GenAI4ELab/papercli-papers huggingface dataset:
A Simple Background Augmentation Method for Object Detection with Diffusion Model
Authors: Yuhang Li; Xin Dong; Chen Chen; Weiming Zhuang; Lingjuan Lyu
Venue: ECCV 2024 (main)

Abstract: "In computer vision, it is well-known that a lack of data diversity will impair model performance. In this study, we address the challenges of enhancing the dataset diversity problem in order to benefit various downstream tasks such as object detection and instance segmentation. We propose a simple yet effective data augmentation approach by leveraging advancements in generative models, specifically text-to-image synthesis technologies like Stable Diffusion. Our method focuses on generating variations of labeled real images, utilizing generative object and background augmentation via inpainting to augment existing training data without the need for additional annotations. We find that background augmentation, in particular, significantly improves the models’ robustness and generalization capabilities. We also investigate how to adjust the prompt and mask to ensure the generated content comply with the existing annotations. The efficacy of our augmentation techniques is validated through comprehensive evaluations of the COCO dataset and several other key object detection benchmarks, demonstrating notable enhancements in model performance across diverse scenarios. This approach offers a promising solution to the challenges of dataset enhancement, contributing to the development of more accurate and robust computer vision models."
Echoes of the Past: Boosting Long-tail Recognition via Reflective Learning
Authors: Qihao Zhao; Yalun Dai; Shen Lin; Wei Hu; Fan Zhang; Jun Liu
Venue: ECCV 2024 (main)

Abstract: "In real-world scenarios, where knowledge distributions exhibit long-tail. Humans manage to master knowledge uniformly across imbalanced distributions, a feat attributed to their diligent practices of reviewing, summarizing, and correcting errors. Motivated by this learning process, we propose a novel learning paradigm, called reflecting learning, in handling long-tail recognition. Our method integrates three processes for reviewing past predictions during training, summarizing and leveraging the feature relation across classes, and correcting gradient conflict for loss functions. These designs are lightweight enough to plug and play with existing long-tail learning methods, achieving state-of-the-art performance in popular long-tail visual benchmarks. The experimental results highlight the great potential of reflecting learning in dealing with long-tail recognition. The code will be available at https://github.com/fistyee/LTRL."
"BlinkVision: A Benchmark for Optical Flow, Scene Flow and Point Tracking Estimation using RGB Frames and Events"
Authors: Yijin Li; Yichen Shen; Zhaoyang Huang; Shuo Chen; Weikang Bian; Xiaoyu Shi; Fu-Yun Wang; Keqiang Sun; Hujun Bao; Zhaopeng Cui; Guofeng Zhang; Hongsheng Li
Venue: ECCV 2024 (main)

Abstract: "Recent advances in event-based vision suggest that they complement traditional cameras by providing continuous observation without frame rate limitations and high dynamic range which are well-suited for correspondence tasks such as optical flow and point tracking. However, so far there is still a lack of comprehensive benchmarks for correspondence tasks with both event data and images. To fill this gap, we propose BlinkVision, a large-scale and diverse benchmark with rich modality and dense annotation of correspondence. BlinkVision has several appealing properties: 1) Rich modalities: It encompasses both event data and RGB images. 2) Rich annotations: It provides dense per-pixel annotations covering optical flow, scene flow, and point tracking. 3) Large vocabulary: It incorporates 410 daily categories, sharing common classes with widely-used 2D and 3D datasets such as LVIS and ShapeNet. 4) Naturalistic: It delivers photorealism data and covers a variety of naturalistic factors such as camera shake and deformation. BlinkVision enables extensive benchmarks on three types of correspondence tasks (i.e., optical flow, point tracking and scene flow estimation) for both image-based methods and event-based methods, leading to new observations, practices, and insights for future research. The benchmark website is https://www.blinkvision.net/."
A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
Authors: Qiyu Chen; Huiyuan Luo; Chengkan Lv; Zhengtao Zhang
Venue: ECCV 2024 (main)

