GenAI4ELab/papercli-papers huggingface dataset:
Closed-Loop Unsupervised Representation Disentanglement with $\\beta$-VAE Distillation and Diffusion Probabilistic Feedback
Authors: Xin Jin; Bohan Li; Baao Xie; Wenyao Zhang; Jinming Liu; Ziqiang Li; Tao Yang; Wenjun Zeng
Venue: ECCV 2024 (main)

Abstract: "Representation disentanglement may help AI fundamentally understand the real world and thus benefit both discrimination and generation tasks. It currently has at least three unresolved core issues: (i) heavy reliance on label annotation and synthetic data — causing poor generalization on natural scenarios; (ii) heuristic/hand-craft disentangling constraints make it hard to adaptively achieve an optimal training trade-off; (iii) lacking reasonable evaluation metric, especially for the real label-free data. To address these challenges, we propose a Closed-Loop unsupervised representation Disentanglement approach dubbed CL-Dis. Specifically, we use diffusion-based autoencoder (Diff-AE) as a backbone while resorting to β-VAE as a co-pilot to extract semantically disentangled representations. The strong generation ability of diffusion model and the good disentanglement ability of VAE model are complementary. To strengthen disentangling, VAE-latent distillation and diffusion-wise feedback are interconnected in a closed-loop system for a further mutual promotion. Then, a self-supervised Navigation strategy is introduced to identify interpretable semantic directions in the disentangled latent space. Finally, a new metric based on content tracking is designed to evaluate the disentanglement effect. Experiments demonstrate the superiority of CL-Dis on applications like real image manipulation and visual analysis."
Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective
Authors: Xiang Fang; Zeyu Xiong; Wanlong Fang; Xiaoye Qu; Chen Chen; Jianfeng Dong; Keke Tang; Pan Zhou; Yu Cheng; Daizong Liu
Venue: ECCV 2024 (main)

Abstract: "This paper addresses the challenging task of weakly-supervised video temporal grounding. Existing approaches are generally based on the moment proposal selection framework that utilizes contrastive learning and reconstruction paradigm for scoring the pre-defined moment proposals. Although they have achieved significant progress, we argue that their current frameworks have overlooked two indispensable issues: 1) Coarse-grained cross-modal learning: previous methods solely capture the global video-level alignment with the query, failing to model the detailed consistency between video frames and query words for accurately grounding the moment boundaries. 2) Complex moment proposals: their performance severely relies on the quality of proposals, which are also time-consuming and complicated for selection. To this end, in this paper, we make the first attempt to tackle this task from a novel game perspective, which effectively learns the uncertain relationship between each vision-language pair with diverse granularity and flexible combination for multi-level cross-modal interaction. Specifically, we creatively model each video frame and query word as game players with multivariate cooperative game theory to learn their contribution to the cross-modal similarity score. By quantifying the trend of frame-word cooperation within a coalition via the game-theoretic interaction, we are able to value all uncertain but possible correspondence between frames and words. Finally, instead of using moment proposals, we utilize the learned query-guided frame-wise scores for better moment localization. Experiments show that our method achieves superior performance on both Charades-STA and ActivityNet Caption datasets."
Every Pixel Has its Moments: Ultra-High-Resolution Unpaired Image-to-Image Translation via Dense Normalization
Authors: Ming-Yang Ho; Che-Ming Wu; Min-Sheng Wu; ‪Yufeng Jane Tseng
Venue: ECCV 2024 (main)

