise knowledge transfer from an adversarially robust teacher model to a less robust student model is paramount. Our Dynamic Guidance Adversarial Distillation (DGAD) framework directly tackles the challenge of differential sample importance, with a keen focus on rectifying the teacher model’s misclassifications. DGAD employs Misclassification-Aware Partitioning (MAP) to dynamically tailor the distillation focus, optimizing the learning process by steering towards the most reliable teacher predictions. Additionally, our Error-corrective Label Swapping (ELS) corrects misclassifications of the teacher on both clean and adversarially perturbed inputs, refining the quality of knowledge transfer. Further, Predictive Consistency Regularization (PCR) guarantees consistent performance of the student model across both clean and adversarial inputs, significantly enhancing its overall robustness. By integrating these methodologies, DGAD significantly improves upon the accuracy of clean data and fortifies the model’s defenses against sophisticated adversarial threats. Our experimental validation on CIFAR10, CIFAR100, and Tiny ImageNet datasets, employing various model architectures, demonstrates the efficacy of DGAD, establishing it as a promising approach for enhancing both the robustness and accuracy of student models in adversarial settings. The code is available at https://github.com/kunsaram01/DGAD."
Decomposition Betters Tracking Everything Everywhere
Authors: Rui Li, Dong Liu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent studies on motion estimation have advocated an optimized motion representation that is globally consistent across the entire video, preferably for every pixel. This is challenging as a uniform representation may not account for the complex and diverse motion and appearance of natural videos. We address this problem and propose a new test-time optimization method, named DecoMotion, for estimating per-pixel and long-range motion. DecoMotion explicitly decomposes video content into static scenes and dynamic objects, either of which uses a quasi-3D canonical volume to represent. DecoMotion separately coordinates the transformations between local and canonical spaces, facilitating an affine transformation for the static scene that corresponds to camera motion. For the dynamic volume, DecoMotion leverages discriminative and temporally consistent features to rectify the non-rigid transformation. The two volumes are finally fused to fully represent motion and appearance. This divide-and-conquer strategy leads to more robust tracking through occlusions and deformations and meanwhile obtains decomposed appearances. We conduct evaluations on the TAP-Vid benchmark. The results demonstrate our method boosts the point-tracking accuracy by a large margin and performs on par with some state-of-the-art dedicated point-tracking solutions."
Straightforward Layer-wise Pruning for More Efficient Visual Adaptation
Authors: Ruizi Han*, Jinglei Tang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Parameter-efficient transfer learning (PETL) aims to adapt large pre-trained models using limited parameters. While most PETL approaches update the added parameters and freeze pre-trained weights during training, the minimal impact of task-specific deep layers on cross-domain data poses a challenge as PETL cannot modify them, resulting in redundant model structures. Structural pruning effectively reduces model redundancy; however, common pruning methods often lead to an excessive increase in stored parameters due to varying pruning structures based on pruning rates and data. Recognizing the storage parameter volume issue, we propose a Straightforward layer-wise pruning method, called SLS, for pruning PETL-transferred models. By evaluating parameters from a feature perspective of each layer and utilizing clustering metrics to assess current parameters based on clustering phenomena in low-dimensional space obtained through t-SNE, SLS facilitates informed pruning decisions. Our study reveals that layer-wise pruning, with a focus on storing pruning indices, addresses storage volume concerns. Notably, mainstream Layer-wise pruning methods may not be suitable for assessing layer importance in PETL-transferred models, where the majority of parameters are pre-trained and have limited relevance to downstream datasets. Comparative analysis against state-of-the-art PETL methods demonstrates that the pruned model achieved a notable balance between model throughput and accuracy. Moreover, SLS effectively reduces storage overhead arising from varying pruned structures while enhancing the accuracy and speed of pruned models compared to conventional pruning methods. The code is available at https://github.com/RuiZiHan/SLS."