Abstract: "Anomaly synthesis strategies can effectively enhance unsupervised anomaly detection. However, existing strategies have limitations in the coverage and controllability of anomaly synthesis, particularly for weak defects that are very similar to normal regions. In this paper, we propose Global and Local Anomaly co-Synthesis Strategy (GLASS), a novel unified framework designed to synthesize a broader coverage of anomalies under the manifold and hypersphere distribution constraints of Global Anomaly Synthesis (GAS) at the feature level and Local Anomaly Synthesis (LAS) at the image level. Our method synthesizes near-in-distribution anomalies in a controllable way using Gaussian noise guided by gradient ascent and truncated projection. GLASS achieves state-of-the-art results on the MVTec AD (detection AUROC of 99.9%), VisA, and MPDD datasets and excels in weak defect detection. The effectiveness and efficiency have been further validated in industrial applications for woven fabric defect detection. The code and dataset are available at: https://github.com/cqylunlun/GLASS."
Deep Polarization Cues for Single-shot Shape and Subsurface Scattering Estimation
Authors: Chenhao Li; Trung Thanh Ngo; Hajime Nagahara
Venue: ECCV 2024 (main)

Abstract: "In this work, we propose a novel learning-based method to jointly estimate the shape and subsurface scattering (SSS) parameters of translucent objects by utilizing polarization cues. Although polarization cues have been used in various applications, such as shape from polarization (SfP), BRDF estimation, and reflection removal, their application in SSS estimation has not yet been explored. Our observations indicate that the SSS affects not only the light intensity but also the polarization signal. Hence, the polarization signal can provide additional cues for SSS estimation. We also introduce the first large-scale synthetic dataset of polarized translucent objects for training our model. Our method outperforms several baselines from the SfP and inverse rendering realms on both synthetic and real data, as demonstrated by qualitative and quantitative results."
Rethinking Features-Fused-Pyramid-Neck for Object Detection
Authors: Hulin Li
Venue: ECCV 2024 (main)

Abstract: "Multi-head detectors typically employ a features-fused-pyramid-neck for multi-scale detection and are widely adopted in the industry. However, this approach faces feature misalignment when representations from different hierarchical levels of the feature pyramid are forcibly fused point-to-point. To address this issue, we designed an independent hierarchy pyramid (IHP) architecture to evaluate the effectiveness of the features-unfused-pyramid-neck for multi-head detectors. Subsequently, we introduced soft nearest neighbor interpolation (SNI) with a weight-downscaling factor to mitigate the impact of feature fusion at different hierarchies while preserving key textures. Furthermore, we present a feature adaptive selection method for downsampling in extended spatial windows (ESD) to retain spatial features and enhance lightweight convolutional techniques (GSConvE). These advancements culminate in our secondary features alignment solution (SA) for real-time detection, achieving state-of-the-art results on Pascal VOC and MS COCO. Code will be released at https://github.com/AlanLi1997/rethinking-fpn."
Spatial-Temporal Multi-level Association for Video Object Segmentation
Authors: Deshui Miao; Xin Li; Zhenyu He; Huchuan Lu; Ming-Hsuan Yang
Venue: ECCV 2024 (main)

Abstract: "Existing semi-supervised video object segmentation methods either focus on temporal feature matching or spatial-temporal feature modeling. However, they do not address the issues of sufficient target interaction and efficient parallel processing simultaneously, thereby constraining the learning of dynamic, target-aware features. To tackle these limitations, this paper proposes a spatial-temporal multi-level association framework, which jointly associates reference frame, test frame, and object features to achieve sufficient interaction and parallel target ID association with a spatial-temporal memory bank for efficient video object segmentation. Specifically, we construct a spatial-temporal multi-level feature association module to learn better target-aware features, which formulates feature extraction and interaction as the efficient operations of object self-attention, reference object enhancement, and test reference correlation. In addition, we propose a spatial-temporal memory to assist feature association and temporal ID assignment and correlation. We evaluate the proposed method by conducting extensive experiments on numerous video object segmentation datasets, including DAVIS 2016/2017 val, DAVIS 2017 test-dev, and YouTube-VOS 2018/2019 val. The favorable performance against the state-of-the-art methods demonstrates the effectiveness of our approach. All source code and trained models will be made publicly available."
Sparse Refinement for Efficient High-Resolution Semantic Segmentation
Authors: Zhijian Liu; Zhuoyang Zhang; Samir Khaki; Shang Yang; Haotian Tang; Chenfeng Xu; Kurt Keutzer; Song Han
Venue: ECCV 2024 (main)

