GenAI4ELab/papercli-papers huggingface dataset:
SpatialFormer: Towards Generalizable Vision Transformers with Explicit Spatial Understanding
Authors: Han Xiao; Wenzhao Zheng; Sicheng Zuo; Peng Gao; Jie Zhou; Jiwen Lu
Venue: ECCV 2024 (main)

Abstract: "Vision transformers have demonstrated promising results and become core components in many tasks. Most existing works focus on context feature extraction and incorporate spatial information through additional positional embedding. However, they only consider the local positional information within each image token and cannot effectively model the global spatial relations of the underlying scene. To address this challenge, we propose an efficient vision transformer architecture, SpatialFormer, with explicit spatial understanding for generalizable image representation learning. Specifically, we accompany the image tokens with adaptive spatial tokens to represent the context and spatial information respectively. We initialize the spatial tokens with positional encoding to introduce general spatial priors and augment them with learnable embeddings to model adaptive spatial information. For better generalization, we employ a decoder-only overall architecture and propose a bilateral cross-attention block for efficient interactions between context and spatial tokens. SpatialFormer learns transferable image representations with explicit scene understanding, where the output spatial tokens can further serve as enhanced initial queries for task-specific decoders for better adaptations to downstream tasks. Extensive experiments on image classification, semantic segmentation, and 2D/3D object detection tasks demonstrate the efficiency and transferability of the proposed SpatialFormer architecture. Code is available at https://github.com/Euphoria16/SpatialFormer."
OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving
Authors: Wenzhao Zheng; Weiliang Chen; Yuanhui Huang; Borui Zhang; Yueqi Duan; Jiwen Lu
Venue: ECCV 2024 (main)

Abstract: "Understanding how the 3D scene evolves is vital for making decisions in autonomous driving. Most existing methods achieve this by predicting the movements of object boxes, which cannot capture more fine-grained scene information. In this paper, we explore a new framework of learning a world model, OccWorld, in the 3D occupancy space to simultaneously predict the movement of the ego car and the evolution of the surrounding scenes. We propose to learn a world model based on 3D occupancy rather than 3D bounding boxes and segmentation maps for three reasons: 1) expressiveness. 3D occupancy can describe the more fine-grained 3D structure of the scene; 2) efficiency. 3D occupancy is more economical to obtain (e.g., from sparse LiDAR points). 3) versatility. 3D occupancy can adapt to both vision and LiDAR. To facilitate the modeling of the world evolution, we learn a reconstruction-based scene tokenizer on the 3D occupancy to obtain discrete scene tokens to describe the surrounding scenes. We then adopt a GPT-like spatial-temporal generative transformer to generate subsequent scene and ego tokens to decode the future occupancy and ego trajectory. Extensive experiments on nuScenes demonstrate the ability of OccWorld to effectively model the driving scene evolutions. OccWorld also produces competitive planning results without using instance and map supervision. Code: https://github.com/wzzheng/OccWorld."
MyVLM: Personalizing VLMs for User-Specific Queries
Authors: Yuval Alaluf; Elad Richardson; Sergey Tulyakov; Kfir Aberman; Danny Cohen-Or
Venue: ECCV 2024 (main)

Abstract: "Recent large-scale vision-language models (VLMs) have demonstrated remarkable capabilities in understanding and generating textual descriptions for visual content. However, these models lack an understanding of user-specific concepts. In this work, we take a first step toward the personalization of VLMs, enabling them to learn and reason over user-provided concepts. For example, we explore whether these models can learn to recognize you in an image and communicate what you are doing, tailoring the model to reflect your personal experiences and relationships. To effectively recognize a variety of user-specific concepts, we augment the VLM with external concept heads that function as toggles for the model, enabling the VLM to identify the presence of specific target concepts in a given image. Having recognized the concept, we learn a new concept embedding in the intermediate feature space of the VLM. This embedding is tasked with guiding the language model to naturally integrate the target concept in its generated response. We apply our technique to BLIP-2 and LLaVA for personalized image captioning and further show its applicability for personalized visual question-answering. Our experiments demonstrate our ability to generalize to unseen images of learned concepts while preserving the model behavior on unrelated inputs. Code and data will be made available upon acceptance."
AMEGO: Active Memory from long EGOcentric videos
Authors: Gabriele Goletto; Tushar Nagarajan; Giuseppe Averta; Dima Damen
Venue: ECCV 2024 (main)

