GenAI4ELab/papercli-papers huggingface dataset:
NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation
Authors: Jingyang Huo; Yikai Wang; Yanwei Fu; Xuelin Qian; Chong Li; Yun Wang; Jianfeng Feng
Venue: ECCV 2024 (main)

Abstract: "Recent fMRI-to-image approaches mainly focused on associating fMRI signals with specific conditions of pre-trained diffusion models. These approaches, while producing high-quality images, capture only a limited aspect of the complex information in fMRI signals and offer little detailed control over image creation. In contrast, this paper proposes to directly modulate the generation process of diffusion models using fMRI signals. Our approach, NeuroPictor, divides the fMRI-to-image process into three steps: i) fMRI calibrated-encoding, to tackle multi-individual pre-training for a shared latent space to minimize individual difference and enable the subsequent multi-subject training; ii) fMRI-to-image multi-subject pre-training, perceptually learning to guide diffusion model with high- and low-level conditions across different individuals; iii) fMRI-to-image single-subject refining, similar with step ii but focus on adapting to particular individual. NeuroPictor extracts high-level semantic features from fMRI signals that characterizing the visual stimulus and incrementally fine-tunes the diffusion model with a low-level manipulation network to provide precise structural instructions. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity, particularly in the within-subject setting, as evidenced in benchmark datasets. Our code and model are available at https://jingyanghuo.github.io/neuropictor/."
Coarse-to-Fine Implicit Representation Learning for 3D Hand-Object Reconstruction from a Single RGB-D Image
Authors: Xingyu Liu; Pengfei Ren; Jingyu Wang; Qi Qi; Haifeng Sun; Zirui Zhuang; Jianxin Liao
Venue: ECCV 2024 (main)

Abstract: "Recent research has explored implicit representations, such as signed distance function (SDF), for interacting hand-object reconstruction. SDF enables modeling hand-held objects with arbitrary topology and overcomes the resolution limitations of parametric models, allowing for finer-grained reconstruction. However, directly modeling detailed SDFs from visual clues presents challenges due to depth ambiguity and appearance similarity, especially in cluttered real-world scenes. In this paper, we propose a coarse-to-fine SDF framework for 3D hand-object reconstruction, which leverages the perceptual advantages of RGB-D modality in visual and geometric aspects, to progressively model the implicit field. Specifically, we model a coarse SDF for visual perception of overall scenes. Then, we propose a 3D Point-Aligned Implicit Function (3D PIFu) for fine-level SDF learning, which leverages both local geometric clues and the coarse-level visual priors to capture intricate details. Additionally, we propose a surface-aware efficient reconstruction strategy that sparsely performs SDF query based on the hand-object semantic priors. Experiments on two challenging hand-object datasets show that our method outperforms existing methods by a large margin."
Efficient Snapshot Spectral Imaging: Calibration-Free Parallel Structure with Aperture Diffraction Fusion
Authors: Tao Lv; Lihao Hu; Shiqiao Li; Chenglong Huang; Xun Cao
Venue: ECCV 2024 (main)

Abstract: "Aiming to address the repetitive need for complex calibration in existing snapshot spectral imaging methods, while better trading off system complexity and spectral reconstruction accuracy, we demonstrate a novel Parallel Coded Calibration-free Aperture Diffraction Imaging Spectrometer (PCCADIS) with simplest parallel architecture and enhanced light throughput. The system integrates monochromatic acquisition of diffraction-induced blurred spatial-spectral projections with uncalibrated chromatic filtering for guidance, enabling portable and lightweight implementation. In the inverse process of PCCADIS, aperture diffraction produces blurred patterns through global replication and band-by-band integration, posing challenges in registration with clearly structured RGB information by feature matching. Therefore, the Self-aware Spectral Fusion Cascaded Transformer (SSFCT) is proposed to realize the fusion and reconstruction of uncalibrated inputs, which demonstrates the potential to substantially improve the accuracy of snapshot spectral imaging while concurrently reducing the associated costs. Our methodology is rigorously evaluated through comprehensive simulation experiments and real reconstruction experiments with a prototype, confirming the high accuracy and user-friendliness of PCCADIS."
Enhancing Recipe Retrieval with Foundation Models: A Data Augmentation Perspective
Authors: Fangzhou Song; Bin Zhu; Yanbin Hao; Shuo Wang
Venue: ECCV 2024 (main)

