mtybilly/Instruct2DS huggingface dataset:
latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction
Authors: Christopher Wewer*, Kevin Raj, Eddy Ilg, Bernt Schiele, Jan E. Lenssen*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We present latentSplat, a method to predict semantic Gaussians in a 3D latent space that can be splatted and decoded by a light-weight generative 2D architecture. Existing methods for generalizable 3D reconstruction either do not scale to large scenes and resolutions, or are limited to interpolation of close input views. latentSplat combines the strengths of regression-based and generative approaches while being trained purely on readily available real video data. The core of our method are variational 3D Gaussians, a representation that efficiently encodes varying uncertainty within a latent space consisting of 3D feature Gaussians. From these Gaussians, specific instances can be sampled and rendered via efficient splatting and a fast, generative decoder. We show that latentSplat outperforms previous works in reconstruction quality and generalization, while being fast and scalable to high-resolution data."
HyperSpaceX: Radial and Angular Exploration of HyperSpherical Dimensions
Authors: Chiranjeev Chiranjeev, Muskan Dosi, Kartik Thakral, Mayank Vatsa*, Richa Singh
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Traditional deep learning models rely on methods such as softmax cross-entropy and ArcFace loss for tasks like classification and face recognition. These methods mainly explore angular features in a hyperspherical space, often resulting in entangled inter-class features due to dense angular data across many classes. In this paper, a new field of feature exploration is proposed known as HyperSpaceX which enhances class discrimination by exploring both angular and radial dimensions in multi-hyperspherical spaces, facilitated by a novel DistArc loss. The proposed DistArc loss encompasses three feature arrangement components: two angular and one radial, enforcing intra-class binding and inter-class separation in multi-radial arrangement, improving feature discriminability. Evaluation of HyperSpaceX framework for the novel representation utilizes a proposed predictive measure that accounts for both angular and radial elements, providing a more comprehensive assessment of model accuracy beyond standard metrics. Experiments across seven object classification and six face recognition datasets demonstrate state-of-the-art (SoTA) results obtained from HyperSpaceX, achieving up to a 20% performance improvement on large-scale object datasets in lower dimensions and up to 6% gain in higher dimensions."
InstructGIE: Towards Generalizable Image Editing
Authors: Zichong Meng, Changdi Yang, Jun Liu, Hao Tang*, Pu Zhao*, Yanzhi Wang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent advances in image editing have been driven by the development of denoising diffusion models, marking a significant leap forward in this field. Despite these advances, the generalization capabilities of recent image editing approaches remain constrained. In response to this challenge, our study introduces a novel image editing framework with enhanced generalization robustness by boosting in-context learning capability and unifying language instruction. This framework incorporates a module specifically optimized for image editing tasks, leveraging the VMamba block and an editing-shift matching strategy to augment in-context learning. Furthermore, we unveil a selective area-matching technique specifically engineered to address and rectify corrupted details in generated images, such as human facial features, to further improve the quality. Another key innovation of our approach is the integration of a language unification technique, which aligns language embeddings with editing semantics to elevate the quality of image editing. Moreover, we compile the first dataset for image editing with visual prompts and editing instructions that could be used to enhance in-context capability. Trained on this dataset, our methodology not only achieves superior synthesis quality for trained tasks, but also demonstrates robust generalization capability across unseen vision tasks through tailored prompts. Our project page is available at https://cr8br0ze.github.io/ InstructGIE."
HandDAGT: A Denoising Adaptive Graph Transformer for 3D Hand Pose Estimation
Authors: WENCAN CHENG, Eunji Kim, Jong Hwan Ko*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The extraction of keypoint positions from input hand frames, known as 3D hand pose estimation, is crucial for various human-computer interaction applications. However, current approaches often struggle with the dynamic nature of self-occlusion of hands and intra-occlusion with interacting objects. To address this challenge, this paper proposes the Denoising Adaptive Graph Transformer, HandDAGT, for hand pose estimation. The proposed HandDAGT leverages a transformer structure to thoroughly explore effective geometric features from input patches. Additionally, it incorporates a novel attention mechanism to adaptively weigh the contribution of kinematic correspondence and local geometric features for the estimation of specific keypoints. This attribute enables the model to adaptively employ kinematic and local information based on the occlusion situation, enhancing its robustness and accuracy. Furthermore, we introduce a novel denoising training strategy aimed at improving the model’s robust performance in the face of occlusion challenges. Experimental results show that the proposed model significantly outperforms the existing methods on four challenging hand pose benchmark datasets. Codes and pre-trained models are publicly available at https://github.com/cwc1260/HandDAGT."
