GenAI4ELab/papercli-papers huggingface dataset:
Efficient Unsupervised Visual Representation Learning with Explicit Cluster Balancing
Authors: Ioannis Maniadis Metaxas; Georgios Tzimiropoulos; Ioannis Patras
Venue: ECCV 2024 (main)

Abstract: "Self-supervised learning has recently emerged as the preeminent pretraining paradigm across and between modalities, with remarkable results. In the image domain specifically, group (or cluster) discrimination has been one of the most successful methods. However, such frameworks need to guard against heavily imbalanced cluster assignments to prevent collapse to trivial solutions. Existing works typically solve this by reweighing cluster assignments to promote balance, or with offline operations (e.g. regular re-clustering) that prevent collapse. However, the former typically requires large batch sizes, which leads to increased resource requirements, and the latter introduces scalability issues with regard to large datasets. In this work, we propose , a framework that tackles this problem with a novel cluster balancing method. estimates the relative size of the clusters across batches and balances them by adjusting cluster assignments, proportionately to their relative size and in an online manner. Thereby, it overcomes previous methods’ dependence on large batch sizes and is fully online, and therefore scalable to any dataset. We conduct extensive experiments to evaluate our approach and demonstrate that : a) achieves state-of-the-art results with significantly reduced resource requirements compared to previous works, b) is fully online, and therefore scalable to large datasets, and c) is stable and effective even with very small batch sizes. Code and models will be made available here."
ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models against Stochastic Perturbation
Authors: Yi Zhang; Yun Tang; Wenjie Ruan; Xiaowei Huang; Siddartha Khastgir; Paul A Jennings; Xingyu Zhao
Venue: ECCV 2024 (main)

Abstract: "Text-to-Image (T2I) Diffusion Models (DMs) excel at creating high-quality images from text descriptions but, like many deep learning models, suffer from robustness issues. While there are attempts to evaluate the robustness of T2I DMs as a binary or worst-case problem, they cannot answer how robust in general the model is whenever an adversarial example (AE) can be found. In this study, we first formalise a probabilistic notion of T2I DMs’ robustness; and then devise an efficient framework, ProTIP, to evaluate it with statistical guarantees. The main challenges stem from: i) the high computational cost of the image generation process; and ii) identifying if a perturbed input is an AE involves comparing two output distributions, which is fundamentally harder compared to other DL tasks like classification where an AE is identified upon misprediction of labels. To tackle the challenges, we employ sequential analysis with efficacy and futility early stopping rules in the statistical testing for identifying AEs, and adaptive concentration inequalities to dynamically determine the “just-right” number of stochastic perturbations whenever the verification target is met. Empirical experiments validate ProTIP’s effectiveness and efficiency, and showcase its application in ranking common defence methods."
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos
Authors: Akshay Paruchuri; Samuel Ehrenstein; Shuxian Wang; Inbar Fried; Stephen Pizer; Marc Niethammer; Roni Sengupta
Venue: ECCV 2024 (main)

Abstract: "Monocular depth estimation in endoscopy videos can enable assistive and robotic surgery to obtain better coverage of the organ and detection of various health issues. Despite promising progress on mainstream, natural image depth estimation, techniques perform poorly on endoscopy images due to a lack of strong geometric features and challenging illumination effects. In this paper, we utilize the photometric cues, i.e., the light emitted from an endoscope and reflected by the surface, to improve monocular depth estimation. We first create two novel loss functions with supervised and self-supervised variants that utilize a per-pixel shading representation. We then propose a novel depth refinement network (PPSNet) that leverages the same per-pixel shading representation. Finally, we introduce teacher-student transfer learning to produce better depth maps from both synthetic data with supervision and clinical data with self-supervision. We achieve state-of-the-art results on the C3VD dataset while estimating high-quality depth maps from clinical data. Our code, pre-trained models, and supplementary materials can be found on our project page: https://ppsnet.github.io/."
OvSW: Overcoming Silent Weights for Accurate Binary Neural Networks
Authors: jingyang xiang; Zuohui Chen; Siqi Li; Qing Wu; Yong Liu
Venue: ECCV 2024 (main)

