mtybilly/Instruct2DS huggingface dataset:
CoTracker: It is Better to Track Together
Authors: Nikita Karaev*, Ignacio Rocco, Ben Graham, Natalia Neverova, Andrea Vedaldi, Christian Rupprecht
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We introduce , a transformer-based model that tracks a large number of 2D points in long video sequences. Differently from most existing approaches that track points independently, tracks them jointly, accounting for their dependencies. We show that joint tracking significantly improves tracking accuracy and robustness, and allows to track occluded points and points outside of the camera view. We also introduce several innovations for this class of trackers, including using token proxies that significantly improve memory efficiency and allow to track 70k points jointly and simultaneously at inference on a single GPU. is an online algorithm that operates causally on short windows. However, it is trained utilizing unrolled windows as a recurrent network, maintaining tracks for long periods of time even when points are occluded or leave the field of view. Quantitatively, substantially outperforms prior trackers on standard point-tracking benchmarks. Code and model weights are available at https://co-tracker.github.io/"
"SPHINX: A Mixer of Weights, Visual Embeddings and Image Scales for Multi-modal Large Language Models"
Authors: Ziyi Lin, Dongyang Liu, Renrui Zhang, Peng Gao*, Longtian Qiu, Han Xiao, Han Qiu, Wenqi Shao, Keqin Chen, Jiaming Han, Siyuan Huang, Yichi Zhang, Xuming He, Yu Qiao*, Hongsheng Li*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We present , a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, visual embeddings and image scales. First, for stronger vision-language alignment, we unfreeze the large language model (LLM) during pre-training, and introduce a weight mix strategy between LLMs trained by real-world and synthetic data. By directly integrating the weights from two domains, the mixed LLM can efficiently incorporate diverse semantics with favorable robustness. Then, we propose to extract comprehensive visual embeddings from various network architectures, pre-training paradigms, and information granularity, providing language models with more robust image representations. We further propose an efficient strategy aiming to better capture fine-grained appearances of high-resolution images. With a mixing of different scales and high-resolution sub-images, attains exceptional visual parsing and reasoning performance on existing evaluation benchmarks. Based on our proposed joint mixing, exhibits superior multi-modal understanding capabilities on a wide range of applications, with highlighted fine-grained visual recognition abilities such as region-level understanding, caption grounding, document layout detection, and human pose estimation. We hope our work may cast a light on the exploration of joint mixing in future MLLM research. Code is released at https://github.com/Alpha-VLLM/LLaMA2-Accessory."
PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology
Authors: Yuxuan Sun*, Hao Wu, Chenglu Zhu, Sunyi Zheng, Qizi Chen, Kai Zhang, Yunlong Zhang, Dan Wan, Xiaoxiao Lan, Mengyue Zheng, Jingxiong Li, Xinheng Lyu, Tao Lin*, Lin Yang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The emergence of Large Multimodal Models (LMMs) has unlocked remarkable potential in AI, particularly in pathology. However, the lack of specialized, high-quality benchmark impeded their development and precise evaluation. To address this, we introduce PathMMU, the largest and highest-quality expert validated pathology benchmark for LMMs. It comprises 33,428 multimodal multi-choice questions and 24,067 images from various sources, each accompanied by an explanation for the correct answer. The construction of PathMMU leverages GPT-4V’s advanced capabilities, utilizing over 30,000 image-caption pairs to enrich the descriptive quality of captions and generate corresponding Q&As in a cascading process. To maximize PathMMU’s authority, we invite seven pathologists to scrutinize each question under strict standards in PathMMU’s validation and test sets, while simultaneously setting an expert-level performance benchmark for PathMMU. We conduct extensive evaluations, including zero-shot assessments of 14 open-sourced and 4 closed-sourced LMMs and their robustness to image corruption. We also fine-tune representative LMMs to assess their adaptability to PathMMU. The empirical findings indicate that advanced LMMs struggle with the challenging PathMMU benchmark, with the top-performing LMM, GPT-4V, achieving only a 49.8% zero-shot performance, significantly lower than the 71.8% demonstrated by human pathologists. After fine-tuning, substantially smaller open-sourced LMMs can outperform GPT-4V but still fall short of the expertise shown by pathologists. We hope that the PathMMU will offer valuable insights and foster the development of more specialized, next-generation LMMs for pathology."
