mtybilly/Instruct2DS huggingface dataset:
Six-Point Method for Multi-Camera Systems with Reduced Solution Space
Authors: Ji Zhao*, Laurent Kneip Banglei Guan
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Relative pose estimation using point correspondences (PC) is a widely used technique. A minimal configuration of six PCs is required for two views of generalized cameras. In this paper, we present several minimal solvers that use six PCs to compute the 6DOF relative pose of multi-camera systems, including a minimal solver for the generalized camera and two minimal solvers for the practical configuration of two-camera rigs. The equation construction is based on the decoupling of rotation and translation. Rotation is represented by Cayley or quaternion parametrization, and translation can be eliminated by using the hidden variable technique. Ray bundle constraints are found and proven when a subset of PCs relate the same cameras across two views. This is the key to reducing the number of solutions and generating numerically stable solvers. Moreover, all configurations of six-point problems for multi-camera systems are enumerated. Extensive experiments demonstrate the superior accuracy and efficiency of our solvers compared to state-of-the-art six-point methods. The code is available at https://github.com/jizhaox/relpose-6pt."
Mew: Multiplexed Immunofluorescence Image Analysis through an Efficient Multiplex Network
Authors: Jie Peng, Alexandro E Trevino, Chanyoung Park, Tianlong Chen* Sukwon Yun
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent advancements in graph-based approaches for multiplexed immunofluorescence (mIF) images have significantly propelled the field forward, offering deeper insights into patient-level phenotyping. However, current graph-based methodologies encounter two primary challenges: 172 Cellular Heterogeneity, where existing approaches fail to adequately address the inductive biases inherent in graphs, particularly the homophily characteristic observed in cellular connectivity; and 173 Scalability, where handling cellular graphs from high-dimensional images faces difficulties in managing a high number of cells. To overcome these limitations, we introduce , a novel framework designed to efficiently process mIF images through the lens of multiplex network. innovatively constructs a multiplex network comprising two distinct layers: a Voronoi network for geometric information and a Cell-type network for capturing cell-wise homogeneity. This framework equips a scalable and efficient Graph Neural Network (GNN), capable of processing the entire graph during training. Furthermore, integrates an interpretable attention module that autonomously identifies relevant layers for image classification. Extensive experiments on a real-world patient dataset from various institutions highlight ’s remarkable efficacy and efficiency, marking a significant advancement in mIF image analysis. The source code of can be found here: https://github. com/UNITES-Lab/Mew"
Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance
Authors: Junming Leo Chen, Zuozhuo Dai, Zilong Dong, Yinghui Xu, Xun Cao, Yao Yao, Hao Zhu*, Siyu Zhu* Shenhao Zhu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion guidance in current human generative techniques. The methodology utilizes the SMPL(Skinned Multi-Person Linear) model as the 3D human parametric model to establish a unified representation of body shape and pose. This facilitates the accurate capture of intricate human geometry and motion characteristics from source videos. Specifically, we incorporate rendered depth images, normal maps, and semantic maps obtained from SMPL sequences, alongside skeleton-based motion guidance, to enrich the conditions of the latent diffusion model with comprehensive 3D shape and detailed pose attributes. A multi-layer motion fusion module, integrating self-attention mechanisms, is employed to fuse the shape and motion latent representations in the spatial domain. By representing the 3D human parametric model as the motion guidance, we can perform parametric shape alignment of the human body between the reference image and the source video motion. Experimental evaluations on benchmark datasets demonstrate the methodology’s superior ability to generate high-quality human animations that accurately capture pose and shape variations. Furthermore, our approach also exhibits superior generalization capabilities on the proposed in-the-wild dataset."