Abstract: "Recent advancements in ultra-high-resolution unpaired image-to-image translation have aimed to mitigate the constraints imposed by limited GPU memory through patch-wise inference. Nonetheless, existing methods often compromise between the reduction of noticeable tiling artifacts and the preservation of color and hue contrast, attributed to the reliance on global image- or patch-level statistics in the instance normalization layers. In this study, we introduce a Dense Normalization (DN) layer designed to estimate pixel-level statistical moments. This approach effectively diminishes tiling artifacts while concurrently preserving local color and hue contrasts. To address the computational demands of pixel-level estimation, we further propose an efficient interpolation algorithm. Moreover, we invent a parallelism strategy that enables the DN layer to operate in a single pass. Through extensive experiments, we demonstrate that our method surpasses all existing approaches in performance. Notably, our DN layer is hyperparameter-free and can be seamlessly integrated into most unpaired image-to-image translation frameworks without necessitating retraining. Overall, our work paves the way for future exploration in handling images of arbitrary resolutions within the realm of unpaired image-to-image translation. Code is available at: https://github.com/Kaminyou/Dense-Normalization."
ZoLA: Zero-Shot Creative Long Animation Generation with Short Video Model
Authors: Fu-Yun Wang; Zhaoyang Huang; Qiang Ma; Guanglu Song; Xudong LU; Weikang Bian; Yijin Li; Yu Liu; Hongsheng Li
Venue: ECCV 2024 (main)

Abstract: "Although video generation has made great progress in capacity and controllability and is gaining increasing attention, currently available video generation models still make minimal progress in the video length they can generate. Due to the lack of well-annotated long video data, high training/inference cost, and flaws in the model designs, current video generation models can only generate videos of 2 ∼ 4 seconds, greatly limiting their applications and the creativity of users. We present , a zero-shot method for creative long animation generation with short video diffusion models and even with short video consistency models (a new family of generative models known for the fast generation with high quality). In addition to the extension for long animation generation (dozens of seconds), as a zero-shot method, can be easily combined with existing community adapters (developed only for image or short video models) for more innovative generation results, including control-guided animation generation/editing, motion customization/alternation, and multi-prompt conditioned animation generation, . And, importantly, all of these can be done with commonly affordable GPU (12 GB for 32-second animations) and inference time (90 seconds for denoising 32-second animations with consistency models). Experiments validate the effectiveness of , bringing great potential for creative long animation generation. More details are available at https://gen-l-2.github.io/."
Parameter-Efficient and Memory-Efficient Tuning for Vision Transformer: A Disentangled Approach
Authors: Taolin Zhang; Jiawang Bai; Zhihe Lu; Dongze Lian; genping wang; Xinchao Wang; Shu-Tao Xia
Venue: ECCV 2024 (main)

Abstract: "Recent works on parameter-efficient transfer learning (PETL) show the potential to adapt a pre-trained Vision Transformer to downstream recognition tasks with only a few learnable parameters. However, since they usually insert new structures into the pre-trained model, entire intermediate features of that model are changed and thus need to be stored to be involved in back-propagation, resulting in memory-heavy training. We solve this problem from a novel disentangled perspective, i.e., dividing PETL into two aspects: task-specific learning and pre-trained knowledge utilization. Specifically, we synthesize the task-specific query with a learnable and lightweight module, which is independent of the pre-trained model. The synthesized query equipped with task-specific knowledge serves to extract the useful features for downstream tasks from the intermediate representations of the pre-trained model in a query-only manner. Built upon these features, a customized classification head is proposed to make the prediction for the input sample. Given that our method employs an extremely lightweight architecture and avoids the use of heavy intermediate features for running gradient descent, it demonstrates limited memory usage in training. Extensive experiments manifest that our method achieves state-of-the-art performance under memory constraints, showcasing its applicability in real-world situations. The code is available at: https://synqt.github.io/."
Restore Anything with Masks: Leveraging Mask Image Modeling for Blind All-in-One Image Restoration
Authors: Chujie Qin; Ruiqi Wu; Zikun Liu; Xin Lin; Chun-Le Guo; Hyun Hee Park; Chongyi Li
Venue: ECCV 2024 (main)

