GenAI4ELab/papercli-papers huggingface dataset:
GOEmbed: Gradient Origin Embeddings for Representation Agnostic 3D Feature Learning
Authors: Animesh Karnewar; Roman Shapovalov; Tom Monnier; Andrea Vedaldi; Niloy J. Mitra; David Novotny
Venue: ECCV 2024 (main)

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
Venue: ECCV 2024 (main)

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
Venue: ECCV 2024 (main)

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
Venue: ECCV 2024 (main)

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
Venue: ECCV 2024 (main)

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
Venue: ECCV 2024 (main)

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."
Modality Translation for Object Detection Adaptation without forgetting prior knowledge
Authors: Heitor Rapela Medeiros; Masih Aminbeidokhti; Fidel A Guerrero Pena; David Latortue; Eric Granger; Marco Pedersoli
Venue: ECCV 2024 (main)

Abstract: "A common practice in deep learning involves training large neural networks on massive datasets to achieve high accuracy across various domains and tasks. While this approach works well in many application areas, it often fails drastically when processing data from a new modality with a significant distribution shift from the data used to pre-train the model. This paper focuses on adapting a large object detection model trained on RGB images to new data extracted from IR images with a substantial modality shift. We propose Modality Translator (ModTr) as an alternative to the common approach of fine-tuning a large model to the new modality. ModTr adapts the IR input image with a small transformation network trained to directly minimize the detection loss. The original RGB model can then work on the translated inputs without any further changes or fine-tuning to its parameters. Experimental results on translating from IR to RGB images on two well-known datasets show that our simple approach provides detectors that perform comparably or better than standard fine-tuning, without forgetting the knowledge of the original model. This opens the door to a more flexible and efficient service-based detection pipeline, where a unique and unaltered server, such as an RGB detector, runs constantly while being queried by different modalities, such as IR with the corresponding translations model. Our code is available at: https://github.com/heitorrapela/ModTr."
FroSSL: Frobenius Norm Minimization for Efficient Multiview Self-Supervised Learning
Authors: Oscar Skean; Aayush Dhakal; Nathan Jacobs; Luis G Sanchez Giraldo
Venue: ECCV 2024 (main)

Abstract: "Self-supervised learning (SSL) is a popular paradigm for representation learning. Recent multiview methods can be classified as sample-contrastive, dimension-contrastive, or asymmetric network-based, with each family having its own approach to avoiding informational collapse. While these families converge to solutions of similar quality, it can be empirically shown that some methods are epoch-inefficient and require longer training to reach a target performance. Two main approaches to improving efficiency are covariance eigenvalue regularization and using more views. However, these two approaches are difficult to combine due to the computational complexity of computing eigenvalues. We present the objective function FroSSL which reconciles both approaches while avoiding eigendecomposition entirely. FroSSL works by minimizing covariance Frobenius norms to avoid collapse and minimizing mean-squared error to achieve augmentation invariance. We show that FroSSL reaches competitive accuracies more quickly than any other SSL method and provide theoretical and empirical support that this faster convergence is due to how FroSSL affects the eigenvalues of the embedding covariance matrices. We also show that FroSSL learns competitive representations on linear probe evaluation when used to train a ResNet18 on several datasets, including STL-10, Tiny Imagenet, and Imagenet-100. Github"
Learning Multimodal Latent Generative Models with Energy-Based Prior
Authors: Shiyu Yuan; Jiali Cui; Hanao Li; Tian Han
Venue: ECCV 2024 (main)

Abstract: "Multimodal generative models have recently gained significant attention for their ability to learn representations across various modalities, enhancing joint and cross-generation coherence. However, most existing works use standard Gaussian or Laplacian distributions as priors, which may struggle to capture the diverse information inherent in multiple data types due to their unimodal and less informative nature. Energy-based models (EBMs), known for their expressiveness and flexibility across various tasks, have yet to be thoroughly explored in the context of multimodal generative models. In this paper, we propose a novel framework that integrates the multimodal latent generative model with the EBM. Both models can be trained jointly through a variational scheme. This approach results in a more expressive and informative prior, better-capturing of information across multiple modalities. Our experiments validate the proposed model, demonstrating its superior generation coherence. Keywords: EBM · Multimodal latent generative model"
On Learning Discriminative Features from Synthesized Data for Self-Supervised Fine-Grained Visual Recognition
Authors: Zihu Wang; Lingqiao Liu; Scott Ricardo Figueroa Weston; Samuel Tian; Peng Li
Venue: ECCV 2024 (main)