Synchronization is All You Need: Exocentric-to-Egocentric Transfer for Temporal Action Segmentation with Unlabeled Synchronized Video Pairs
Authors: Camillo Quattrocchi*, Antonino Furnari, Daniele Di Mauro, Mario Valerio Giuffrida, Giovanni Maria Farinella
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We consider the problem of transferring a temporal action segmentation system initially designed for exocentric (fixed) cameras to an egocentric scenario, where wearable cameras capture video data. The conventional supervised approach requires the collection and labeling of a new set of egocentric videos to adapt the model, which is costly and time-consuming. Instead, we propose a novel methodology which performs the adaptation leveraging existing labeled exocentric videos and a new set of unlabeled, synchronized exocentric-egocentric video pairs, for which temporal action segmentation annotations do not need to be collected. We implement the proposed methodology with an approach based on knowledge distillation, which we investigate both at the feature and Temporal Action Segmentation model level. Experiments on Assembly101 and EgoExo4D demonstrate the effectiveness of the proposed method against classic unsupervised domain adaptation and temporal alignment approaches. Without bells and whistles, our best model performs on par with supervised approaches trained on labeled egocentric data, without ever seeing a single egocentric label, achieving a +15.99 improvement in the edit score (28.59 vs 12.60) on the Assembly101 dataset compared to a baseline model trained solely on exocentric data. In similar settings, our method also improves edit score by +3.32 on the challenging EgoExo4D benchmark. Code is available here: https://github.com/fpv-iplab/ synchronization-is-all-you-need."
LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Models
Authors: Yabin Zhang*, Wenjie Zhu, Chenhang He, Lei Zhang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Out-of-distribution (OOD) detection is crucial for model reliability, as it identifies samples from unknown classes and reduces errors due to unexpected inputs. Vision-Language Models (VLMs) such as CLIP are emerging as powerful tools for OOD detection by integrating multi-modal information. However, the practical application of such systems is challenged by manual prompt engineering, which demands domain expertise and is sensitive to linguistic nuances. In this paper, we introduce Label-driven Automated Prompt Tuning (LAPT), a novel approach to OOD detection that reduces the need for manual prompt engineering. We develop distribution-aware prompts with in-distribution (ID) class names and negative labels mined automatically. Training samples linked to these class labels are collected autonomously via image synthesis and retrieval methods, allowing for prompt learning without manual effort. We utilize a simple cross-entropy loss for prompt optimization, with cross-modal and cross-distribution mixing strategies to reduce image noise and explore the intermediate space between distributions, respectively. The LAPT framework operates autonomously, requiring only ID class names as input and eliminating the need for manual intervention. With extensive experiments, LAPT consistently outperforms manually crafted prompts, setting a new standard for OOD detection. Moreover, LAPT not only enhances the distinction between ID and OOD samples, but also improves the ID classification accuracy and strengthens the generalization robustness to covariate shifts, resulting in outstanding performance in challenging full-spectrum OOD detection tasks. Codes are available at https://github.com/YBZh/LAPT."
Domain Shifting: A Generalized Solution for Heterogeneous Cross-Modality Person Re-Identification
Authors: Yan Jiang, Xu Cheng*, Hao Yu, Xingyu Liu, Haoyu Chen, Guoying Zhao
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Cross-modality person re-identification (ReID) is a challenging task that aims to match cross-modality pedestrian images across multiple camera views. Existing methods are tailored to specific tasks and perform well for visible-infrared or visible-sketch ReID. However, the performance exhibits a notable decline when the same method is utilized for multiple cross-modality ReIDs, limiting its generalization and applicability. To address this issue, we propose a generalized domain shifting method (DNS) for cross-modality ReID, which can address the generalization and perform well in both visible-infrared and visible-sketch modalities. Specifically, we propose the heterogeneous space shifting and common space shifting modules to augment specific and shared representations in heterogeneous space and common space, respectively, thereby regulating the model to learn the consistency between modalities. Further, a domain alignment loss is developed to alleviate the cross-modality discrepancies by aligning the patterns across modalities. In addition, a domain distillation loss is designed to distill identity-invariant knowledge by learning the distribution of different modalities. Extensive experiments on two cross-modality ReID tasks (, visible-infrared ReID, visible-sketch ReID) demonstrate that the proposed method outperforms the state-of-the-art methods by a large margin."