Navigating Text-to-Image Generative Bias across Indic Languages
Authors: Surbhi Mittal*, Arnav Sudan, Mayank Vatsa*, Richa Singh, Tamar Glaser, Tal Hassner
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This research investigates biases in text-to-image (TTI) models for the Indic languages widely spoken across India. It evaluates and compares the generative performance and cultural relevance of leading TTI models in these languages against their performance in English. Using the proposed IndicTTI benchmark, we comprehensively assess the performance of 30 Indic languages with two open-source diffusion models and two commercial generation APIs. The primary objective of this benchmark is to evaluate the support for Indic languages in these models and identify areas needing improvement. Given the linguistic diversity of 30 languages spoken by over 1.4 billion people, this benchmark aims to provide a detailed and insightful analysis of TTI models’ effectiveness within the Indic linguistic landscape. The data and code for the IndicTTI benchmark can be accessed at https://iab-rubric.org/resources/other-databases/ indictti."
Correspondence-Free SE(3) Point Cloud Registration in RKHS via Unsupervised Equivariant Learning
Authors: Ray Zhang*, Zheming Zhou, Min Sun, Omid Ghasemalizadeh, Cheng-Hao Kuo, Ryan M. Eustice, Maani Ghaffari Jadidi, Arnie Sen
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This paper introduces a robust unsupervised SE(3) point cloud registration method that operates without requiring point correspondences. The method frames point clouds as functions in a reproducing kernel Hilbert space (RKHS), leveraging SE(3)-equivariant features for direct feature space registration. A novel RKHS distance metric is proposed, offering reliable performance amidst noise, outliers, and asymmetrical data. An unsupervised training approach is introduced to effectively handle limited ground truth data, facilitating adaptation to real datasets. The proposed method outperforms classical and supervised methods in terms of registration accuracy on both synthetic (ModelNet40) and real-world (ETH3D) noisy, outlier-rich datasets. To our best knowledge, this marks the first instance of successful real RGB-D odometry data registration using an equivariant method. The code is available at https://sites.google. com/view/eccv24-equivalign."
CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models
Authors: Nick Stracke*, Stefan Andreas Baumann, Joshua Susskind, Miguel Angel Bautista, Bjorn Ommer
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Text-to-image generative models have become a prominent and powerful tool that excels at generating high-resolution realistic images. However, guiding the generative process of these models to take into account detailed forms of conditioning reflecting style and/or structure information remains an open problem. In this paper, we present , an approach that unifies both style and structure conditioning under the same formulation using a novel conditional LoRA block that enables zero-shot control. is an efficient and powerful approach to condition text-to-image diffusion models, which enables fine-grained control conditioning during generation and outperforms recent state-of-the-art approaches. Project page: compvis.github.io/LoRAdapter/"
Nickel and Diming Your GAN: A Dual-Method Approach to Enhancing GAN Efficiency via Knowledge Distillation
Authors: Sangyeop Yeo, Yoojin Jang, Jaejun Yoo*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this paper, we address the challenge of compressing generative adversarial networks (GANs) for deployment in resource-constrained environments by proposing two novel methods: Distribution Matching for Efficient compression (DiME) and Network Interactive Compression via Knowledge Exchange and Learning (NICKEL). DiME employs foundation models as embedding kernels for efficient distribution matching, leveraging maximum mean discrepancy to facilitate effective knowledge distillation. NICKEL employs an interactive compression method that enhances the communication between the student generator and discriminator, achieving a balanced and stable compression process. Our comprehensive evaluation on the StyleGAN2 architecture with the FFHQ dataset shows the effectiveness of our approach, with NICKEL & DiME achieving FID scores of 10.45 and 15.93 at compression rates of 95.73% and 98.92%, respectively. Remarkably, our methods sustain generative quality even at an extreme compression rate of 99.69%, surpassing the previous state-of-the-art performance by a large margin. These findings not only show our methodologies’ capacity to significantly lower GANs’ computational demands but also pave the way for deploying high-quality GAN models in settings with limited resources. Our code is available at Nickel & Dime."
VividDreamer: Invariant Score Distillation for Hyper-Realistic Text-to-3D Generation
Authors: Wenjie Zhuo*, Fan Ma, Hehe Fan, Yi Yang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This paper presents Invariant Score Distillation (ISD), a novel method for high-fidelity text-to-3D generation. ISD aims to tackle the over-saturation and over-smoothing problems in Score Distillation Sampling (SDS). In this paper, SDS is decoupled into a weighted sum of two components: the reconstruction term and the classifier-free guidance term. We experimentally found that over-saturation stems from the large classifier-free guidance scale and over-smoothing comes from the reconstruction term. To overcome these problems, ISD utilizes an invariant score term derived from DDIM sampling to replace the reconstruction term in SDS. This operation allows the utilization of a medium classifier-free guidance scale and mitigates the reconstruction-related errors, thus preventing the over-smoothing and over-saturation of results. Extensive experiments demonstrate that our method greatly enhances SDS and produces realistic 3D objects through single-stage optimization."