Abstract: "Self-Supervised Learning (SSL) has become a prominent approach for acquiring visual representations across various tasks, yet its application in fine-grained visual recognition (FGVR) is challenged by the intricate task of distinguishing subtle differences between categories. To overcome this, we introduce an novel strategy that boosts SSL’s ability to extract critical discriminative features vital for FGVR. This approach creates synthesized data pairs to guide the model to focus on discriminative features critical for FGVR during SSL. We start by identifying non-discriminative features using two main criteria: features with low variance that fail to effectively separate data and those deemed less important by Grad-CAM induced from the SSL loss. We then introduce perturbations to these non-discriminative features while preserving discriminative ones. A decoder is employed to reconstruct images from both perturbed and original feature vectors to create data pairs. An encoder is trained on such generated data pairs to become invariant to variations in non-discriminative dimensions while focusing on discriminative features, thereby improving the model’s performance in FGVR tasks. We demonstrate the promising FGVR performance of the proposed approach through extensive evaluation on a wide variety of datasets."
LaWa: Using Latent Space for In-Generation Image Watermarking
Authors: Ahmad Rezaei; Mohammad Akbari; Saeed Ranjbar Alvar; Arezou Fatemi; Yong Zhang
Venue: ECCV 2024 (main)

Abstract: "With generative models producing high quality images that are indistinguishable from real ones, there is growing concern regarding the malicious usage of AI-generated images. Imperceptible image watermarking is one viable solution towards such concerns. Prior watermarking methods map the image to a latent space for adding the watermark. Moreover, Latent Diffusion Models (LDM) generate the image in the latent space of a pre-trained autoencoder. We argue that this latent space can be used to integrate watermarking into the generation process. To this end, we present LaWa, an in-generation image watermarking method designed for LDMs. By using coarse-to-fine watermark embedding modules, LaWa modifies the latent space of pre-trained autoencoders and achieves high robustness against a wide range of image transformations while preserving perceptual quality of the image. We show that LaWa can also be used as a general image watermarking method. Through extensive experiments, we demonstrate that LaWa outperforms previous works in perceptual quality, robustness against attacks, and computational complexity, while having very low false positive rate. Code is available here."
Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution
Authors: Mridul Khurana; Arka Daw; M. Maruf; Josef C. Uyeda; Wasila Dahdul; Caleb Charpentier; Yasin Bakış; Henry L. Bart Jr.; Paula M. Mabee; Hilmar Lapp; James P. Balhoff; Wei-Lun Chao; Charles Stewart; Tanya Berger-Wolf; Anuj Karpatne
Venue: ECCV 2024 (main)

Abstract: "A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of large-scale image repositories in biology and recent advances in generative modeling, there is an opportunity to accelerate the discovery of evolutionary traits automatically from images. Toward this goal, we introduce Phylo-Diffusion, a novel framework for conditioning diffusion models with phylogenetic knowledge represented in the form of HIERarchical Embeddings (HIER-Embeds). We also propose two new experiments for perturbing the embedding space of Phylo-Diffusion: trait masking and trait swapping, inspired by counterpart experiments of gene knockout and gene editing/swapping. Our work represents a novel methodological advance in generative modeling to structure the embedding space of diffusion models using tree-based knowledge. Our work also opens a new chapter of research in evolutionary biology by using generative models to visualize evolutionary changes directly from images. We empirically demonstrate the usefulness of Phylo-Diffusion in capturing meaningful trait variations for fishes and birds, revealing novel insights about the biological mechanisms of their evolution. 1 1 Model and code can be found at imageomics.github.io/phylo-diffusion"
Markov Knowledge Distillation: Make Nasty Teachers trained by Self-undermining Knowledge Distillation Fully Distillable
Authors: En-hui Yang; Linfeng Ye
Venue: ECCV 2024 (main)

