GenAI4ELab/papercli-papers huggingface dataset:
Model Stock: All we need is just a few fine-tuned models
Authors: Dong-Hwan Jang; Sangdoo Yun; Dongyoon Han
Venue: ECCV 2024 (main)

Abstract: "This paper introduces an efficient fine-tuning method for large pre-trained models, offering strong in-distribution (ID) and out-of-distribution (OOD) performance. Breaking away from traditional practices that need a multitude of fine-tuned models for averaging, our approach employs significantly fewer models to achieve final weights yet yield superior accuracy. Drawing from key insights in the weight space of fine-tuned weights, we uncover a strong link between the performance and proximity to the center of weight space. Based on this, we introduce a method that approximates a center-close weight using only two fine-tuned models, applicable during or after training. Our innovative layer-wise weight averaging technique surpasses state-of-the-art model methods such as Model Soup, utilizing only two fine-tuned models. This strategy can be aptly coined , highlighting its reliance on selecting a minimal number of models to draw a more optimized-averaged model. We demonstrate the efficacy of with fine-tuned models based upon pre-trained CLIP architectures, achieving remarkable performance on both ID and OOD tasks on the standard benchmarks, all while barely bringing extra computational demands. Our code and pre-trained models are available at https://github.com/naver-ai/model-stock."
Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution
Authors: Xi Yang; Chenhang He; Jianqi Ma; Lei Zhang
Venue: ECCV 2024 (main)

Abstract: "Real-world low-resolution (LR) videos have diverse and complex degradations, imposing great challenges on video super-resolution (VSR) algorithms to reproduce their high-resolution (HR) counterparts with high quality. Recently, the diffusion models have shown compelling performance in generating realistic details for image restoration tasks. However, the diffusion process has randomness, making it hard to control the contents of restored images. This issue becomes more serious when applying diffusion models to VSR tasks because temporal consistency is crucial to the perceptual quality of videos. In this paper, we propose an effective real-world VSR algorithm by leveraging the strength of pre-trained latent diffusion models. To ensure the content consistency among adjacent frames, we exploit the temporal dynamics in LR videos to guide the diffusion process by optimizing the latent sampling path with a motion-guided loss, ensuring that the generated HR video maintains a coherent and continuous visual flow. To further mitigate the discontinuity of generated details, we insert temporal module to the decoder and fine-tune it with an innovative sequence-oriented loss. The proposed motion-guided latent diffusion (MGLD) based VSR algorithm achieves significantly better perceptual quality than state-of-the-arts on real-world VSR benchmark datasets, validating the effectiveness of the proposed model design and training strategies. Codes and models are available at https://github.com/IanYeung/MGLD-V"
PoseCrafter: One-Shot Personalized Video Synthesis Following Flexible Pose Control
Authors: Yong Zhong; Min Zhao; Zebin You; Xiaofeng Yu; Changwang Zhang; Chongxuan Li
Venue: ECCV 2024 (main)

Abstract: "In this paper, we introduce PoseCrafter, a one-shot method for personalized video generation following the control of flexible poses. Built upon Stable Diffusion and ControlNet, we carefully design an inference process to produce high-quality videos without the corresponding ground-truth frames. First, we select an appropriate reference frame from the training video and invert it to initialize all latent variables for generation. Then, we insert the corresponding training pose into the target pose sequences to enhance faithfulness through a trained temporal attention module. Furthermore, to alleviate the face and hand degradation resulting from discrepancies between poses of training videos and inference poses, we implement simple latent editing through an affine transformation matrix involving facial and hand landmarks. Extensive experiments on several datasets demonstrate that PoseCrafter achieves superior results to baselines pre-trained on a vast collection of videos under 8 commonly used metrics. Besides, PoseCrafter can follow poses from different individuals or artificial edits and simultaneously retain the human identity in an open-domain training video. Our project page is available at https://ml-gsai.github.io/PoseCrafter-demo/."
MAD-DR: Map Compression for Visual Localization with Matchness Aware Descriptor Dimension Reduction
Authors: Qiang Wang
Venue: ECCV 2024 (main)

