GenAI4ELab/papercli-papers huggingface dataset:
Two-Stage Video Shadow Detection via Temporal-Spatial Adaption
Authors: Xin Duan; Yu Cao; Lei Zhu; Gang Fu; Xin Wang; Renjie ZHANG; Ping Li
Venue: ECCV 2024 (main)

Abstract: "Video Shadow Detection (VSD) is an important computer vision task focusing on detecting and segmenting shadows throughout the entire video sequence. Despite their remarkable performance, existing VSD methods and datasets mainly focus on the dominant and isolated shadows. Consequently, VSD under complex scenes is still an unexplored challenge. To address this issue, we built a new dataset, Complex Video Shadow Dataset (CVSD), which contains 196 video clips including 19,757 frames with complex shadow patterns, to enhance the practical applicability of VSD. We propose a two-stage training paradigm and a novel network to handle complex dynamic shadow scenarios. Regarding the complex video shadow detection as conditioned feature adaption, we propose temporal- and spatial-adaption blocks for incorporating temporal information and attaining high-quality shadow detection, respectively. To the best of our knowledge, we are the first to construct the dataset and model tailored for the complex VSD task. Experimental results show the superiority of our model over state-of-the-art VSD methods. Our project will be publicly available at: https://hizuka590.github.io/CVSD."
Towards Physical World Backdoor Attacks against Skeleton Action Recognition
Authors: Qichen Zheng; Yi Yu; SIYUAN YANG; Jun Liu; Kwok-Yan Lam; Alex Kot
Venue: ECCV 2024 (main)

Abstract: "Skeleton Action Recognition (SAR) has attracted significant interest for its efficient representation of the human skeletal structure. Despite its advancements, recent studies have raised security concerns in SAR models, particularly their vulnerability to adversarial attacks. However, such strategies are limited to digital scenarios and ineffective in physical attacks, limiting their real-world applicability. To investigate the vulnerabilities of SAR in the physical world, we introduce the Physical Skeleton Backdoor Attacks (PSBA), the first exploration of physical backdoor attacks against SAR. Considering the practicalities of physical execution, we introduce a novel trigger implantation method that integrates infrequent and imperceivable actions as triggers into the original skeleton data. By incorporating a minimal amount of this manipulated data into the training set, PSBA enables the system misclassify any skeleton sequences into the target class when the trigger action is present. We examine the resilience of PSBA in both poisoned and clean-label scenarios, demonstrating its efficacy across a range of datasets, poisoning ratios, and model architectures. Additionally, we introduce a trigger-enhancing strategy to strengthen attack performance in the clean label setting. The robustness of PSBA is tested against three distinct backdoor defenses, and the stealthiness of PSBA is evaluated using two quantitative metrics. Furthermore, by employing a Kinect V2 camera, we compile a dataset of human actions from the real world to mimic physical attack situations, with our findings confirming the effectiveness of our proposed attacks. Our project website can be found at https://qichenzheng.github. io/psba-website."
SAM-guided Graph Cut for 3D Instance Segmentation
Authors: Haoyu Guo; He Zhu; Sida Peng; Yuang Wang; Yujun Shen; Ruizhen Hu; Xiaowei Zhou
Venue: ECCV 2024 (main)

Abstract: "∗ Equal contribution † Corresponding authors This paper addresses the challenge of 3D instance segmentation by simultaneously leveraging 3D geometric and multi-view image information. Many previous works have applied deep learning techniques to 3D point clouds for instance segmentation. However, these methods often failed to generalize to various types of scenes due to the scarcity and low-diversity of labeled 3D point cloud data. Some recent works have attempted to lift 2D instance segmentations to 3D within a bottom-up framework. The inconsistency in 2D instance segmentations among views can substantially degrade the performance of 3D segmentation. In this work, we introduce a novel 3D-to-2D query framework to effectively exploit 2D segmentation models for 3D instance segmentation. Specifically, we pre-segment the scene into several superpoints in 3D, and formulate the task into a graph cut problem. The superpoint graph is constructed based on 2D segmentation models, enabling great segmentation performance on various types of scenes. We employ a GNN to further improve the robustness, which can be trained using pseudo 3D labels generated from 2D segmentation models. Experimental results on the ScanNet200, ScanNet++ and KITTI-360 datasets demonstrate that our method achieves state-of-the-art segmentation performance. Code will be made publicly available for reproducibility."
Fully Authentic Visual Question Answering Dataset from Online Communities
Authors: Chongyan Chen; Mengchen Liu; Noel C Codella; Yunsheng Li; Lu Yuan; Danna Gurari
Venue: ECCV 2024 (main)

