GenAI4ELab/papercli-papers huggingface dataset:
Efficient Vision Transformers with Partial Attention
Authors: Xuan-Thuy Vo; Duy-Linh Nguyen; Adri Priadana; Kang-Hyun Jo
Venue: ECCV 2024 (main)

Abstract: "As a core of Vision Transformer (ViT), self-attention has high versatility in modeling long-range spatial interactions because every query attends to all spatial locations. Although ViT achieves promising performance in visual tasks, self-attention’s complexity is quadratic with token lengths. This leads to challenging problems when adapting ViT models to downstream tasks that require high input resolutions. Previous arts have tried to solve this problem by introducing sparse attention such as spatial reduction attention, and window attention. One common point of these methods is that all image/window tokens are joined during computing attention weights. In this paper, we find out that there exist high similarities between attention weights and incur computation redundancy. To address this issue, this paper introduces novel attention, called partial attention, that learns spatial interactions more efficiently, by reducing redundant information in attention maps. Each query in our attention only interacts with a small set of relevant tokens. Based on partial attention, we propose an efficient and general vision Transformer, named PartialFormer, that attains good trade-offs between accuracy and computational costs across vision tasks. For example, on ImageNet-1K, PartialFormer-B3 surpasses Swin-T by 1.7% Top-1 accuracy while saving 25% GFLOPs, and Focal-T by 0.8% while saving 30% GFLOPs."
Generalized Coverage for More Robust Low-Budget Active Learning
Authors: Wonho Bae; Junhyug Noh; Danica J. Sutherland
Venue: ECCV 2024 (main)

Abstract: "The ProbCover method of Yehuda et al. is a well-motivated algorithm for active learning in low-budget regimes, which attempts to “cover” the data distribution with balls of a given radius at selected data points. We demonstrate, however, that the performance of this algorithm is extremely sensitive to the choice of this radius hyper-parameter, and that tuning it is quite difficult, with the original heuristic frequently failing. We thus introduce (and theoretically motivate) a generalized notion of “coverage,” including ProbCover’s objective as a special case, but also allowing smoother notions that are far more robust to hyper-parameter choice. We propose an efficient greedy method to optimize this coverage, generalizing ProbCover’s algorithm; due to its close connection to kernel herding, we call it “MaxHerding.” The objective can also be optimized non-greedily through a variant of k-medoids, clarifying the relationship to other low-budget active learning methods. In comprehensive experiments, MaxHerding surpasses existing active learning methods across multiple low-budget image classification benchmarks, and does so with less computational cost than most competitive methods."
Rasterized Edge Gradients: Handling Discontinuities Differentially
Authors: Stanislav Pidhorskyi; Tomas Simon; Gabriel Schwartz; He Wen; Yaser Sheikh; Jason Saragih
Venue: ECCV 2024 (main)

Abstract: "Computing the gradients of a rendering process is paramount for diverse applications in computer vision and graphics. However, accurate computation of these gradients is challenging due to discontinuities and rendering approximations, particularly for surface-based representations and rasterization-based rendering. We present a novel method for computing gradients at visibility discontinuities for rasterization-based differentiable renderers. Our method elegantly simplifies the traditionally complex problem through a carefully designed approximation strategy, allowing for a straightforward, effective, and performant solution. We introduce a novel concept of micro-edges, which allows us to treat the rasterized images as outcomes of a differentiable, continuous process aligned with the inherently non-differentiable, discrete-pixel rasterization. This technique eliminates the necessity for rendering approximations or other modifications to the forward pass, preserving the integrity of the rendered image, which makes it applicable to rasterized masks, depth, and normals images where filtering is prohibitive. Utilizing micro-edges simplifies gradient interpretation at discontinuities and enables handling of geometry intersections, offering an advantage over the prior art. We showcase our method in dynamic human head scene reconstruction, demonstrating effective handling of camera images and segmentation masks."
Enhancing Cross-Subject fMRI-to-Video Decoding with Global-Local Functional Alignment
Authors: Chong Li; Xuelin Qian; Yun Wang; Jingyang Huo; Xiangyang Xue; Yanwei Fu; Jianfeng Feng
Venue: ECCV 2024 (main)