Abstract: "All-in-one image restoration aims to handle multiple degradation types using one model. This paper proposes a simple pipeline for all-in-one blind image restoration to Restore Anything with Masks (). We focus on the image content by utilizing Mask Image Modeling to extract intrinsic image information rather than distinguishing degradation types like other methods. Our pipeline consists of two stages: masked image pre-training and fine-tuning with mask attribute conductance. We design a straightforward masking pre-training approach specifically tailored for all-in-one image restoration. This approach enhances networks to prioritize the extraction of image content priors from various degradations, resulting in a more balanced performance across different restoration tasks and achieving stronger overall results. To bridge the gap of input integrity while preserving learned image priors as much as possible, we selectively fine-tuned a small portion of the layers. Specifically, the importance of each layer is ranked by the proposed Mask Attribute Conductance (MAC), and the layers with higher contributions are selected for finetuning. Extensive experiments demonstrate that our method achieves state-of-the-art performance. Our code and model will be released at https://github.com/Dragonisss/RAM."
When Fast Fourier Transform Meets Transformer for Image Restoration
Authors: Xingyu Jiang; Xiuhui Zhang; Ning Gao; Yue Deng
Venue: ECCV 2024 (main)

Abstract: "Natural images can suffer from various degradation phenomena caused by adverse atmospheric conditions or unique degradation mechanism. Such diversity makes it challenging to design a universal framework for kinds of restoration tasks. Instead of exploring the commonality across different degradation phenomena, existing image restoration methods focus on the modification of network architecture under limited restoration priors. In this work, we first review various degradation phenomena from a frequency perspective as prior. Based on this, we propose an efficient image restoration framework, dubbed SFHformer, which incorporates the Fast Fourier Transform mechanism into Transformer architecture. Specifically, we design a dual domain hybrid structure for multi-scale receptive fields modeling, in which the spatial domain and the frequency domain focuses on local modeling and global modeling, respectively. Moreover, we design unique positional coding and frequency dynamic convolution for each frequency component to extract rich frequency-domain features. Extensive experiments on thirty-one restoration datasets for a range of ten restoration tasks such as deraining, dehazing, deblurring, desnowing, denoising, super-resolution and underwater/low-light enhancement, demonstrate that our SFHformer surpasses the state-of-the-art approaches and achieves a favorable trade-off between performance, parameter size and computational cost. The code is available at: https://github.com/deng-ai-lab/SFHformer."
Dolphins: Multimodal Language Model for Driving
Authors: Yingzi Ma; Yulong Cao; Jiachen Sun; Marco Pavone; Chaowei Xiao
Venue: ECCV 2024 (main)

Abstract: "The quest for fully autonomous vehicles (AVs) capable of navigating complex real-world scenarios with human-like understanding and responsiveness. In this paper, we introduce , a novel vision-language model architected to imbibe human-like abilities as a conversational driving assistant. is adept at processing multimodal inputs comprising video (or image) data, text instructions, and historical control signals to generate informed outputs corresponding to the provided instructions. Building upon the open-sourced pretrained Vision-Language Model, OpenFlamingo, we first enhance ’s reasoning capabilities through an innovative Grounded Chain of Thought (GCoT) process in the general domain. Then we tailored to the driving domain by constructing driving-specific instruction data and conducting instruction tuning. Through the utilization of the BDD-X dataset, we designed and consolidated four distinct AV tasks into to foster a holistic understanding of intricate driving scenarios. As a result, the distinctive features of are characterised into two dimensions: (1) the ability to provide a comprehensive understanding of complex and long-tailed open-world driving scenarios and solve a spectrum of AV tasks, and (2) the emergence of human-like capabilities including gradient-free instant adaptation via in-context learning and error recovery via reflection. The anonymous demo is available at https: //vlm-driver.github.io/."
Rethinking Video Deblurring with Wavelet-Aware Dynamic Transformer and Diffusion Model
Authors: Chen Rao; Guangyuan Li; Zehua Lan; Jiakai Sun; Junsheng Luan; Wei Xing; Lei Zhao; Huaizhong Lin; Jianfeng Dong; Dalong Zhang
Venue: ECCV 2024 (main)