Abstract: "3D-structure based methods remain the top-performing solution for long-term visual localization tasks. However, the dimension of existing local descriptors is usually high and the map takes huge storage space, especially for large-scale scenes. We propose an asymmetric framework which learns to reduce the dimension of local descriptors and match them jointly. We can compress existing local descriptor to 1/256 of original size while maintaining high matching performance. Experiments on public visual localization datasets show that our pipeline obtains better results than existing map compression methods and non-structure based alternatives."
Benchmarking Object Detectors with COCO: A New Path Forward
Authors: Shweta Singh; Aayan Yadav; Jitesh Jain; Humphrey Shi; Justin Johnson; Karan Desai
Venue: ECCV 2024 (main)

Abstract: "The Common Objects in Context (COCO) dataset has been instrumental in benchmarking object detectors over the past decade. Like every dataset, COCO contains subtle errors and imperfections stemming from its annotation procedure. With the advent of high-performing models, we ask whether these errors of COCO are hindering its utility in reliably benchmarking further progress. In search for an answer, we inspect thousands of masks from COCO (2017 version) and uncover different types of errors such as imprecise mask boundaries, non-exhaustively annotated instances, and mislabeled masks. Due to the prevalence of COCO, we choose to correct these errors to maintain continuity with prior research. We develop COCO-ReM (Refined Masks), a cleaner set of annotations with visibly better mask quality than COCO-2017. We evaluate fifty object detectors and find that models that predict visually sharper masks score higher on COCO-ReM, affirming that they were being incorrectly penalized due to errors in COCO-2017. Moreover, our models trained using COCO-ReM converge faster and score higher than their larger variants trained using COCO-2017, highlighting the importance of data quality in improving object detectors. With these findings, we advocate using COCO-ReM for future object detection research. Our dataset is available at https://cocorem.xyz"
Adaptive High-Frequency Transformer for Diverse Wildlife Re-Identification
Authors: Chenyue Li; Shuoyi Chen; Mang Ye
Venue: ECCV 2024 (main)

Abstract: "Wildlife ReID involves utilizing visual technology to identify specific individuals of wild animals in different scenarios, holding significant importance for wildlife conservation, ecological research, and environmental monitoring. Existing wildlife ReID methods are predominantly tailored to specific species, exhibiting limited applicability. Although some approaches leverage extensively studied person ReID techniques, they struggle to address the unique challenges posed by wildlife. Therefore, in this paper, we present a unified, multi-species general framework for wildlife ReID. Given that high-frequency information is a consistent representation of unique features in various species, significantly aiding in identifying contours and details such as fur textures, we propose the Adaptive High-Frequency Transformer model with the goal of enhancing high-frequency information learning. To mitigate the inevitable high-frequency interference in the wilderness environment, we introduce an object-aware high-frequency selection strategy to adaptively capture more valuable high-frequency components. Notably, we unify the experimental settings of multiple wildlife datasets for ReID, achieving superior performance over state-of-the-art ReID methods. In domain generalization scenarios, our approach demonstrates robust generalization to unknown species. Code is available at https://github.com/JigglypuffStitch/AdaFreq.git."
WPS-SAM: Towards Weakly-Supervised Part Segmentation with Foundation Models
Authors: Xin-Jian Wu; Ruisong Zhang; Jie Qin; Shijie Ma; Cheng-Lin Liu
Venue: ECCV 2024 (main)