AdaDistill: Adaptive Knowledge Distillation for Deep Face Recognition
Authors: Vitomir Struc, Naser Damer Fadi Boutros*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Knowledge distillation (KD) aims at improving the performance of a compact student model by distilling the knowledge from a high-performing teacher model. In this paper, we present an adaptive KD approach, namely AdaDistill, for deep face recognition. The proposed AdaDistill embeds the KD concept into the softmax loss by training the student using a margin penalty softmax loss with distilled class centers from the teacher. Being aware of the relatively low capacity of the compact student model, we propose to distill less complex knowledge at an early stage of training and more complex one at a later stage of training. This relative adjustment of the distilled knowledge is controlled by the progression of the learning capability of the student over the training iterations without the need to tune any hyper-parameters. Extensive experiments and ablation studies show that AdaDistill can enhance the discriminative learning capability of the student and demonstrate superiority over various state-of-the-art competitors on several challenging benchmarks, such as IJB-B, IJB-C, and ICCV2021-MFR 1 . 1 https://github.com/fdbtrs/AdaDistill"
HERGen: Elevating Radiology Report Generation with Longitudinal Data
Authors: Shenghui Du, Lequan Yu* Fuying Wang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Radiology reports provide detailed descriptions of medical imaging integrated with patients’ medical histories, while report writing is traditionally labor-intensive, increasing radiologists’ workload and the risk of diagnostic errors. Recent efforts in automating this process seek to mitigate these issues by enhancing accuracy and clinical efficiency. However, existing automated approaches are based on a single timestamp and often neglect the critical temporal aspect of patients’ imaging histories, which is essential for accurate longitudinal analysis. To address this gap, we propose a novel History Enhanced Radiology Report Generation () framework that employs a group causal transformer to efficiently integrate longitudinal data across patient visits. Our approach not only allows for comprehensive analysis of varied historical data but also improves the quality of generated reports through an auxiliary contrastive objective that aligns image sequences with their corresponding reports. More importantly, we introduce a curriculum learning-based strategy to adeptly handle the inherent complexity of longitudinal radiology data and thus stabilize the optimization of our framework. The extensive evaluations across three datasets demonstrate that our framework surpasses existing methods in generating accurate radiology reports and effectively predicting disease progression from medical images."
Labeled Data Selection for Category Discovery
Authors: Nico Lang, Serge Belongie, Oisin Mac Aodha* Bingchen Zhao*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Visual category discovery methods aim to find novel categories in unlabeled visual data. At training time, a set of labeled and unlabeled images are provided, where the labels correspond to the categories present in the images. The labeled data provides guidance during training by indicating what types of visual properties and features are relevant for performing discovery in the unlabeled data. As a result, changing the categories present in the labeled set can have a large impact on what is ultimately discovered in the unlabeled set. Despite its importance, the impact of labeled data selection has not been explored in the category discovery literature to date. We show that changing the labeled data does indeed significantly impact discovery performance. Motivated by this, we propose two new approaches for automatically selecting the most suitable labeled data based on the similarity between the labeled and unlabeled data. Our observation is that, unlike in conventional supervised transfer learning, the most informative labeled data is neither too similar nor too dissimilar, to the unlabeled categories. Our resulting approaches obtain state-of-the-art discovery performance across a range of methods and challenging fine-grained benchmark datasets."
Dependency-aware Differentiable Neural Architecture Search
Authors: Xinle Wu, Hao Miao, Bin Yang, Chenjuan Guo Buang Zhang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "UTF8gbsn Neural architecture search (NAS) reduces the burden of manual design by automatically building neural network architectures, among which differential NAS approaches such as DARTS, have gained popularity for the search efficiency. Despite achieving promising performance, the DARTS series methods still suffer two issues: 1) It does not explicitly establish dependencies between edges, potentially leading to suboptimal performance. 2) The high degree of parameter sharing results in inaccurate performance evaluations of subnets. To tackle these issues, we propose to model dependencies explicitly between different edges to construct a high-performance architecture distribution. Specifically, we model the architecture distribution in DARTS as a multivariate normal distribution with learnable mean vector and correlation matrix, representing the base architecture weights of each edge and the dependencies between different edges, respectively. Then, we sample architecture weights from this distribution and alternately train these learnable parameters and network weights by gradient descent. With the learned dependencies, we prune the search space dynamically to alleviate the inaccurate evaluation by only sharing weights among high-performance architectures. Besides, we identify good motifs by analyzing the learned dependencies, which guide human experts to manually design high-performance neural architectures. Extensive experiments and competitive results on multiple NAS Benchmarks demonstrate the effectiveness of our method."
WAS: Dataset and Methods for Artistic Text Segmentation
Authors: Yuzhe Li, Yang Liu, Zhifei Zhang, Zhaowen Wang, Wei Xiong, Xiang Bai* Xudong Xie
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Accurate text segmentation results are crucial for text-related generative tasks, such as text image generation, text editing, text removal, and text style transfer. Recently, some scene text segmentation methods have made significant progress in segmenting regular text. However, these methods perform poorly in scenarios containing artistic text. Therefore, this paper focuses on the more challenging task of artistic text segmentation and constructs a real artistic text segmentation dataset. One challenge of the task is that the local stroke shapes of artistic text are changeable with diversity and complexity. We propose a decoder with the layer-wise momentum query to prevent the model from ignoring stroke regions of special shapes. Another challenge is the complexity of the global topological structure. We further design a skeleton-assisted head to guide the model to focus on the global structure. Additionally, to enhance the generalization performance of the text segmentation model, we propose a strategy for training data synthesis, based on the large multi-modal model and the diffusion model. Experimental results show that our proposed method and synthetic dataset can significantly enhance the performance of artistic text segmentation and achieve state-of-the-art results on other public datasets. The datasets and codes are available at: https://github.com/xdxie/WAS WordArt-Segmentation."