"A Framework for Efficient Model Evaluation through Stratification, Sampling, and Estimation"
Authors: Riccardo Fogliato*, Pratik Patil, Mathew Monfort, Pietro Perona
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Model performance evaluation is a critical and expensive task in machine learning and computer vision. Without clear guidelines, practitioners often estimate model accuracy using a one-time completely random selection of the data. However, by employing tailored sampling and estimation strategies, one can obtain more precise estimates and reduce annotation costs. In this paper, we propose a statistical framework for model evaluation that includes stratification, sampling, and estimation components. We examine the statistical properties of each component and evaluate their efficiency (precision). One key result of our work is that stratification via clustering based on accurate predictions of model performance yields efficient estimators. Our experiments on computer vision datasets show that this method consistently provides more precise accuracy estimates than the traditional simple random sampling, even with substantial efficiency gains of 10x. We also find that model-assisted estimators, which leverage predictions of model accuracy on the unlabeled portion of the dataset, are generally more efficient than the traditional estimates based solely on the labeled data."
Towards Scene Graph Anticipation
Authors: Rohith Peddi*, Saksham Singh, Saurabh ., Parag Singla, Vibhav Gogate
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Spatio-temporal scene graphs represent interactions in a video by decomposing scenes into individual objects and their pair-wise temporal relationships. Long-term anticipation of the fine-grained pair-wise relationships between objects is a challenging problem. To this end, we introduce the task of Scene Graph Anticipation (SGA). We adapt state-of-the-art scene graph generation methods as baselines to anticipate future pair-wise relationships between objects and propose a novel approach SceneSayer. In SceneSayer, we leverage object-centric representations of relationships to reason about the observed video frames and model the evolution of relationships between objects. We take a continuous time perspective and model the latent dynamics of the evolution of object interactions using concepts of NeuralODE and NeuralSDE, respectively. We infer representations of future relationships by solving an Ordinary Differential Equation and a Stochastic Differential Equation, respectively. Extensive experimentation on the Action Genome dataset validates the efficacy of the proposed methods. 0∗ denotes equal contribution with names in alphabetical order."
Non-Line-of-Sight Estimation of Fast Human Motion with Slow Scanning Imagers
Authors: Javier Grau Chopite*, Patrick Hähn, Matthias B Hullin*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Non-line-of-sight (NLoS) reconstruction, i.e., the task of imaging scenes beyond the camera’s field of view, is often implemented using source-and-sensor systems that scan the visible region and analyze secondary reflections of light that has interacted with the hidden static scene. Estimating human activity around the corner will be a task of major interest for emerging NLoS applications, and some attempts have been reported in the recent literature. However, due to the long exposure times and comprehensive scans needed for NLoS sensing, the reconstruction of continuous movement remains prone to artifacts and is unreliable. In this paper, we analyze the interplay between dynamic scenes and scanning hardware to identify possible failure cases for filtering and data-driven approaches. Our studies indicate that existing reconstruction methods are prone to systematic error due to the space-time skew introduced by scanning setups. To alleviate this issue, we propose an image formation model for dynamic scenes that explicitly integrates motion skew. Using this model, we construct a baseline method for human pose estimation that achieves high accuracy, even at very slow scan rates."
Distributed Semantic Segmentation with Efficient Joint Source and Task Decoding
Authors: Danish Nazir*, Timo Bartels, Jan Piewek, Thorsten Bagdonat, Tim Fingscheidt
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Distributed computing in the context of deep neural networks (DNNs) implies the execution of one part of the network on edge devices and the other part typically on a large-scale cloud platform. Conventional methods propose to employ a serial concatenation of a learned image and source encoder, the latter projecting the image encoder output (bottleneck features) into a quantized representation for bitrate-efficient transmission. In the cloud, a respective source decoder reprojects the quantized representation to the original feature representation, serving as an input for the downstream task decoder performing, e.g., semantic segmentation. In this work, we propose joint source and task decoding, as it allows for a smaller network size in the cloud. This further enables the scalability of such services in large numbers without requiring extensive computational load on the cloud per channel. We demonstrate the effectiveness of our method by achieving a distributed semantic segmentation SOTA over a wide range of bitrates on the mean intersection over union metric, while using only 9.8% ... 11.59% of cloud DNN parameters used in previous SOTA on the COCO and Cityscapes datasets."