Abstract: "Semantic segmentation empowers numerous real-world applications, such as autonomous driving and augmented/mixed reality. These applications often operate on high-resolution images (, 8 megapixels) to capture the fine details. However, this comes at the cost of considerable computational complexity, hindering the deployment in latency-sensitive scenarios. In this paper, we introduce SparseRefine, a novel approach that enhances dense low-resolution predictions with sparse high-resolution refinements. Based on coarse low-resolution outputs, SparseRefine first uses an entropy selector to identify a sparse set of pixels with high entropy. It then employs a sparse feature extractor to efficiently generate the refinements for those pixels of interest. Finally, it leverages a gated ensembler to apply these sparse refinements to the initial coarse predictions. SparseRefine can be seamlessly integrated into any existing semantic segmentation model, regardless of CNN- or ViT-based. SparseRefine achieves significant speedup: 1.5 to 3.7 times when applied to HRNet-W48, SegFormer-B5, Mask2Former-T/L and SegNeXt-L on Cityscapes, with negligible to no loss of accuracy. Our “dense+sparse” paradigm paves the way for efficient high-resolution visual computing."
Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion
Authors: Sanghyun Kim; Seohyeon Jung; Balhae Kim; Moonseok Choi; Jinwoo Shin; Juho Lee
Venue: ECCV 2024 (main)

Abstract: "This paper addresses the societal concerns arising from large-scale text-to-image diffusion models for generating potentially harmful or copyrighted content. Existing models rely heavily on internet-crawled data, wherein problematic concepts persist due to incomplete filtration processes. While previous approaches somewhat alleviate the issue, they often rely on text-specified concepts, introducing challenges in accurately capturing nuanced concepts and aligning model knowledge with human understandings. In response, we propose a framework named Human Feedback Inversion (HFI), where human feedback on model-generated images is condensed into textual tokens guiding the mitigation or removal of problematic images. The proposed framework can be built upon existing techniques for the same purpose, enhancing their alignment with human judgment. By doing so, we simplify the training objective with a self-distillation-based technique, providing a strong baseline for concept removal. Our experimental results demonstrate our framework significantly reduces objectionable content generation while preserving image quality, contributing to the ethical deployment of AI in the public sphere. Code is available at https://github.com/nannullna/ safeguard-hfi. Caution: This paper contains discussions and examples related to harmful content, including text and images. Reader discretion is advised."
An Explainable Vision Question Answer Model via Diffusion Chain-of-Thought
Authors: Chunhao LU; Qiang Lu; Jake Luo
Venue: ECCV 2024 (main)

Abstract: "Explainable visual question-answering research focuses on generating explanations for answers. However, in complex VQA scenarios, there can be a significant semantic distance between the question and the answer. This means that generating explanations solely for the answer can lead to a semantic discrepancy between the content of the explanation and the question-answering content. To address this, we propose a step-by-step reasoning approach to reduce such semantic discrepancies. Additionally, the task of explaining VQA should include generating explanations for the reasoning steps to obtain explanations for the final answer. We introduce a diffusion chain-of-thought model to implement this step-by-step reasoning and the explanation process. The model consists of two processes: the external diffusion and the internal diffusion. The external diffusion process generates explanations for each reasoning step, while the internal diffusion process describes the probability of the question transitioning to each step of the explanation. Through experiments on eight sub-tasks in the ScienceQA dataset, we demonstrate that our diffusion chain-of-thought model outperforms GPT-3.5 in terms of the answer accuracy and the explanation ability while only using 1% of GPT-3.5’s parameters. Furthermore, the model approaches GPT-4, Llama, and so on in eight sub-tasks."
RaFE: Generative Radiance Fields Restoration
Authors: Zhongkai Wu; Ziyu Wan; Jing Zhang; Jing Liao; Dong Xu
Venue: ECCV 2024 (main)