Abstract: "Egocentric videos provide a unique perspective into individuals’ daily experiences, yet their unstructured nature presents challenges for perception. In this paper, we introduce , a novel approach aimed at enhancing the comprehension of very-long egocentric videos. Inspired by the human’s ability to maintain information from a single watching, focuses on constructing a self-contained representations from one egocentric video, capturing key locations and object interactions. This representation is semantic-free and facilitates multiple queries without the need to reprocess the entire visual content. Additionally, to evaluate our understanding of very-long egocentric videos, we introduce the new (), composed of more than 20K of highly challenging visual queries from EPIC-KITCHENS. These queries cover different levels of video reasoning (sequencing, concurrency and temporal grounding) to assess detailed video understanding capabilities. We showcase improved performance of on , surpassing other video QA baselines by a substantial margin."
Power Variable Projection for Initialization-Free Large-Scale Bundle Adjustment
Authors: Simon Weber; Je Hyeong Hong; Daniel Cremers
Venue: ECCV 2024 (main)

Abstract: "Most Bundle Adjustment (BA) solvers like the Levenberg-Marquard algorithm require a good initialization. Instead, initialization-free BA remains a largely uncharted territory. The under-explored Variable Projection algorithm (VarPro) exhibits a wide convergence basin even without initialization. Coupled with object space error formulation, recent works have shown its ability to solve small-scale initialization-free bundle adjustment problem. To make such initialization-free BA approaches scalable, we introduce Power Variable Projection (PoVar), extending a recent inverse expansion method based on power series. Importantly, we link the power series expansion to Riemannian manifold optimization. This projective framework is crucial to solve large-scale bundle adjustment problems without initialization. Using the real-world BAL dataset, we experimentally demonstrate that our solver achieves state-of-the-art results in terms of speed and accuracy. To our knowledge, this work is the first to address the scalability of BA without initialization opening new venues for initialization-free structure-from-motion."
Collaborative Control for Geometry-Conditioned PBR Image Generation
Authors: Shimon Vainer; Mark Boss; Mathias Parger; Konstantin Kutsy; Dante De Nigris; Ciara Rowles; Nicolas Perony; Simon Donné
Venue: ECCV 2024 (main)

Abstract: "Graphics pipelines require physically-based rendering (PBR) materials, yet current 3D content generation approaches are built on RGB models. We propose to model the PBR image distribution directly, avoiding photometric inaccuracies in RGB generation and the inherent ambiguity in extracting PBR from RGB. As existing paradigms for cross-modal fine-tuning are not suited for PBR generation due to both a lack of data and the high dimensionality of the output modalities, we propose to train a new PBR model that is tightly linked to a frozen RGB model using a novel cross-network communication paradigm. As the base RGB model is fully frozen, the proposed method retains its general performance and remains compatible with IPAdapters for that base model."
Co-synthesis of Histopathology Nuclei Image-Label Pairs using a Context-Conditioned Joint Diffusion Model
Authors: Seonghui Min; Hyun-Jic Oh; Won-Ki Jeong
Venue: ECCV 2024 (main)

Abstract: "In multi-class histopathology nuclei analysis tasks, the lack of training data becomes a main bottleneck for the performance of learning-based methods. To tackle this challenge, previous methods have utilized generative models to increase data by generating synthetic samples. However, existing methods often overlook the importance of considering the context of biological tissues (e.g., shape, spatial layout, and tissue type) in the synthetic data. Moreover, while generative models have shown superior performance in synthesizing realistic histopathology images, none of the existing methods are capable of producing image-label pairs at the same time. In this paper, we introduce a novel framework for co-synthesizing histopathology nuclei images and paired semantic labels using a context-conditioned joint diffusion model. We propose conditioning of a diffusion model using nucleus centroid layouts with structure-related text prompts to incorporate spatial and structural context information into the generation targets. Moreover, we enhance the granularity of our synthesized semantic labels by generating instance-wise nuclei labels using distance maps synthesized concurrently in conjunction with the images and semantic labels. We demonstrate the effectiveness of our framework in generating high-quality samples on multi-institutional, multi-organ, and multi-modality datasets. Our synthetic data consistently outperforms existing augmentation methods in the downstream tasks of nuclei segmentation and classification."
One-stage Prompt-based Continual Learning
Authors: Youngeun Kim; Yuhang Li; Priyadarshini Panda
Venue: ECCV 2024 (main)