Self-Supervised Video Desmoking for Laparoscopic Surgery
Authors: Renlong Wu, Zhilu Zhang*, Shuohao Zhang, Longfei Gou, Haobin Chen, Lei Zhang, Hao Chen*, Wangmeng Zuo
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Due to the difficulty of collecting real paired data, most existing desmoking methods train the models by synthesizing smoke, generalizing poorly to real surgical scenarios. Although a few works have explored single-image real-world desmoking in unpaired learning manners, they still encounter challenges in handling dense smoke. In this work, we address these issues together by introducing the self-supervised surgery video desmoking (SelfSVD). On the one hand, we observe that the frame captured before the activation of high-energy devices is generally clear (named pre-smoke frame, PS frame), thus it can serve as supervision for other smoky frames, making real-world self-supervised video desmoking practically feasible. On the other hand, in order to enhance the desmoking performance, we further feed the valuable information from PS frame into models, where a masking strategy and a regularization term are presented to avoid trivial solutions. In addition, we construct a real surgery video dataset for desmoking, which covers a variety of smoky scenes. Extensive experiments on the dataset show that our SelfSVD can remove smoke more effectively and efficiently while recovering more photo-realistic details than the state-of-the-art methods. The dataset, codes, and pre-trained models are available at https: //github.com/ZcsrenlongZ/SelfSVD."
Removing Rows and Columns of Tokens in Vision Transformer enables Faster Dense Prediction without Retraining
Authors: Diwei Su, cheng fei, Jianxu Luo*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In recent years, vision transformers based on self-attention mechanisms have demonstrated remarkable abilities in various tasks such as natural language processing, computer vision (CV), and multimodal applications. However, due to the high computational costs and the structural nature of images, the application of transformers to CV tasks faces challenges, particularly when handling ultra-high-resolution images. Recently, several token reduction methods have been proposed to improve the computational efficiency of transformers by reducing the number of tokens without the need for retraining. These methods primarily involve fusion based on matching or clustering. The former exhibits faster speed but suffers more accuracy loss compared to the latter. In this work, we propose a simple matching-based fusion method called Token Adapter, which achieves comparable accuracy to the clustering-based fusion method with faster speed and demonstrates higher potential in terms of robustness. Our method was applied to Segmenter, MaskDINO and SWAG, exhibiting promising performance on four tasks, including semantic segmentation, instance segmentation, panoptic segmentation, and image classification. Specifically, our method can be applied to Segmenter on ADE20k, providing 41% frames per second (FPS) acceleration while maintaining full performance without retraining or fine-tuning off-the-shelf weights. Our code will be released at https://github.com/MilknoCandy/ Token-Adapter."
Continuity Preserving Online CenterLine Graph Learning
Authors: Yunhui Han, Kun Yu, Zhiwei Li*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Lane topology, which is usually modeled by a centerline graph, is essential for high-level autonomous driving. For a high-quality graph, both topology connectivity and spatial continuity of centerline segments are critical. However, most of existing approaches pay more attention to connectivity while neglect the continuity. Such kind of centerline graph usually cause problem to planning of autonomous driving. To overcome this problem, we present an end-to-end network, CGNet, with three key modules: 1) Junction Aware Query Enhancement module, which provides positional prior to accurately predict junction points; 2) Bézier Space Connection module, which enforces continuity constraints on any two topologically connected segments in a Bézier space; 3) Iterative Topology Refinement module, which is a graph-based network with memory to iteratively refine the predicted topological connectivity. CGNet achieves state-of-the-art performance on both nuScenes and Argoverse2 datasets. Our code is available at https://github.com/XiaoMi/CGNet."