NePhi: Neural Deformation Fields for Approximately Diffeomorphic Medical Image Registration
Authors: Lin Tian*, Thomas H Greer, Raul San Jose Estepar, Roni Sengupta, Marc Niethammer
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This work proposes NePhi, a generalizable neural deformation model which results in approximately diffeomorphic transformations. In contrast to the predominant voxel-based transformation fields used in learning-based registration approaches, NePhi represents deformations functionally, leading to great flexibility within the design space of memory consumption during training and inference, inference time, registration accuracy, as well as transformation regularity. Specifically, NePhi 1) requires less memory compared to voxel-based learning approaches, 2) improves inference speed by predicting latent codes, compared to current existing neural deformation based registration approaches that only rely on optimization, 3) improves accuracy via instance optimization, and 4) shows excellent deformation regularity which is highly desirable for medical image registration. We demonstrate the performance of NePhi on a 2D synthetic dataset as well as for real 3D medical image datasets (e.g., lungs and brains). Our results show that NePhi can match the accuracy of voxel-based representations in a single-resolution registration setting. For multi-resolution registration, our method matches the accuracy of current SOTA learning-based registration approaches with instance optimization while reducing memory requirements by a factor of five. Our code is available at https://github.com/uncbiag/NePhi."
Aligning Neuronal Coding of Dynamic Visual Scenes with Foundation Vision Models
Authors: Rining Wu*, Feixiang Zhou, Ziwei Yin, Jian Liu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Our brains represent the ever-changing environment with neurons in a highly dynamic fashion. The temporal features of visual pixels in dynamic natural scenes are entrapped in the neuronal responses of the retina. It is crucial to establish the intrinsic temporal relationship between visual pixels and neuronal responses. Recent foundation vision models have paved an advanced way of understanding image pixels. Yet, neuronal coding in the brain largely lacks a deep understanding of its alignment with pixels. Most previous studies employ static images or artificial videos derived from static images for emulating more real and complicated stimuli. Despite these simple scenarios effectively help to separate key factors influencing visual coding, complex temporal relationships receive no consideration. To decompose the temporal features of visual coding in natural scenes, here we propose Vi-ST, a spatiotemporal convolutional neural network fed with a self-supervised Vision Transformer (ViT) prior, aimed at unraveling the temporal-based encoding patterns of retinal neuronal populations. The model demonstrates robust predictive performance in generalization tests. Furthermore, through detailed ablation experiments, we demonstrate the significance of each temporal module. Furthermore, we introduce a visual coding evaluation metric designed to integrate temporal considerations and compare the impact of different numbers of neuronal populations on complementary coding. In conclusion, our proposed Vi-ST demonstrates a novel modeling framework for neuronal coding of dynamic visual scenes in the brain, effectively aligning our brain representation of video with neuronal activity. The code is available at github.com/wurining/Vi-ST."
Image Manipulation Detection With Implicit Neural Representation and Limited Supervision
Authors: Zhenfei Zhang*, Mingyang Li, Xin Li, Ming-Ching Chang, Jun-Wei Hsieh
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Image Manipulation Detection (IMD) is becoming increasingly important as tampering technologies advance. However, most state-of-the-art (SoTA) methods require high-quality training datasets featuring image- and pixel-level annotations. The effectiveness of these methods suffers when applied to manipulated or noisy samples that differ from the training data. To address these challenges, we present a unified framework that combines unsupervised and weakly supervised approaches for IMD. Our approach introduces a novel pre-processing stage based on a controllable fitting function from Implicit Neural Representation (INR). Additionally, we introduce a new selective pixel-level contrastive learning approach, which concentrates exclusively on high-confidence regions, thereby mitigating uncertainty associated with the absence of pixel-level labels. In weakly supervised mode, we utilize ground-truth image-level labels to guide predictions from an adaptive pooling method, facilitating comprehensive exploration of manipulation regions for image-level detection. The unsupervised model is trained using a self-distillation training method with selected high-confidence pseudo-labels obtained from the deepest layers via different sources. Extensive experiments demonstrate that our proposed method outperforms existing unsupervised and weakly supervised methods. Moreover, it competes effectively against fully supervised methods on novel manipulation detection tasks."