Abstract: "Prompt-based Continual Learning (PCL) has gained considerable attention as a promising continual learning solution because it achieves state-of-the-art performance while preventing privacy violations and memory overhead problems. Nonetheless, existing PCL approaches face significant computational burdens because of two Vision Transformer (ViT) feed-forward stages; one is for the query ViT that generates a prompt query to select prompts inside a prompt pool; the other one is a backbone ViT that mixes information between selected prompts and image tokens. To address this, we introduce a one-stage PCL framework by directly using the intermediate layer’s token embedding as a prompt query. This design removes the need for an additional feed-forward stage for query ViT, resulting in ∼ 50% computational cost reduction for both training and inference with marginal accuracy drop (≤ 1%). We further introduce a Query-Pool Regularization (QR) loss that regulates the relationship between the prompt query and the prompt pool to improve representation power. The QR loss is only applied during training time, so there is no computational overhead at inference from the QR loss. With the QR loss, our approach maintains ∼ 50% computational cost reduction during inference as well as outperforms the prior two-stage PCL methods by ∼ 1.4% on public class-incremental continual learning benchmarks including CIFAR-100, ImageNet-R, and DomainNet."
SpaceJAM: a Lightweight and Regularization-free Method for Fast Joint Alignment of Images
Authors: Nir Barel; Ron A Shapira Weber; Nir Mualem; Shahaf E Finder; Oren Freifeld
Venue: ECCV 2024 (main)

Abstract: "The unsupervised task of Joint Alignment (JA) of images is beset by challenges such as high complexity, geometric distortions, and convergence to poor local or even global optima. Although Vision Transformers (ViT) have recently provided valuable features for JA, they fall short of fully addressing these issues. Consequently, researchers frequently depend on expensive models and numerous regularization terms, resulting in long training times and challenging hyperparameter tuning. We introduce the Spatial Joint Alignment Model (SpaceJAM), a novel approach that addresses the JA task with efficiency and simplicity. SpaceJAM leverages a compact architecture with only ∼16K trainable parameters and uniquely operates without the need for regularization or atlas maintenance. Evaluations on SPair-71K and CUB datasets demonstrate that SpaceJAM matches the alignment capabilities of existing methods while significantly reducing computational demands and achieving at least a 10x speedup. SpaceJAM sets a new standard for rapid and effective image alignment, making the process more accessible and efficient. Our code is available at: https://bgu-cs-vil.github.io/ SpaceJAM/."
APL: Anchor-based Prompt Learning for One-stage Weakly Supervised Referring Expression Comprehension
Authors: Yaxin Luo; Jiayi Ji; Xiaofu Chen; Yuxin Zhang; Tianhe Ren; Gen Luo
Venue: ECCV 2024 (main)