Abstract: "Binary Neural Networks (BNNs) have been proven to be highly effective for deploying deep neural networks on mobile and embedded platforms. Most existing works focus on minimizing quantization errors, improving representation ability, or designing gradient approximations to alleviate gradient mismatch in BNNs, while leaving the weight sign flipping, a critical factor for achieving powerful BNNs, untouched. In this paper, we investigate the efficiency of weight sign updates in BNNs. We observe that, for vanilla BNNs, over 50% of the weights remain their signs unchanged during training, and these weights are not only distributed at the tails of the weight distribution but also universally present in the vicinity of zero. We refer to these weights as “silent weights”, which slow down convergence and lead to a significant accuracy degradation. Theoretically, we reveal this is due to the independence of the BNNs gradient from the latent weight distribution. To address the issue, we propose Overcome Silent Weights (OvSW). OvSW first employs Adaptive Gradient Scaling (AGS) to establish a relationship between the gradient and the latent weight distribution, thereby improving the overall efficiency of weight sign updates. Additionally, we design Silence Awareness Decaying (SAD) to automatically identify “silent weights” by tracking weight flipping state, and apply an additional penalty to “silent weights” to facilitate their flipping. By efficiently updating weight signs, our method achieves faster convergence and state-of-the-art performance on CIFAR10 and ImageNet1K dataset with various architectures. For example, OvSW obtains 61.6% and 65.5% top-1 accuracy on the ImageNet1K using binarized ResNet18 and ResNet34 architecture respectively. Codes are available at https://github.com/JingyangXiang/OvSW."
Multistain Pretraining for Slide Representation Learning in Pathology
Authors: Guillaume Jaume; Anurag J Vaidya; Andrew Zhang; Andrew Song; Richard J Chen; Sharifa Sahai; Dandan Mo; Emilio Madrigal; Long P Le; Faisal Mahmood
Venue: ECCV 2024 (main)

Abstract: "Developing self-supervised learning (SSL) models that can learn universal and transferable representations of H&E gigapixel whole-slide images (WSIs) is becoming increasingly valuable in computational pathology. These models hold the potential to advance critical tasks such as few-shot classification, slide retrieval, and patient stratification. Existing approaches for slide representation learning extend the principles of SSL from small images (e.g., 224×224 patches) to entire slides, usually by aligning two different augmentations (or views) of the slide. Yet the resulting representation remains constrained by the limited clinical and biological diversity of the views. Instead, we postulate that slides stained with multiple markers, such as immunohistochemistry, can be used as different views to form a rich task-agnostic training signal. To this end, we introduce , a multimodal pretraining strategy for slide representation learning. is trained with a dual global-local cross-stain alignment objective on large cohorts of breast cancer samples (N=4,211 WSIs across five stains) and kidney transplant samples (N=12,070 WSIs across four stains). We demonstrate the quality of slide representations learned by on various downstream evaluations, ranging from morphological and molecular classification to prognostic prediction, comprising 21 tasks using 7,299 WSIs from multiple medical centers. Code at https://github.com/mahmoodlab/MADELEIN"
T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy
Authors: Qing Jiang; Feng Li; Zhaoyang Zeng; Shilong Liu; Tianhe Ren; Lei Zhang
Venue: ECCV 2024 (main)

Abstract: "We present , a highly practical model for open-set object detection. Previous open-set object detection methods relying on text prompts effectively encapsulate the abstract concept of common objects, but struggle with rare or complex object representation due to data scarcity and descriptive limitations. Conversely, visual prompts excel in depicting novel objects through concrete visual examples, but fall short in conveying the abstract concept of objects as effectively as text prompts. Recognizing the complementary strengths and weaknesses of both text and visual prompts, we introduce that synergizes both prompts within a single model through contrastive learning. accepts inputs in diverse formats, including text prompts, visual prompts, and the combination of both, so that it can handle different scenarios by switching between the two prompt modalities. Comprehensive experiments demonstrate that exhibits remarkable zero-shot object detection capabilities across a wide spectrum of scenarios. We show that text prompts and visual prompts can benefit from each other within the synergy, which is essential to cover massive and complicated real-world scenarios and pave the way towards generic object detection. API code is available at https://github.com/IDEA-Research/T-Rex."
Harmonizing knowledge Transfer in Neural Network with Unified Distillation
Authors: yaomin huang; Faming Fang; Zaoming Yan; Chaomin Shen; Guixu Zhang
Venue: ECCV 2024 (main)