Abstract: "Advancements in brain imaging enable the decoding of thoughts and intentions from neural activities. However, the fMRI-to-video decoding of brain signals across multiple subjects encounters challenges arising from structural and coding disparities among individual brains, further compounded by the scarcity of paired fMRI-stimulus data. Addressing this issue, this paper introduces the fMRI Global-Local Functional Alignment (GLFA) projection, a novel approach that aligns fMRI frames from diverse subjects into a unified brain space, thereby enhancing cross-subject decoding. Additionally, we present a meticulously curated fMRI-video paired dataset comprising a total of 75k fMRI-stimulus paired samples from 8 individuals. This dataset is approximately 4.5 times larger than the previous benchmark dataset. Building on this, we augment a transformer-based fMRI encoder with a diffusion video generator, delving into the realm of cross-subject fMRI-based video reconstruction. This innovative methodology faithfully captures semantic information from diverse brain signals, resulting in the generation of vivid videos and achieving an impressive average accuracy of 84.7% in cross-subject semantic classification tasks."
FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning
Authors: Boyu Fan; Chenrui Wu; Xiang Su; Pan HUI
Venue: ECCV 2024 (main)

Abstract: "Despite extensive research into data heterogeneity in federated learning (FL), system heterogeneity remains a significant yet often overlooked challenge. Traditional FL approaches typically assume homogeneous hardware resources across FL clients, implying that clients can train a global model within a comparable time frame. However, in practical FL systems, clients often have heterogeneous resources, which impacts their training capacity. This discrepancy underscores the importance of exploring model-heterogeneous FL, a paradigm allowing clients to train different models based on their resource capabilities. To address this challenge, we introduce FedTSA, a cluster-based two-stage aggregation method tailored for system heterogeneity in FL. FedTSA begins by clustering clients based on their capabilities, then performs a two-stage aggregation: conventional weight averaging for homogeneous models in Stage 1, and deep mutual learning with a diffusion model for aggregating heterogeneous models in Stage 2. Extensive experiments demonstrate that FedTSA not only outperforms the baselines but also explores various factors influencing model performance, validating FedTSA as a promising approach for model-heterogeneous FL."
LLaVA-UHD: an LMM Perceiving any Aspect Ratio and High-Resolution Images
Authors: Zonghao Guo; Ruyi Xu; Yuan Yao; Junbo Cui; Zanlin Ni; Chunjiang Ge; Tat-Seng Chua; Zhiyuan Liu; Gao Huang
Venue: ECCV 2024 (main)

Abstract: "Visual encoding constitutes the basis of large multimodal models (LMMs) in understanding the visual world. Conventional LMMs process images in fixed sizes and limited resolutions, while recent explorations in this direction are limited in adaptivity, efficiency, and even correctness. In this work, we first take GPT-4V and LLaVA 1.5 as representative examples and expose systematic flaws rooted in their visual encoding strategy. To address the challenges, we present LLaVA-UHD, a large multimodal model that can efficiently perceive images in any aspect ratio and high resolution. LLaVA-UHD includes three key components: (1) An image modularization strategy that divides native-resolution images into smaller variable-sized slices for efficient and extensible encoding, (2) a compression module that further condenses image tokens from visual encoders, and (3) a spatial schema to organize slice tokens for LLMs. Comprehensive experiments show that LLaVA-UHD outperforms established LMMs trained with 2-3 orders of magnitude more data on black8 benchmarks. Notably, our model built on LLaVA-1.5 336×336 supports 6 times larger (i.e., 672×1008) resolution images, and achieves 5.7 accuracy improvement on TextVQA."
Learning Natural Consistency Representation for Face Forgery Video Detection
Authors: Daichi Zhang; Zihao Xiao; Shikun Li; Fanzhao Lin; Jianmin Li; Shiming Ge
Venue: ECCV 2024 (main)