Abstract: "Visual Question Answering (VQA) entails answering questions about images. We introduce the first VQA dataset in which all contents originate from an authentic use case. Sourced from online question answering community forums, we call it VQAonline. We characterize this dataset and how it relates to eight mainstream VQA datasets. Observing that answers in our dataset tend to be much longer (i.e., a mean of 173 words) and so incompatible with standard VQA evaluation metrics, we instead utilize popular metrics for longer text evaluation for evaluating six state-of-the-art VQA models on VQAonline and report where they struggle most. Finally, we analyze which evaluation metrics align best with human judgments. We publicly-share the dataset at: https://vqaonline.github.io/."
Active Generation for Image Classification
Authors: Tao Huang; Jiaqi Liu; Shan You; Chang Xu
Venue: ECCV 2024 (main)

Abstract: "Recently, the growing capabilities of deep generative models have underscored their potential in enhancing image classification accuracy. However, existing methods often demand the generation of a disproportionately large number of images compared to the original dataset, while having only marginal improvements in accuracy. This computationally expensive and time-consuming process hampers the practicality of such approaches. In this paper, we propose to address the efficiency of image generation by focusing on the specific needs and characteristics of the model. With a central tenet of active learning, our method, named ActGen, takes a training-aware approach to image generation. It aims to create images akin to the challenging or misclassified samples encountered by the current model and incorporates these generated images into the training set to augment model performance. ActGen introduces an attentive image guidance technique, using real images as guides during the denoising process of a diffusion model. The model’s attention on class prompt is leveraged to ensure the preservation of similar foreground object while diversifying the background. Furthermore, we introduce a gradient-based generation guidance method, which employs two losses to generate more challenging samples and prevent the generated images from being too similar to previously generated ones. Experimental results on the CIFAR and ImageNet datasets demonstrate that our method achieves better performance with a significantly reduced number of generated images. Code is available at https://github.com/hunto/ActGen."
FuseTeacher: Modality-fused Encoders are Strong Vision Supervisors
Authors: Chen-Wei Xie; Siyang Sun; Liming Zhao; Pandeng Li; Shuailei Ma; Yun Zheng
Venue: ECCV 2024 (main)

Abstract: "Learning visual representation with image-text datasets attracts a lot of attention in recent years. Existing approaches primarily rely on cross-modality supervision, and incorporate intra-modality supervision if necessary. They overlook the potential benefits of modality-fused supervision. Since modality-fused representation augments the image representation with textual information, we conjecture it is more discriminative and potential to be a strong teacher for visual representation learning. In this paper, we validate this hypothesis by experiments and propose a novel method that learns visual representation by modality-fused supervision. Specifically, we introduce a fusion encoder that encodes image and text into a fusion representation. This representation can be utilized to supervise the visual representation learning in two distillation ways: (i) Classification Distillation: we cluster image-text pairs into K clusters using the fusion representation and assign each pair a soft cluster assignment, which is served as a pseudo classification label for supervising the image encoder. (ii) Retrieval Distillation: we calculate the similarities between the fusion representation and all text representations in the same batch. By using the similarity distribution as pseudo retrieval similarity between the corresponding image and all texts, we can prevent one-to-one contrastive learning from separating relevant but unpaired pairs. The is compatible with existing language supervised visual representation learning methods. Experimental results demonstrate that it is able to bring significant improvements and achieves state-of-the-art methods on various datasets. Code, datasets and pre-trained models are available at https://github.com/Eniac-Xie/FuseTeacher."
Learning Local Pattern Modularization for Point Cloud Reconstruction from Unseen Classes
Authors: Chao Chen; Yu-Shen Liu; Zhizhong Han
Venue: ECCV 2024 (main)