Abstract: "Knowledge distillation (KD), known for its ability to transfer knowledge from a cumbersome network (teacher) to a lightweight one (student) without altering the architecture, has been garnering increasing attention. Two primary categories emerge within KD methods: feature-based, focusing on intermediate layers’ features, and logits-based, targeting the final layer’s logits. This paper introduces a novel perspective by leveraging diverse knowledge sources within a unified KD framework. Specifically, we aggregate features from intermediate layers into a comprehensive representation, effectively gathering semantic information from different stages and scales. Subsequently, we predict the distribution parameters from this representation. These steps transform knowledge from the intermediate layers into corresponding distributive forms, thereby allowing for knowledge distillation through a unified distribution constraint at different stages of the network, ensuring the comprehensiveness and coherence of knowledge transfer. Numerous experiments were conducted to validate the effectiveness of the proposed method."
Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data
Authors: Shufan Li; Aditya Grover; Harkanwar Singh
Venue: ECCV 2024 (main)

Abstract: "In recent years, Transformers have become the de-facto architecture for sequence modeling on text and multi-dimensional data, such as images and video. However, the use of self-attention layers in a Transformer incurs prohibitive compute and memory complexity that scales quadratically w.r.t. the sequence length. A recent architecture, Mamba, based on state space models has been shown to achieve comparable performance for modeling text sequences, while scaling linearly with the sequence length. In this work, we present Mamba-ND, a generalized design extending the Mamba architecture to arbitrary multi-dimensional data. Our design alternatively unravels the input data across different dimensions following row-major orderings. We provide a systematic comparison of Mamba-ND with several other alternatives, based on prior multi-dimensional extensions such as Bi-directional LSTMs and S4ND. Empirically, we show that Mamba-ND demonstrates performance competitive with the state-of-the-art on various multi-dimensional benchmarks, including ImageNet-1K classification, HMDB-51 and UCF-101 action recognition, ERA5 weather forecasting and BTCV 3D segmentation. Code is available at https://github.com/jacklishufan/Mamba-ND"
Click Prompt Learning with Optimal Transport for Interactive Segmentation
Authors: Jie Liu; Haochen wang; Wenzhe Yin; Jan-Jakob Sonke; Efstratios Gavves
Venue: ECCV 2024 (main)

Abstract: "Click-based interactive segmentation aims to segment target objects conditioned on user-provided clicks. Existing methods typically interpret user intention by learning multiple click prompts to generate corresponding prompt-activated masks, and selecting one from these masks. However, directly matching each prompt to the same visual feature often leads to homogeneous prompt-activated masks, as it pushes the click prompts to converge to one point. To address this problem, we propose Click Prompt Learning with Optimal Transport (CPlot), which leverages optimal transport theory to capture diverse user intentions with multiple click prompts. Specifically, we first introduce a prompt-pixel alignment module (PPAM), which aligns each click prompts with the visual features in the same feature space by plain transformer blocks. In such way, PPAM enables all click prompts to encode more general knowledge about regions of interest, indicating a consistent user intention. To capture diverse user intentions, we further propose the click prompt optimal transport module (CPOT) to match click prompts and visual features. CPOT is designed to learn an optimal mapping between click prompts and visual features. Such unique mapping facilities click prompts to effectively focus on distinct visual regions, which reflect underlying diverse user intentions. Furthermore, CPlot learns click prompts with a two-stage optimization strategy: the inner loop optimizes the optimal transport distance to align visual features with click prompts through the Sinkhorn algorithm, while the outer loop adjusts the click prompts from the supervised data. Extensive experiments on eight interactive segmentation benchmarks demonstrate the superiority of our method for interactive segmentation. Project page: https://jliu4ai.github.io/cplot_projectpage/."
3D Human Pose Estimation via Non-Causal Retentive Networks
Authors: Kaili Zheng; Feixiang Lu; Yihao Lv; Liangjun Zhang; Chenyi Guo; Ji Wu
Venue: ECCV 2024 (main)