Abstract: "Learning recipe and food image representation in common embedding space is non-trivial but crucial for cross-modal recipe retrieval. In this paper, we propose a new perspective for this problem by utilizing foundation models for data augmentation. Leveraging on the remarkable capabilities of foundation models (i.e., Llama2 and SAM), we propose to augment recipe and food image by extracting alignable information related to the counterpart. Specifically, Llama2 is employed to generate a textual description from the recipe, aiming to capture the visual cues of a food image, and SAM is used to produce image segments that correspond to key ingredients in the recipe. To make full use of the augmented data, we introduce Data Augmented Retrieval framework (DAR) to enhance recipe and image representation learning for cross-modal retrieval. We first inject adapter layers to pre-trained CLIP model to reduce computation cost rather than fully fine-tuning all the parameters. In addition, multi-level circle loss is proposed to align the original and augmented data pairs, which assigns different penalties for positive and negative pairs. On the Recipe1M dataset, our DAR outperforms all existing methods by a large margin. Extensive ablation studies validate the effectiveness of each component of DAR. Code is available at magentahttps://github.com/Noah888/DAR."
PapMOT: Exploring Adversarial Patch Attack against Multiple Object Tracking
Authors: Jiahuan Long; Tingsong Jiang; Wen Yao; Shuai Jia; Weijia Zhang; Weien Zhou; Chao Ma; Xiaoqian Chen
Venue: ECCV 2024 (main)

Abstract: "Tracking multiple objects in a continuous video stream is crucial for many computer vision tasks. It involves detecting and associating objects with their respective identities across successive frames. Despite significant progress made in multiple object tracking (MOT), recent studies have revealed the vulnerability of existing MOT methods to adversarial attacks. Nevertheless, all of these attacks belong to digital attacks that inject pixel-level noise into input images, and are therefore ineffective in physical scenarios. To fill this gap, we propose PapMOT, which can generate physical adversarial patches against MOT for both digital and physical scenarios. Besides attacking the detection mechanism, PapMOT also optimizes a printable patch that can be detected as new targets to mislead the identity association process. Moreover, we introduce a patch enhancement strategy to further degrade the temporal consistency of tracking results across video frames, resulting in more aggressive attacks. We further develop new evaluation metrics to assess the robustness of MOT against such attacks. Extensive evaluations on multiple datasets demonstrate that our PapMOT can successfully attack various architectures of MOT trackers in digital scenarios. We also validate the effectiveness of PapMOT for physical attacks by deploying printed adversarial patches in the real world."
HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models
Authors: Shen Zhang; Zhaowei CHEN; Zhenyu Zhao; Yuhao Chen; Yao Tang; Jiajun Liang
Venue: ECCV 2024 (main)

Abstract: "Diffusion models have become a mainstream approach for high-resolution image synthesis. However, directly generating higher-resolution images from pretrained diffusion models will encounter unreasonable object duplication and exponentially increase the generation time. In this paper, we discover that object duplication arises from feature duplication in the deep blocks of the U-Net. Concurrently, We pinpoint the extended generation times to self-attention redundancy in U-Net’s top blocks. To address these issues, we propose a tuning-free higher-resolution framework named HiDiffusion. Specifically, HiDiffusion contains Resolution-Aware U-Net (RAU-Net) that dynamically adjusts the feature map size to resolve object duplication and engages Modified Shifted Window Multi-head Self-Attention (MSW-MSA) that utilizes optimized window attention to reduce computations. we can integrate HiDiffusion into various pretrained diffusion models to scale image generation resolutions even to 4096×4096 at 1.5-6× the inference speed of previous methods. Extensive experiments demonstrate that our approach can address object duplication and heavy computation issues, achieving state-of-the-art performance on higher-resolution image synthesis tasks."
On the Approximation Risk of Few-Shot Class-Incremental Learning
Authors: Xuan Wang; Zhong Ji; Xiyao Liu; Yanwei Pang; Jungong Han
Venue: ECCV 2024 (main)