CLIFF: Continual Latent Diffusion for Open-Vocabulary Object Detection
Authors: Xinyu Liu, Jiayi Ma, Yixuan Yuan* Wuyang Li
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Open-vocabulary object detection (OVD) utilizes image-level cues to expand the linguistic space of region proposals, thereby facilitating the detection of diverse novel classes. Recent works adapt CLIP embedding by minimizing the object-image and object-text discrepancy combinatorially in a discriminative paradigm. However, they ignore the underlying distribution and the disagreement between the image and text objective, leading to the misaligned distribution between the vision and language sub-space. To address the deficiency, we explore the advanced generative paradigm with distribution perception and propose a novel framework based on the diffusion model, coined Continual Latent Diffusion (CLIFF), which formulates a continual distribution transfer among the object, image, and text latent space probabilistically. CLIFF consists of a Variational Latent Sampler (VLS) enabling the probabilistic modeling and a Continual Diffusion Module (CDM) for the distribution transfer. Specifically, in VLS, we first establish a probabilistic object space with region proposals by estimating distribution parameters. Then, the object-centric noise is sampled from the estimated distribution to generate text embedding for OVD. To achieve this generation process, CDM conducts a short-distance object-to-image diffusion from the sampled noise to generate image embedding as the medium, which guides the long-distance diffusion to generate text embedding. Extensive experiments verify that CLIFF can significantly surpass state-of-the-art methods on benchmarks. The code is available at https://github.com/CUHK-AIM-Group/CLIFF."
GMT: Enhancing Generalizable Neural Rendering via Geometry-Driven Multi-Reference Texture Transfer
Authors: Hyun-Kurl Jang, Kuk-Jin Yoon* Youngho Yoon
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Novel view synthesis (NVS) aims to generate images at arbitrary viewpoints using multi-view images, and recent insights from neural radiance fields (NeRF) have contributed to remarkable improvements. Recently, studies on generalizable NeRF (G-NeRF) have addressed the challenge of per-scene optimization in NeRFs. The construction of radiance fields on-the-fly in G-NeRF simplifies the NVS process, making it well-suited for real-world applications. Meanwhile, G-NeRF still struggles in representing fine details for a specific scene due to the absence of per-scene optimization, even with texture-rich multi-view source inputs. As a remedy, we propose a Geometry-driven Multi-reference Texture transfer network (GMT) available as a plug-and-play module designed for G-NeRF. Specifically, we propose ray-imposed deformable convolution (RayDCN), which aligns input and reference features reflecting scene geometry. Additionally, the proposed texture preserving transformer (TPFormer) aggregates multi-view source features while preserving texture information. Consequently, our module enables direct interaction between adjacent pixels during the image enhancement process, which is deficient in G-NeRF models with an independent rendering process per pixel. This addresses constraints that hinder the ability to capture high-frequency details. Experiments show that our plug-and-play module consistently improves G-NeRF models on various benchmark datasets."
Norface: Improving Facial Expression Analysis by Identity Normalization
Authors: Rudong An, Zhimeng Zhang, Bowen Ma, Wei Zhang, Yan Song, Yujing Hu, Chen Wei, Yu Ding* Hanwei Liu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Facial Expression Analysis remains a challenging task due to unexpected task-irrelevant noise, such as identity, head pose, and background. To address this issue, this paper proposes a novel framework, called Norface, that is unified for both Action Unit (AU) analysis and Facial Emotion Recognition (FER) tasks. Norface consists of a normalization network and a classification network. First, the carefully designed normalization network struggles to directly remove the above task-irrelevant noise, by maintaining facial expression consistency but normalizing all original images to a common identity with consistent pose, and background. Then, these additional normalized images are fed into the classification network. Due to consistent identity and other factors (e.g. head pose, background, etc.), the normalized images enable the classification network to extract useful expression information more effectively. Additionally, the classification network incorporates a Mixture of Experts to refine the latent representation, including handling the input of facial representations and the output of multiple (AU or emotion) labels. Extensive experiments validate the carefully designed framework with the insight of identity normalization. The proposed method outperforms existing SOTA methods in multiple facial expression analysis tasks, including AU detection, AU intensity estimation, and FER tasks, as well as their cross-dataset tasks. For the normalized datasets and code please visit project page."