Abstract: "It is challenging to reconstruct 3D point clouds in unseen classes from single 2D images. Instead of object-centered coordinate system, current methods generalized global priors learned in seen classes to reconstruct 3D shapes from unseen classes in viewer-centered coordinate system. However, the reconstruction accuracy and interpretability are still eager to get improved. To resolve this issue, we introduce to learn local pattern modularization for reconstructing 3D shapes in unseen classes, which achieves both good generalization ability and high reconstruction accuracy. Our insight is to learn a local prior which is class-agnostic and easy to generalize in object-centered coordinate system. Specifically, the local prior is learned via a process of learning and customizing local pattern modularization in seen classes. During this process, we first learn a set of patterns in local regions, which is the basis in the object-centered coordinate system to represent an arbitrary region on shapes across different classes. Then, we modularize each region on an initially reconstructed shape using the learned local patterns. Based on that, we customize the local pattern modularization using the input image by refining the reconstruction with more details. Our method enables to reconstruct high fidelity point clouds from unseen classes in object-centered coordinate system without requiring a large number of patterns or any additional information, such as segmentation supervision or camera poses. Our experimental results under widely used benchmarks show that our method achieves the state-of-the-art reconstruction accuracy for shapes from unseen classes. The code is available at https://github.com/ chenchao15/Unseen."
Understanding Multi-compositional learning in Vision and Language models via Category Theory
Authors: Sotirios Panagiotis Chytas; Hyunwoo J Kim; Vikas Singh
Venue: ECCV 2024 (main)

Abstract: "Pre-trained large language models (and multi-modal models) offer excellent performance across a wide range of tasks. Despite their effectiveness, we have limited knowledge of their internal knowledge representation. To get started, we use the classic problem of Compositional Zero-Shot Learning (CZSL) as an example, and first provide a structured view of the latent space that any general model (LLM or otherwise) should nominally respect. We obtain a practical solution to the CZSL problem that can deal with both Open and Closed-World single-attribute compositions as well as multi-attribute compositions with relative ease, where we achieve performance competitive with methods designed solely for that task (i.e., adaptations to other tasks are difficult). Then, we extend this perspective to analysis of existing LLMs and ask to what extent they satisfy our axiomatic definitions. Our analysis shows a mix of interesting and unsurprising findings, but nonetheless suggests that our criteria is meaningful and may yield a more structured approach for potential incorporation in training such models, strategies for additional data collection, and diagnostics beyond visual inspection. The code is available at https://github.com/SPChytas/CatCom."
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
Authors: Shangchao Su; Bin Li; Xiangyang Xue
Venue: ECCV 2024 (main)

Abstract: "With the increasing availability of Foundation Models, federated tuning has garnered attention in the field of federated learning, utilizing data and computation resources from multiple clients to collaboratively fine-tune foundation models. However, in real-world federated scenarios, there often exist a multitude of heterogeneous clients with varying computation and communication resources, rendering them incapable of supporting the entire model fine-tuning process. In response to this challenge, we propose a novel federated tuning algorithm, FedRA. The implementation of FedRA is straightforward and can be seamlessly integrated into any Transformer-based model without the need for further modification to the original model. Specifically, in each communication round, FedRA randomly generates an allocation matrix. For resource-constrained clients, it reorganizes a small number of layers from the original model based on the allocation matrix and fine-tunes using adapters. Subsequently, the server aggregates the updated adapter parameters from the clients according to the current allocation matrix into the corresponding layers of the original model. It is worth noting that FedRA also supports scenarios where none of the clients can support the entire global model, which is an impressive advantage. We conduct experiments on two large-scale image datasets, DomainNet and NICO++, under various non-iid settings. The results demonstrate that FedRA outperforms the compared methods significantly."
Panel-Specific Degradation Representation for Raw Under-Display Camera Image Restoration
Authors: Youngjin Oh; Keuntek Lee; Jooyoung Lee; Dae-Hyun Lee; Nam Ik Cho
Venue: ECCV 2024 (main)