Improving Adversarial Transferability via Model Alignment
Authors: Avery Ma*, Amir-massoud Farahmand, Yangchen Pan, Philip Torr, Jindong Gu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Neural networks are susceptible to adversarial perturbations that are transferable across different models. In this paper, we introduce a novel model alignment technique aimed at improving a given source model’s ability in generating transferable adversarial perturbations. During the alignment process, the parameters of the source model are fine-tuned to minimize an alignment loss. This loss measures the divergence in the predictions between the source model and another, independently trained model, referred to as the witness model. To understand the effect of model alignment, we conduct a geometric analysis of the resulting changes in the loss landscape. Extensive experiments on the ImageNet dataset, using a variety of model architectures, demonstrate that perturbations generated from aligned source models exhibit significantly higher transferability than those from the original source model. Our source code is available at https://github.com/averyma/model-alignment."
RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios
Authors: Wenhao Ding*, Yulong Cao, DING ZHAO, Chaowei Xiao, Marco Pavone
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Simulation plays a crucial role in the development of autonomous vehicles (AVs) due to the potential risks associated with real-world testing. Although significant progress has been made in the visual aspects of simulators, generating complex behavior among agents remains a formidable challenge. It is not only imperative to ensure realism in the scenarios generated but also essential to incorporate preferences and conditions to facilitate controllable generation for AV training and evaluation. Traditional methods, which rely mainly on memorizing the distribution of training datasets, often fail to generate unseen scenarios. Inspired by the success of retrieval augmented generation in large language models, we present RealGen, a novel retrieval-based in-context learning framework for traffic scenario generation. RealGen synthesizes new scenarios by combining behaviors from multiple retrieved examples in a gradient-free way, which may originate from templates or tagged scenarios. This in-context learning framework endows versatile generative capabilities, including the ability to edit scenarios, compose various behaviors, and produce critical scenarios. Evaluations show that RealGen offers considerable flexibility and controllability, marking a new direction in the field of controllable traffic scenario generation. Check our project website for more information: https://realgen.github.io."
ADen: Adaptive Density Representations for Sparse-view Camera Pose Estimation
Authors: Hao Tang, Weiyao Wang, Pierre Gleize, Matt Feiszli*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recovering camera poses from a set of images is a foundational task in 3D computer vision, which powers key applications such as 3D scene/object reconstructions. Classic methods often depend on feature correspondence, such as keypoints, which require the input images to have large overlap and small viewpoint changes. Such requirements present considerable challenges in scenarios with sparse views. Recent data-driven approaches aim to directly output camera poses, either through regressing the 6DoF camera poses or formulating rotation as a probability distribution. However, each approach has its limitations. On one hand, directly regressing the camera poses can be ill-posed, since it assumes a single mode, which is not true under symmetry and leads to sub-optimal solutions. On the other hand, probabilistic approaches are capable of modeling the symmetry ambiguity, yet they sample the entire space of rotation uniformly by brute-force. This leads to an inevitable trade-off between high sample density, which improves model precision, and sample efficiency that determines the runtime. In this paper, we propose to unify the two frameworks by employing a generator and a discriminator: the generator is trained to output multiple hypotheses of 6DoF camera pose to represent a distribution and handle multi-mode ambiguity, and the discriminator is trained to identify the hypothesis that best explains the data. This allows to combine the best of both worlds, achieving substantially higher precision as well as lower runtime than previous methods in empirical evaluations."
Embodied Understanding of Driving Scenarios
Authors: Yunsong Zhou*, Linyan Huang, Qingwen Bu, Jia Zeng, Tianyu Li, Hang Qiu, Hongzi Zhu, Minyi Guo, Yu Qiao, Hongyang Li
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded upon Vision-Language Models (VLMs). Nevertheless, existing VLMs are restricted to the 2D domain, devoid of spatial awareness and long-horizon extrapolation proficiencies. We revisit the key aspects of autonomous driving and formulate appropriate rubrics. Hereby, we introduce the Embodied Language Model (ELM), a comprehensive framework tailored for agents’ understanding of driving scenes with large spatial and temporal spans. ELM incorporates space-aware pre-training to endow the agent with robust spatial localization capabilities. Besides, the model employs time-aware token selection to accurately inquire about temporal cues. We instantiate ELM on the reformulated multi-faced benchmark, and it surpasses previous state-of-the-art approaches in all aspects. All code, data, and models are accessible at https://github.com/OpenDriveLab/ELM."
Learning to Drive via Asymmetric Self-Play
Authors: Chris Zhang*, Sourav Biswas, Kelvin Wong, Kion Fallah, Lunjun Zhang, Dian Chen, Sergio Casas, Raquel Urtasun
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Large-scale data is crucial for learning realistic and capable driving policies. However, it can be impractical to rely on scaling datasets with real data alone. The majority of driving data is uninteresting, and deliberately collecting new long-tail scenarios is expensive and unsafe. We propose asymmetric self-play to scale beyond real data with additional challenging, solvable, and realistic synthetic scenarios. Our approach pairs a teacher that learns to generate scenarios it can solve but the student cannot, with a student that learns to solve them. When applied to traffic simulation, we learn realistic policies with significantly fewer collisions in both nominal and long-tail scenarios. Our policies further zero-shot transfer to generate training data for end-to-end autonomy, significantly outperforming state-of-the-art adversarial approaches, or using real data alone. For more information, visit waabi.ai/selfplay."
OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation
Authors: Zhening Huang, Xiaoyang Wu, Xi Chen, Hengshuang Zhao*, Lei Zhu, Joan Lasenby*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this work, we introduce OpenIns3D, a new 3D-input-only framework for 3D open-vocabulary scene understanding. The OpenIns3D framework employs a “Mask-Snap-Lookup” scheme. The “Mask” module learns class-agnostic mask proposals in 3D point clouds, the “Snap” module generates synthetic scene-level images at multiple scales and leverages 2D vision-language models to extract interesting objects, and the “Lookup” module searches through the outcomes of “Snap” to assign category names to the proposed masks. This approach yet simple, achieves state-of-the-art performance across a wide range of 3D open-vocabulary tasks, including recognition, object detection, and instance segmentation, on both indoor and outdoor datasets. Moreover, OpenIns3D facilitates effortless switching between different 2D detectors without requiring retraining. When integrated with powerful 2D open-world models, it achieves excellent results in scene understanding tasks. Furthermore, when combined with LLM-powered 2D models, OpenIns3D exhibits an impressive capability to comprehend and process highly complex text queries that demand intricate reasoning and real-world knowledge."
ViLA: Efficient Video-Language Alignment for Video Question Answering
Authors: Xijun Wang*, Junbang Liang, Chun-Kai Wang, Kenan Deng, Yu Lou, Ming C Lin, Shan Yang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We propose an efficient Video-Language Alignment (ViLA) network. Our ViLA model addresses both efficient frame sampling and effective cross-modal alignment in a unified way. In our ViLA network, we design a new learnable text-guided Frame-Prompter together with a cross-modal distillation (QFormer-Distiller) module. Pre-trained large image-language models have shown promising results on problems such as visual question answering (VQA). However, how to efficiently and effectively sample video frames when adapting pre-trained large image-language model to video-language alignment is still the major challenge. Compared with prior work, our ViLA model demonstrates the capability of selecting key frames with critical contents, thus improving the video-language alignment accuracy while reducing the inference latency (+3.3% on NExT-QA Temporal with 3.0× speed up). Overall, our ViLA network outperforms the state-of-the-art methods on the video question-answering benchmarks: +4.6% on STAR Interaction, +2.2% on STAR average with 3.0× speed up, ours 2-frames out-perform SeViLA 4-frames on the VLEP dataset with 4.2× speed-up. Code will be available at https://github.com/xijun-cs/ViLA."
Factorizing Text-to-Video Generation by Explicit Image Conditioning
Authors: Rohit Girdhar*, Mannat Singh, Andrew Brown, Quentin Duval, Samaneh Azadi, Sai Saketh Rambhatla, Mian Akbar Shah, Xi Yin, Devi Parikh, Ishan Misra
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We present , a text-to-video generation model that factorizes the generation into two steps: first generating an image conditioned on the text, and then generating a video conditioned on the text and the generated image. We identify critical design decisions–adjusted noise schedules for diffusion, and multi-stage training–that enable us to directly generate high quality and high resolution videos, without requiring a deep cascade of models as in prior work. In human evaluations, our generated videos are strongly preferred in quality compared to all prior work–81% vs. Google’s , 90% vs. Nvidia’s PYOCO, and 96% vs. Meta’s Make-A-Video. Our model outperforms commercial solutions such as RunwayML’s Gen2 and Pika Labs. Finally, our factorizing approach naturally lends itself to animating images based on a user’s text prompt, where our generations are preferred 96% over prior work."
MobileDiffusion: Instant Text-to-Image Generation on Mobile Devices
Authors: Yang Zhao*, Zhisheng Xiao*, Yanwu Xu, Haolin Jia, Tingbo Hou
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The deployment of large-scale text-to-image diffusion models on mobile devices is impeded by their substantial model size and high latency. In this paper, we present MobileDiffusion, an ultra-efficient text-to-image diffusion model obtained through extensive optimizations in both architecture and sampling techniques. We conduct a comprehensive examination of model architecture design to minimize model size and FLOPs, while preserving image generation quality. Additionally, we revisit the advanced sampling technique by diffusion-GAN, and make one-step sampling compatible to downstream applications trained on the base model. Empirical studies, conducted both quantitatively and qualitatively, demonstrate the effectiveness of our proposed technologies. With them, MobileDiffusion achieves instant text-to-image generation on mobile devices, establishing a new state of the art."