Unlocking Attributes' Contribution to Successful Camouflage: A Combined Textual and Visual Analysis Strategy
Authors: Yixuan Lyu, Qian Yu, Hanyang Liu, Huimin Ma, Yuan Ding, Yifan Yang* Hong Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In the domain of Camouflaged Object Segmentation (COS), despite continuous improvements in segmentation performance, the underlying mechanisms of effective camouflage remain poorly understood, akin to a black box. To address this gap, we present the first comprehensive study to examine the impact of camouflage attributes on the effectiveness of camouflage patterns, offering a quantitative framework for the evaluation of camouflage designs. To support this analysis, we have compiled the first dataset comprising descriptions of camouflaged objects and their attribute contributions, termed COD-Text And X-attributions (COD-TAX). Moreover, drawing inspiration from the hierarchical process by which humans process information: from high-level textual descriptions of overarching scenarios, through mid-level summaries of local areas, to low-level pixel data for detailed analysis. We have developed a robust framework that combines textual and visual information for the task of COS, named Attribution CUe Modeling with Eye-fixation Network (ACUMEN). ACUMEN demonstrates superior performance, outperforming nine leading methods across three widely-used datasets. We conclude by highlighting key insights derived from the attributes identified in our study. Code: https://github.com/lyu-yx/ACUMEN."
SNeRV: Spectra-preserving Neural Representation for Video
Authors: Jihoo Lee*, Jewon Kang* Jina Kim*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Neural representation for video (NeRV), which employs a neural network to parameterize video signals, introduces a novel methodology in video representations. However, existing NeRV-based methods have difficulty in capturing fine spatial details and motion patterns due to spectral bias, in which a neural network learns high-frequency (HF) components at a slower rate than low-frequency (LF) components. In this paper, we propose spectra-preserving NeRV (SNeRV) as a novel approach to enhance implicit video representations by efficiently handling various frequency components. SNeRV uses 2D discrete wavelet transform (DWT) to decompose video into LF and HF features, preserving spatial structures and directly addressing the spectral bias issue. To balance the compactness, we encode only the LF components, while HF components that include fine textures are generated by a decoder. Specialized modules, including a multi-resolution fusion unit (MFU) and a high-frequency restorer (HFR), are integrated into a backbone to facilitate the representation. Furthermore, we extend SNeRV to effectively capture temporal correlations between adjacent video frames, by casting the extension as additional frequency decomposition to a temporal domain. This approach allows us to embed spatio-temporal LF features into the network, using temporally extended up-sampling blocks (TUBs). Experimental results demonstrate that SNeRV outperforms existing NeRV models in capturing fine details and achieves enhanced reconstruction, making it a promising approach in the field of implicit video representations. The codes are available at https: //github.com/qwertja/SNeRV."
COMO: Compact Mapping and Odometry
Authors: Andrew Davison Eric Dexheimer*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We present COMO, a real-time monocular mapping and odometry system that encodes dense geometry via a compact set of 3D anchor points. Decoding anchor point projections into dense geometry via per-keyframe depth covariance functions guarantees that depth maps are joined together at visible anchor points. The representation enables joint optimization of camera poses and dense geometry, intrinsic 3D consistency, and efficient second-order inference. To maintain a compact yet expressive map, we introduce a frontend that leverages the covariance function for tracking and initializing potentially visually indistinct 3D points across frames. Altogether, we introduce a real-time system capable of estimating accurate poses and consistent geometry."
OAT: Object-Level Attention Transformer for Gaze Scanpath Prediction
Authors: Jingling Yu, Haozheng Zhang, Ralf van der Lans, Bertram E Shi Yini Fang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Visual search is important in our daily life. The efficient allocation of visual attention is critical to effectively complete visual search tasks. Prior research has predominantly modelled the spatial allocation of visual attention in images at the pixel level, e.g. using a saliency map. However, emerging evidence shows that visual attention is guided by objects rather than pixel intensities. This paper introduces the Object-level Attention Transformer (OAT), which predicts human scanpaths as they search for a target object within a cluttered scene of distractors. OAT uses an encoder-decoder architecture. The encoder captures information about the position and appearance of the objects within an image and about the target. The decoder predicts the gaze scanpath as a sequence of object fixations, by integrating output features from both the encoder and decoder. We also propose a new positional encoding that better reflects spatial relationships between objects. We evaluated OAT on the Amazon book cover dataset and a new dataset for visual search that we collected. OAT’s predicted gaze scanpaths align more closely with human gaze patterns, compared to predictions by algorithms based on spatial attention on both established metrics and a novel behavioural-based metric. Our results demonstrate the generalization ability of OAT, as it accurately predicts human scanpaths for unseen layouts and target objects. The code is available at: https://github.com/HKUST-NISL/ oat_eccv24. 1 The first two authors contributed equally in the paper."