Abstract: "Current video deblurring methods have limitations in recovering high-frequency information since the regression losses are conservative with high-frequency details. Since Diffusion Models (DMs) have strong capabilities in generating high-frequency details, we consider introducing DMs into the video deblurring task. However, we found that directly applying DMs to the video deblurring task has the following problems: (1) DMs require many iteration steps to generate videos from Gaussian noise, which consumes many computational resources. (2) DMs are easily misled by the blurry artifacts in the video, resulting in irrational content and distortion of the deblurred video. To address the above issues, we propose a novel video deblurring framework VD-Diff that integrates the diffusion model into the Wavelet-Aware Dynamic Transformer (WADT). Specifically, we perform the diffusion model in a highly compact latent space to generate prior features containing high-frequency information that conforms to the ground truth distribution. We design the WADT to preserve and recover the low-frequency information in the video while utilizing the high-frequency information generated by the diffusion model. Extensive experiments show that our proposed VD-Diff outperforms SOTA methods on GoPro, DVD, BSD, and Real-World Video datasets. The codes will be available at https://github.com/Chen-Rao/VD-Diff."
CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection
Authors: xunfa lai; Zhiyu Yang; Jie Hu; ShengChuan Zhang; Liujuan Cao; Guannan Jiang; Songan Zhang; zhiyu wang; Rongrong Ji
Venue: ECCV 2024 (main)

Abstract: "Existing camouflaged object detection (COD) methods depend heavily on large-scale pixel-level annotations. However, acquiring such annotations is laborious due to the inherent camouflage characteristics of the objects. Semi-supervised learning offers a promising solution to this challenge. Yet, its application in COD is hindered by significant pseudo-label noise, both pixel-level and instance-level. We introduce CamoTeacher, a novel semi-supervised COD framework, utilizing Dual-Rotation Consistency Learning (DRCL) to effectively address these noise issues. Specifically, DRCL minimizes pseudo-label noise by leveraging rotation views’ consistency in pixel-level and instance-level. First, it employs Pixel-wise Consistency Learning (PCL) to deal with pixel-level noise by reweighting the different parts within the pseudo-label. Second, Instance-wise Consistency Learning (ICL) is used to adjust weights for pseudo-labels, which handles instance-level noise. Extensive experiments on four COD benchmark datasets demonstrate that the proposed CamoTeacher not only achieves state-of-the-art compared with semi-supervised learning methods, but also rivals established fully-supervised learning methods. Our code will be available soon."
Placing Objects in Context via Inpainting for Out-of-distribution Segmentation
Authors: Pau de Jorge Aranda; Riccardo Volpi; Puneet Dokania; Philip Torr; Gregory Rogez
Venue: ECCV 2024 (main)

Abstract: "When deploying a semantic segmentation model into the real world, it will inevitably encounter semantic classes that were not seen during training. To ensure a safe deployment of such systems, it is crucial to accurately evaluate and improve their anomaly segmentation capabilities. However, acquiring and labelling semantic segmentation data is expensive and unanticipated conditions are long-tail and potentially hazardous. Indeed, existing anomaly segmentation datasets capture a limited number of anomalies, lack realism or have strong domain shifts. In this paper, we propose the Placing Objects in Context (POC) pipeline to realistically add any object into any image via diffusion models. POC can be used to easily extend any dataset with an arbitrary number of objects. In our experiments, we present different anomaly segmentation datasets based on POC-generated data and show that POC can improve the performance of recent state-of-the-art anomaly fine-tuning methods across several standardized benchmarks. POC is also effective for learning new classes. For example, we utilize it to augment Cityscapes samples by incorporating a subset of Pascal classes and demonstrate that models trained on such data achieve comparable performance to the Pascal-trained baseline. This corroborates the low synth2real gap of models trained on POC-generated images. Code: https://github.com/ naver/poc"
Textual Grounding for Open-vocabulary Visual Information Extraction in Layout-diversified Documents
Authors: Mengjun Cheng; Chengquan Zhang; Chang Liu; Yuke Li; Bohan Li; Kun Yao; Xiawu Zheng; Rongrong Ji; Jie Chen
Venue: ECCV 2024 (main)