Abstract: "Under-display camera (UDC) image restoration aims to restore images distorted by the OLED display panel covering the frontal camera on a smartphone. Previous deep learning-based UDC restoration methods focused on restoring the image within the RGB domain with the collection of real or synthetic RGB datasets. However, UDC images in these datasets exhibit domain differences from real commercial smartphone UDC images while inherently constraining the problem and solution within the RGB domain. To address this issue, we collect well-aligned sensor-level real UDC images using panels from two commercial smartphones equipped with UDC. We also propose a new UDC restoration method to exploit the disparities between degradations caused by different panels, considering that UDC degradations are specific to the type of OLED panel. For this purpose, we train an encoder with an unsupervised learning scheme using triplet loss that aims to extract the inherent degradations caused by different panels from degraded UDC images as implicit representations. The learned panel-specific degradation representations are then provided as priors to our restoration network based on an efficient Transformer network. Extensive experiments show that our proposed method achieves state-of-the-art performance on our real raw image dataset and generalizes well to previous datasets. Our dataset and code is available at https://github.com/OBAKSA/ DREUDC."
Unlocking Textual and Visual Wisdom: Open-Vocabulary 3D Object Detection Enhanced by Comprehensive Guidance from Text and Image
Authors: Pengkun Jiao; Na Zhao; Jingjing Chen; Yu-Gang Jiang
Venue: ECCV 2024 (main)

Abstract: "Open-vocabulary 3D object detection (OV-3DDet) aims to localize and recognize both seen and previously unseen object categories within any new 3D scene. While language and vision foundation models have achieved success in handling various open-vocabulary tasks with abundant training data, OV-3DDet faces a significant challenge due to the limited availability of training data. Although some pioneering efforts have integrated vision-language models (VLM) knowledge into OV-3DDet learning, the full potential of these foundational models has yet to be fully exploited. In this paper, we unlock the textual and visual wisdom to tackle the open-vocabulary 3D detection task by leveraging the language and vision foundation models. We leverage a vision foundation model to provide image-wise guidance for discovering novel classes in 3D scenes. Specifically, we utilize a object detection vision foundation model to enable the zero-shot discovery of objects in images, which serves as the initial seeds and filtering guidance to identify novel 3D objects. Additionally, to align the 3D space with the powerful vision-language space, we introduce a hierarchical alignment approach, where the 3D feature space is aligned with the vision-language feature space using a pretrained VLM at the instance, category, and scene levels. Through extensive experimentation, we demonstrate significant improvements in accuracy and generalization, highlighting the potential of foundation models in advancing open-vocabulary 3D object detection in real-world scenarios."
Diffusion-Guided Weakly Supervised Semantic Segmentation
Authors: Sung-Hoon Yoon; Hoyong Kwon; Jaeseok Jeong; Daehee Park; Kuk-Jin Yoon
Venue: ECCV 2024 (main)

Abstract: "Weakly Supervised Semantic Segmentation (WSSS) with classification labels typically uses Class Activation Maps to localize the object based on Convolutional Neural Networks (CNN). With limited receptive fields, CNN-based CAMs often fail to localize the whole object. The emergence of a Vision Transformer (ViT) alleviates the problem with superior performance, but the lack of locality in ViT introduces a new challenge. Inspired by the ability of Denoising Diffusion Probabilistic Models (DDPM) to capture high-level semantic information, we bring diffusion models to WSSS to resolve the problem. Firstly, to fuse and semantically align the information between DDPM and ViT, we design the Locality Fusion Cross Attention (LFCA) module. Using the aggregated features from the denoising process of the pretrained DDPM, LFCA generates CAMs (Diffusion-CAMs) that provide locality information to CAMs from ViT (ViT-CAMs). Secondly, by adding noise to the original image and denoising it with DDPM, we obtain a denoised image that can be leveraged as an augmented sample. To effectively guide ViT in excavating the relation between the patches, we devise the Patch Affinity Consistency (PAC) between the outputs of the original image and the denoised image. Extensive ablation studies support the superiority of the proposed method. Our method achieves new state-of-the-art performance on two widely used datasets in WSSS; PASCAL VOC 2012 and MS-COCO 2014. The code is available at https://github.com/yoon307/DiG."
Weakly-Supervised Spatio-Temporal Video Grounding with Variational Cross-Modal Alignment
Authors: Yang Jin; Yadong Mu
Venue: ECCV 2024 (main)