SelfSwapper: Self-Supervised Face Swapping via Shape Agnostic Masked AutoEncoder
Authors: Junha Hyung*, Sohyun Jeong, Jaegul Choo* Jaeseong Lee*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Face swapping has gained significant attention for its varied applications. Most previous face swapping approaches have relied on the seesaw game training scheme, also known as the target-oriented approach. However, this often leads to instability in model training and results in undesired samples with blended identities due to the target identity leakage problem. Source-oriented methods achieve more stable training with self-reconstruction objective but often fail to accurately reflect target image’s skin color and illumination. This paper introduces the Shape Agnostic Masked AutoEncoder (SAMAE) training scheme, a novel self-supervised approach that combines the strengths of both target-oriented and source-oriented approaches. Our training scheme addresses the limitations of traditional training methods by circumventing the conventional seesaw game and introducing clear ground truth through its self-reconstruction training regime. Our model effectively mitigates identity leakage and reflects target albedo and illumination through learned disentangled identity and non-identity features. Additionally, we closely tackle the shape misalignment and volume discrepancy problems with new techniques, including perforation confusion and random mesh scaling. SAMAE establishes a new state-of-the-art, surpassing other baseline methods, preserving both identity and non-identity attributes without sacrificing on either aspect."
EgoPoseFormer: A Simple Baseline for Stereo Egocentric 3D Human Pose Estimation
Authors: Anastasia Tkach, Shreyas Hampali, Linguang Zhang, Elliot J Crowley, Cem Keskin Chenhongyi Yang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We present , a simple yet effective transformer-based model for stereo egocentric human pose estimation. The main challenge in egocentric pose estimation is overcoming joint invisibility, which is caused by self-occlusion or a limited field of view (FOV) of head-mounted cameras. Our approach overcomes this challenge by incorporating a two-stage pose estimation paradigm: in the first stage, our model leverages the global information to estimate each joint’s coarse location, then in the second stage, it employs a DETR style transformer to refine the coarse locations by exploiting fine-grained stereo visual features. In addition, we present a operation to enable our transformer to effectively process multi-view features, which enables it to accurately localize each joint in the 3D world. We evaluate our method on the stereo UnrealEgo dataset and show it significantly outperforms previous approaches while being computationally efficient: it improves MPJPE by 27.4mm (45% improvement) with only 7.9% model parameters and 13.1% FLOPs compared to the state-of-the-art. Surprisingly, with proper training settings, we find that even our first-stage pose proposal network can achieve superior performance compared to previous arts. We also show that our method can be seamlessly extended to monocular settings, which achieves state-of-the-art performance on the SceneEgo dataset, improving MPJPE by 25.5mm (21% improvement) compared to the best existing method with only 60.7% model parameters and 36.4% FLOPs. Code is available at https://github.com/ChenhongyiYang/egoposeformer."
An Information Theoretical View for Out-Of-Distribution Detection
Authors: Wenrui Liu, Hong Chang*, Bingpeng MA, Shiguang Shan, Xilin Chen Hu Jinjing
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Detecting out-of-distribution (OOD) inputs are pivotal for real-world applications. However, due to the inaccessibility of OODs during training phase, applying supervised binary classification with in-distribution (ID) and OOD labels is not feasible. Therefore, previous works typically employ the proxy ID classification task to learn feature representation for OOD detection task. In this study, we delve into the relationship between the two tasks through the lens of Information Theory. Our analysis reveals that optimizing the classification objective could inevitably cause the over-confidence and undesired compression of OOD detection-relevant information. To address these two problems, we propose OOD Entropy Regularization (OER) to regularize the information captured in classification-oriented representation learning for detecting OOD samples. Both theoretical analyses and experimental results underscore the consistent improvement of OER on OOD detection."
DMiT: Deformable Mipmapped Tri-Plane Representation for Dynamic Scenes
Authors: Jia-Mu Sun, Yong-Liang Yang, Jie Yang, Ying Shan, Yan-Pei Cao, Lin Gao* Jing-Wen Yang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Neural Radiance Fields (NeRF) have achieved remarkable progress on dynamic scenes with deformable objects. Nonetheless, most previous works required multi-view inputs or long training time (several hours), making it hard to apply them for real-world scenarios. Recent works dedicated to addressing blurry artifacts may fail to predict stable and accurate deformation while keeping high-frequency details when rendering at various resolutions. To this end, we introduce a novel framework DMiT (Deformable Mipmapped Tri-Plane) that adopts the mipmaps to render dynamic scenes at various resolutions from novel views. With the help of hierarchical mipmapped tri-planes, we incorporate an MLP to effectively predict a mapping between the observation space and the canonical space, enabling not only high-fidelity dynamic scene rendering but also high-performance training and inference. Moreover, a training scheme for joint geometry and deformation refinement is designed for canonical regularization to reconstruct high-quality geometries. Extensive experiments on both synthetic and real dynamic scenes demonstrate the efficacy and efficiency of our method."