Open-Set Biometrics: Beyond Good Closed-Set Models
Authors: Yiyang Su, Minchul Kim, Feng Liu, Anil Jain, Xiaoming Liu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Biometric recognition has primarily addressed closed-set identification, assuming all probe subjects are in the gallery. However, most practical applications involve open-set biometrics, where probe subjects may or may not be present in the gallery. This poses distinct challenges in effectively distinguishing individuals in the gallery while minimizing false detections. While it is commonly believed that powerful biometric models can excel in both closed- and open-set scenarios, existing loss functions are inconsistent with open-set evaluation. They treat genuine (mated) and imposter (non-mated) similarity scores symmetrically and neglect the relative magnitudes of imposter scores. To address these issues, we simulate open-set evaluation using minibatches during training and introduce novel loss functions: (1) the identification-detection loss optimized for open-set performance under selective thresholds and (2) relative threshold minimization to reduce the maximum negative score for each probe. Across diverse biometric tasks, including face recognition, gait recognition, and person re-identification, our experiments demonstrate the effectiveness of the proposed loss functions, significantly enhancing open-set performance while positively impacting closed-set performance. Our code and models are available here."
UNIT: Backdoor Mitigation via Automated Neural Distribution Tightening
Authors: Siyuan Cheng*, Guangyu Shen, Kaiyuan Zhang, Guanhong Tao, Shengwei An, Hanxi Guo, Shiqing Ma, Xiangyu Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Deep neural networks (DNNs) have demonstrated effectiveness in various fields. However, DNNs are vulnerable to backdoor attacks, which inject a unique pattern, called trigger, into the input to cause misclassification to an attack-chosen target label. While existing works have proposed various methods to mitigate backdoor effects in poisoned models, they tend to be less effective against recent advanced attacks. In this paper, we introduce a novel post-training defense technique that can effectively eliminate backdoor effects for a variety of attacks. In specific, approximates a unique and tight activation distribution for each neuron in the model. It then proactively dispels substantially large activation values that exceed the approximated boundaries. Our experimental results demonstrate that outperforms 7 popular defense methods against 14 existing backdoor attacks, including 2 advanced attacks, using only 5% of clean training data. is also cost efficient. The code is accessible at https://github.com/Megum1/UNIT."
Which Model Generated This Image? A Model-Agnostic Approach for Origin Attribution
Authors: Fengyuan Liu, Haochen Luo, Yiming Li, Philip Torr, Jindong Gu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent progress in visual generative models enables the generation of high-quality images. To prevent the misuse of generated images, it is important to identify the origin model that generates them. In this work, we study the origin attribution of generated images in a practical setting where only a few images generated by a source model are available and the source model cannot be accessed. The goal is to check if a given image is generated by the source model. We first formulate this problem as a few-shot one-class classification task. To solve the task, we propose OCC-CLIP, a CLIP-based framework for few-shot one-class classification, enabling the identification of an image’s source model, even among multiple candidates. Extensive experiments corresponding to various generative models verify the effectiveness of our OCC-CLIP framework. Furthermore, an experiment based on the recently released DALL·E-3 API verifies the real-world applicability of our solution. Our source code is available at https://github. com/uwFengyuan/OCC-CLIP."
Osmosis: RGBD Diffusion Prior for Underwater Image Restoration
Authors: Opher Bar Nathan*, Deborah Levy, Tali Treibitz, Dan Rosenbaum
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Underwater image restoration is a challenging task because of water effects that increase dramatically with distance. This is worsened by lack of ground truth data of clean scenes without water. Diffusion priors have emerged as strong image restoration priors. However, they are often trained with a dataset of the desired restored output, which is not available in our case. We also observe that using only color data is insufficient, and therefore augment the prior with a depth channel. We train an unconditional diffusion model prior on the joint space of color and depth, using standard RGBD datasets of natural outdoor scenes in air. Using this prior together with a novel guidance method based on the underwater image formation model, we generate posterior samples of clean images, removing the water effects. Even though our prior did not see any underwater images during training, our method outperforms state-of-the-art baselines for image restoration on very challenging scenes. Our code, models and data are available on the project’s website."