Abstract: "Current methodologies have achieved notable success in the closed-set visual information extraction (VIE) task, while the exploration into open-vocabulary settings is comparatively underdeveloped, which is practical for individual users in terms of inferring information across documents of diverse types. Existing proposal solutions, including named entity recognition methods and large language model-based methods, fall short in processing the unlimited range of open-vocabulary keys and missing explicit layout modeling. This paper introduces a novel method for tackling the given challenge by transforming the process of categorizing text tokens into a task of locating regions based on given queries also called textual grounding. Particularly, we take this a step further by pairing open-vocabulary key language embedding with corresponding grounded text visual embedding. We design a document-tailored grounding framework by incorporating layout-aware context learning and document-tailored two-stage pre-training, which significantly improves the model’s understanding of documents. Our method outperforms current proposal solutions on the SVRD benchmark for the open-vocabulary VIE task, offering lower costs and faster inference speed. Specifically, our method infers 20× faster than the QwenVL model and achieves an improvement of 24.3% in the F-score metric."
Teddy: Efficient Large-Scale Dataset Distillation via Taylor-Approximated Matching
Authors: Ruonan Yu; Songhua Liu; Jingwen Ye; Xinchao Wang
Venue: ECCV 2024 (main)

Abstract: "Dataset distillation or condensation refers to compressing a large-scale dataset into a much smaller one, enabling models trained on this synthetic dataset to generalize effectively on real data. Tackling this challenge, as defined, relies on a bi-level optimization algorithm: a novel model is trained in each iteration within a nested loop, with gradients propagated through an unrolled computation graph. However, this approach incurs high memory and time complexity, posing difficulties in scaling up to large datasets such as ImageNet. Addressing these concerns, this paper introduces Teddy, a Taylor-approximated dataset distillation framework designed to handle large-scale dataset and enhance efficiency. On the one hand, backed up by theoretical analysis, we propose a memory-efficient approximation derived from Taylor expansion, which transforms the original form dependent on multi-step gradients to a first-order one. On the other hand, rather than repeatedly training a novel model in each iteration, we unveil that employing a pre-cached pool of weak models, which can be generated from a single base model, enhances both time efficiency and performance concurrently, particularly when dealing with large-scale datasets. Extensive experiments demonstrate that the proposed Teddy attains state-of-the-art efficiency and performance on the Tiny-ImageNet and original-sized ImageNet-1K dataset, notably surpassing prior methods by up to 12.8%, while reducing 46.6% runtime. Our code will be available at https://github.com/Lexie-YU/Teddy."
Rethinking and Improving Visual Prompt Selection for In-Context Learning Segmentation Framework
Authors: Wei Suo; Lanqing Lai; Mengyang Sun; Hanwang Zhang; Peng Wang; Yanning Zhang
Venue: ECCV 2024 (main)

Abstract: "As a fundamental and extensively studied task in computer vision, image segmentation aims to locate and identify different semantic concepts at the pixel level. Recently, inspired by In-Context Learning (ICL), several generalist segmentation frameworks have been proposed, providing a promising paradigm for segmenting specific objects. However, existing works mostly ignore the value of visual prompts or simply apply similarity sorting to select contextual examples. In this paper, we focus on rethinking and improving the example selection strategy. By comprehensive comparisons, we first demonstrate that ICL-based segmentation models are sensitive to different contexts. Furthermore, empirical evidence indicates that the diversity of contextual prompts plays a crucial role in guiding segmentation. Based on the above insights, we propose a new stepwise context search method. Different from previous works, we construct a small yet rich candidate pool and adaptively search the well-matched contexts. More importantly, this method effectively reduces the annotation cost by compacting the search space. Extensive experiments show that our method is an effective strategy for selecting examples and enhancing segmentation performance. https://github.com/LanqingL/SCS"