Gated Temporal Diffusion for Stochastic Long-term Dense Anticipation
Authors: Emad Bahrami*, Yazan Abu Farha, Gianpiero Francesca, Jürgen Gall* Olga Zatsarynna*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Long-term action anticipation has become an important task for many applications such as autonomous driving and human-robot interaction. Unlike short-term anticipation, predicting more actions into the future imposes a real challenge with the increasing uncertainty in longer horizons. While there has been a significant progress in predicting more actions into the future, most of the proposed methods address the task in a deterministic setup and ignore the underlying uncertainty. In this paper, we propose a novel Gated Temporal Diffusion (GTD) network that models the uncertainty of both the observation and the future predictions. As generator, we introduce a Gated Anticipation Network (GTAN) to model both observed and unobserved frames of a video in a mutual representation. On the one hand, using a mutual representation for past and future allows us to jointly model ambiguities in the observation and future, while on the other hand GTAN can by design treat the observed and unobserved parts differently and steer the information flow between them. Our model achieves state-of-the-art results on the Breakfast, Assembly101 and 50Salads datasets in both stochastic and deterministic settings."
Gradient-Aware for Class-Imbalanced Semi-supervised Medical Image Segmentation
Authors: Jiafei Wu*, S. C. Chan* Wenbo Qi
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Class imbalance poses a significant challenge in semi-supervised medical image segmentation (SSLMIS). Existing techniques face problems such as poor performance on tail classes, instability, and slow convergence speed. We propose a novel Gradient-Aware (GA) method, structured on a clear paradigm: identify extrinsic data-bias → analyze intrinsic gradient-bias → propose solutions, to address this issue. Through theoretical analysis, we identify the intrinsic gradient bias instigated by extrinsic data bias in class-imbalanced SSLMIS. To combat this, we propose a GA loss, featuring GADice loss, which leverages a probability-aware gradient for absent classes, and GACE, designed to alleviate gradient bias through class equilibrium and dynamic weight equilibrium. Our proposed method is plug-and-play, simple yet very effective and robust, exhibiting a fast convergence speed. Comprehensive experiments on three public datasets (CT&MRI, 2D&3D) demonstrate our method’s superior performance, significantly outperforming other SOTA of SSLMIS and class-imbalanced designs (+ 17.90% with CPS on 20% labeled Synapse). Code is available at https://github.com/cicailalala/GALoss."
HowToCaption: Prompting LLMs to Transform Video Annotations at Scale
Authors: Anna Kukleva, Xudong Hong, Christian Rupprecht, Bernt Schiele, Hilde Kuehne Nina Shvetsova*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Instructional videos are a common source for learning text-video or even multimodal representations by leveraging subtitles extracted with automatic speech recognition systems (ASR) from the audio signal in the videos. However, in contrast to human-annotated captions, both speech and subtitles naturally differ from the visual content of the videos and thus provide only noisy supervision. As a result, large-scale annotation-free web video training data remains sub-optimal for training text-video models. In this work, we propose to leverage the capabilities of large language models (LLMs) to obtain high-quality video descriptions aligned with videos at scale. Specifically, we prompt an LLM to create plausible video captions based on ASR subtitles of instructional videos. To this end, we introduce a prompting method that is able to take into account a longer text of subtitles, allowing us to capture the contextual information beyond one single sentence. We further prompt the LLM to generate timestamps for each produced caption based on the timestamps of the subtitles and finally align the generated captions to the video temporally. In this way, we obtain human-style video captions at scale without human supervision. We apply our method to the subtitles of the HowTo100M dataset, creating a new large-scale dataset, HowToCaption. Our evaluation shows that the resulting captions not only significantly improve the performance over many different benchmark datasets for zero-shot text-video retrieval and video captioning, but also lead to a disentangling of textual narration from the audio, boosting the performance in text-video-audio tasks.1 1 All data and code is available at https://github.com/ninatu/howtocaption."