Towards Adaptive Pseudo-label Learning for Semi-Supervised Temporal Action Localization
Authors: Feixiang Zhou, Bryan Williams, Hossein Rahmani*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Alleviating noisy pseudo labels remains a key challenge in Semi-Supervised Temporal Action Localization (SS-TAL). Existing methods often filter pseudo labels based on strict conditions, but they typically assess classification and localization quality separately, leading to suboptimal pseudo-label ranking and selection. In particular, there might be inaccurate pseudo labels within selected positives, alongside reliable counterparts erroneously assigned to negatives. To tackle these problems, we propose a novel Adaptive Pseudo-label Learning (APL) framework to facilitate better pseudo-label selection. Specifically, to improve the ranking quality, Adaptive Label Quality Assessment (ALQA) is proposed to jointly learn classification confidence and localization reliability, followed by dynamically selecting pseudo labels based on the joint score. Additionally, we propose an Instance-level Consistency Discriminator (ICD) for eliminating ambiguous positives and mining potential positives simultaneously based on inter-instance intrinsic consistency, thereby leading to a more precise selection. We further introduce a general unsupervised Action-aware Contrastive Pre-training (ACP) to enhance the discrimination both within actions and between actions and backgrounds, which benefits SS-TAL. Extensive experiments on THUMOS14 and ActivityNet v1.3 demonstrate that our method achieves state-of-the-art performance under various semi-supervised settings."
Computing the Lipschitz constant needed for fast scene recovery from CASSI measurements
Authors: Niels Chr Overgaard*, Anders Holst
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The linear inverse problem associated with the standard model for hyperspectral image recovery from CASSI measurements is considered. This is formulated as the minimization of an objective function which is the sum of a total variation regularizer and a least squares loss function. Standard first-order iterative minimization algorithms, such as ISTA, FISTA and TwIST, require as input the value of the Lipschitz constant for the gradient of the loss function, or at least a good upper bound on this value, in order to select appropriate step lengths. For the loss term considered here, this Lipschitz constant equals the square of the largest singular value of the measurement map. In applications, this number is usually computed directly as the largest eigenvalue of a huge square matrix. This can sometimes become a bottleneck in an otherwise optimized algorithm. In the present paper we effectively eliminate this bottleneck for CASSI reconstructions by showing how the Lipschitz constant can be calculated from a square matrix whose size is easily three orders of magnitudes smaller than in the direct approach."
DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance Fields
Authors: Yu Chi*, Fangneng Zhan, Sibo Wu, Christian Theobalt, Adam Kortylewski
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Progress in 3D computer vision tasks demands a huge amount of data, yet annotating multi-view images with 3D-consistent annotations, or point clouds with part segmentation is both time-consuming and challenging. This paper introduces DatasetNeRF, a novel approach capable of generating infinite, high-quality 3D-consistent 2D annotations alongside 3D point cloud segmentations, while utilizing minimal 2D human-labeled annotations. Specifically, we leverage the semantic prior within a 3D generative model to train a semantic decoder, requiring only a handful of fine-grained labeled samples. Once trained, the decoder generalizes across the latent space, enabling the generation of infinite data. The generated data is applicable across various computer vision tasks, including video segmentation and 3D point cloud segmentation in both synthetic and real-world scenarios. Our approach not only surpasses baseline models in segmentation quality, achieving superior 3D-Consistency and segmentation precision on individual images, but also demonstrates versatility by being applicable to both articulated and non-articulated generative models. Furthermore, we explore applications stemming from our approach, such as 3D-aware semantic editing and 3D inversion. Code can be found at /GenIntel/DatasetNeRF."
Flowed Time of Flight Radiance Fields
Authors: Mikhail Okunev*, Marc Mapeke, Benjamin Attal, Christian Richardt, Matthew O'Toole, James Tompkin
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Flowed time of flight radiance fields () is a method to correct for motion artifacts in continuous-wave time of flight imaging (C-ToF). As C-ToF cameras must capture multiple exposures over time to derive depth, any moving object will exhibit depth errors. We formulate an optimization problem to reconstruct the raw frames captured by the camera via an underlying 4D volumetric scene and a physically-based differentiable C-ToF simulator. With weak optical flow supervision, we can infer a 3D volume with scene flow that explains the raw captures, even though any particular time instant does not provide sufficient constraints upon the depth or motion. On synthetic sequences, we find that our approach reduces depth errors on dynamic objects by up to 20× compared to C-ToF, particularly for axial motions and large disparities (≥ 25 pixels) between raw frames. On real-world sequences, we see qualitatively similar gains with artifacts resolved on falling pillows and swinging baseball bats. All experiments were performed by university authors; CR’s role was as an advisor."