Scalar Function Topology Divergence: Comparing Topology of 3D Objects
Authors: Ilya Trofimov*, Daria Voronkova, Eduard Tulchinskii, Evgeny Burnaev, Serguei Barannikov
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We propose a new topological tool for computer vision - Scalar Function Topology Divergence (SFTD), which measures the dissimilarity of multi-scale topology between sublevel sets of two functions having a common domain. Functions can be defined on an undirected graph or Euclidean space of any dimensionality. Most of the existing methods for comparing topology are based on Wasserstein distance between persistence barcodes and they don’t take into account the localization of topological features. The minimization of SFTD ensures that the corresponding topological features of scalar functions are located in the same places. The proposed tool provides useful visualizations depicting areas where functions have topological dissimilarities. We provide applications of the proposed method to 3D computer vision. In particular, experiments demonstrate that SFTD as an additional loss improves the reconstruction of cellular 3D shapes from 2D fluorescence microscopy images, and helps to identify topological errors in 3D segmentation. Additionally, we show that SFTD outperforms Betti matching loss in 2D segmentation problems. The code is publicly available: https://github.com/IlyaTrofimov/SFTD."
Introducing Routing Functions to Vision-Language Parameter-Efficient Fine-Tuning with Low-Rank Bottlenecks
Authors: Tingyu Qu*, Tinne Tuytelaars, Marie-Francine Moens
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Mainstream parameter-efficient fine-tuning (PEFT) methods, such as LoRA or Adapter, project a model’s hidden states to a lower dimension, allowing pre-trained models to adapt to new data through this low-rank bottleneck. However, PEFT tasks involving multiple modalities, like vision-language (VL) tasks, require not only adaptation to new data but also learning the relationship between different modalities. Targeting at VL PEFT tasks, we propose a family of operations, called routing functions, to enhance VL alignment in the low-rank bottlenecks. These feature routing functions adopt linear operations and do not introduce new trainable parameters. In-depth analyses are conducted to study their behavior. In various VL PEFT settings, the routing functions significantly improve performance of the original PEFT methods, achieving over 20% improvement on VQAv2 (RoBERT alarge +ViT-L/16) and 30% on COCO Captioning (GPT2-medium+ViT-L/16). Also when fine-tuning a pre-trained multimodal model such as CLIP-BART, we observe smaller but consistent improvements across a range of VL PEFT tasks. Our code is available at https://github. com/tingyu215/Routing_VLPEFT."
Concept Arithmetics for Circumventing Concept Inhibition in Diffusion Models
Authors: Vitali Petsiuk*, Kate Saenko
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Motivated by ethical and legal concerns, the scientific community is actively developing methods to limit the misuse of Text-to-Image diffusion models for reproducing copyrighted, violent, explicit, or personal information in the generated images. Simultaneously, researchers put these newly developed safety measures to the test by assuming the role of an adversary to find vulnerabilities and backdoors in them. We use the compositional property of diffusion models, which allows us to leverage multiple prompts in a single image generation. This property allows us to combine other concepts that should not have been affected by the inhibition to reconstruct the vector responsible for target concept generation, even though the direct computation of this vector is no longer accessible. We provide theoretical and empirical evidence of why the proposed attacks are possible and discuss the implications of these findings for safe model deployment. We argue that it is essential to consider all possible approaches to image generation with diffusion models that can be employed by an adversary. Our work opens up the discussion about the implications of concept arithmetics and compositional inference for safety mechanisms in diffusion models. Content Advisory: This paper contains discussions and model-generated content that may be considered offensive. Reader discretion is advised. Project page: https://cs-people.bu.edu/vpetsiuk/arc"
DeTra: A Unified Model for Object Detection and Trajectory Forecasting
Authors: Sergio Casas*, Ben T Agro, Jiageng Mao, Thomas Gilles, ALEXANDER Y CUI, Enxu Li, Raquel Urtasun
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The tasks of object detection and trajectory forecasting play a crucial role in understanding the scene for autonomous driving. These tasks are typically executed in a cascading manner, making them prone to compounding errors. Furthermore, there is usually a very thin interface between the two tasks, creating a lossy information bottleneck. To address these challenges, our approach formulates the union of the two tasks as a trajectory refinement problem, where the first pose is the detection (current time), and the subsequent poses are the waypoints of the multiple forecasts (future time). To tackle this unified task, we design a refinement transformer that infers the presence, pose, and multi-modal future behaviors of objects directly from LiDAR point clouds and high-definition maps. We call this model , short for object Detection and Trajectory forecasting. In our experiments, we observe that outperforms the state-of-the-art on Argoverse 2 Sensor and Waymo Open Dataset by a large margin, across a broad range of metrics. Finally, we perform extensive ablation studies that show the value of refinement for this task and that key design choices were made."
