mtybilly/Instruct2DS huggingface dataset:
Real Appearance Modeling for More General Deepfake Detection
Authors: Jiahe Tian, Cai Yu, Xi Wang, Peng Chen, Zihao Xiao, Jiao Dai, Yesheng Chai*, Jizhong Han
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent studies in deepfake detection have shown promising results when detecting deepfakes of the same type as those present in training. However, their ability to generalize to unseen deepfakes remains limited. This work improves the generalizable deepfake detection from a simple principle: an ideal detector classifies any face that contains anomalies not found in real faces as fake. Namely, detectors should learn consistent real appearances rather than fake patterns in the training set that may not apply to unseen deepfakes. Guided by this principle, we propose a learning task named Real Appearance Modeling (RAM) that guides the model to learn real appearances by recovering original faces from slightly disturbed faces. We further propose Face Disturbance to produce disturbed faces while preserving original information that enables recovery, which aids the model in learning the fine-grained appearance of real faces. Extensive experiments demonstrate the effectiveness of modeling real appearances to spot richer deepfakes. Our method surpasses existing state-of-the-art methods by a large margin on multiple popular deepfake datasets."
6DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Model
Authors: Matteo Bortolon*, Theodore Tsesmelis, Stuart James, Fabio Poiesi, Alessio Del Bue
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We propose to estimate the camera pose of a target RGB image given a 3D Gaussian Splatting (3DGS) model representing the scene. avoids the iterative process typical of analysis-by-synthesis methods (iNeRF) that also require an initialization of the camera pose in order to converge. Instead, our method estimates a 6DoF pose by inverting the 3DGS rendering process. Starting from the object surface, we define a radiant that uniformly generates rays departing from each ellipsoid that parameterize the 3DGS model. Each ray is associated with the rendering parameters of each ellipsoid, which in turn is used to obtain the best bindings between the target image pixels and the cast rays. These pixel-ray bindings are then ranked to select the best scoring bundle of rays, which their intersection provides the camera center and, in turn, the camera rotation. The proposed solution obviates the necessity of an “a priori” pose for initialization, and it solves 6DoF pose estimation in closed form, without the need for iterations. Moreover, compared to the existing Novel View Synthesis (NVS) baselines for pose estimation, can improve the overall average rotational accuracy by 12% and translation accuracy by 22% on real scenes, despite not requiring any initialization pose. At the same time, our method operates near real-time, reaching 15f ps on consumer hardware. Project page: https://mbortolon97.github.io/6dgs/ Corresponding author: mbortolon@fbk.eu"
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning
Authors: Jia-Hao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations. Unlike semi-supervised learning, one cannot select the most probable label as the pseudo-label in SSMLL due to multiple semantics contained in an instance. To solve this problem, the mainstream method developed an effective thresholding strategy to generate accurate pseudo-labels. Unfortunately, the method neglected the quality of model predictions and its potential impact on pseudo-labeling performance. In this paper, we propose a dual-perspective method to generate high-quality pseudo-labels. To improve the quality of model predictions, we perform dual-decoupling to boost the learning of correlative and discriminative features, while refining the generation and utilization of pseudo-labels. To obtain proper class-wise thresholds, we propose the metric-adaptive thresholding strategy to estimate the thresholds, which maximize the pseudo-label performance for a given metric on labeled data. Experiments on multiple benchmark datasets show the proposed method can achieve the state-of-the-art performance and outperform the comparative methods with a significant margin. The implementation is available at JiahaoXxX/SSMLL-D2L MAT."
V2X-Real: a Largs-Scale Dataset for Vehicle-to-Everything Cooperative Perception
Authors: Hao Xiang, Xin Xia, Zhaoliang Zheng, Runsheng Xu, Letian Gao, Zewei Zhou, xu han, Xinkai Ji, Mingxi Li, Zonglin Meng, Li Jin, Mingyue Lei, Zhaoyang Ma, Zihang He, Haoxuan Ma, Yunshuang Yuan, Yingqian Zhao, Jiaqi Ma*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the perception capability. However, there are no real-world datasets to facilitate the real V2X cooperative perception research – existing datasets either only support Vehicle-to-Infrastructure cooperation or Vehicle-to-Vehicle cooperation. In this paper, we present V2X-Real, a large-scale dataset that includes a mixture of multiple vehicles and smart infrastructure to facilitate the V2X cooperative perception development with multi-modality sensing data. Our V2X-Real is collected using two connected automated vehicles and two smart infrastructure, which are all equipped with multi-modal sensors including LiDAR sensors and multi-view cameras. The whole dataset contains 33K LiDAR frames and 171K camera data with over 1.2M annotated bounding boxes of 10 categories in very challenging urban scenarios. According to the collaboration mode and ego perspective, we derive four types of datasets for Vehicle-Centric, Infrastructure-Centric, Vehicle-to-Vehicle, and Infrastructure-to-Infrastructure cooperative perception. Comprehensive multi-class multi-agent benchmarks of SOTA cooperative perception methods are provided. The V2X-Real dataset and codebase are available at https://mobility-lab.seas.ucla.edu/v2x-real."