3D-GOI: 3D GAN Omni-Inversion for Multifaceted and Multi-object Editing
Authors: Haoran Li, Long Ma, Haolin Shi, Yanbin Hao, Yong Liao*, Lechao Cheng, Peng Yuan Zhou*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The current GAN inversion methods typically can only edit the appearance and shape of a single object and background while overlooking spatial information. In this work, we propose a 3D editing framework, to enable multifaceted editing of affine information (scale, translation, and rotation) on multiple objects. realizes the complex editing function by inverting the abundance of attribute codes (object shape/ appearance/ scale/ rotation/ translation, background shape/ appearance, and camera pose) controlled by GIRAFFE, a renowned 3D GAN. Accurately inverting all the codes is challenging, 3D-GOI solves this challenge following three main steps. First, we segment the objects and the background in a multi-object image. Second, we use a custom Neural Inversion Encoder to obtain coarse codes of each object. Finally, we use a round-robin optimization algorithm to get precise codes to reconstruct the image. To the best of our knowledge, is the first framework to enable multifaceted editing on multiple objects. Both qualitative and quantitative experiments demonstrate that holds immense potential for flexible, multifaceted editing in complex multi-object scenes. Our project and code are released at https://3d-goi.github.io."
Fast Registration of Photorealistic Avatars for VR Facial Animation
Authors: Chaitanya Patel*, Shaojie Bai, Te-Li Wang, Jason Saragih, Shih-En Wei
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Virtual Reality (VR) bares promise of social interactions that can feel more immersive than other media. Key to this is the ability to accurately animate a personalized photorealistic avatar, and hence the acquisition of the labels for headset-mounted camera (HMC) images need to be efficient and accurate, while wearing a VR headset. This is challenging due to oblique camera views and differences in image modality. In this work, we first show that the domain gap between the avatar and HMC images is one of the primary sources of difficulty, where a transformer-based architecture achieves high accuracy on domain-consistent data, but degrades when the domain-gap is re-introduced. Building on this finding, we propose a system split into two parts: an iterative refinement module that takes in-domain inputs, and a generic avatar-guided image-to-image domain transfer module conditioned on current estimates. These two modules reinforce each other: domain transfer becomes easier when close-to-groundtruth examples are shown, and better domain-gap removal in turn improves the registration. Our system obviates the need for costly offline optimization, and produces online registration of higher quality than direct regression method. We validate the accuracy and efficiency of our approach through extensive experiments on a commodity headset, demonstrating significant improvements over these baselines. To stimulate further research in this direction, we make our large-scale dataset and code publicly available."
CoPT: Unsupervised Domain Adaptive Segmentation using Domain-Agnostic Text Embeddings
Authors: Cristina Mata*, Kanchana N Ranasinghe, Michael S Ryoo
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Unsupervised domain adaptation (UDA) involves learning class semantics from labeled data within a source domain that generalize to an unseen target domain. UDA methods are particularly impactful for semantic segmentation, where annotations are more difficult to collect than in image classification. Despite recent advances in large-scale vision-language representation learning, UDA methods for segmentation have not taken advantage of the domain-agnostic properties of text. To address this, we present a novel Covariance-based Pixel-Text loss, CoPT, that uses domain-agnostic text embeddings to learn domain-invariant features in an image segmentation encoder. The text embeddings are generated through our LLM Domain Template process, where an LLM is used to generate source and target domain descriptions that are fed to a frozen CLIP model and combined. In experiments on four benchmarks we show that a model trained using CoPT achieves the new state of the art performance on UDA for segmentation. The code can be found at https://github.com/cfmata/CoPT."
HiFi-Score: Fine-grained Image Description Evaluation with Hierarchical Parsing Graphs
Authors: Ziwei Yao, Ruiping Wang*, Xilin Chen
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "With the advancements of vision-language models, the growing demand for generating customized image descriptions under length, target regions, and other various control conditions brings new challenges for evaluation. Most existing metrics, designed primarily for single-sentence image captioning with an overall matching score, struggle to accommodate complex description requirements, resulting in insufficient accuracy and interpretability of evaluation. Therefore, we propose HiFi-Score, a hierarchical parsing graph-based fine-grained evaluation metric. Specifically, we model both text and images as parsing graphs, which organize multi-granular instances into a hierarchical structure according to their inclusion relationships, which provides a comprehensive scene analysis for both modalities from global to local. Based on the fine-grained matching between the graphs, we can evaluate the fidelity to ensure text contents are related to image and the adequacy to ensure the image is covered by text at multiple levels. Furthermore, we employ the large language model to evaluate fluency of the language expression. Human correlation experiments on four caption-level benchmarks show that the proposed metric outperforms existing metrics. At the paragraph-level, we construct a novel dataset ParaEval and demonstrate the accuracy of the HiFi-Score in evaluating long texts. We further show its superiority in assessing vision-language models and its flexibility when applied to various image description tasks."