Decomposition of Neural Discrete Representations for Large-Scale 3D Mapping
Authors: Minseong Park, Suhan Woo, Euntai Kim*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Learning efficient representations of local features is a key challenge in feature volume-based 3D neural mapping, especially in large-scale environments. In this paper, we introduce Decomposition-based Neural Mapping (DNMap), a storage-efficient large-scale 3D mapping method that employs a discrete representation based on a decomposition strategy. This decomposition strategy aims to efficiently capture repetitive and representative patterns of shapes by decomposing each discrete embedding into component vectors that are shared across the embedding space. Our DNMap optimizes a set of component vectors, rather than entire discrete embeddings, and learns composition rather than indexing the discrete embeddings. Furthermore, to complement the mapping quality, we additionally learn low-resolution continuous embeddings that require tiny storage space. By combining these representations with a shallow neural network and an efficient octree-based feature volume, our DNMap successfully approximates signed distance functions and compresses the feature volume while preserving mapping quality. Our source code is available at https://github.com/minseong-p/dnmap."
MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections
Authors: Jiayue Liu, Xiao Tang, Freeman Cheng, Zihao Yang, Zhihao Li*, Jianzhuang Liu, Yi Huang, Jiaqi Lin, Shiyong Liu, Xiaofei Wu, Songcen Xu, Chun Yuan*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "3D Gaussian Splatting showcases notable advancements in photo-realistic and real-time novel view synthesis. However, it faces challenges in modeling mirror reflections, which exhibit substantial appearance variations from different viewpoints. To tackle this problem, we present MirrorGaussian, the first method for mirror scene reconstruction with real-time rendering based on 3D Gaussian Splatting. The key insight is grounded on the mirror symmetry between the real-world space and the virtual mirror space. We introduce an intuitive dual-rendering strategy that enables differentiable rasterization of both the real-world 3D Gaussians and the mirrored counterpart obtained by reflecting the former about the mirror plane. All 3D Gaussians are jointly optimized with the mirror plane in an end-to-end framework. MirrorGaussian achieves high-quality and real-time rendering in scenes with mirrors, empowering scene editing like adding new mirrors and objects. Comprehensive experiments on multiple datasets demonstrate that our approach significantly outperforms existing methods, achieving state-of-the-art results. Project page: https://mirror-gaussian.github.io/."
Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection
Authors: Christos Koutlis*, Symeon Papadopoulos
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The recently developed and publicly available synthetic image generation methods and services make it possible to create extremely realistic imagery on demand, raising great risks for the integrity and safety of online information. State-of-the-art Synthetic Image Detection (SID) research has led to strong evidence on the advantages of feature extraction from foundation models. However, such extracted features mostly encapsulate high-level visual semantics instead of fine-grained details, which are more important for the SID task. On the contrary, shallow layers encode low-level visual information. In this work, we leverage the image representations extracted by intermediate Transformer blocks of CLIP’s image-encoder via a lightweight network that maps them to a learnable forgery-aware vector space capable of generalizing exceptionally well. We also employ a trainable module to incorporate the importance of each Transformer block to the final prediction. Our method is compared against the state-of-the-art by evaluating it on 20 test datasets and exhibits an average +10.6% absolute performance improvement. Notably, the best performing models require just a single epoch for training (∼8 minutes). Code available at https://github.com/mever-team/rine."
Exploring Vulnerabilities in Spiking Neural Networks: Direct Adversarial Attacks on Raw Event Data
Authors: Yanmeng Yao, Xiaohan Zhao, Bin Gu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In the field of computer vision, event-based Dynamic Vision Sensors (DVSs) have emerged as a significant complement to traditional pixel-based imaging due to their low power consumption and high temporal resolution. These sensors, particularly when combined with Spiking Neural Networks (SNNs), offer a promising direction for energy-efficient and fast-reacting vision systems. Typically, DVS data are converted into grid-based formats for processing with SNNs, with this transformation process often being an opaque step in the pipeline. As a result, the grid representation becomes an intermediate yet inaccessible stage during the implementation of attacks, highlighting the importance of attacking raw event data. Existing attack methodologies predominantly target grid-based representations, hindered by the complexity of three-valued optimization and the broad optimization space associated with raw event data. Our study addresses this gap by introducing a novel adversarial attack approach that directly targets raw event data. We tackle the inherent challenges of three-valued optimization and the need to preserve data sparsity through a strategic amalgamation of methods: 1) Treating Discrete Event Values as Probabilistic Samples: This allows for continuous optimization by considering discrete event values as probabilistic space samples. 2) Focusing on Specific Event Positions: We prioritize specific event positions that merge original data with additional target label data, enhancing attack precision. 3) Employing a Sparsity Norm: To retain the original data’s sparsity, a sparsity norm is utilized, ensuring the adversarial data’s comparability. Our empirical findings demonstrate the effectiveness of our combined approach, achieving noteworthy success in targeted attacks and highlighting vulnerabilities in models based on raw event data."