LabelDistill: Label-guided Cross-modal Knowledge Distillation for Camera-based 3D Object Detection
Authors: Youngseok Kim, Sihwan Hwang, Hyeonjun Jeong, Dongsuk Kum* Sanmin Kim
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent advancements in camera-based 3D object detection have introduced cross-modal knowledge distillation to bridge the performance gap with LiDAR 3D detectors, leveraging the precise geometric information in LiDAR point clouds. However, existing cross-modal knowledge distillation methods tend to overlook the inherent imperfections of LiDAR, such as the ambiguity of measurements on distant or occluded objects, which should not be transferred to the image detector. To mitigate these imperfections in LiDAR teacher, we propose a novel method that leverages aleatoric uncertainty-free features from ground truth labels. In contrast to conventional label guidance approaches, we approximate the inverse function of the teacher’s head to effectively embed label inputs into feature space. This approach provides additional accurate guidance alongside LiDAR teacher, thereby boosting the performance of the image detector. Additionally, we introduce feature partitioning, which effectively transfers knowledge from the teacher modality while preserving the distinctive features of the student, thereby maximizing the potential of both modalities. Experimental results demonstrate that our approach improves mAP and NDS by 5.1 points and 4.9 points compared to the baseline model, proving the effectiveness of our approach. The code is available at https://github.com/ sanmin0312/LabelDistill"
Beyond the Data Imbalance: Employing the Heterogeneous Datasets for Vehicle Maneuver Prediction
Authors: Sanmin Kim, Abi Rahman Syamil, Junsoo Kim, Dongsuk Kum* Hyeongseok Jeon
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Predicting the maneuvers of surrounding vehicles is imperative for the safe navigation of autonomous vehicles. However, naturalistic driving datasets tend to be highly imbalanced, with a bias towards the ”going straight” maneuver. Consequently, learning and accurately predicting turning maneuvers pose significant challenges. In this study, we propose a novel two-stage maneuver learning method that can overcome such strong biases by leveraging two heterogeneous datasets in a complementary manner. In the first training phase, we utilize an intersection-centric dataset characterized by balanced distribution of maneuver classes to learn the representations of each maneuver. Subsequently, in the second training phase, we incorporate an ego-centric driving dataset to account for various geometrical road shapes, by transferring the knowledge of geometric diversity to the maneuver prediction model. To facilitate this, we constructed an in-house intersection-centric trajectory dataset with a well-balanced maneuver distribution. By harnessing the power of heterogeneous datasets, our framework significantly improves maneuver prediction performance, particularly for minority maneuver classes such as turning maneuvers. The dataset is available at https: //github.com/KAIST-VDCLab/VDC-Trajectory-Dataset."
On Pretraining Data Diversity for Self-Supervised Learning
Authors: Tuhin Das, Fabio Pizzati*, Philip Torr, Adel Bibi, Bernard Ghanem Hasan Abed Al Kader Hammoud*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We explore the impact of training with more diverse datasets, characterized by the number of unique samples, on the performance of self-supervised learning (SSL) under a fixed computational budget. Our findings demonstrate that increasing pretraining data diversity enhances SSL performance, albeit only when the distribution distance to the downstream data is minimal. Notably, even with an exceptionally large pretraining data diversity achieved through methods like web crawling or diffusion-generated data, among other ways, the distribution shift remains a challenge. Our experiments are comprehensive with seven SSL methods using large-scale datasets such as ImageNet and YFCC100M amounting to over 200 GPU days. The code and trained models will be available at https://github.com/hammoudhasan/DiversitySSL."
Look Around and Learn: Self-Training Object Detection by Exploration
Authors: Stefano Rosa*, Pietro Morerio, Lorenzo Natale, Alessio Del Bue Gianluca Scarpellini*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "When an object detector is deployed in a novel setting it often experiences a drop in performance. This paper studies how an embodied agent can automatically fine-tune a pre-existing object detector while exploring and acquiring images in a new environment without relying on human intervention, i.e., a fully self-supervised approach. In our setting, an agent initially learns to explore the environment using a pre-trained off-the-shelf detector to locate objects and associate pseudo-labels. By assuming that pseudo-labels for the same object must be consistent across different views, we learn the exploration policy “Look Around” to mine hard samples, and we devise a novel mechanism called “Disagreement Reconciliation” for producing refined pseudo-labels from the consensus among observations. We implement a unified benchmark of the current state-of-the-art and compare our approach with pre-existing exploration policies and perception mechanisms. Our method is shown to outperform existing approaches, improving the object detector by 6.2% in a simulated scenario, a 3.59% advancement over other state-of-the-art methods, and by 9.97% in the real robotic test without relying on ground-truth. Code for the proposed approach and baselines are available at https:// iit-pavis.github.io/Look_Around_And_Learn/."
Bayesian Self-Training for Semi-Supervised 3D Segmentation
Authors: Christos Sakaridis, Luc Van Gool Ozan Unal*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "3D segmentation is a core problem in computer vision and, similarly to many other dense prediction tasks, it requires large amounts of annotated data for adequate training. However, densely labeling 3D point clouds to employ fully-supervised training remains too labor intensive and expensive. Semi-supervised training provides a more practical alternative, where only a small set of labeled data is given, accompanied by a larger unlabeled set. This area thus studies the effective use of unlabeled data to reduce the performance gap that arises due to the lack of annotations. In this work, inspired by Bayesian deep learning, we first propose a Bayesian self-training framework for semi-supervised 3D semantic segmentation. Employing stochastic inference, we generate an initial set of pseudo-labels and then filter these based on estimated point-wise uncertainty. By constructing a heuristic n-partite matching algorithm, we extend the method to semi-supervised 3D instance segmentation, and finally, with the same building blocks, to dense 3D visual grounding. We demonstrate state-of-the-art results for our semi-supervised method on SemanticKITTI and ScribbleKITTI for 3D semantic segmentation and on ScanNet and S3DIS for 3D instance segmentation. We further achieve substantial improvements in dense 3D visual grounding over supervised-only baselines on ScanRefer. Our project page is available at ouenal.github.io/bst/."