Image-to-Lidar Relational Distillation for Autonomous Driving Data
Authors: Anas Mahmoud*, Ali Harakeh, Steven Waslander
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Pre-trained on extensive and diverse multi-modal datasets, 2D foundation models excel at addressing 2D tasks with little or no downstream supervision, owing to their robust representations. The emergence of 2D-to-3D distillation frameworks has extended these capabilities to 3D models. However, distilling 3D representations for autonomous driving datasets presents challenges like self-similarity, class imbalance, and point cloud sparsity, hindering the effectiveness of contrastive distillation, especially in zero-shot learning contexts. Whereas other methodologies, such as similarity-based distillation, enhance zero-shot performance, they tend to yield less discriminative representations, diminishing few-shot performance. We investigate the gap in structure between the 2D and the 3D representations that result from state-of-the-art distillation frameworks and reveal a significant mismatch between the two. Additionally, we demonstrate that the observed structural gap is negatively correlated with the efficacy of the distilled representations on zero-shot and few-shot 3D semantic segmentation. To bridge this gap, we propose a relational distillation framework enforcing intra-modal and cross-modal constraints, resulting in distilled 3D representations that closely capture the structure of the 2D representation. This alignment significantly enhances 3D representation performance over those learned through contrastive distillation in zero-shot segmentation tasks. Furthermore, our relational loss consistently improves the quality of 3D representations in both in-distribution and out-of-distribution few-shot segmentation tasks, outperforming approaches that rely on the similarity loss."
Thinking Outside the BBox: Unconstrained Generative Object Compositing
Authors: Gemma Canet Tarrés*, Zhe Lin, Zhifei Zhang, Jianming Zhang, Yizhi Song, Dan Ruta, Andrew Gilbert, John Collomosse, Soo Ye Kim
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Compositing an object into an image involves multiple non-trivial sub-tasks such as object placement and scaling, color/lighting harmonization, viewpoint/geometry adjustment, and shadow/reflection generation. Recent generative image compositing methods leverage diffusion models to handle multiple sub-tasks at once. However, existing models face limitations due to their reliance on masking the original object during training, which constrains their generation to the input mask. Furthermore, obtaining an accurate input mask specifying the location and scale of the object in a new image can be highly challenging. To overcome such limitations, we define a novel problem of unconstrained generative object compositing, i.e., the generation is not bounded by the mask, and train a diffusion-based model on a synthesized paired dataset. Our first-of-its-kind model is able to generate object effects such as shadows and reflections that go beyond the mask, enhancing image realism. Additionally, if an empty mask is provided, our model automatically places the object in diverse natural locations and scales, accelerating the compositing workflow. Our model outperforms existing object placement and compositing models in various quality metrics and user studies."
Large-scale Reinforcement Learning for Diffusion Models
Authors: Yinan Zhang*, Eric Tzeng, Yilun Du, Dmitry Kislyuk*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Text-to-image diffusion models are cutting-edge deep generative models that have demonstrated impressive capabilities in generating high-quality images. However, these models are susceptible to implicit biases originating from web-scale text-image training pairs, potentially leading to inaccuracies in modeling image attributes. This susceptibility can manifest as suboptimal samples, model bias, and images that do not align with human ethics and preferences. In this paper, we propose a scalable algorithm for enhancing diffusion models using Reinforcement Learning (RL) with a diverse range of reward functions, including human preference, compositionality, and social diversity over millions of images. We demonstrate how our approach significantly outperforms existing methods for aligning diffusion models with human preferences. We further illustrate how this substantially improves pretrained Stable Diffusion (SD) models, generating samples that are preferred by humans 80.3% of the time over those from the base SD model, while simultaneously enhancing object composition and diversity of the samples."
CoMusion: Towards Consistent Stochastic Human Motion Prediction via Motion Diffusion
Authors: Jiarui Sun*, Girish Chowdhary*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Stochastic Human Motion Prediction (HMP) aims to predict multiple possible future human pose sequences from observed ones. Most prior works learn motion distributions through encoding-decoding in the latent space, which does not preserve motion’s spatial-temporal structure. While effective, these methods often require complex, multi-stage training and yield predictions that are inconsistent with the provided history and can be physically unrealistic. To address these issues, we propose , a single-stage, end-to-end diffusion-based stochastic HMP framework. is inspired from the insight that a smooth future pose initialization improves prediction performance, a strategy not previously utilized in stochastic models but evidenced in deterministic works. To generate such initialization, ’s motion predictor starts with a Transformer-based network for initial reconstruction of corrupted motion. Then, a graph convolutional network (GCN) is employed to refine the prediction considering past observations in the discrete cosine transformation (DCT) space. Our method, facilitated by the Transformer-GCN module design and a proposed variance scheduler, excels in predicting accurate, realistic, and consistent motions, while maintaining appropriate diversity. Experimental results on benchmark datasets demonstrate that surpasses prior methods across metrics, while demonstrating superior generation quality."