Abstract: "This paper explores the spatio-temporal video grounding (STVG) task, which aims at localizing a particular object corresponding to a given textual description in an untrimmed video. Existing approaches mainly resort to object-level manual annotations as the supervision for addressing this challenging task. Such a paradigm heavily constrains the scalability of processing large-scale unlabeled data. To this end, we present a novel framework that is capable of grounding the target object relying only on the video-sentence correspondence. Specifically, our model re-formulates the original STVG task as two cross-modal alignment sub-problems: region-phrase and frame-sentence. Since the absence of ground-truth alignments during the training stage, we treat them as latent variables and learn to model the joint conditional distribution by reconstructing the interactions of entities in the video. The entire framework can be effectively optimized by the variational Expectation-Maximization (EM) algorithm, which alternates between two updating steps for progressively maximizing the likelihood of query sentence, thereby approximating the real cross-modal assignment. Extensive experiments on two video benchmarks (VidSTG and HC-STVG) further show the effectiveness of the proposed method."
When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset
Authors: Yi Zhang; Wang Zeng; Sheng Jin; Chen Qian; Ping Luo; Wentao Liu
Venue: ECCV 2024 (main)

Abstract: "Recent years have witnessed increasing research attention towards pedestrian detection by taking the advantages of different sensor modalities (RGB, IR, Depth, LiDAR and Event). However, designing a unified generalist model that can effectively process diverse sensor modalities remains a challenge. This paper introduces MMPedestron, a novel generalist model for multimodal perception. Unlike previous specialist models that only process one or a pair of specific modality inputs, MMPedestron is able to process multiple modal inputs and their dynamic combinations. The proposed approach comprises a unified encoder for modal representation and fusion and a general head for pedestrian detection. We introduce two extra learnable tokens, MAA and MAF, for adaptive multi-modal feature fusion. In addition, we construct the MMPD dataset, the first large-scale benchmark for multi-modal pedestrian detection. This benchmark incorporates existing public datasets and a newly collected dataset called EventPed, covering a wide range of sensor modalities including RGB, IR, Depth, LiDAR, and Event data. With multi-modal joint training, our model achieves state-of-the-art performance on a wide range of pedestrian detection benchmarks, surpassing leading models tailored for specific sensor modality. For example, it achieves 71.1 AP on COCO-Persons and 72.6 AP on LLVIP. Notably, our model achieves comparable performance to the InternImage-H model on CrowdHuman with 30× smaller parameters. Codes and data are available at https://github. com/BubblyYi/MMPedestron. : Corresponding author."
NVS-Adapter: Plug-and-Play Novel View Synthesis from a Single Image
Authors: Yoonwoo Jeong; Jinwoo Lee; Chiheon Kim; Minsu Cho; Doyup Lee
Venue: ECCV 2024 (main)

Abstract: "Recent advancements in Novel View Synthesis (NVS) from a single image have produced impressive results by leveraging the generation capabilities of pre-trained Text-to-Image (T2I) models. However, previous NVS approaches require extra optimization to use other plug-and-play image generation modules such as ControlNet and LoRA, as they fine-tune the T2I parameters. In this study, we propose an efficient plug-and-play adaptation module, NVS-Adapter, that is compatible with existing plug-and-play modules without extensive fine-tuning. We introduce target views and reference view alignment to improve the geometric consistency of multi-view predictions. Experimental results demonstrate the compatibility of our NVS-Adapter with existing plug-and-play modules. Moreover, our NVS-Adapter shows superior performance over state-of-the-art methods on NVS benchmarks although it does not fine-tune billions of parameters of the pre-trained T2I models. The code and data are publicly available at postech-cvlab.github.io/nvsadapter/ 1 1 This work was done during the summer internship program at KakaoBrain."