Abstract: "NeRF (Neural Radiance Fields) has demonstrated tremendous potential in novel view synthesis and 3D reconstruction, but its performance is sensitive to input image quality, which struggles to achieve high-fidelity rendering when provided with low-quality sparse input viewpoints. Previous methods for NeRF restoration are tailored for specific degradation type, ignoring the generality of restoration. To overcome this limitation, we propose a generic radiance fields restoration pipeline, named RaFE, which applies to various types of degradations, such as low resolution, blurriness, noise, compression artifacts, or their combinations. Our approach leverages the success of off-the-shelf 2D restoration methods to recover the multi-view images individually. Instead of reconstructing a blurred NeRF by averaging inconsistencies, we introduce a novel approach using Generative Adversarial Networks (GANs) for NeRF generation to better accommodate the geometric and appearance inconsistencies present in the multi-view images. Specifically, we adopt a two-level tri-plane architecture, where the coarse level remains fixed to represent the low-quality NeRF, and a fine-level residual tri-plane to be added to the coarse level is modeled as a distribution with GAN to capture potential variations in restoration. We validate RaFE on both synthetic and real cases for various restoration tasks, demonstrating superior performance in both quantitative and qualitative evaluations, surpassing other 3D restoration methods specific to single task. Please see our project website zkaiwu.github.io/RaFE."
UniProcessor: A Text-induced Unified Low-level Image Processor
Authors: Huiyu Duan; Xiongkuo Min; Sijing Wu; Wei Shen; Guangtao Zhai
Venue: ECCV 2024 (main)

Abstract: "Image processing, including image restoration, image enhancement, etc., involves generating a high-quality clean image from a degraded input. Deep learning-based methods have shown superior performance for various image processing tasks in terms of single-task conditions. However, they require to train separate models for different degradations and levels, which limits the generalization abilities of these models and restricts their applications in real-world. In this paper, we propose a text-induced Unified image Processor for low-level vision tasks, termed UniProcessor, which can effectively process various degradation types and levels, and support multimodal control. Specifically, our UniProcessor encodes degradation-specific information with the subject prompt and process degradations with the manipulation prompt. These context control features are injected into the UniProcessor backbone via cross-attention to control the processing procedure. For automatic subject-prompt generation, we further build a vision-language model for general-purpose low-level degradation perception via instruction tuning techniques. Our UniProcessor covers 30 degradation types, and extensive experiments demonstrate that our UniProcessor can well process these degradations without additional training or tuning and outperforms other competing methods. Moreover, with the help of degradation-aware context control, our UniProcessor first shows the ability to individually handle a single distortion in an image with multiple degradations. Code is available at: https: //github.com/IntMeGroup/UniProcessor."
Fast Sprite Decomposition from Animated Graphics
Authors: Tomoyuki Suzuki; Kotaro Kikuchi; Kota Yamaguchi
Venue: ECCV 2024 (main)

Abstract: "This paper presents an approach to decomposing animated graphics into sprites, a set of basic elements or layers. Our approach builds on the optimization of sprite parameters to fit the raster video. For efficiency, we assume static textures for sprites to reduce the search space while preventing artifacts using a texture prior model. To further speed up the optimization, we introduce the initialization of the sprite parameters utilizing a pre-trained video object segmentation model and user input of single frame annotations. For our study, we construct the Crello Animation dataset from an online design service and define quantitative metrics to measure the quality of the extracted sprites. Experiments show that our method significantly outperforms baselines for similar decomposition tasks in terms of the quality/efficiency tradeoff."
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection
Authors: Liren He; Zhengkai Jiang; Jinlong Peng; Wenbing Zhu; Liang Liu; Qiangang Du; Xiaobin Hu; Mingmin Chi; Yabiao Wang; Chengjie Wang
Venue: ECCV 2024 (main)