VQ-HPS: Human Pose and Shape Estimation in a Vector-Quantized Latent Space
Authors: Guénolé Fiche*, Simon Leglaive, Xavier Alameda-Pineda, Antonio Agudo, Francesc Moreno
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Previous works on Human Pose and Shape Estimation (HPSE) from RGB images can be broadly categorized into two main groups: parametric and non-parametric approaches. Parametric techniques leverage a low-dimensional statistical body model for realistic results, whereas recent non-parametric methods achieve higher precision by directly regressing the 3D coordinates of the human body mesh. This work introduces a novel paradigm to address the HPSE problem, involving a low-dimensional discrete latent representation of the human mesh and framing HPSE as a classification task. Instead of predicting body model parameters or 3D vertex coordinates, we focus on predicting the proposed discrete latent representation, which can be decoded into a registered human mesh. This innovative paradigm offers two key advantages. Firstly, predicting a low-dimensional discrete representation confines our predictions to the space of anthropomorphic poses and shapes even when little training data is available. Secondly, by framing the problem as a classification task, we can harness the discriminative power inherent in neural networks. The proposed model, VQ-HPS, predicts the discrete latent representation of the mesh. The experimental results demonstrate that VQ-HPS outperforms the current state-of-the-art non-parametric approaches while yielding results as realistic as those produced by parametric methods when trained with few data. VQ-HPS also shows promising results when training on large-scale datasets, highlighting the significant potential of the classification approach for HPSE. See the project page at https://g-fiche.github.io/research-pages/vqhps/."
Attention Beats Linear for Fast Implicit Neural Representation Generation
Authors: Shuyi Zhang, Ke Liu, Jingjun Gu, Xiaoxu Cai, Zhihua Wang, Jiajun Bu, Haishuai Wang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Implicit Neural Representation (INR) has gained increasing popularity as a data representation method, serving as a prerequisite for innovative generation models. Unlike gradient-based methods, which exhibit lower efficiency in inference, the adoption of hyper-network for generating parameters in Multi-Layer Perceptrons (MLP), responsible for executing INR functions, has surfaced as a promising and efficient alternative. However, as a global continuous function, MLP is challenging in modeling highly discontinuous signals, resulting in slow convergence during the training phase and inaccurate reconstruction performance. Moreover, MLP requires massive representation parameters, which implies inefficiencies in data representation. In this paper, we propose a novel Attention-based Localized INR (ANR) composed of a localized attention layer (LAL) and a global MLP that integrates coordinate features with data features and converts them to meaningful outputs. Subsequently, we design an instance representation framework that delivers a transformer-like hyper-network to represent data instances as a compact representation vector. With instance-specific representation vector and instance-agnostic ANR parameters, the target signals are well reconstructed as a continuous function. We further address aliasing artifacts with variational coordinates when obtaining the super-resolution inference results. Extensive experimentation across four datasets showcases the notable efficacy of our ANR method, e.g. enhancing the PSNR value from 37.95dB to 47.25dB on the CelebA dataset. Code is released at https://github.com/Roninton/ANR."