Abstract: "Temporal dependencies are essential in 3D human pose estimation to mitigate depth ambiguity. Previous methods typically use a fixed-length sliding window to capture these dependencies. However, they treat past and future frames equally, ignoring the fact that relying on too many future frames increases the inference latency. In this paper, we present a 3D human pose estimation model based on Retentive Networks (RetNet) that incorporates temporal information by utilizing a large number of past frames and a few future frames. The Non-Causal RetNet (NC-RetNet) is designed to allow the originally causal RetNet to be aware of future information. Additionally, we propose a knowledge transfer strategy, i.e., training the model with a larger chunk size and using a smaller chunk size during inference, to reduce latency while maintaining comparable accuracy. Extensive experiments have been conducted on the Human3.6M and MPI-INF-3DHP datasets, and the results demonstrate that our method achieves state-of-the-art performance. Code and models are available at https://github.com/Kelly510/PoseRetN"
OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection
Authors: Dongkwon Jin; Chang-Su Kim
Venue: ECCV 2024 (main)

Abstract: "A novel algorithm for video lane detection is proposed in this paper. First, we extract a feature map for a current frame and detect a latent mask for obstacles occluding lanes. Then, we enhance the feature map by developing an occlusion-aware memory-based refinement (OMR) module. It takes the obstacle mask and feature map from the current frame, previous output, and memory information as input, and processes them recursively in a video. Moreover, we apply a novel data augmentation scheme for training the OMR module effectively. Experimental results show that the proposed algorithm outperforms existing techniques on video lane datasets. Our codes are available at https://github.com/dongkwonjin/OMR."
6DoF Head Pose Estimation through Explicit Bidirectional Interaction with Face Geometry
Authors: Sungho Chun; Ju Yong Chang
Venue: ECCV 2024 (main)

Abstract: "This study addresses the nuanced challenge of estimating head translations within the context of six-degrees-of-freedom (6DoF) head pose estimation, placing emphasis on this aspect over the more commonly studied head rotations. Identifying a gap in existing methodologies, we recognized the underutilized potential synergy between facial geometry and head translation. To bridge this gap, we propose a novel approach called the head Translation, Rotation, and face Geometry network (TRG), which stands out for its explicit bidirectional interaction structure. This structure has been carefully designed to leverage the complementary relationship between face geometry and head translation, marking a significant advancement in the field of head pose estimation. Our contributions also include the development of a strategy for estimating bounding box correction parameters and a technique for aligning landmarks to image. Both of these innovations demonstrate superior performance in 6DoF head pose estimation tasks. Extensive experiments conducted on ARKitFace and BIWI datasets confirm that the proposed method outperforms current state-of-the-art techniques. Codes are released at https://github. com/asw91666/TRG-Release."
Latent Diffusion Prior Enhanced Deep Unfolding for Snapshot Spectral Compressive Imaging
Authors: Zongliang Wu; Ruiying Lu; Ying Fu; Xin Yuan
Venue: ECCV 2024 (main)

Abstract: "Snapshot compressive spectral imaging reconstruction aims to reconstruct three-dimensional spatial-spectral images from a single-shot two-dimensional compressed measurement. Existing state-of-the-art methods are mostly based on deep unfolding structures but have intrinsic performance bottlenecks: i) the ill-posed problem of dealing with heavily degraded measurement, and ii) the regression loss-based reconstruction models being prone to recover images with few details. In this paper, we introduce a generative model, namely the latent diffusion model (LDM), to generate degradation-free prior to enhance the regression-based deep unfolding method by a two-stage training procedure. Furthermore, we propose a Trident Transformer (TT), which extracts correlations among prior knowledge, spatial, and spectral features, to integrate knowledge priors in deep unfolding denoiser, and guide the reconstruction for compensating high-quality spectral signal details. To our knowledge, this is the first approach to integrate physics-driven deep unfolding with generative LDM in the context of CASSI reconstruction. Comparisons on synthetic and real-world datasets illustrate the superiority of our proposed method in both reconstruction quality and computational efficiency. The code is available at https://github.com/ Zongliang-Wu/LADE-DUN."
Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognition
Authors: Masashi Hatano; Ryo Hachiuma; Ryo Fujii; Hideo Saito
Venue: ECCV 2024 (main)