Abstract: "In the field of multi-class anomaly detection, reconstruction-based methods derived from single-class anomaly detection face the well-known challenge of “learning shortcuts”, wherein the model fails to learn the patterns of normal samples as it should, opting instead for shortcuts such as identity mapping or artificial noise elimination. Consequently, the model becomes unable to reconstruct genuine anomalies as normal instances, resulting in a failure of anomaly detection. To counter this issue, we present a novel unified feature reconstruction-based anomaly detection framework termed RLR (Reconstruct features from a Learnable Reference representation). Unlike previous methods, RLR utilizes learnable reference representations to compel the model to learn normal feature patterns explicitly, thereby prevents the model from succumbing to the “learning shortcuts” issue. Additionally, RLR incorporates locality constraints into the learnable reference to facilitate more effective normal pattern capture and utilizes a masked learnable key attention mechanism to enhance robustness. Evaluation of RLR on the 15-category MVTec-AD dataset and the 12-category VisA dataset shows superior performance compared to state-of-the-art methods under the unified setting. Code is available at RLR."
IRSAM: Advancing Segment Anything Model for Infrared Small Target Detection
Authors: Mingjin Zhang; Yuchun Wang; Jie Guo; Yunsong Li; Xinbo Gao; Jing Zhang
Venue: ECCV 2024 (main)

Abstract: "The recent Segment Anything Model (SAM) is a significant advancement in natural image segmentation, exhibiting potent zero-shot performance suitable for various downstream image segmentation tasks. However, directly utilizing the pretrained SAM for Infrared Small Target Detection (IRSTD) task falls short in achieving satisfying performance due to a notable domain gap between natural and infrared images. Unlike a visible light camera, a thermal imager reveals an object’s temperature distribution by capturing infrared radiation. Small targets often show a subtle temperature transition at the object’s boundaries. To address this issue, we propose the IRSAM model for IRSTD, which improves SAM’s encoder-decoder architecture to learn better feature representation of infrared small objects. Specifically, we design a Perona-Malik diffusion (PMD)-based block and incorporate it into multiple levels of SAM’s encoder to help it capture essential structural features while suppressing noise. Additionally, we devise a Granularity-Aware Decoder (GAD) to fuse the multi-granularity feature from the encoder to capture structural information that may be lost in long-distance modeling. Extensive experiments on the public datasets, including NUAA-SIRST, NUDT-SIRST, and IRSTD-1K, validate the design choice of IRSAM and its significant superiority over representative state-of-the-art methods. The source code are available at: github.com/IPIC-Lab/IRSAM."
PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth Estimation
Authors: Zhenyu Li; Shariq Farooq Bhat; Peter Wonka
Venue: ECCV 2024 (main)

Abstract: "This paper introduces PatchRefiner, an advanced framework for metric single image depth estimation aimed at high-resolution real-domain inputs. While depth estimation is crucial for applications such as autonomous driving, 3D generative modeling, and 3D reconstruction, achieving accurate high-resolution depth in real-world scenarios is challenging due to the constraints of existing architectures and the scarcity of detailed real-world depth data. PatchRefiner adopts a tile-based methodology, reconceptualizing high-resolution depth estimation as a refinement process, which results in notable performance enhancements. Utilizing a pseudo-labeling strategy that leverages synthetic data, PatchRefiner incorporates a Detail and Scale Disentangling (DSD) loss to enhance detail capture while maintaining scale accuracy, thus facilitating the effective transfer of knowledge from synthetic to real-world data. Our extensive evaluations demonstrate PatchRefiner’s superior performance, significantly outperforming existing benchmarks on the Unreal4KStereo dataset by 18.1% in terms of the root mean squared error (RMSE) and showing marked improvements in detail accuracy and consistent scale estimation on diverse real-world datasets like CityScape, ScanNet++, and ETH3D."