HARIVO: Harnessing Text-to-Image Models for Video Generation
Authors: Mingi Kwon, Seoung Wug Oh, Yang Zhou, Joon-Young Lee, Difan Liu, Haoran Cai, Baqiao Liu, Feng Liu, Youngjung Uh*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We present a method to create diffusion-based video models from pretrained Text-to-Image (T2I) models. Recently, AnimateDiff proposed freezing the T2I model while only training temporal layers. We advance this method by proposing a unique architecture, incorporating a mapping network and frame-wise tokens, tailored for video generation while maintaining the diversity and creativity of the original T2I model. Key innovations include novel loss functions for temporal smoothness and a mitigating gradient sampling technique, ensuring realistic and temporally consistent video generation despite limited public video data. We have successfully integrated video-specific inductive biases into the architecture and loss functions. Our method, built on the frozen StableDiffusion model, simplifies training processes and allows for seamless integration with off-the-shelf models like ControlNet and DreamBooth. project page: https://kwonminki.github.io/HARIVO/"
Deep Online Probability Aggregation Clustering
Authors: Yuxuan Yan, Na Lu*, Ruofan Yan
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Combining machine clustering with deep models has shown remarkable superiority in deep clustering. It modifies the data processing pipeline into two alternating phases: feature clustering and model training. However, such alternating schedules may lead to instability and computational burden issues. To tackle these problems, we propose a centerless clustering algorithm called Probability Aggregation Clustering (PAC), enabling easy deployment in online deep clustering. PAC circumvents the cluster center and aligns the probability space and distribution space by formulating clustering as an optimization problem with a novel objective function. Based on the computation mechanism of the PAC, we propose a general online probability aggregation module to perform stable and flexible feature clustering over mini-batch data and further construct a deep visual clustering framework deep PAC (DPAC). Extensive experiments demonstrate that DPAC remarkably outperforms the state-of-the-art deep clustering methods.1 1 The code is available at Deep-Probability-Aggregation-Clustering https://github.com/aomandechenai/"
WRIM-Net: Wide-Ranging Information Mining Network for Visible-Infrared Person Re-Identification
Authors: Yonggan Wu, Ling-Chao Meng*, Yuan Zichao, Sixian Chan, Hong-Qiang Wang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "For the visible-infrared person re-identification (VI-ReID) task, one of the primary challenges lies in significant cross-modality discrepancy. Existing methods struggle to conduct modality-invariant information mining. They often focus solely on mining singular dimensions like spatial or channel, and overlook the extraction of specific-modality multi-dimension information. To fully mine modality-invariant information across a wide range, we introduce the Wide-Ranging Information Mining Network (WRIM-Net), which mainly comprises a Multi-dimension Interactive Information Mining (MIIM) module and an Auxiliary-Information-based Contrastive Learning (AICL) approach. Empowered by the proposed Global Region Interaction (GRI), MIIM comprehensively mines non-local spatial and channel information through intra-dimension interaction. Moreover, Thanks to the low computational complexity design, separate MIIM can be positioned in shallow layers, enabling the network to better mine specific-modality multi-dimension information. AICL, by introducing the novel Cross-Modality Key-Instance Contrastive (CMKIC) loss, effectively guides the network in extracting modality-invariant information. We conduct extensive experiments not only on the well-known SYSU-MM01 and RegDB datasets but also on the latest large-scale cross-modality LLCM dataset. The results demonstrate WRIM-Net’s superiority over state-of-the-art methods."
Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models
Authors: Chao Gong*, Kai Chen, Zhipeng Wei, Jingjing Chen*, Yu-Gang Jiang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Text-to-image models encounter safety issues, including concerns related to copyright and Not-Safe-For-Work (NSFW) content. Despite several methods have been proposed for erasing inappropriate concepts from diffusion models, they often exhibit incomplete erasure, consume a lot of computing resources, and inadvertently damage generation ability. In this work, we introduce Reliable and Efficient Concept Erasure (RECE), a novel approach that modifies the model in 3 seconds without necessitating additional fine-tuning. Specifically, RECE efficiently leverages a closed-form solution to derive new target embeddings, which are capable of regenerating erased concepts within the unlearned model. To mitigate inappropriate content potentially represented by derived embeddings, RECE further aligns them with harmless concepts in cross-attention layers. The derivation and erasure of new representation embeddings are conducted iteratively to achieve a thorough erasure of inappropriate concepts. Besides, to preserve the model’s generation ability, RECE introduces an additional regularization term during the derivation process, resulting in minimizing the impact on unrelated concepts during the erasure process. All the processes above are in closed-form, guaranteeing extremely efficient erasure in only 3 seconds. Benchmarking against previous approaches, our method achieves more efficient and thorough erasure with minor damage to original generation ability and demonstrates enhanced robustness against red-teaming tools. Code is available at https://github. com/CharlesGong12/RECE. redWARNING: This paper contains model outputs that may be offensive."
Visual Text Generation in the Wild
Authors: Yuanzhi Zhu, Jiawei Liu, Feiyu Gao, Wenyu Liu*, Xinggang Wang, Peng Wang, Fei Huang, Cong Yao, Zhibo Yang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recently, with the rapid advancements of generative models, the field of visual text generation has witnessed significant progress. However, it is still challenging to render high-quality text images in real-world scenarios, as three critical criteria should be satisfied: (1) Fidelity: the generated text images should be photo-realistic and the contents are expected to be the same as specified in the given conditions; (2) Reasonability: the regions and contents of the generated text should cohere with the scene; (3) Utility: the generated text images can facilitate related tasks (, text detection and recognition). Upon investigation, we find that existing methods, either rendering-based or diffusion-based, can hardly meet all these aspects simultaneously, limiting their application range. Therefore, we propose in this paper a visual text generator (termed SceneVTG), which can produce high-quality text images in the wild. Following a two-stage paradigm, SceneVTG leverages a Multimodal Large Language Model to recommend reasonable text regions and contents across multiple scales and levels, which are used by a conditional diffusion model as conditions to generate text images. Extensive experiments demonstrate that the proposed SceneVTG significantly outperforms traditional rendering-based methods and recent diffusion-based methods in terms of fidelity and reasonability. Besides, the generated images provide superior utility for tasks involving text detection and text recognition. Code and datasets are available at magentaAdvancedLiterateMachinery."