Motion and Structure from Event-based Normal Flow
Authors: Bangyan Liao, Delei Kong, Jinghang Li, Peidong Liu, Laurent Kneip, Guillermo Gallego, Yi Zhou* Zhongyang Ren
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recovering the camera motion and scene geometry from visual data is a fundamental problem in computer vision. Its success in conventional (frame-based) vision is attributed to the maturity of feature extraction, data association and multi-view geometry. The emergence of asynchronous (event-based) cameras calls for new approaches that use raw event data as input to solve this fundamental problem. State-of-the-art solutions typically infer data association implicitly by iteratively reversing the event data generation process. However, the nonlinear nature of these methods limits their applicability in real-time tasks, and the constant-motion assumption leads to unstable results under agile motion. To this end, we reformulate the problem in a way that aligns better with the differential working principle of event cameras. We show that event-based normal flow can be used, via the proposed geometric error term, as an alternative to the full (optical) flow in solving a family of geometric problems that involve instantaneous first-order kinematics and scene geometry. Furthermore, we develop a fast linear solver and a continuous-time nonlinear solver on top of the proposed geometric error term. Experiments on both synthetic and real data show the superiority of our linear solver in terms of accuracy and efficiency, and its practicality as an initializer for previous nonlinear solvers. Besides, our continuous-time non-linear solver exhibits exceptional capabilities in accommodating sudden variations in motion since it does not rely on the constant-motion assumption. Our project page can be found at https://nail-hnu.github.io/EvLinearSolver/."
ParCo: Part-Coordinating Text-to-Motion Synthesis
Authors: Shangyuan Yuan, Shian Du, Yu Wang, Chang Liu, Yi Xu, Jie Chen, Xiangyang Ji* Qiran Zou
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We study a challenging task: text-to-motion synthesis, aiming to generate motions that align with textual descriptions and exhibit coordinated movements. Currently, the part-based methods introduce part partition into the motion synthesis process to achieve finer-grained generation. However, these methods encounter challenges such as the lack of coordination between different part motions and difficulties for networks to understand part concepts. Moreover, introducing finer-grained part concepts poses computational complexity challenges. In this paper, we propose Part-Coordinating Text-to-Motion Synthesis (ParCo), endowed with enhanced capabilities for understanding part motions and communication among different part motion generators, ensuring a coordinated and fined-grained motion synthesis. Specifically, we discretize whole-body motion into multiple part motions to establish the prior concept of different parts. Afterward, we employ multiple lightweight generators designed to synthesize different part motions and coordinate them through our part coordination module. Our approach demonstrates superior performance on common benchmarks with economic computations, including HumanML3D and KIT-ML, providing substantial evidence of its effectiveness. Code is available at: https://github.com/qrzou/ParCo."
Learning to Complement and to Defer to Multiple Users
Authors: Wenjie Ai, Kevin Wells, David M Rosewarne, Thanh-Toan Do, Gustavo Carneiro* Zheng Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "With the development of Human-AI Collaboration in Classification (HAI-CC), integrating users and AI predictions becomes challenging due to the complex decision-making process. This process has three options: 1) AI autonomously classifies, 2) learning to complement, where AI collaborates with users, and 3) learning to defer, where AI defers to users. Despite their interconnected nature, these options have been studied in isolation rather than as components of a unified system. In this paper, we address this weakness with the novel HAI-CC methodology, called Learning to Complement and to Defer to Multiple Users (LECODU). LECODU not only combines learning to complement and learning to defer strategies, but it also incorporates an estimation of the optimal number of users to engage in the decision process. The training of LECODU maximises classification accuracy and minimises collaboration costs associated with user involvement. Comprehensive evaluations across real-world and synthesized datasets demonstrate LECODU’s superior performance compared to state-of-the-art HAI-CC methods. Remarkably, even when relying on unreliable users with high rates of label noise, LECODU exhibits significant improvement over both human decision-makers alone and AI alone1 . Code is available at https://github.com/zhengzhang37/LECODU.git . 1 Supported by the Engineering and Physical Sciences Research Council (EPSRC) through grant EP/Y018036/1."