Abstract: "Referring Expression Comprehension (REC) aims to ground the target object based on a given referring expression, which requires expensive instance-level annotations for training. To address this issue, recent advances explore an efficient one-stage weakly supervised REC model called RefCLIP. Particularly, RefCLIP utilizes anchor features of pre-trained one-stage detection networks to represent candidate objects and conducts anchor-text ranking to locate the referent. Despite the effectiveness, we identify that visual semantics of RefCLIP are ambiguous and insufficient for weakly supervised REC modeling. To address this issue, we propose a novel method that enriches visual semantics with various prompt information, called anchor-based prompt learning (APL). Specifically, APL contains an innovative anchor-based prompt encoder (APE) to produce discriminative prompts covering three aspects of REC modeling, e.g., position, color and category. These prompts are dynamically fused into anchor features to improve the visual description power. In addition, we propose two novel auxiliary objectives to achieve accurate vision-language alignment in APL, namely text reconstruction loss and visual alignment loss. To validate APL, we conduct extensive experiments on four REC benchmarks, namely RefCOCO, RefCOCO+, RefCOCOg and ReferIt. Experimental results not only show the state-of-the-art performance of APL against existing methods on four benchmarks, e.g., +6.44% over RefCLIP on RefCOCO, but also confirm its strong generalization ability on weakly supervised referring expression segmentation. Source codes released at: https://github.com/Yaxin9Luo/APL."
GenQ: Quantization in Low Data Regimes with Generative Synthetic Data
Authors: Yuhang Li; Youngeun Kim; Donghyun Lee; Souvik Kundu; Priyadarshini Panda
Venue: ECCV 2024 (main)

Abstract: "In the realm of deep neural network deployment, low-bit quantization presents a promising avenue for enhancing computational efficiency. However, it often hinges on the availability of training data to mitigate quantization errors, a significant challenge when data availability is scarce or restricted due to privacy or copyright concerns. Addressing this, we introduce , a novel approach employing an advanced Generative AI model to generate photorealistic, high-resolution synthetic data, overcoming the limitations of traditional methods that struggle to accurately mimic complex objects in extensive datasets like ImageNet. Our methodology is underscored by two robust filtering mechanisms designed to ensure the synthetic data closely aligns with the intrinsic characteristics of the actual training data. In case of limited data availability, the actual data is used to guide the synthetic data generation process, enhancing fidelity through the inversion of learnable token embeddings. Through rigorous experimentation, establishes new benchmarks in data-free and data-scarce quantization, significantly outperforming existing methods in accuracy and efficiency, thereby setting a new standard for quantization in low data regimes. Code is released at https: //github.com/Intelligent-Computing-Lab-Yale/GenQ."
MVDD: Multi-View Depth Diffusion Models
Authors: Zhen Wang; Qiangeng Xu; Feitong Tan; Menglei Chai; Shichen Liu; Rohit Pandey; Sean Fanello; Achuta Kadambi; Yinda Zhang
Venue: ECCV 2024 (main)

Abstract: "Denoising diffusion models have demonstrated outstanding results in 2D image generation, yet it remains a challenge to replicate its success in 3D shape generation. In this paper, we propose leveraging multi-view depth, which represents complex 3D shapes in a 2D data format that is easy to denoise. We pair this representation with a diffusion model, MVDD, that is capable of generating high-quality dense point clouds with 20K+ points with fine-grained details. To enforce 3D consistency in multi-view depth, we introduce an epipolar line segment attention that conditions the denoising step for a view on its neighboring views. Additionally, a depth fusion module is incorporated into diffusion steps to further ensure the alignment of depth maps. When augmented with surface reconstruction, MVDD can also produce high-quality 3D meshes. Furthermore, MVDD stands out in other tasks such as depth completion, and can serve as a 3D prior, significantly boosting many downstream tasks, such as GAN inversion. State-of-the-art results from extensive experiments demonstrate MVDD’s excellent ability in 3D shape generation, depth completion, and its potential as a 3D prior for downstream tasks."
Rethinking Video-Text Understanding: Retrieval from Counterfactually Augmented Data
Authors: Wufei Ma; Kai Li; Zhongshi Jiang; Moustafa Meshry; Qihao Liu; Huiyu Wang; Christian Haene; Alan Yuille
Venue: ECCV 2024 (main)