Length-Aware Motion Synthesis via Latent Diffusion
Authors: Alessio Sampieri*, Alessio Palma, Indro Spinelli, Fabio Galasso
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The target duration of a synthesized human motion is a critical attribute that requires modeling control over the motion dynamics and style. Speeding up an action performance is not merely fast-forwarding it. However, state-of-the-art techniques for human behavior synthesis have limited control over the target sequence length. We introduce the problem of generating length-aware 3D human motion sequences from textual descriptors, and we propose a novel model to synthesize motions of variable target lengths, which we dub “Length-Aware Latent Diffusion” (LADiff ). LADiff consists of two new modules: 1) a length-aware variational auto-encoder to learn motion representations with length-dependent latent codes; 2) a length-conforming latent diffusion model to generate motions with a richness of details that increases with the required target sequence length. LADiff significantly improves over the state-of-the-art across most of the existing motion synthesis metrics on the two established benchmarks of HumanML3D and KIT-ML. The code is available at https://github.com/AlessioSam/LADiff."
Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification
Authors: Yunlong Zhang*, Honglin Li, YUXUAN SUN, Chenglu Zhu, Sunyi Zheng, Lin Yang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In the application of Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) classification, attention mechanisms often focus on a subset of discriminative instances, which are closely linked to overfitting. To mitigate overfitting, we present Attention-Challenging MIL (ACMIL). ACMIL combines two techniques based on separate analyses for attention value concentration. Firstly, UMAP of instance features reveals various patterns among discriminative instances, with existing attention mechanisms capturing only some of them. To remedy this, we introduce Multiple Branch Attention (MBA) to capture more discriminative instances using multiple attention branches. Secondly, the examination of the cumulative value of Top-K attention scores indicates that a tiny number of instances dominate the majority of attention. In response, we present Stochastic Top-K Instance Masking (STKIM), which masks out a portion of instances with Top-K attention values and allocates their attention values to the remaining instances. The extensive experimental results on three WSI datasets with two pre-trained backbones reveal that our ACMIL outperforms state-of-the-art methods. Additionally, through heatmap visualization and UMAP visualization, this paper extensively illustrates ACMIL’s effectiveness in suppressing attention value concentration and overcoming the overfitting challenge. The source code is available at https://github.com/dazhangyu123/ACMIL."
An Optimal Control View of LoRA and Binary Controller Design for Vision Transformers
Authors: Chi Zhang*, Jingpu Cheng, Qianxiao Li
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "While recent advancements in model fine-tuning predominantly emphasize the utilization of low-rank adaptation (LoRA), we propose an alternative approach centered on reducing the precision of adaptation matrices. In particular, we depart from the common viewpoint that considers adaptation matrices solely as weight differences, and reinterpret them as “control variables” to perturb pre-trained ViT systems. This new perspective enables the establishment of a control-oriented framework, facilitating the exploration of optimal controls guided by the Pontryagin Maximum Principle. Furthermore, we demonstrate that for bounded control sets such as hypercubes, the optimal controls often take on boundary values, leading naturally to a binary controller design. Theoretical analysis reveals that employing a binary control strategy achieves the same reachable state as its full-precision counterpart in the continuous idealisation of deep residual structures, a finding corroborated by later empirical investigations. Our studies further indicate that the controller’s rank holds greater significance than its precision. As such, opting for low-precision yet high-rank controls is demonstrated to obtain better performance for practical vision tasks."
Exploring Phrase-Level Grounding with Text-to-Image Diffusion Model
Authors: Danni Yang, Ruohan Dong, Jiayi Ji, Yiwei Ma, Haowei Wang, Xiaoshuai Sun*, Rongrong Ji
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recently, diffusion models have increasingly demonstrated their capabilities in vision understanding. By leveraging prompt-based learning to construct sentences, these models have shown proficiency in classification and visual grounding tasks. However, existing approaches primarily showcase their ability to perform sentence-level localization, leaving the potential for leveraging contextual information for phrase-level understanding largely unexplored. In this paper, we utilize Panoptic Narrative Grounding (PNG) as a proxy task to investigate this capability further. PNG aims to segment object instances mentioned by multiple noun phrases within a given narrative text. Specifically, we introduce the DiffPNG framework, a straightforward yet effective approach that fully capitalizes on the diffusion’s architecture for segmentation by decomposing the process into a sequence of localization, segmentation, and refinement steps. The framework initially identifies anchor points using cross-attention mechanisms and subsequently performs segmentation with self-attention to achieve zero-shot PNG. Moreover, we introduce a refinement module based on SAM to enhance the quality of the segmentation masks. Our extensive experiments on the PNG dataset demonstrate that DiffPNG achieves strong performance in the zero-shot PNG task setting, conclusively proving the diffusion model’s capability for context-aware, phrase-level understanding. Source code is available at https://github.com/nini0919/DiffPNG."
FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection
Authors: Jianwei Zhao*, Xin Li, Fan Yang, Qiang Zhai*, Ao Luo, Zhicheng Jiao, Hong Cheng
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Detecting objects seamlessly blended into their surroundings represents a complex task for both human cognitive capabilities and advanced artificial intelligence algorithms. Currently, the majority of methodologies for detecting camouflaged objects mainly focus on utilizing discriminative models with various unique designs. However, it has been observed that generative models, such as Stable Diffusion, possess stronger capabilities for understanding various objects in complex environments; Yet their potential for the cognition and detection of camouflaged objects has not been extensively explored. In this study, we present a novel denoising diffusion model, namely FocusDiffuser, to investigate how generative models can enhance the detection and interpretation of camouflaged objects. We believe that the secret to spotting camouflaged objects lies in catching the subtle nuances in details. Consequently, our FocusDiffuser innovatively integrates specialized enhancements, notably the Boundary-Driven LookUp (BDLU) module and Cyclic Positioning (CP) module, to elevate standard diffusion models, significantly boosting the detail-oriented analytical capabilities. Our experiments demonstrate that FocusDiffuser, from a generative perspective, effectively addresses the challenge of camouflaged object detection, surpassing leading models on benchmarks like CAMO, COD10K and NC4K. Code and pre-trained models are available at https://github.com/JWZhao-uestc/FocusDiffuser."
Improving image synthesis with diffusion-negative sampling
Authors: Alakh Desai*, Nuno Vasconcelos
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "For image generation with diffusion models (DMs), a negative prompt n can be used to complement the text prompt p, helping define properties not desired in the synthesized image. While this improves prompt adherence and image quality, finding good negative prompts is challenging. We argue that this is due to a semantic gap between humans and DMs, which makes good negative prompts for DMs appear unintuitive to humans. To bridge this gap, we propose a new diffusion-negative prompting () strategy. is based on a new procedure to sample images that are least compliant with p under the distribution of the DM, denoted as diffusion-negative sampling (). Given p, one such image is sampled, which is then translated into natural language by the user or a captioning model, to produce the negative prompt n∗ . The pair (p, n∗ ) is finally used to prompt the DM. is straightforward to implement and requires no training. Experiments and human evaluations show that performs well both quantitatively and qualitatively and can be easily combined with several DM variants."
AvatarPose: Avatar-guided 3D Pose Estimation of Close Human Interaction from Sparse Multi-view Videos
Authors: Feichi Lu*, Zijian Dong*, Jie Song, Otmar Hilliges
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Despite progress in human motion capture, existing multi-view methods often face challenges in estimating the 3D pose and shape of multiple closely interacting people. This difficulty arises from reliance on accurate 2D joint estimations, which are hard to obtain due to occlusions and body contact when people are in close interaction. To address this, we propose a novel method leveraging the personalized implicit neural avatar of each individual as a prior, which significantly improves the robustness and precision of this challenging pose estimation task. Concretely, the avatars are efficiently reconstructed via layered volume rendering from sparse multi-view videos. The reconstructed avatar prior allows for the direct optimization of 3D poses based on color and silhouette rendering loss, bypassing the issues associated with noisy 2D detections. To handle interpenetration, we propose a collision loss on the overlapping shape regions of avatars to add penetration constraints. Moreover, both 3D poses and avatars are optimized in an alternating manner. Our experimental results demonstrate state-of-the-art performance on several public datasets."
FedVAD: Enhancing Federated Video Anomaly Detection with GPT-Driven Semantic Distillation
Authors: Fan Qi*, Ruijie Pan, Huaiwen Zhang, Changsheng Xu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The imperative for smart surveillance systems to robustly detect anomalies poses a unique challenge given the sensitivity of visual data and privacy concerns. We propose a novel Federated Learning framework for Video Anomaly Detection that operates under the constraints of data heterogeneity and privacy preservation. We utilize Federated Visual Consistency Clustering to group clients on the server side. Further innovation is realized with an Adaptive Semantic-Enhanced Distillation strategy that infuses public video knowledge into our framework. During this process, Large Language Models are utilized for semantic generation and calibration of public videos. These video-text pairs are then used to fine-tune a multimodal network, which serves as a teacher in updating the global model. This approach not only refines video representations but also increases sensitivity to anomalous events. Our extensive evaluations showcase FedVAD’s proficiency in boosting unsupervised and weakly supervised anomaly detection, rivaling centralized training paradigms while preserving privacy. The code will be made available publicly at https://github.com/Eurekaer/FedVAD."