Abstract: "Face Forgery videos have elicited critical social public concerns and various detectors have been proposed. However, fully-supervised detectors may lead to easily overfitting to specific forgery methods or videos, and existing self-supervised detectors are strict on auxiliary tasks, such as requiring audio or multi-modalities, leading to limited generalization and robustness. In this paper, we examine whether we can address this issue by leveraging visual-only real face videos. To this end, we propose to learn the Natural Consistency representation (NACO) of real face videos in a self-supervised manner, which is inspired by the observation that fake videos struggle to maintain the natural spatiotemporal consistency even under unknown forgery methods and different perturbations. Our NACO first extracts spatial features of each frame by CNNs then integrates them into Transformer to learn the long-range spatiotemporal representation, leveraging the advantages of CNNs and Transformer on local spatial receptive field and long-term memory respectively. Furthermore, a Spatial Predictive Module (SPM) and a Temporal Contrastive Module (TCM) are introduced to enhance the natural consistency representation learning. The SPM aims to predict random masked spatial features from spatiotemporal representation, and the TCM regularizes the latent distance of spatiotemporal representation by shuffling the natural order to disturb the consistency, which could both force our NACO more sensitive to the natural spatiotemporal consistency. After the representation learning stage, a MLP head is fine-tuned to perform the usual forgery video classification task. Extensive experiments show that our method outperforms other state-of-the-art competitors with impressive generalization and robustness."
ZeroI2V: Zero-Cost Adaptation of Pre-Trained Transformers from Image to Video
Authors: Xinhao Li; Yuhan Zhu; Limin Wang
Venue: ECCV 2024 (main)

Abstract: "Adapting image models to the video domain has emerged as an efficient paradigm for solving video recognition tasks. Due to the huge number of parameters and effective transferability of image models, performing full fine-tuning is less efficient and even unnecessary. Thus, recent research is shifting its focus toward parameter-efficient image-to-video adaptation. However, these adaptation strategies inevitably introduce extra computational costs to deal with the domain gap and temporal modeling in videos. In this paper, we present a new adaptation paradigm (ZeroI2V) to transfer the image transformers to video recognition tasks (, introduce zero extra cost to the original models during inference). To achieve this goal, we present two core designs. First, to capture the dynamics in videos and reduce the difficulty of image-to-video adaptation, we exploit the flexibility of self-attention and introduce spatial-temporal dual-headed attention (STDHA). This approach efficiently endows the image transformers with temporal modeling capability at zero extra parameters and computation. Second, to handle the domain gap between images and videos, we propose a linear adaption strategy that utilizes lightweight densely placed linear adapters to fully transfer the frozen image models to video recognition. Thanks to the customized linear design, all newly added adapters could be easily merged with the original modules through structural reparameterization after training, enabling zero extra cost during inference. Extensive experiments on representative fully-supervised and few-shot video recognition benchmarks showcase that ZeroI2V can match or even outperform previous state-of-the-art methods while enjoying superior parameter and inference efficiency."
Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse Problems
Authors: Yasar U Alcalar; Mehmet Akcakaya
Venue: ECCV 2024 (main)