Abstract: "Few-Shot Class-Incremental Learning (FSCIL) aims to learn new concepts with few training samples while preserving previously acquired knowledge. Although promising performance has been achieved, there remains an underexplored aspect regarding the basic statistical principles underlying FSCIL. Therefore, we thoroughly explore the approximation risk of FSCIL, encompassing both transfer and consistency risks. By tightening the upper bounds of these risks, we derive practical guidelines for designing and training FSCIL models. These guidelines include (1) expanding training datasets for base classes, (2) preventing excessive focus on specific features, (3) optimizing classification margin discrepancy, and (4) ensuring unbiased classification across both base and novel classes. Leveraging these insights, we conduct comprehensive experiments to validate our principles, achieving state-of-the-art performance on three FSCIL benchmark datasets. Code is available at https://github.com/xwangrs/Approximation_FSCIL-ECCV2024. git."
Syn-to-Real Domain Adaptation for Point Cloud Completion via Part-based Approach
Authors: Yunseo Yang; Jihun Kim; Kuk-Jin Yoon
Venue: ECCV 2024 (main)

Abstract: "Acquiring complete point clouds for real-world scenarios is labor-intensive, making it impractical for conventional learning-based approaches. Numerous methods have been proposed to overcome this limitation by leveraging synthetic complete point clouds. While access to complete point clouds offers a notable advantage, they often struggle to bridge domain gaps, leading to sub-optimal performance. As a remedy, we propose a novel part-based framework for synthetic-to-real domain adaptation in point cloud completion. Our approach starts on the observation that domain gaps inherent in part information are relatively small, as parts are shared properties across categories regardless of domains. To employ part-based approach to point cloud completion, we introduce Part-Based Decomposition (PBD) module to generate part input point clouds. Subsequently, we design a Part-Aware Completion (PAC) module, which operates in a part-wise manner to produce complete point clouds. Within PAC, we devise a novel part-aware transformer to learn relationships between parts and utilize this information to infer missing parts in incomplete point clouds. Extensive experiments demonstrate that our part-based framework significantly outperforms existing studies on real-world point cloud datasets. The code is available at https: //github.com/yun-seo/PPCC"
Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain Generalization
Authors: Jiajun Hu; Jian Zhang; Lei Qi; Yinghuan Shi; Yang Gao
Venue: ECCV 2024 (main)

Abstract: "Domain generalization (DG) aims to avoid the performance degradation of the model when the distribution shift between the limited training data and unseen test data occurs. Recently, foundation models with enormous parameters have been pre-trained with huge datasets, demonstrating strong generalization ability and showing promising direction for solving the DG problem. However, fully Fine-Tuning (FT) the foundation models results in unsatisfactory out-of-distribution accuracy due to the destroyed pre-trained generalized features. Recently, Parameter-Efficient Fine-Tuning (PEFT) alleviates the above problem by fine-tuning a small portion of the model parameters while keeping the rest frozen, which achieves better generalization performance compared to FT. Nevertheless, PEFT still suffers from the issue of overfitting to the training domains. To address the above issue, we propose Parameter-Efficient Group with Orthogonal regularization (PEGO) for vision transformers, which effectively preserves the generalization ability of the pre-trained network and learns more diverse knowledge compared with conventional PEFT. Specifically, we inject a group of trainable Low-Rank Adaptation (LoRA) modules into the pre-trained model and propose an orthogonal regularization loss to enhance the generalization ability of the model. Our framework achieves SOTA performance on five DG benchmarks, while only requiring training a small number of parameters without adding additional testing cost."
SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning
Authors: Zerun Wang; Liuyu Xiang; Lang Huang; Jiafeng Mao; Ling Xiao; Toshihiko Yamasaki
Venue: ECCV 2024 (main)