SignGen: End-to-End Sign Language Video Generation with Latent Diffusion
Authors: Fan Qi*, Yu Duan, Changsheng Xu, Huaiwen Zhang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The seamless transformation of textual input into natural and expressive sign language holds profound societal significance. Sign language is not solely about hand gestures. It encompasses vital facial expressions and mouth movements essential for nuanced communication. Achieving both semantic precision and emotional resonance in text-to-sign language translation is of paramount importance. Our work pioneers direct end-to-end translation of text into sign language videos, encompassing a realistic representation of the entire body and facial expressions. We go beyond traditional diffusion models by tailoring the multi-modal conditions for sign language videos. Additionally, our modified motion-aware sign generation framework enhances alignment between text and visual cues in sign language, further improving the quality of the generated sign language videos. Extensive experiments show that our approach significantly outperforms the state-of-the-art approaches in terms of semantic consistency, naturalness, and expressiveness, presenting benchmark quantitative results on the RWTH-2014, RWTH-2014-T, WLASL, CSL-Daily, and AUTSL. Our code is available at https://github.com/mingtiannihao/SignGen."
"Idling Neurons, Appropriately Lenient Workload During Fine-tuning Leads to Better Generalization"
Authors: Hongjing Niu*, Hanting Li, Bin Li, Feng Zhao*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Pre-training on large-scale datasets has become a fundamental method for training deep neural networks. Pre-training provides a better set of parameters than random initialization, which reduces the training cost of deep neural networks on the target task. In addition, pre-training also provides a large number of feature representations, which may help improve generalization capabilities. However, this potential advantage has not received enough attention and has been buried by rough fine-tuning. Based on some exploratory experiments, this paper rethinks the fine-tuning process and gives a new perspective on understanding fine-tuning. Moreover, this paper proposes some plug-and-play fine-tuning strategies as alternatives for simple fine-tuning. These fine-tuning strategies all preserve pre-trained features better by creating idling of some neurons, leading to better generalization."
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems
Authors: Sojin Lee, Dogyun Park, Inho Kong, Hyunwoo J. Kim*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent studies on inverse problems have proposed posterior samplers that leverage the pre-trained diffusion models as powerful priors. These attempts have paved the way for using diffusion models in a wide range of inverse problems. However, the existing methods entail computationally demanding iterative sampling procedures and optimize a separate solution for each measurement, which leads to limited scalability and lack of generalization capability across unseen samples. To address these limitations, we propose a novel approach, Diffusion prior-based Amortized Variational Inference (DAVI) that solves inverse problems with a diffusion prior from an amortized variational inference perspective. Specifically, instead of separate measurement-wise optimization, our amortized inference learns a function that directly maps measurements to the implicit posterior distributions of corresponding clean data, enabling a single-step posterior sampling even for unseen measurements. Extensive experiments on image restoration tasks, , Gaussian deblur, 4× super-resolution, and box inpainting with two benchmark datasets, demonstrate our approach’s superior performance over strong baselines. Code is available at https://github.com/mlvlab/DAVI."
The Gaussian Discriminant Variational Autoencoder (GdVAE): A Self-Explainable Model with Counterfactual Explanations
Authors: Anselm Haselhoff*, Kevin Trelenberg, Fabian Küppers, Jonas Schneider
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Visual counterfactual explanation (CF) methods modify image concepts, , shape, to change a prediction to a predefined outcome while closely resembling the original query image. Unlike self-explainable models (SEMs) and heatmap techniques, they grant users the ability to examine hypothetical ”what-if” scenarios. Previous CF methods either entail post-hoc training, limiting the balance between transparency and CF quality, or demand optimization during inference. To bridge the gap between transparent SEMs and CF methods, we introduce the GdVAE, a self-explainable model based on a conditional variational autoencoder (CVAE), featuring a Gaussian discriminant analysis (GDA) classifier and integrated CF explanations. Full transparency is achieved through a generative classifier that leverages class-specific prototypes for the downstream task and a closed-form solution for CFs in the latent space. The consistency of CFs is improved by regularizing the latent space with the explainer function. Extensive comparisons with existing approaches affirm the effectiveness of our method in producing high-quality CF explanations while preserving transparency. Code and models are public."