Abstract: "We address a novel cross-domain few-shot learning task (CD-FSL) with multimodal input and unlabeled target data for egocentric action recognition. This paper simultaneously tackles two critical challenges associated with egocentric action recognition in CD-FSL settings: (1) the extreme domain gap in egocentric videos (, daily life vs. industrial domain) and (2) the computational cost for real-world applications. We propose MM-CDFSL, a domain-adaptive and computationally efficient approach designed to enhance adaptability to the target domain and improve inference cost. To address the first challenge, we propose the incorporation of multimodal distillation into the student RGB model using teacher models. Each teacher model is trained independently on source and target data for its respective modality. Leveraging only unlabeled target data during multimodal distillation enhances the student model’s adaptability to the target domain. We further introduce ensemble masked inference, a technique that reduces the number of input tokens through masking. In this approach, ensemble prediction mitigates the performance degradation caused by masking, effectively addressing the second issue. Our approach outperformed the state-of-the-art CD-FSL approaches with a substantial margin on multiple egocentric datasets, improving by an average of 6.12/6.10 points for 1-shot/5-shot settings while achieving 2.2 times faster inference speed. Project page: https://masashi-hatano. github.io/MM-CDFSL/"
Enhancing Tampered Text Detection through Frequency Feature Fusion and Decomposition
Authors: Zhongxi Chen; Shen Chen; Taiping Yao; Ke Sun; Shouhong Ding; Xianming Lin; Liujuan Cao; Rongrong Ji
Venue: ECCV 2024 (main)

Abstract: "Document image tampering poses a grave risk to the veracity of information, with potential consequences ranging from misinformation dissemination to financial and identity fraud. Current detection methods use frequency information to uncover tampering that is invisible to the naked eye. However, these methods often fail to integrate this information effectively, thereby compromising RGB detection capabilities and missing the high-frequency details necessary to detect subtle tampering. To address these gaps, we introduce a Feature Fusion and Decomposition Network (FFDN) that combines a Visual Enhancement Module (VEM) with a Wavelet-like Frequency Enhancement (WFE). Specifically, the VEM makes tampering traces visible while preserving the integrity of original RGB features using zero-initialized convolutions. Meanwhile, the WFE decomposes the features to explicitly retain high-frequency details that are often overlooked during downsampling, focusing on small but critical tampering clues. Rigorous testing on the DocTamper dataset confirms FFDN’s preeminence, significantly outperforming existing state-of-the-art methods in detecting tampering."
Modeling Label Correlations with Latent Context for Multi-Label Recognition
Authors: Zhaomin Chen; Quan Cui; Ruoxi Deng; Jie Hu; Guodao Zhang
Venue: ECCV 2024 (main)

Abstract: "Label dependencies have been widely studied in multi-label image recognition for improving performances. Previous methods mainly considered label co-occurrences as label correlations. In this paper, we show that label co-occurrences may be insufficient to represent label correlations, and modeling label correlations relies on latent context information. To this end, we propose a latent context embedding information network for multi-label image recognition. Our proposal is straightforward and contains three key modules to correspondingly tackle three questions, , where to locate the latent context information, how to utilize the latent context information, and how to model label correlations with context-aware features. First, the multi-level context feature fusion module fuses the multi-level feature pyramids to obtain sufficient latent context information. Second, the latent context information embedding module aggregates the latent context information into categorical features, and thus the label correlation can be directly established. Moreover, we use the label correlation capturing module to model label correlations with full and partial manners, respectively. Comprehensive experiments validate the correctness of our arguments and the effectiveness of our method. In both generic multi-label classification and partial-label multi-label classification, our proposed method consistently achieves promising results."