Abstract: "Open-set semi-supervised learning (OSSL) leverages practical open-set unlabeled data, comprising both in-distribution (ID) samples from seen classes and out-of-distribution (OOD) samples from unseen classes, for semi-supervised learning (SSL). Prior OSSL methods initially learned the decision boundary between ID and OOD with labeled ID data, subsequently employing self-training to refine this boundary. These methods, however, suffer from the tendency to overtrust the labeled ID data: the scarcity of labeled data caused the distribution bias between the labeled samples and the entire ID data, which misleads the decision boundary to overfit. The subsequent self-training process, based on the overfitted result, fails to rectify this problem. In this paper, we address the overtrusting issue by treating OOD samples as an additional class, forming a new SSL process. Specifically, we propose SCOMatch, a novel OSSL method that 1) selects reliable OOD samples as new labeled data with an OOD memory queue and a corresponding update strategy and 2) integrates the new SSL process into the original task through our Simultaneous Close-set and Open-set self-training. SCOMatch refines the decision boundary of ID and OOD classes across the entire dataset, thereby leading to improved results. Extensive experimental results show that SCOMatch significantly outperforms the state-of-the-art methods on various benchmarks. The effectiveness is further verified through ablation studies and visualization. Our code will be available at https://github.com/komejisatori/ SCOMatch."
Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning
Authors: Meixuan Li; Tianyu Li; Guoqing Wang; Peng Wang; Yang Yang; Jie Zou
Venue: ECCV 2024 (main)

Abstract: "In this study, we address the intricate challenge of multi-task dense prediction, encompassing tasks such as semantic segmentation, depth estimation, and surface normal estimation, particularly when dealing with partially annotated data (MTPSL). The complexity arises from the absence of complete task labels for each training image. Given the inter-related nature of these pixel-wise dense tasks, our focus is on mining and capturing cross-task relationships. Existing solutions typically rely on learning global image representations for global cross-task image matching, imposing constraints that, unfortunately, sacrifice the finer structures within the images. Attempting local matching as a remedy faces hurdles due to the lack of precise region supervision, making local alignment a challenging endeavor. The introduction of Segment Anything Model (SAM) sheds light on addressing local alignment challenges by providing free and high-quality solutions for region detection. Leveraging SAM-detected regions, the subsequent challenge lies in aligning the representations within these regions. Diverging from conventional methods that directly learn a monolithic image representation, our proposal involves modeling region-wise representations using Gaussian Distributions. Aligning these distributions between corresponding regions from different tasks imparts higher flexibility and capacity to capture intra-region structures, accommodating a broader range of tasks. This innovative approach significantly enhances our ability to effectively capture cross-task relationships, resulting in improved overall performance in partially supervised multi-task dense prediction scenarios. Extensive experiments conducted on two widely used benchmarks underscore the superior effectiveness of our proposed method, showcasing state-of-the-art performance even when compared to fully supervised methods. https://github.com/HereNowL/Region-aware-Distribution-Contrast"
MasterWeaver: Taming Editability and Face Identity for Personalized Text-to-Image Generation
Authors: Yuxiang Wei; Zhilong Ji; Jinfeng Bai; Hongzhi Zhang; Lei Zhang; Wangmeng Zuo
Venue: ECCV 2024 (main)

Abstract: "Text-to-image (T2I) diffusion models have shown significant success in personalized text-to-image generation, which aims to generate novel images with human identities indicated by the reference images. Despite promising identity fidelity has been achieved by several tuning-free methods, they often suffer from overfitting issues. The learned identity tends to entangle with irrelevant information, resulting in unsatisfied text controllability, especially on faces. In this work, we present MasterWeaver, a test-time tuning-free method designed to generate personalized images with both high identity fidelity and flexible editability. Specifically, MasterWeaver adopts an encoder to extract identity features and steers the image generation through additionally introduced cross attention. To improve editability while maintaining identity fidelity, we propose an editing direction loss for training, which aligns the editing directions of our MasterWeaver with those of the original T2I model. Additionally, a face-augmented dataset is constructed to facilitate disentangled identity learning, and further improve the editability. Extensive experiments demonstrate that our MasterWeaver can not only generate personalized images with faithful identity, but also exhibit superiority in text controllability. Our code can be found at https://github. com/csyxwei/MasterWeaver."
PointRegGPT: Boosting 3D Point Cloud Registration using Generative Point-Cloud Pairs for Training
Authors: Suyi Chen; Hao Xu; Haipeng Li; Kunming Luo; Guanghui Liu; Chi-Wing Fu; Ping Tan; Shuaicheng Liu
Venue: ECCV 2024 (main)