Accelerating Image Generation with Sub-path Linear Approximation Model
Authors: Chen Xu, Tianhui Song, Weixin Feng, Xubin Li, Tiezheng Ge, Bo Zheng, Limin Wang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Diffusion models have significantly advanced the state of the art in image, audio, and video generation tasks. However, their applications in practical scenarios are hindered by slow inference speed. Drawing inspiration from the consistency models, we propose the Sub-Path Linear Approximation Model (SPLAM), which can accelerate diffusion models while maintaining high-quality image generation. SPLAM treats the PF-ODE trajectory as a series of PF-ODE sub-paths divided by sampled points, and harnesses sub-path linear (SL) ODEs to form a progressive and continuous error estimation along each individual PF-ODE sub-path. The optimization on such SL-ODEs allows SPLAM to construct denoising mapping with smaller cumulative approximated error. An efficient distillation method is also developed to facilitate the incorporation of pre-trained diffusion models, such as latent diffusion models. The extensive experimental results demonstrate SPLAM achieves remarkable training efficiency, requiring only 6 A100 GPU days to produce a high-quality generative model capable of 2 to 4-step generation. Comprehensive evaluations on LAION, MS COCO 2014, and MS COCO 2017 datasets also illustrate that SPLAM surpasses the existing acceleration methods in few-step generation tasks, achieving state-of-the-art performance both on FID and the quality of the generated images."
Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models
Authors: Samuele Poppi*, Tobia Poppi*, Federico Cocchi, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Large-scale vision-and-language models, such as CLIP, are typically trained on web-scale data, which can introduce inappropriate content and lead to the development of unsafe and biased behavior. This, in turn, hampers their applicability in sensitive and trustworthy contexts and could raise significant concerns in their adoption. Our research introduces a novel approach to enhancing the safety of vision-and-language models by diminishing their sensitivity to NSFW (not safe for work) inputs. In particular, our methodology seeks to sever “toxic” linguistic and visual concepts, unlearning the linkage between unsafe linguistic or visual items and unsafe regions of the embedding space. We show how this can be done by fine-tuning a CLIP model on synthetic data obtained from a large language model trained to convert between safe and unsafe sentences, and a text-to-image generator. We conduct extensive experiments on the resulting embedding space for cross-modal retrieval, text-to-image, and image-to-text generation, where we show that our model can be remarkably employed with pre-trained generative models. Our source code and trained models are available at: https://github.com/aimagelab/safe-clip. ∗ Equal contribution."
TetraDiffusion: Tetrahedral Diffusion Models for 3D Shape Generation
Authors: Nikolai Kalischek*, Torben Peters, Jan Dirk Wegner, Konrad Schindler
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Probabilistic denoising diffusion models (DDMs) have set a new standard for 2D image generation. Extending DDMs for 3D content creation is an active field of research. Here, we propose TetraDiffusion, a diffusion model that operates on a tetrahedral partitioning of 3D space to enable efficient, high-resolution 3D shape generation. Our model introduces operators for convolution and transpose convolution that act directly on the tetrahedral partition, and seamlessly includes additional attributes like color. Our design generates mesh geometry much more efficiently: Compared to existing mesh diffusion techniques, TetraDiffusion is up to 200× faster. At the same time, it reduces memory consumption and can operate at substantially higher resolution than existing mesh generators. Using only standard consumer hardware, it sets a new standard in terms of spatial detail and outperforms other mesh generators across a range of quality metrics. For additional results and code see our project page tetradiffusion.github. io."
Camera Calibration using a Collimator System
Authors: Shunkun Liang, Banglei Guan*, Zhenbao Yu, Pengju Sun, Yang Shang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Camera calibration is a crucial step in photogrammetry and 3D vision applications. In practical scenarios with a long working distance to cover a wide area, target-based calibration methods become complicated and inflexible due to site limitations. This paper introduces a novel camera calibration method using a collimator system, which can provide a reliable and controllable calibration environment for cameras with varying working distances. Based on the optical geometry of the collimator system, we prove that the relative motion between the target and camera conforms to the spherical motion model, reducing the original 6DOF relative motion to 3DOF pure rotation motion. Furthermore, a closed-form solver for multiple views and a minimal solver for two views are proposed for camera calibration. The performance of our method is evaluated in both synthetic and real-world experiments, which verify the feasibility of calibration using the collimator system and demonstrate that our method is superior to the state-of-the-art methods. Demo code is available at https : //github.com/LiangSK98/CollimatorCalibration"
Label-free Neural Semantic Image Synthesis
Authors: Jiayi Wang*, Kevin A Laube, Yumeng Li, Jan Hendrik Metzen, Shin-I Cheng, Julio Borges, Anna Khoreva
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent work has shown great progress in integrating spatial conditioning to control large, pre-trained text-to-image diffusion models. Despite these advances, existing methods describe the spatial image content using hand-crafted conditioning inputs, which are either semantically ambiguous (e.g., edges) or require expensive manual annotations (e.g., semantic segmentation). To address these limitations, we propose a new label-free way of conditioning diffusion models to enable fine-grained spatial control. We introduce the concept of neural semantic image synthesis, which uses neural layouts extracted from pre-trained foundation models as conditioning. These layouts provide rich descriptions of the desired image, containing both semantics and detailed geometry of the scene. We experimentally show that images synthesized via neural semantic image synthesis achieve similar or superior pixel-level alignment of semantic classes compared to those created using expensive semantic label maps. At the same time, they capture better semantics, instance separation, and object orientation than other label-free conditioning options, such as edges or depth. Moreover, we show that images generated by neural layout conditioning can effectively augment real data for training various perception tasks."