Abstract: "Segmenting and recognizing diverse object parts is crucial in computer vision and robotics. Despite significant progress in object segmentation, part-level segmentation remains underexplored due to complex boundaries and scarce annotated data. To address this, we propose a novel Weakly-supervised Part Segmentation (WPS) setting and an approach called WPS-SAM, built on the large-scale pre-trained vision foundation model, Segment Anything Model (SAM). WPS-SAM is an end-to-end framework designed to extract prompt tokens directly from images and perform pixel-level segmentation of part regions. During its training phase, it only uses weakly supervised labels in the form of bounding boxes or points. Extensive experiments demonstrate that, through exploiting the rich knowledge embedded in pre-trained foundation models, WPS-SAM outperforms other segmentation models trained with pixel-level strong annotations. Specifically, WPS-SAM achieves 68.93% mIOU and 79.53% mACC on the PartImageNet dataset, surpassing state-of-the-art fully supervised methods by approximately 4% in terms of mIOU."
Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction
Authors: Bencheng Liao; Shaoyu Chen; Bo Jiang; Tianheng Cheng; Qian Zhang; Wenyu Liu; Chang Huang; Xinggang Wang
Venue: ECCV 2024 (main)

Abstract: "Online lane graph construction is a promising but challenging task in autonomous driving. Previous methods usually model the lane graph at the pixel or piece level, and recover the lane graph by pixel-wise or piece-wise connection, which breaks down the continuity of the lane and results in suboptimal performance. Human drivers focus on and drive along the continuous and complete paths instead of considering lane pieces. Autonomous vehicles also require path-specific guidance from lane graph for trajectory planning. We argue that the path, which indicates the traffic flow, is the primitive of the lane graph. Motivated by this, we propose to model the lane graph in a novel path-wise manner, which well preserves the continuity of the lane and encodes traffic information for planning. We present a path-based online lane graph construction method, termed LaneGAP, which end-to-end learns the path and recovers the lane graph via a Path2Graph algorithm. We qualitatively and quantitatively demonstrate the superior accuracy and efficiency of LaneGAP over conventional pixel-based and piece-based methods on the challenging nuScenes and Argoverse2 datasets under controllable and fair conditions. Compared to the recent state-of-the-art piece-wise method TopoNet on the OpenLane-V2 dataset, LaneGAP still outperforms by 1.6 mIoU, further validating the effectiveness of path-wise modeling. Abundant visualizations in the supplementary material show LaneGAP can cope with diverse traffic conditions. Code is released at https://github.com/hustvl/ LaneGAP."
DeCo: Decoupled Human-Centered Diffusion Video Editing with Motion Consistency
Authors: Xiaojing Zhong; Xinyi Huang; Xiaofeng Yang; Guosheng Lin; Qingyao Wu
Venue: ECCV 2024 (main)

Abstract: "Diffusion models usher a new era of video editing, flexibly manipulating the video contents with text prompts. Despite the widespread application demand in editing human-centered videos, these models face significant challenges in handling complex objects like humans. In this paper, we introduce DeCo, a novel video editing framework specifically designed to treat humans and the background as separate editable targets, ensuring global spatial-temporal consistency by maintaining the coherence of each individual component. Specifically, we propose a decoupled dynamic human representation that utilizes a parametric human body prior to generate tailored humans while preserving the consistent motions as the original video. In addition, we consider the background as a layered atlas to apply text-guided image editing approaches on it. To further enhance the geometry and texture of humans during the optimization, we extend the calculation of score distillation sampling into normal space and image space. Moreover, we tackle inconsistent lighting between the edited targets by leveraging a lighting-aware video harmonizer, a problem previously overlooked in decompose-edit-combine approaches. Extensive qualitative and numerical experiments demonstrate that DeCo outperforms prior video editing methods in human-centered videos, especially in longer videos."
Unleashing the Potential of the Semantic Latent Space in Diffusion Models for Image Dehazing
Authors: Zizheng Yang; Hu Yu; Bing Li; Jinghao Zhang; Jie Huang; Feng Zhao
Venue: ECCV 2024 (main)