Abstract: "To protect intellectual property of a deep neural network (DNN), two knowledge distillation (KD) related concepts are proposed: distillable DNN and KD-resistant DNN. A DNN is said to be distillable if used as a black-box input-output teacher, it can be distilled by a KD method to train a student model so that the distilled student outperforms the student trained alone with label smoothing (LS student) in terms of accuracy. A DNN is said to be KD-resistant with respect to a specific KD method if used as a black-box input-output teacher, it cannot be distilled by that specific KD method to yield a distilled student outperforming LS student in terms of accuracy. A new KD method called Markov KD (MKD) is further presented. When applied to nasty teachers trained by self-undermining KD, MKD makes those nasty teachers fully distillable, although those nasty teachers are shown to be KD-resistant with respect to state-of-the-art KD methods existing in the literature before our work. When applied to normal teachers, MKD yields distilled students outperforming those trained by KD from the same normal teachers by a large margin. More interestingly, MKD is capable of transferring knowledge from teachers trained in one domain to students trained in another domain."
Co-speech Gesture Video Generation with 3D Human Meshes
Authors: Aniruddha Mahapatra; Richa Mishra; Ziyi Chen; Boyang Ding; Renda Li; Shoulei Wang; Jun-Yan Zhu; Peng Chang; Mei Han; Jing Xiao
Venue: ECCV 2024 (main)

Abstract: "Co-speech gesture video generation is an enabling technique for many digital human applications. Substantial progress has been made in creating high-quality talking head videos. However, existing hand gesture video generation methods are primarily limited by the widely adopted 2D skeleton-based gesture representation and still struggle to generate realistic hands. We introduce an audio-driven co-speech video generation pipeline to synthesize human speech videos leveraging 3D human mesh-based representations. By adopting a 3D human mesh-based gesture representation, we present a mesh-grounded video generator that includes a mesh texture map optimization step followed by a conditional GAN network and outputs photorealistic gesture videos with realistic hands. Our experiments on the TalkSHOW dataset demonstrate the effectiveness of our method over 2D skeleton-based baselines."
When and How do negative prompts take effect?
Authors: Yuanhao Ban; Ruochen Wang; Tianyi Zhou; Minhao Cheng; Boqing Gong; Cho-Jui Hsieh
Venue: ECCV 2024 (main)

Abstract: "The concept of negative prompts, emerging from conditional generation models like Stable Diffusion, allows users to specify what to exclude from the generated images.Despite the widespread use of negative prompts, their intrinsic mechanisms remain largely unexplored. This paper presents the first comprehensive study to uncover how and when negative prompts take effect. Our extensive empirical analysis identifies two primary behaviors of negative prompts. Delayed Effect: The impact of negative prompts is observed after positive prompts render corresponding content. Deletion Through Neutralization: Negative prompts delete concepts from the generated image through a mutual cancellation effect in latent space with positive prompts. These insights reveal significant potential real-world applications; for example, we demonstrate that negative prompts can facilitate object inpainting with minimal alterations to the background via a simple adaptive algorithm. We believe our findings will offer valuable insights for the community in capitalizing on the potential of negative prompts."
GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo Views
Authors: Yaniv Wolf; Amit Bracha; Ron Kimmel
Venue: ECCV 2024 (main)

Abstract: "Recently, 3D Gaussian Splatting (3DGS) has emerged as an efficient approach for accurately representing scenes. However, despite its superior novel view synthesis capabilities, extracting the geometry of the scene directly from the Gaussian properties remains a challenge, as those are optimized based on a photometric loss. While some concurrent models have tried adding geometric constraints during the Gaussian optimization process, they still produce noisy, unrealistic surfaces. We propose a novel approach for bridging the gap between the noisy 3DGS representation and the smooth 3D mesh representation, by injecting real-world knowledge into the depth extraction process. Instead of extracting the geometry of the scene directly from the Gaussian properties, we instead extract the geometry through a pre-trained stereo-matching model. We render stereo-aligned pairs of images corresponding to the original training poses, feed the pairs into a stereo model to get a depth profile, and finally fuse all of the profiles together to get a single mesh. The resulting reconstruction is smoother, more accurate and shows more intricate details compared to other methods for surface reconstruction from Gaussian Splatting, while only requiring a small overhead on top of the fairly short 3DGS optimization process. We performed extensive testing of the proposed method on in-the-wild scenes, obtained using a smartphone, showcasing its superior reconstruction abilities. Additionally, we tested the method on the Tanks and Temples and DTU benchmarks, achieving state-of-the-art results."