Abstract: "Diffusion models have emerged as powerful generative techniques for solving inverse problems. Despite their success in a variety of inverse problems in imaging, these models require many steps to converge, leading to slow inference time. Recently, there has been a trend in diffusion models for employing sophisticated noise schedules that involve more frequent iterations of timesteps at lower noise levels, thereby improving image generation and convergence speed. However, application of these ideas for solving inverse problems with diffusion models remain challenging, as these noise schedules do not perform well when using empirical tuning for the forward model log-likelihood term weights. To tackle these challenges, we propose zero-shot approximate posterior sampling (ZAPS) that leverages connections to zero-shot physics-driven deep learning. ZAPS fixes the number of sampling steps, and uses zero-shot training with a physics-guided loss function to learn log-likelihood weights at each irregular timestep. We apply ZAPS to the recently proposed diffusion posterior sampling method as baseline, though ZAPS can also be used with other posterior sampling diffusion models. We further approximate the Hessian of the logarithm of the prior using a diagonalization approach with learnable diagonal entries for computational efficiency. These parameters are optimized over a fixed number of epochs with a given computational budget. Our results for various noisy inverse problems, including Gaussian and motion deblurring, inpainting, and super-resolution show that ZAPS reduces inference time, provides robustness to irregular noise schedules and improves reconstruction quality. Code is available at https://github.com/ualcalar17/ZAPS."
R.A.C.E.: Robust Adversarial Concept Erasure for Secure Text-to-Image Diffusion Model
Authors: Changhoon Kim; Kyle Min; Yezhou Yang
Venue: ECCV 2024 (main)

Abstract: "In the evolving landscape of text-to-image (T2I) diffusion models, the remarkable capability to generate high-quality images from textual descriptions faces challenges with the potential misuse of reproducing sensitive content. To address this critical issue, we introduce Robust Adversarial Concept Erase (RACE), a novel approach designed to mitigate these risks by enhancing the robustness of concept erasure method for T2I models. RACE utilizes a sophisticated adversarial training framework to identify and mitigate adversarial text embeddings, significantly reducing the Attack Success Rate (ASR). Impressively, RACE achieves a 30% reduction in ASR for the “nudity” concept against the leading white-box attack method. Our extensive evaluations demonstrate RACE’s effectiveness in defending against both white-box and black-box attacks, marking a significant advancement in protecting T2I diffusion models from generating inappropriate or misleading imagery. This work underlines the essential need for proactive defense measures in adapting to the rapidly advancing field of adversarial challenges. Our code is publicly available: https://github.com/chkimmmmm/R.A.C.E."
OpenSight: A Simple Open-Vocabulary Framework for LiDAR-Based Object Detection
Authors: Hu Zhang; xu jianhua; Tao Tang; Haiyang Sun; Xin Yu; Zi Helen Huang; Kaicheng Yu
Venue: ECCV 2024 (main)

Abstract: "Traditional LiDAR-based object detection research primarily focuses on closed-set scenarios, which falls short in complex real-world applications. Directly transferring existing 2D open-vocabulary models with some known LiDAR classes for open-vocabulary ability, however, tends to suffer from over-fitting problems: The obtained model will detect the known objects, even presented with a novel category. In this paper, we propose OpenSight, a more advanced 2D-3D modeling framework for LiDAR-based open-vocabulary detection. OpenSight utilizes 2D-3D geometric priors for the initial discernment and localization of generic objects, followed by a more specific semantic interpretation of the detected objects. The process begins by generating 2D boxes for generic objects from the accompanying camera images of LiDAR. These 2D boxes, together with LiDAR points, are then lifted back into the LiDAR space to estimate corresponding 3D boxes. For better generic object perception, our framework integrates both temporal and spatial-aware constraints. Temporal awareness correlates the predicted 3D boxes across consecutive timestamps, recalibrating the missed or inaccurate boxes. The spatial awareness randomly places some “precisely” estimated 3D boxes at varying distances, increasing the visibility of generic objects. To interpret the specific semantics of detected objects, we develop a cross-modal alignment and fusion module to first align 3D features with 2D image embeddings and then fuse the aligned 3D-2D features for semantic decoding. Our experiments indicate that our method establishes state-of-the-art open-vocabulary performance on widely used 3D detection benchmarks and effectively identifies objects for new categories of interest."
Few-Shot Image Generation by Conditional Relaxing Diffusion Inversion
Authors: Yu Cao; Shaogang Gong
Venue: ECCV 2024 (main)