Abstract: "Diffusion models have recently been investigated as powerful generative solvers for image dehazing, owing to their remarkable capability to model the data distribution. However, the massive computational burden imposed by the retraining of diffusion models, coupled with the extensive sampling steps during the inference, limit the broader application of diffusion models in image dehazing. To address these issues, we explore the properties of hazy images in the semantic latent space of frozen pre-trained diffusion models, and propose a Diffusion Latent Inspired network for Image Dehazing, dubbed DiffLI2 D. Specifically, we first reveal that the semantic latent space of pre-trained diffusion models can represent the content and haze characteristics of hazy images, as the diffusion time-step changes. Building upon this insight, we integrate the diffusion latent representations at different time-steps into a delicately designed dehazing network to provide instructions for image dehazing. Our DiffLI2 D avoids re-training diffusion models and iterative sampling process by effectively utilizing the informative representations derived from the pre-trained diffusion models, which also offers a novel perspective for introducing diffusion models to image dehazing. Extensive experiments on multiple datasets demonstrate that the proposed method achieves superior performance to existing image dehazing methods."
Uncertainty-aware sign language video retrieval with probability distribution modeling
Authors: Xuan Wu; Hongxiang Li; yuanjiang luo; Xuxin Cheng; Xianwei Zhuang; Meng Cao; Keren Fu
Venue: ECCV 2024 (main)

Abstract: "Sign language video retrieval plays a key role in facilitating information access for the deaf community. Despite significant advances in video-text retrieval, the complexity and inherent uncertainty of sign language preclude direct applications of these techniques. Previous methods achieve mapping between sign language videos and text through fine-grained modal alignment. However, due to the scarcity of fine-grained annotations, the uncertainty inherent in sign language videos is underestimated, limiting further development of sign language retrieval tasks. To address this challenge, we propose a new Uncertainty-aware Probability Distribution Retrieval (UPRet), which conceptualizes the mapping process of sign language videos and texts in terms of probability distributions, explores their potential interrelationships, and enables flexible mappings. Experiments on three benchmarks demonstrate the effectiveness of our method, which achieves state-of-the-art results on How2Sign (59.1%), PHOENIX-2014T (72.0%), and CSL-Daily (78.4%). Our source code is available: https://github.com/xua222/ UPRet."
NeRMo: Learning Implicit Neural Representations for 3D Human Motion Prediction
Authors: Dong Wei; Huaijiang Sun; Xiaoning Sun; Shengxiang Hu
Venue: ECCV 2024 (main)

Abstract: "Predicting accurate future human poses from historically observed motions remains a challenging task due to the spatial-temporal complexity and continuity of motions. Previous historical-value methods typically interpret the motion as discrete consecutive frames, which neglects the continuous temporal dynamics and impedes the capability of handling incomplete observations (with missing values). In this paper, we propose a novel implicit Neural Representation method for the task of human Motion prediction, dubbed NeRMo, which represents the motion as a continuous function parameterized by a neural network. The core idea is to explicitly disentangle the spatial-temporal context and output the corresponding 3D skeleton positions. This separate and flexible treatment of space and time allows NeRMo to combine the following advantages. It extrapolates at arbitrary temporal locations; it can learn from both complete and incomplete observed past motions; it provides a unified framework for repairing missing values and forecasting future poses using a single trained model. In addition, we show that NeRMo exhibits compatibility with meta-learning methods, enabling it to effectively generalize to unseen time steps. Extensive experiments conducted on classical benchmarks have confirmed the superior repairing and prediction performance of our proposed method compared to existing historical-value baselines."
Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors
Authors: Tongkun Guan; Wei Shen; Xue Yang; Xuehui Wang; Xiaokang Yang
Venue: ECCV 2024 (main)