FedHARM: Harmonizing Model Architectural Diversity in Federated Learning
Authors: Anestis Kastellos*, Athanasios Psaltis, Charalampos Z Patrikakis, Petros Daras
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In the domain of Federated Learning (FL), the issue of managing variability in model architectures surpasses a mere technical barrier, representing a crucial aspect of the field’s evolution, especially considering the ever-increasing number of model architectures emerging in the literature. This focus on architecture variability emerges from the unique nature of FL, where diverse devices or participants, each with their own data and computational constraints, collaboratively train a shared model. The proposed FL system architecture facilitates the deployment of diverse convolutional neural network (CNN) architectures across distinct clients, while outperforming the state-of-the-art FL methodologies. F edHARM 1 capitalizes on the strengths of different architectures while limiting their weaknesses by converging each local client on a shared dataset to achieve superior performance on the test set. 1 Code: https://github.com/Kastellos/FedHARM"
EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS
Authors: Sharath Girish*, Kamal Gupta, Abhinav Shrivastava
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recently, 3D Gaussian splatting (3D-GS) has gained popularity in novel-view scene synthesis. It addresses the challenges of lengthy training times and slow rendering speeds associated with Neural Radiance Fields (NeRFs). Through rapid, differentiable rasterization of 3D Gaussians, 3D-GS achieves real-time rendering and accelerated training. They, however, demand substantial memory resources for both training and storage, as they require millions of Gaussians in their point cloud representation for each scene. We present a technique utilizing quantized embeddings to significantly reduce per-point memory storage requirements and a coarse-to-fine training strategy for a faster and more stable optimization of the Gaussian point clouds. Our approach develops a pruning stage which results in scene representations with fewer Gaussians, leading to faster training times and rendering speeds for real-time rendering of high resolution scenes. We reduce storage memory by more than an order of magnitude all while preserving the reconstruction quality. We validate the effectiveness of our approach on a variety of datasets and scenes preserving the visual quality while consuming 10-20× less memory and faster training/inference speed. Code is available here."
Global Counterfactual Directions
Authors: Bartłomiej Sobieski*, Przemyslaw Biecek*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Despite increasing progress in development of methods for generating visual counterfactual explanations, previous works consider them as an entirely local technique. In this work, we take the first step at globalizing them. Specifically, we discover that the latent space of Diffusion Autoencoders encodes the inference process of a given classifier in the form of global directions. We propose a novel proxy-based approach that discovers two types of these directions with the use of only single image in an entirely black-box manner. Precisely, g-directions allow for flipping the decision of a given classifier on an entire dataset of images, while h-directions further increase the diversity of explanations. We refer to them in general as Global Counterfactual Directions (GCDs). Moreover, we show that GCDs can be naturally combined with Latent Integrated Gradients resulting in a new black-box attribution method, while simultaneously enhancing the understanding of counterfactual explanations. We validate our approach on existing benchmarks and show that it generalizes to real-world use-cases."
TCLC-GS: Tightly Coupled LiDAR-Camera Gaussian Splatting for Autonomous Driving
Authors: Cheng Zhao*, su sun, Ruoyu Wang, Yuliang Guo, Jun-Jun Wan, Zhou Huang, Xinyu Huang, Yingjie Victor Chen, Liu Ren
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Most 3D Gaussian Splatting (3D-GS) based methods for urban scenes initialize 3D Gaussians directly with 3D LiDAR points, which not only underutilizes LiDAR data capabilities but also overlooks the potential advantages of fusing LiDAR with camera data. In this paper, we design a novel tightly coupled LiDAR-Camera Gaussian Splatting (TCLC-GS) to fully leverage the combined strengths of both LiDAR and camera sensors, enabling rapid, high-quality 3D reconstruction and novel view RGB/depth synthesis. TCLC-GS designs a hybrid explicit (colorized 3D mesh) and implicit (hierarchical octree feature) 3D representation derived from LiDAR-camera data, to enrich the properties of 3D Gaussians for splatting. 3D Gaussian’s properties are not only initialized in alignment with the 3D mesh which provides more completed 3D shape and color information, but are also endowed with broader contextual information through retrieved octree implicit features. During the Gaussian Splatting optimization process, the 3D mesh offers dense depth information as supervision, which enhances the training process by learning of a robust geometry. Comprehensive evaluations conducted on the Waymo Open Dataset and nuScenes Dataset validate our method’s state-of-the-art (SOTA) performance. Utilizing a single NVIDIA RTX 3090 Ti, our method demonstrates fast training and achieves real-time RGB and depth rendering at 90 FPS in resolution of 1920×1280 (Waymo), and 120 FPS in resolution of 1600×900 (nuScenes) in urban scenarios."