GenAI4ELab/papercli-papers huggingface dataset:
Chains of Diffusion Models
Authors: Yanheng Wei; Lianghua Huang; Zhi-Fan Wu; Wei Wang; Yu Liu; Mingda Jia; Shuailei Ma
Venue: ECCV 2024 (main)

Abstract: "Recent generative models excel in creating high-quality single-human images but fail in complex multi-human scenarios, failing to capture accurate structural details like quantities, identity accuracy, layouts and postures. We introduce a novel approach, Chains, which enhances initial text prompts into detailed human conditions using a step-by-step process. Chains utilize a series of condition nodes—text, quantity, layout, skeleton, and 3D mesh—each undergoing an independent diffusion process. This enables high-quality human generation and advanced scene layout management in diffusion models. We evaluate Chains against a new benchmark for complex multi-human scene synthesis, showing superior performance in human quality and scene accuracy over existing methods. Remarkably, Chains achieves this with under 0.45 seconds for a 20-step inference, demonstrating both effectiveness and efficiency."
Time-Efficient and Identity-Consistent Virtual Try-On Using A Variant of Altered Diffusion Models
Authors: Phuong Hoang Dam; Jihoon Jeong; Anh T Tran; Daeyoung Kim
Venue: ECCV 2024 (main)

Abstract: "This study discusses the critical issues of Virtual Try-On in contemporary e-commerce and the prospective metaverse, emphasizing the challenges of preserving intricate texture details and distinctive features of the target person and the clothes in various scenarios, such as clothing texture and identity characteristics like tattoos or accessories. In addition to the fidelity of the synthesized images, the efficiency of the synthesis process presents a significant hurdle. Various existing approaches are explored, highlighting the limitations and unresolved aspects, e.g., identity information omission, uncontrollable artifacts, and low synthesis speed. It then proposes a novel diffusion-based solution that addresses garment texture preservation and user identity retention during virtual try-on. The proposed network comprises two primary modules - a warping module aligning clothing with individual features and a try-on module refining the attire and generating missing parts integrated with a mask-aware post-processing technique ensuring the integrity of the individual’s identity. It demonstrates impressive results, surpassing the state-of-the-art in speed by nearly 20 times during inference, with superior fidelity in qualitative assessments. Quantitative evaluations confirm comparable performance with the recent SOTA method on the VITON-HD and Dresscode datasets. We named our model Fast and Identity Preservation Virtual TryON (FIP-VITON)."
Feature Diversification and Adaptation for Federated Domain Generalization
Authors: Seunghan Yang; Seokeon Choi; Hyunsin Park; Sungha Choi; Simyung Chang; Sungrack Yun
Venue: ECCV 2024 (main)

Abstract: "Federated learning, a distributed learning paradigm, utilizes multiple clients to build a robust global model. In real-world applications, local clients often operate within their limited domains, leading to a ‘domain shift’ across clients. Privacy concerns limit each client’s learning to its own domain data, which increase the risk of overfitting. Moreover, the process of aggregating models trained on own limited domain can be potentially lead to a significant degradation in the global model performance. To deal with these challenges, we introduce the concept of federated feature diversification. Each client diversifies the own limited domain data by leveraging global feature statistics, i.e., the aggregated average statistics over all participating clients, shared through the global model’s parameters. This data diversification helps local models to learn client-invariant representations while preserving privacy. Our resultant global model shows robust performance on unseen test domain data. To enhance performance further, we develop an instance-adaptive inference approach tailored for test domain data. Our proposed instance feature adapter dynamically adjusts feature statistics to align with the test input, thereby reducing the domain gap between the test and training domains. We show that our method achieves state-of-the-art performance on several domain generalization benchmarks within a federated learning setting."
Grounding Image Matching in 3D with MASt3R
Authors: Vincent Leroy; Yohann Cabon; Jerome Revaud
Venue: ECCV 2024 (main)

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

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

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

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

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

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

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

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

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

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

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

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