Abstract: "Existing scene text detection methods typically rely on extensive real data for training. Due to the lack of annotated real images, recent works have attempted to exploit large-scale labeled synthetic data (LSD) for pre-training text detectors. However, a synth-to-real domain gap emerges, further limiting the performance of text detectors. Differently, in this work, we propose FreeReal, a real-domain-aligned pre-training paradigm that enables the complementary strengths of both LSD and unlabeled real data (URD). Specifically, to bridge real and synthetic worlds for pre-training, a glyph-based mixing mechanism (GlyphMix) is tailored for text images. GlyphMix delineates the character structures of synthetic images and embeds them as graffiti-like units onto real images. Without introducing real domain drift, GlyphMix freely yields real-world images with partial annotations derived from synthetic labels. Furthermore, when given free fine-grained synthetic labels, GlyphMix can effectively bridge the linguistic domain gap stemming from English-dominated LSD to URD in various languages. Without bells and whistles, FreeReal achieves average gains of 1.97%, 3.90%, 3.85%, and 4.56% in improving the performance of FCENet, PSENet, PANet, and DBNet methods, respectively, consistently outperforming previous pre-training methods by a substantial margin across four public datasets. Code will be available at https://github.com/SJTU-DeepVisionLab/FreeReal."
VLAD-BuFF: Burst-aware Fast Feature Aggregation for Visual Place Recognition
Authors: Ahmad Khaliq; Ming Xu; Stephen Hausler; Michael J Milford; Sourav Garg
Venue: ECCV 2024 (main)

Abstract: "Visual Place Recognition (VPR) is a crucial component of many visual localization pipelines for embodied agents. VPR is often formulated as an image retrieval task aimed at jointly learning local features and an aggregation method. The current state-of-the-art VPR methods rely on VLAD aggregation, which can be trained to learn a weighted contribution of features through their soft assignment to cluster centers. However, this process has two key limitations. Firstly, the feature-to-cluster weighting does not account for over-represented repetitive structures within a cluster, e.g., shadows or window panes; this phenomenon is also referred to as the ‘burstiness’ problem, classically solved by discounting repetitive features before aggregation. Secondly, feature to cluster comparisons are compute-intensive for state-of-the-art image encoders with high-dimensional local features. This paper addresses these limitations by introducing VLAD-BuFF with two novel contributions: i) a self-similarity based feature discounting mechanism to learn Burst-aware features within end-to-end VPR training, and ii) Fast Feature aggregation by reducing local feature dimensions specifically through PCA-initialized learnable pre-projection. We benchmark our method on 9 public datasets, where VLAD-BuFF sets a new state of the art. Our method is able to maintain its high recall even for 12× reduced local feature dimensions, thus enabling fast feature aggregation without compromising on recall. Through additional qualitative studies, we show how our proposed weighting method effectively downweights the non-distinctive features. Source code: https://github.com/Ahmedest61/VLAD-BuFF/."
DSA: Discriminative Scatter Analysis for Early Smoke Segmentation
Authors: Lujian Yao; Haitao Zhao; Jingchao Peng; Zhongze Wang; Kaijie Zhao
Venue: ECCV 2024 (main)

Abstract: "Early smoke segmentation (ESS) plays a crucial role in accurately locating the source of smoke, facilitating prompt fire rescue operations and gas leak detection. Unlike regular objects, which are typically rigid, opaque, and have clear boundaries, ESS presents challenges due to the large areas of high transparency in early smoke. This leads to a significant similarity between smoke features and the surrounding background features. The key solution is to obtain a discriminative embedding space. Some distance-based methods have pursued this goal by using specific loss functions (e.g., pair-based Triplet loss and proxy-based NCA loss) to constrain the feature extractor. In this paper, we propose a novel approach called discriminative scatter analysis (DSA). Instead of solely measuring Euclidean distance, DSA assesses the compactness and separation of the embedding space from a sample scatter perspective. DSA is performed on both pixel-proxy scatter (IOS) and proxy-proxy scatter (OOS), and a unified loss function is designed to optimize the feature extractor. DSA can be easily integrated with regular segmentation methods. It is applied only during training and without incurring any additional computational cost during inference. Extensive experiments have demonstrated that DSA can consistently improve the performance of various models in ESS."