ControlNet-XS: Rethinking the Control of Text-to-Image Diffusion Models as Feedback-Control Systems
Authors: Denis Zavadski*, Johann-Friedrich Feiden, Carsten Rother
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The field of image synthesis has made tremendous strides forward in the last years. Besides defining the desired output image with text-prompts, an intuitive approach is to additionally use spatial guidance in form of an image, such as a depth map. In state-of-the-art approaches, this guidance is realized by a separate controlling model that controls a pre-trained image generation network, such as a latent diffusion model [?]. Understanding this process from a control system perspective shows that it forms a feedback-control system, where the control module receives a feedback signal from the generation process and sends a corrective signal back. When analysing existing systems, we observe that the feedback signals are timely sparse and have a small number of bits. As a consequence, there can be long delays between newly generated features and the respective corrective signals for these features. It is known that this delay is the most unwanted aspect of any control system. In this work, we take an existing controlling network (ControlNet [?]) and change the communication between the controlling network and the generation process to be of high-frequency and with large-bandwidth. By doing so, we are able to considerably improve the quality of the generated images, as well as the fidelity of the control. Also, the controlling network needs noticeably fewer parameters and hence is about twice as fast during inference and training time. Another benefit of small-sized models is that they help to democratise our field and are likely easier to understand. We call our proposed network ControlNet-XS. When comparing with the state-of-the-art approaches, we outperform them for pixel-level guidance, such as depth, canny-edges, and semantic segmentation, and are on a par for loose keypoint-guidance of human poses. All code and pre-trained models will be made publicly available."
Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction
Authors: Alexander Timans*, Christoph-Nikolas Straehle, Kaspar Sakmann, Eric Nalisnick
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Quantifying a model’s predictive uncertainty is essential for safety-critical applications such as autonomous driving. We consider quantifying such uncertainty for multi-object detection. In particular, we leverage conformal prediction to obtain uncertainty intervals with guaranteed coverage for object bounding boxes. One challenge in doing so is that bounding box predictions are conditioned on the object’s class label. Thus, we develop a novel two-step conformal approach that propagates uncertainty in predicted class labels into the uncertainty intervals of bounding boxes. This broadens the validity of our conformal coverage guarantees to include incorrectly classified objects, thus offering more actionable safety assurances. Moreover, we investigate novel ensemble and quantile regression formulations to ensure the bounding box intervals are adaptive to object size, leading to a more balanced coverage. Validating our two-step approach on real-world datasets for 2D bounding box localization, we find that desired coverage levels are satisfied with practically tight predictive uncertainty intervals."
Common Sense Reasoning for Deep Fake Detection
Authors: Yue Zhang*, Ben Colman, Xiao Guo, Ali Shahriyari, Gaurav Bharaj*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "State-of-the-art deepfake detection approaches rely on image-based features extracted via neural networks. While these approaches trained in a supervised manner extract likely fake features, they may fall short in representing unnatural ‘non-physical’ semantic facial attributes – blurry hairlines, double eyebrows, rigid eye pupils, or unnatural skin shading. However, such facial attributes are easily perceived by humans and used to discern the authenticity of an image based on human common sense. Furthermore, image-based feature extraction methods that provide visual explanations via saliency maps can be hard to interpret for humans. To address these challenges, we frame deepfake detection as a Deepfake Detection VQA (DD-VQA) task and model human intuition by providing textual explanations that describe common sense reasons for labeling an image as real or fake. We introduce a new annotated dataset and propose a Vision and Language Transformer-based framework for the DD-VQA task. We also incorporate text and image-aware feature alignment formulation to enhance multi-modal representation learning. As a result, we improve upon existing deepfake detection models by integrating our learned vision representations, which reason over common sense knowledge from the DD-VQA task. We provide extensive empirical results demonstrating that our method enhances detection performance, generalization ability, and language-based interpretability in the deepfake detection task. Our dataset is available at https://github.com/Reality-Defender/Research-DD-VQA."
Let the Avatar Talk using Texts without Paired Training Data
Authors: Xiuzhe Wu, Yang-Tian Sun, Handi Chen, Hang Zhou, Jingdong Wang, Zhengzhe Liu, Xiaojuan Qi*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This paper introduces text-driven talking avatar generation, a task that uses text to instruct both the generation and animation of an avatar. One significant obstacle in this task is the absence of paired text and talking avatar data for model training, limiting data-driven methodologies. To this end, we present a zero-shot approach that adapts an existing 3D-aware image generation model, trained on a large-scale image dataset for high-quality avatar creation, to align with textual instructions and be animated to produce talking avatars, eliminating the need for paired text and talking avatar data. Our approach’s core lies in the seamless integration of a 3D-aware image generation model (i.e., EG3D), the explicit 3DMM model, and a newly developed self-supervised inpainting technique, to create and animate the avatar and generate a temporal consistent talking video. Thorough evaluations demonstrate the effectiveness of our proposed approach in generating realistic avatars based on textual descriptions and empowering avatars to express user-specified text. Notably, our approach is highly controllable and can generate rich expressions and head poses."
NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields
Authors: Muhammad Zubair Irshad*, Sergey Zakharov, Vitor Guizilini, Adrien Gaidon, Zsolt Kira, Rares Ambrus
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Neural fields excel in computer vision and robotics due to their ability to understand the 3D visual world such as inferring semantics, geometry, and dynamics. Given the capabilities of neural fields in densely representing a 3D scene from 2D images, we ask the question: Can we scale their self-supervised pretraining, specifically using masked autoencoders, to generate effective 3D representations from posed RGB images. Owing to the astounding success of extending transformers to novel data modalities, we employ standard 3D Vision Transformers to suit the unique formulation of NeRFs. We leverage NeRF’s volumetric grid as a dense input to the transformer, contrasting it with other 3D representations such as pointclouds where the information density can be uneven, and the representation is irregular. Due to the difficulty of applying masked autoencoders to an implicit representation, such as NeRF, we opt for extracting an explicit representation that canonicalizes scenes across domains by employing the camera trajectory for sampling. Our goal is made possible by masking random patches from NeRF’s radiance and density grid and employing a standard 3D Swin Transformer to reconstruct the masked patches. In doing so, the model can learn the semantic and spatial structure of complete scenes. We pretrain this representation at scale on our proposed curated posed-RGB data, totaling over 1.8 million images. Once pretrained, the encoder is used for effective 3D transfer learning. Our novel self-supervised pretraining for NeRFs, NeRF-MAE, scales remarkably well and improves performance on various challenging 3D tasks. Utilizing unlabeled posed 2D data for pretraining, NeRF-MAE significantly outperforms self-supervised 3D pretraining and NeRF scene understanding baselines on Front3D and ScanNet datasets with an absolute performance improvement of over 20% AP50 and 8% AP25 for 3D object detection. Project Page: magentanerf-mae.github.io"
GOEmbed: Gradient Origin Embeddings for Representation Agnostic 3D Feature Learning
Authors: Animesh Karnewar*, Roman Shapovalov, Tom Monnier, Andrea Vedaldi, Niloy J. Mitra*, David Novotny*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Encoding information from 2D views of an object into a 3D representation is crucial for generalized 3D feature extraction. Such features can then enable 3D reconstruction, 3D generation, and other applications. We propose (Gradient Origin Embeddings) that encodes input 2D images into any 3D representation, without requiring a pre-trained image feature extractor; unlike typical prior approaches in which input images are either encoded using 2D features extracted from large pre-trained models, or customized features are designed to handle different 3D representations; or worse, encoders may not yet be available for specialized 3D neural representations such as MLPs and hash-grids. We extensively evaluate our proposed under different experimental settings on the OmniObject3D benchmark. First, we evaluate how well the mechanism compares against prior encoding mechanisms on multiple 3D representations using an illustrative experiment called Plenoptic-Encoding. Second, the efficacy of the GOEmbed mechanism is further demonstrated by achieving a new SOTA FID of 22.12 on the OmniObject3D generation task using a combination of and DFM (Diffusion with Forward Models), which we call Fusion. Finally, we evaluate how the mechanism bolsters sparse-view 3D reconstruction pipelines."
Causal Subgraphs and Information Bottlenecks: Redefining OOD Robustness in Graph Neural Networks
Authors: Weizhi An, Wenliang Zhong, Feng Jiang, Hehuan Ma, Junzhou Huang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Graph Neural Networks (GNNs) are increasingly popular in processing graph-structured data, yet they face significant challenges when training and testing distributions diverge, common in real-world scenarios. This divergence often leads to substantial performance drops in GNN models. To address this, we introduce a novel approach that effectively enhances GNN performance in Out-of-Distribution (OOD) scenarios, called Causal Subgraphs and Information Bottlenecks (CSIB). CSIB is guided by causal modeling principles to generate causal subgraphs while concurrently considering both Fully Informative Invariant Features (FIIF) and Partially Informative Invariant Features (PIIF) situations. Our approach uniquely combines the principles of invariant risk minimization and graph information bottleneck. This integration not only guides the generation of causal subgraphs but also underscores the necessity of balancing invariant principles with information compression in the face of various distribution shifts. We validate our model through extensive experiments across diverse shift types, demonstrating its effectiveness in maintaining robust performance under OOD conditions."
AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale
Authors: Keenon Werling*, Janelle M Kaneda, Tian Tan, Rishi Agarwal, Six Skov, Tom Van Wouwe, Scott Uhlrich, Scott Delp, Karen Liu, Nicholas A Bianco, Carmichael Ong, Antoine Falisse, Shardul Sapkota, Aidan Jai Chandra, Joshua A Carter, Ezio Preatoni, Benjamin J Fregly, Jennifer Hicks
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "While reconstructing human poses in 3D from inexpensive sensors has advanced significantly in recent years, quantifying the dynamics of human motion, including the muscle-generated joint torques and external forces, remains a challenge. Prior attempts to estimate physics from reconstructed human poses have been hampered by a lack of datasets with high-quality pose and force data for a variety of movements. We present the AddBiomechanics Dataset 1.0, which includes physically accurate human dynamics of 273 human subjects, over 70 hours of motion and force plate data, totaling more than 24 million frames. To construct this dataset, novel analytical methods were required, which are also reported here. We propose a benchmark for estimating human dynamics from motion using this dataset, and present several baseline results. The AddBiomechanics Dataset is publicly available at addbiomechanics.org/download data.html."
How to Train the Teacher Model for Effective Knowledge Distillation
Authors: Shayan Mohajer Hamidi*, Xizhen Deng, Renhao Tan, Linfeng Ye, Ahmed Hussein Salamah
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recently, it was shown that the role of the teacher in knowledge distillation (KD) is to provide the student with an estimate of the true Bayes conditional probability density (BCPD). Notably, the new findings propose that the student’s error rate can be upper-bounded by the mean squared error (MSE) between the teacher’s output and BCPD. Consequently, to enhance KD efficacy, the teacher should be trained such that its output is close to BCPD in MSE sense. This paper elucidates that training the teacher model with MSE loss equates to minimizing the MSE between its output and BCPD, aligning with its core responsibility of providing the student with a BCPD estimate closely resembling it in MSE terms. In this respect, through a comprehensive set of experiments, we demonstrate that substituting the conventional teacher trained with cross-entropy loss with one trained using MSE loss in state-of-the-art KD methods consistently boosts the student’s accuracy, resulting in improvements of up to 2.6%. The code for this paper is publicly available at: https://github.com/ECCV2024MSE/ ECCV_MSE_Teacher."
Tight and Efficient Upper Bound on Spectral Norm of Convolutional Layers
Authors: Ekaterina Grishina*, Mikhail Gorbunov, Maxim Rakhuba
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Controlling the spectral norm of the Jacobian matrix, which is related to the convolution operation, has been shown to improve generalization, training stability and robustness in CNNs. Existing methods for computing the norm either tend to overestimate it or their performance may deteriorate quickly with increasing the input and kernel sizes. In this paper, we demonstrate that the tensor version of the spectral norm of a four-dimensional convolution kernel, up to a constant factor, serves as an upper bound for the spectral norm of the Jacobian matrix associated with the convolution operation. This new upper bound is independent of the input image resolution, differentiable and can be efficiently calculated during training. Through experiments, we demonstrate how this new bound can be used to improve the performance of convolutional architectures."
Deciphering the Role of Representation Disentanglement: Investigating Compositional Generalization in CLIP Models
Authors: Reza Abbasi, Mohammad Rohban, Mahdieh Soleymani Baghshah*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "CLIP models have recently shown to exhibit Out of Distribution (OoD) generalization capabilities. However, Compositional Out of Distribution (C-OoD) generalization, which is a crucial aspect of a model’s ability to understand unseen compositions of known concepts, is relatively unexplored for the CLIP models. Our goal is to address this problem and identify the factors that contribute to the C-OoD in CLIPs. We noted that previous studies regarding compositional understanding of CLIPs frequently fail to ensure that test samples are genuinely novel relative to the CLIP training data. To this end, we carefully synthesized a large and diverse dataset in the single object setting, comprising attributes for objects that are highly unlikely to be encountered in the combined training datasets of various CLIP models. This dataset enables an authentic evaluation of C-OoD generalization. Our observations reveal varying levels of C-OoD generalization across different CLIP models. We propose that the disentanglement of CLIP representations serves as a critical indicator in this context. By utilizing our synthesized datasets and other existing datasets, we assess various disentanglement metrics of text and image representations. Our study reveals that the disentanglement of image and text representations, particularly with respect to their compositional elements, plays a crucial role in improving the generalization of CLIP models in out-of-distribution settings. This finding suggests promising opportunities for advancing out-of-distribution generalization in CLIPs. For more details and access to our dataset, please visit https://github.com/abbasiReza/CLIP-COoD."