Abstract: "In the field of Few-Shot Image Generation (FSIG) using Deep Generative Models (DGMs), accurately estimating the distribution of target domain with minimal samples poses a significant challenge. This requires a method that can both capture the broad diversity and the true characteristics of the target domain distribution. We present Conditional Relaxing Diffusion Inversion (CRDI), an innovative ‘training-free’ approach designed to enhance distribution diversity in synthetic image generation. Distinct from conventional methods, CRDI does not rely on fine-tuning based on only a few samples. Instead, it focuses on reconstructing each target image instance and expanding diversity through a few-shot learning. The approach initiates by identifying a Sample-wise Guidance Embedding (SGE) for the diffusion model, which serves a purpose analogous to the explicit latent codes in certain Generative Adversarial Network (GAN) models. Subsequently, the method involves a scheduler that progressively introduces perturbations to the SGE, thereby augmenting diversity. Comprehensive experiments demonstrate that our method outperforms GAN-based reconstruction techniques and achieves comparable performance to state-of-the-art (SOTA) FSIG methods. Additionally, it effectively mitigates overfitting and catastrophic forgetting, common drawbacks of fine-tuning approaches. Code is available at GitHub."
Data Poisoning Quantization Backdoor Attack
Authors: Tran Huynh; Anh Tran; Khoa Doan; Tung Pham
Venue: ECCV 2024 (main)

Abstract: "Deep learning (DL) models are often large and require a lot of computing power. Hence, model quantization is frequently used to reduce their size and complexity, making them more suitable for deployment on edge devices or achieving real-time performance. It has been previously shown that standard quantization frameworks can be exploited to activate the backdoor in a DL model. This means that an attacker could create a hijacked model that appears normal and free from backdoors (even when examined by state-of-the-art defenses), but when it is quantized, the backdoor is activated, and the attacker can control the model’s output. Existing backdoor attack methods on quantization models require full access to the victim model, which might not hold in practice. In this work, we focus on designing a novel quantization backdoor based on data poisoning, which requires zero knowledge of the target model. The key component is a trigger pattern generator, which is trained together with a surrogate model in an alternating manner. The attack’s effectiveness is tested on multiple benchmark datasets, including CIFAR10, CelebA, and ImageNet10, as well as state-of-the-art backdoor defenses."
DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition
Authors: Qi Wang; Zhou Xu; Yuming Lin; Jingtao Ye; Hongsheng Li; Guangming Zhu; Syed Afaq Ali Shah; Mohammed Bennamoun; Liang Zhang
Venue: ECCV 2024 (main)

Abstract: "Neuromorphic sensors, specifically event cameras, revolutionize visual data acquisition by capturing pixel intensity changes with exceptional dynamic range, minimal latency, and energy efficiency, setting them apart from conventional frame-based cameras. The distinctive capabilities of event cameras have ignited significant interest in the domain of event-based action recognition, recognizing their vast potential for advancement. However, the development in this field is currently slowed by the lack of comprehensive, large-scale datasets, which are critical for developing robust recognition frameworks. To bridge this gap, we introduces DailyDVS-200 , a meticulously curated benchmark dataset tailored for the event-based action recognition community. DailyDVS-200 is extensive, covering 200 action categories across real-world scenarios, recorded by 47 participants, and comprises more than 22,000 event sequences. This dataset is designed to reflect a broad spectrum of action types, scene complexities, and data acquisition diversity. Each sequence in the dataset is annotated with 14 attributes, ensuring a detailed characterization of the recorded actions. Moreover, DailyDVS-200 is structured to facilitate a wide range of research paths, offering a solid foundation for both validating existing approaches and inspiring novel methodologies. By setting a new benchmark in the field, we challenge the current limitations of neuromorphic data processing and invite a surge of new approaches in event-based action recognition techniques, which paves the way for future explorations in neuromorphic computing and beyond. The dataset and source code are available at https://github.com/QiWang233/DailyDVS-200."