HSR: Holistic 3D Human-Scene Reconstruction from Monocular Videos
Authors: Lixin Xue*, Chen Guo, Chengwei Zheng, Fangjinhua Wang, Tianjian Jiang, Hsuan-I Ho, Manuel Kaufmann, Jie Song, Otmar Hilliges
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "An overarching goal for computer-aided perception systems is the holistic understanding of the human-centric 3D world, including faithful reconstructions of humans, scenes, and their global spatial relationships. While recent progress in monocular 3D reconstruction has been made for footage of either humans or scenes alone, the joint reconstruction of both humans and scenes, along with their global spatial information, remains an unsolved challenge. To address this, we introduce a novel and unified framework that simultaneously achieves temporally and spatially coherent 3D reconstruction of static scenes with dynamic humans from monocular RGB videos. Specifically, we parameterize temporally consistent canonical human models and static scene representations using two neural fields in a shared 3D space. Additionally, we develop a global optimization framework that considers physical constraints imposed by potential human-scene interpenetration and occlusion. Compared to separate reconstructions, our framework enables detailed and holistic geometry reconstructions of both humans and scenes. Furthermore, we introduce a synthetic dataset for quantitative evaluations. Extensive experiments and ablation studies on both real-world and synthetic videos demonstrate the efficacy of our framework in monocular human-scene reconstruction. Code and data are publicly available on our project page."
Online Video Quality Enhancement with Spatial-Temporal Look-up Tables
Authors: Zefan Qu, Xinyang Jiang*, Yifan Yang, Dongsheng Li, Cairong Zhao*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Low latency rates are crucial for online video-based applications, such as video conferencing and cloud gaming, which make improving video quality in online scenarios increasingly important. However, existing quality enhancement methods are limited by slow inference speed and the requirement for temporal information contained in future frames, making it challenging to deploy them directly in online tasks. In this paper, we propose a novel method, STLVQE, specifically designed to address the rarely studied online video quality enhancement (Online-VQE) problem. Our STLVQE designs a new VQE framework which contains a Module-Agnostic Feature Extractor that greatly reduces the redundant computations and redesign the propagation, alignment, and enhancement module of the network. A Spatial-Temporal Look-up Tables (STL) is proposed, which extracts spatial-temporal information in videos while saving substantial inference time. To the best of our knowledge, we are the first to exploit the LUT structure to extract temporal information in video tasks. Extensive experiments on the MFQE 2.0 dataset demonstrate our STLVQE achieves a satisfactory performance-speed trade-off."
PARIS3D: Reasoning-based 3D Part Segmentation Using Large Multimodal Model
Authors: Amrin Kareem*, Jean Lahoud, Hisham Cholakkal*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent advancements in 3D perception systems have significantly improved their ability to perform visual recognition tasks such as segmentation. However, these systems still heavily rely on explicit human instruction to identify target objects or categories, lacking the capability to actively reason and comprehend implicit user intentions. We introduce a novel segmentation task known as reasoning part segmentation for 3D objects, aiming to output a segmentation mask based on complex and implicit textual queries about specific parts of a 3D object. To facilitate evaluation and benchmarking, we present a large 3D dataset comprising over 60k instructions paired with corresponding ground-truth part segmentation annotations specifically curated for reasoning-based 3D part segmentation. We propose a model that is capable of segmenting parts of 3D objects based on implicit textual queries and generating natural language explanations corresponding to 3D object segmentation requests. Experiments show that our method achieves competitive performance to models that use explicit queries, with the additional abilities to identify part concepts, reason about them, and complement them with world knowledge. Our source code, dataset, and trained models are accessible at this url."
Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
Authors: Donghoon Ahn, Hyoungwon Cho, Jaewon Min, Jungwoo Kim, Wooseok Jang, SeonHwa Kim, Hyun Hee Park, Kyong Hwan Jin*, Seungryong Kim*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent studies have demonstrated that diffusion models can generate high-quality samples, but their quality heavily depends on sampling guidance techniques, such as classifier guidance (CG) and classifier-free guidance (CFG). These techniques are often not applicable in unconditional generation or various downstream tasks such as the solving inverse problems. In this paper, we propose novel sampling guidance, called Perturbed-Attention Guidance (PAG), which improves diffusion sample quality across both unconditional and conditional settings, achieving this without requiring additional training or the integration of external modules. PAG progressively enhances the structure of samples throughout the denoising process by generating intermediate samples with degraded structures and guiding the denoising process away from these degraded samples. These degraded samples are created by substituting selected self-attention maps in the diffusion U-Net, which capture structural information between image patches, with an identity matrix. In both ADM and Stable Diffusion, PAG surprisingly improves sample quality in conditional and even unconditional generation. Moreover, PAG significantly enhances baseline performance in various downstream tasks where existing guidance methods such as CG or CFG cannot be fully utilized, including ControlNet with empty prompts and solving inverse problems such as inpainting and deblurring. To the best of our knowledge, this is the first approach to apply guidance in solving inverse problems using diffusion models."
Localization and Expansion: A Decoupled Framework for Point Cloud Few-shot Semantic Segmentation
Authors: Zhaoyang Li*, Yuan Wang, Wangkai Li, Rui Sun, Tianzhu Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Point cloud few-shot semantic segmentation (PC-FSS) aims to segment targets of novel categories in a given query point cloud with only a few annotated support samples. The current top-performing prototypical learning methods employ prototypes originating from support samples to direct the classification of query points. However, the inherent fragility of point-level matching and the prevalent intra-class diversity pose great challenges to this cross-instance matching paradigm, leading to erroneous background activations or incomplete target excavation. In this work, we propose a simple yet effective framework in the spirit of Decoupled Localization and Expansion (DLE). The proposed DLE, including a structural localization module (SLM) and a self-expansion module (SEM), enjoys several merits. First, structural information is injected into the matching process through the agent-level correlation in SLM, and the confident target region can thus be precisely located. Second, more reliable intra-object similarity is harnessed in SEM to derive the complete target, and the conservative expansion strategy is introduced to reasonably constrain the expansion. Extensive experiments on two challenging benchmarks under different settings demonstrate that DLE outperforms previous state-of-the-art approaches by large margins."
Think before Placement: Common Sense Enhanced Transformer for Object Placement
Authors: Yaxuan Qin, Jiayu Xu, Ruiping Wang*, Xilin Chen
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Object placement is a task to insert a foreground object into a background scene at a suitable position and size. Existing methods mainly focus on extracting better visual features, while neglecting common sense about the objects and background. It leads to semantically unrealistic object positions. In this paper, we introduce Think Before Placement, a novel framework that effectively combines the implicit and explicit knowledge to generate placements that are both visually coherent and contextually appropriate. Specifically, we first adopt a large multi-modal model to generate a descriptive caption that identifies an appropriate position in the background for placing foreground object(Think ), then output proper position and size of the object (Place). The caption serves as an explicit semantic guidance for the subsequent placement of objects. Using this framework, we implement our model named CSENet, which outperforms baseline methods on the OPA dataset in extensive experiments. Further, we establish the OPAZ dataset to evaluate the zero-shot transfer capabilities of CSENet, where it also shows impressive performance across different foreground objects and scenes."
Oulu Remote-photoplethysmography Physical Domain Attacks Database (ORPDAD)
Authors: Marko Savic, Guoying Zhao*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Remote photoplethysmography (rPPG) is an emerging technology that can detect the pulse rate remotely from face videos. However, it is easily influenced by the recording environment, as robustness to noise is still an open problem. This vulnerability can therefore be exploited to inject fake signals or impair predictions physically. In this study we propose the first dataset containing a wide set of physical domain attack scenarios divided in three categories (illumination, movement, concealment) that directly target the main weaknesses of rPPG. We propose the Oulu rPPG Physical Domain Attacks Database (ORPDAD) as a benchmark for evaluation of robustness to physical attacks. We perform extensive experiments on conventional hand-crafted and deep learning (end-to-end, non-end-to-end, CNN, transformer, self-supervised) methods and study their susceptibility to the attacks. We conclude by discussing the most critical vulnerabilities discovered and stress the importance of designing more secure solutions. Our code and instructions for requesting the dataset (with trained models) are available at: https://github.com/marukosan93/ORPDAD/."