Abstract: "Data plays a crucial role in training learning-based methods for 3D point cloud registration. However, the real-world dataset is expensive to build, while rendering-based synthetic data suffers from domain gaps. In this work, we present , boosting 3D Point cloud Registration using Generative Point-cloud pairs for Training. Given a single depth map, we first apply a random camera motion to re-project it into a target depth map. Converting them to point clouds gives a training pair. To enhance the data realism, we formulate a generative model as a depth inpainting diffusion to process the target depth map with the re-projected source depth map as the condition. Also, we design a depth correction module to alleviate artifacts caused by point penetration during the re-projection. To our knowledge, this is the first generative approach that explores realistic data generation for indoor 3D point cloud registration. When equipped with our approach, several recent algorithms can improve their performance significantly and achieve SOTA consistently on two common benchmarks. The code and dataset will be released on https://github.com/Chen-Suyi/PointRegGPT."
General Geometry-aware Weakly Supervised 3D Object Detection
Authors: Guowen Zhang; Junsong Fan; Liyi Chen; Zhaoxiang Zhang; Zhen Lei; Lei Zhang
Venue: ECCV 2024 (main)

Abstract: "3D object detection is an indispensable component for scene understanding. However, the annotation of large-scale 3D datasets requires significant human effort. To tackle this problem, many methods adopt weakly supervised 3D object detection that estimates 3D boxes by leveraging 2D boxes and scene/class-specific priors. However, these approaches generally depend on sophisticated manual priors, which is hard to generalize to novel categories and scenes. In this paper, we are motivated to propose a general approach, which can be easily adapted to new scenes and/or classes. A unified framework is developed for learning 3D object detectors from RGB images and associated 2D boxes. In specific, we propose three general components: prior injection module to obtain general object geometric priors from LLM model, 2D space projection constraint to minimize the discrepancy between the boundaries of projected 3D boxes and their corresponding 2D boxes on the image plane, and 3D space geometry constraint to build a Point-to-Box alignment loss to further refine the pose of estimated 3D boxes. Experiments on KITTI and SUN-RGBD datasets demonstrate that our method yields surprisingly high-quality 3D bounding boxes with only 2D annotation. The source code is available at https://github.com/gwenzhang/GGA."
Long-CLIP: Unlocking the Long-Text Capability of CLIP
Authors: Beichen Zhang; Pan Zhang; Xiaoyi Dong; Yuhang Zang; Jiaqi Wang
Venue: ECCV 2024 (main)

Abstract: "Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for zero-shot classification, text-image retrieval, and text-image generation by aligning image and text modalities. Despite its widespread adoption, a significant limitation of CLIP lies in the inadequate length of text input. The length of the text token is restricted to 77, and an empirical study shows the actual effective length is even less than 20. This prevents CLIP from handling detailed descriptions, limiting its applications for image retrieval and text-to-image generation with extensive prerequisites. To this end, we propose Long-CLIP as a plug-and-play alternative to CLIP that supports long-text input, retains or even surpasses its zero-shot generalizability, and aligns the CLIP latent space, making it readily replace CLIP without any further adaptation in downstream frameworks. Nevertheless, achieving this goal is far from straightforward, as simplistic fine-tuning can result in a significant degradation of CLIP’s performance. Moreover, substituting the text encoder with a language model supporting longer contexts necessitates pretraining with vast amounts of data, incurring significant expenses. Accordingly, Long-CLIP introduces an efficient fine-tuning solution on CLIP with two novel strategies designed to maintain the original capabilities, including (1) a knowledge-preserved stretching of positional embedding and (2) a primary component matching of CLIP features. With leveraging just one million extra long text-image pairs, Long-CLIP has shown the superiority to CLIP for about 20% in long caption text-image retrieval and 6% in traditional text-image retrieval tasks, , COCO and Flickr30k. Furthermore, Long-CLIP offers enhanced capabilities for generating images from detailed text descriptions by replacing CLIP in a plug-and-play manner. Codes and models are released at https://github.com/beichenzbc/ Long-CLIP."