Exploring Reliable Matching with Phase Enhancement for Night-time Semantic Segmentation
Authors: Yuwen Pan*, Rui Sun, Naisong Luo, Tianzhu Zhang, Yongdong Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Semantic segmentation of night-time images holds significant importance in computer vision, particularly for applications like night environment perception in autonomous driving systems. However, existing methods tend to parse night-time images from a day-time perspective, leaving the inherent challenges in low-light conditions (such as compromised texture and deceiving matching errors) unexplored. To address these issues, we propose a novel end-to-end optimized approach, named NightFormer, tailored for night-time semantic segmentation, avoiding the conventional practice of forcibly fitting night-time images into day-time distributions. Specifically, we design a pixel-level texture enhancement module to acquire texture-aware features hierarchically with phase enhancement and amplified attention, and an object-level reliable matching module to realize accurate association matching via reliable attention in low-light environments. Extensive experimental results on various challenging benchmarks including NightCity, BDD and Cityscapes demonstrate that our proposed method performs favorably against state-of-the-art night-time semantic segmentation methods."
Multiscale Sliced Wasserstein Distances as Perceptual Color Difference Measures
Authors: Jiaqi He, Zhihua Wang, Leon Wang, Tsein-I Liu, Yuming Fang, Qilin Sun*, Kede Ma
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Contemporary color difference (CD) measures for photographic images typically operate by comparing co-located pixels, patches in a “perceptually uniform” color space, or features in a learned latent space. Consequently, these measures inadequately capture the human color perception of misaligned image pairs, which are prevalent in digital photography (, the same scene captured by different smartphones). In this paper, we describe a perceptual CD measure based on the multiscale sliced Wasserstein distance, which facilitates efficient comparisons between non-local patches of similar color and structure. This aligns with the modern understanding of color perception, where color and structure are inextricably interdependent as a unitary process of perceptual organization. Meanwhile, our method is easy to implement and training-free. Experimental results indicate that our CD measure performs favorably in assessing CDs in photographic images, and consistently surpasses competing models in the presence of image misalignment. Additionally, we empirically verify that our measure functions as a metric in the mathematical sense, and show its promise as a loss function for image and video color transfer tasks. The code is available at https://github.com/real-hjq/MS-SWD."
DiscoMatch: Fast Discrete Optimisation for Geometrically Consistent 3D Shape Matching
Authors: Paul Roetzer*, Ahmed Abbas*, Dongliang Cao, Florian Bernard, Paul Swoboda
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this work we propose to combine the advantages of learning-based and combinatorial formalisms for 3D shape matching. While learning-based methods lead to state-of-the-art matching performance, they do not ensure geometric consistency, so that obtained matchings are locally non-smooth. On the contrary, axiomatic, optimisation-based methods allow to take geometric consistency into account by explicitly constraining the space of valid matchings. However, existing axiomatic formalisms do not scale to practically relevant problem sizes, and require user input for the initialisation of non-convex optimisation problems. We work towards closing this gap by proposing a novel combinatorial solver that combines a unique set of favourable properties: our approach (i) is initialisation free, (ii) is massively parallelisable and powered by a quasi-Newton method, (iii) provides optimality gaps, and (iv) delivers improved matching quality with decreased runtime and globally optimal results for many instances. all_papers.txt decode_tex_noligatures.sh decode_tex_noligatures.sh~ decode_tex.sh decode_tex.sh~ ECCV_abstracts.csv ECCV_abstracts_good.csv ECCV.csv ECCV.csv~ ECCV_new.csv generate_list.sh generate_list.sh~ generate_overview.sh gen.sh gen.sh~ HOWTO HOWTO~ pdflist pdflist.copied RCS snippet.html These authors contributed equally to this work."