Leveraging Imperfect Restoration for Data Availability Attack
Authors: YI HUANG*, Jeremy Styborski*, Mingzhi Lyu*, Fan Wang*, Wai-Kin Adams Kong*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The abundance of online data is at risk of unauthorized usage in training deep learning models. To counter this, various Data Availability Attacks (DAAs) have been devised to make data unlearnable for such models by subtly perturbing the training data. However, existing attacks often excel against either Supervised Learning (SL) or Self-Supervised Learning (SSL) scenarios. Among these, a model-free approach that generates a Convolution-based Unlearnable Dataset (CUDA) stands out as the most robust DAA across both SSL and SL. Nonetheless, CUDA’s effectiveness against SSL is underwhelming and it faces a severe trade-off between image quality and its poisoning effect. In this paper, we conduct a theoretical analysis of CUDA, uncovering the sub-optimal gradients it introduces and elucidating the strategy it employs to induce class-wise bias for data poisoning. Building on this, we propose a novel poisoning method named Imperfect Restoration Poisoning (IRP), aiming to preserve high image quality while achieving strong poisoning effects. Through extensive comparisons of IRP with eight baselines across SL and SSL, coupled with evaluations alongside five representative defense methods, we showcase the superiority of IRP. Code: https://github.com/ lyumingzhi/IRP"
3D Weakly Supervised Semantic Segmentation with 2D Vision-Language Guidance
Authors: Xiaoxu Xu, Yitian Yuan, Jinlong Li, Qiudan Zhang, Zequn Jie, Lin Ma, Hao Tang, Nicu Sebe, Xu Wang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this paper, we propose 3DSS-VLG, a weakly supervised approach for 3D Semantic Segmentation with 2D Vision-Language Guidance, an alternative approach that a 3D model predicts dense-embedding for each point which is co-embedded with both the aligned image and text spaces from the 2D vision-language model. Specifically, our method exploits the superior generalization ability of the 2D vision-language models and proposes the Embeddings Soft-Guidance Stage to utilize it to implicitly align 3D embeddings and text embeddings. Moreover, we introduce the Embeddings Specialization Stage to purify the feature representation with the help of a given scene-level label, specifying a better feature supervised by the corresponding text embedding. Thus, the 3D model is able to gain informative supervisions both from the image embedding and text embedding, leading to competitive segmentation performances. To the best of our knowledge, this is the first work to investigate 3D weakly supervised semantic segmentation by using the textual semantic information of text category labels. Moreover, with extensive quantitative and qualitative experiments, we present that our 3DSS-VLG is able not only to achieve the state-of-the-art performance on both S3DIS and ScanNet datasets, but also to maintain strong generalization capability. The code will be available at https://github.com/xuxiaoxxxx/3DSS-VLG/."
Open-set Domain Adaptation via Joint Error based Multi-class Positive and Unlabeled Learning
Authors: Dexuan Zhang*, Thomas Westfechtel, Tatsuya Harada
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Open-set domain adaptation aims to improve the generalization performance of a learning algorithm on a more realistic problem of open-set domain shift where the target data contains an additional unknown class that is not present in the source data. Most existing algorithms include two phases that can be described as closed-set domain adaptation given heuristic unknown class separation. Therefore, the generalization error cannot be strictly bounded due to the gap between the true distribution and samples inferred from heuristics. In this paper, we propose an end-to-end algorithm that tightly bound the risk of the entire target task by positive-unlabeled (PU) learning theory and the joint error from domain adaptation. Extensive experiments on various data sets demonstrate the effectiveness and efficiency of our proposed algorithm over open-set domain adaptation baselines."