Abstract: "Recent video-text foundation models have demonstrated strong performance on a wide variety of downstream video understanding tasks. Can these video-text models genuinely understand the contents of natural videos? Standard video-text evaluations could be misleading as many questions can be inferred merely from the objects and contexts in a single frame or biases inherent in the datasets. In this paper, we aim to better assess the capabilities of current video-text models and understand their limitations. We propose a novel evaluation task for video-text understanding, namely retrieval from counterfactually augmented data (RCAD), and a new dataset. To succeed on our new evaluation task, models must derive a comprehensive understanding of the video from cross-frame reasoning. Analyses show that previous video-text foundation models can be easily fooled by counterfactually augmented data and are far behind human-level performance. In order to narrow the gap between video-text models and human performance on RCAD, we identify a key limitation of current contrastive approaches on video-text data and introduce , a more effective approach to learn action semantics by leveraging knowledge obtained from a pretrained large language model. Experiments and analyses show that our approach successfully learn more discriminative action embeddings and improves results on when applied to multiple video-text models. Our dataset and project page is available here."
Risk-Aware Self-Consistent Imitation Learning for Trajectory Planning in Autonomous Driving
Authors: Yixuan Fan; Ya-Li Li; Shengjin Wang
Venue: ECCV 2024 (main)

Abstract: "Planning for the ego vehicle is the ultimate goal of autono-mous driving. Although deep learning-based methods have been widely applied to predict future trajectories of other agents in traffic scenes, directly using them to plan for the ego vehicle is often unsatisfactory. This is due to misaligned objectives during training and deployment: a planner that only aims to imitate human driver trajectories is insufficient to accomplish driving tasks well. We argue that existing training processes may not endow models with an understanding of how the physical world evolves. To address this gap, we propose RaSc, which stands for Risk-aware Self-consistent imitation learning. RaSc not only imitates driving trajectories, but also learns the motivations behind human driver behaviors (to be risk-aware) and the consequences of its own actions (by being self-consistent). These two properties stem from our novel prediction branch and training objectives regarding Time-To-Collision (TTC). Moreover, we enable the model to better mine hard samples during training by checking its self-consistency. Our experiments on the large-scale real-world nuPlan dataset demonstrate that RaSc outperforms previous state-of-the-art learning-based methods, in both open-loop and, more importantly, closed-loop settings."
Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud Segmentation
Authors: Ruijie Xu; CHUYU ZHANG; Hui Ren; Xuming He
Venue: ECCV 2024 (main)

Abstract: "We tackle the novel class discovery in point cloud segmentation, which discovers novel classes based on the semantic knowledge of seen classes. Existing work proposes an online point-wise clustering method with a simplified equal class-size constraint on the novel classes to avoid degenerate solutions. However, the inherent imbalanced distribution of novel classes in point clouds typically violates the equal class-size constraint. Moreover, point-wise clustering ignores the rich spatial context information of objects, which results in less expressive representation for semantic segmentation. To address the above challenges, we propose a novel self-labeling strategy that adaptively generates high-quality pseudo-labels for imbalanced classes during model training. In addition, we develop a dual-level representation that incorporates regional consistency into the point-level classifier learning, reducing noise in generated segmentation. Finally, we conduct extensive experiments on two widely used datasets, SemanticKITTI and SemanticPOSS, and the results show our method outperforms the state of the art by a large margin."
EBDM: Exemplar-guided Image Translation with Brownian-bridge Diffusion Models
Authors: Eungbean Lee; Somi Jeong; Kwanghoon Sohn
Venue: ECCV 2024 (main)

Abstract: "Exemplar-guided image translation, synthesizing photo-realistic images that conform to both structural control and style exemplars, is attracting attention due to its ability to enhance user control over style manipulation. Previous methodologies have predominantly depended on establishing dense correspondences across cross-domain inputs. Despite these efforts, they incur quadratic memory and computational costs for establishing dense correspondence, resulting in limited versatility and performance degradation. In this paper, we propose a novel approach termed Exemplar-guided Image Translation with Brownian-Bridge Diffusion Models (EBDM). Our method formulates the task as a stochastic Brownian bridge process, a diffusion process with a fixed initial point as structure control and translates into the corresponding photo-realistic image while being conditioned solely on the given exemplar image. To efficiently guide the diffusion process toward the style of exemplar, we delineate three pivotal components: the Global Encoder, the Exemplar Network, and the Exemplar Attention Module to incorporate global and detailed texture information from exemplar images. Leveraging Bridge diffusion, the network can translate images from structure control while exclusively conditioned on the exemplar style, leading to more robust training and inference processes. We illustrate the superiority of our method over competing approaches through comprehensive benchmark evaluations and visual results."