GenAI4ELab/papercli-papers huggingface dataset:
DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs
Authors: DongHyun Kim; Byeongho Heo; Dongyoon Han
Venue: ECCV 2024 (main)

Abstract: "This paper revives Densely Connected Convolutional Networks (DenseNets) and reveals the underrated effectiveness over predominant ResNet-style architectures. We believe DenseNets’ potential was overlooked due to untouched training methods and traditional design elements not fully revealing their capabilities. Our pilot study shows dense connections through concatenation are strong, demonstrating that DenseNets can be revitalized to compete with modern architectures. We methodically refine suboptimal components - architectural adjustments, block redesign, and improved training recipes towards widening DenseNets and boosting memory efficiency while keeping concatenation shortcuts. Our models, employing simple architectural elements, ultimately surpass Swin Transformer, ConvNeXt, and DeiT-III — key architectures in the residual learning lineage. Furthermore, our models exhibit near state-of-the-art performance on ImageNet-1K, competing with the very recent models and downstream tasks, ADE20k semantic segmentation, and COCO object detection/instance segmentation. Finally, we provide empirical analyses that uncover the merits of the concatenation over additive shortcuts, steering a renewed preference towards DenseNet-style designs. Our code is available at https://github.com/naver-ai/rdnet."
Eliminating Feature Ambiguity for Few-Shot Segmentation
Authors: Qianxiong Xu; Guosheng Lin; Chen Change Loy; Cheng Long; Ziyue Li; Rui Zhao
Venue: ECCV 2024 (main)

Abstract: "Recent advancements in few-shot segmentation (FSS) have exploited pixel-by-pixel matching between query and support features, typically based on cross attention, which selectively activate query foreground (FG) features that correspond to the same-class support FG features. However, due to the large receptive fields in deep layers of the backbone, the extracted query and support FG features are inevitably mingled with background (BG) features, impeding the FG-FG matching in cross attention. Hence, the query FG features are fused with less support FG features, , the support information is not well utilized. This paper presents a novel plug-in termed ambiguity elimination network (AENet), which can be plugged into any existing cross attention-based FSS methods. The main idea is to mine discriminative query FG regions to rectify the ambiguous FG features, increasing the proportion of FG information, so as to suppress the negative impacts of the doped BG features. In this way, the FG-FG matching is naturally enhanced. We plug AENet into three baselines CyCTR, SCCAN and HDMNet for evaluation, and their scores are improved by large margins, , the 1-shot performance of SCCAN can be improved by 3.0%+ on both PASCAL-5i and COCO-20i . The code is available at https://github.com/Sam1224/AENet."
Soft Prompt Generation for Domain Generalization
Authors: Shuanghao Bai; Yuedi Zhang; Wanqi Zhou; Zhirong Luan; Badong Chen
Venue: ECCV 2024 (main)

Abstract: "Large pre-trained vision language models (VLMs) have shown impressive zero-shot ability on downstream tasks with manually designed prompt. To further adapt VLMs to downstream tasks, soft prompt is proposed to replace manually designed prompt, which undergoes fine-tuning based on specific domain data. Prior prompt learning methods primarily learn a fixed prompt or residuled prompt from training samples. However, the learned prompts lack diversity and ignore information about unseen domains. In this paper, we reframe the prompt learning framework from a generative perspective and propose a simple yet efficient method for the Domain Generalization (DG) task, namely Soft Prompt Generation (SPG). Specifically, SPG consists of a two-stage training phase and an inference phase. During the training phase, we introduce soft prompt label for each domain, aiming to incorporate the generative model domain knowledge. During the inference phase, the generator of the generative model is employed to obtain instance-specific soft prompts for the unseen target domain. Extensive experiments on five domain generalization benchmarks of three DG tasks demonstrate that SPG achieves state-of-the-art performance. The code is available at https://github.com/renytek13/Soft-Prompt-Generation-with-CGAN."
Shedding More Light on Robust Classifiers under the lens of Energy-based Models
Authors: Mujtaba Hussain Mirza; Maria Rosaria Briglia; Senad Beadini; Iacopo Masi
Venue: ECCV 2024 (main)

Abstract: "By reinterpreting a robust discriminative classifier as Energy-based Model (EBM), we offer a new take on the dynamics of adversarial training (AT). Our analysis of the energy landscape during AT reveals that untargeted attacks generate adversarial images much more in-distribution (lower energy) than the original data from the point of view of the model. Conversely, we observe the opposite for targeted attacks. On the ground of our thorough analysis, we present new theoretical and practical results that show how interpreting AT energy dynamics unlocks a better understanding: (1) AT dynamic is governed by three phases and robust overfitting occurs in the third phase with a drastic divergence between natural and adversarial energies (2) by rewriting TRADES loss in terms of energies, we show that TRADES implicitly alleviates overfitting by means of aligning the natural energy with the adversarial one (3) we empirically show that all recent state-of-the-art robust classifiers are smoothing the energy landscape and we reconcile a variety of studies about understanding AT and weighting the loss function under the umbrella of EBMs. Motivated by rigorous evidence, we propose Weighted Energy Adversarial Training (WEAT), a novel sample weighting scheme that yields robust accuracy matching the state-of-the-art on multiple benchmarks such as CIFAR-10 and SVHN and going beyond in CIFAR-100 and Tiny-ImageNet. We further show that robust classifiers vary in the intensity and quality of their generative capabilities, and offer a simple method to push this capability, reaching a remarkable Inception Score (IS) and FID using a robust classifier without training for generative modeling. The code to reproduce our results is available at github.com/OmnAI-Lab/Robust-Classifiers-under-the-lens-of-EBM."
LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation
Authors: Jiaxiang Tang; Zhaoxi Chen; Xiaokang Chen; Tengfei Wang; Gang Zeng; Ziwei Liu
Venue: ECCV 2024 (main)

Abstract: "3D content creation has achieved significant progress in terms of both quality and speed. Although current feed-forward models can produce 3D objects in seconds, their resolution is constrained by the intensive computation required during training. In this paper, we introduce Large Multi-View Gaussian Model (LGM), a novel framework designed to generate high-resolution 3D models from text prompts or single-view images. Our key insights are two-fold: 1) 3D Representation: We propose multi-view Gaussian features as an efficient yet powerful representation, which can then be fused together for differentiable rendering. 2) 3D Backbone: We present an asymmetric U-Net as a high-throughput backbone operating on multi-view images, which can be produced from text or single-view image input by leveraging multi-view diffusion models. Extensive experiments demonstrate the high fidelity and efficiency of our approach. Notably, we maintain the fast speed to generate 3D objects within 5 seconds while boosting the training resolution to 512, thereby achieving high-resolution 3D content generation. Our project page is available at https://me.kiui.moe/ lgm/."
Mahalanobis Distance-based Multi-view Optimal Transport for Multi-view Crowd Localization
Authors: Qi Zhang; Kaiyi Zhang; Antoni B. Chan; Hui Huang
Venue: ECCV 2024 (main)

Abstract: "Multi-view crowd localization predicts the ground locations of all people in the scene. Typical methods usually estimate the crowd density maps on the ground plane first, and then obtain the crowd locations. However, existing methods’ performances are limited by the ambiguity of the density maps in crowded areas, where local peaks can be smoothed away. To mitigate the weakness of density map supervision, optimal transport-based point supervision methods have been proposed in the single-image crowd localization tasks, but have not been explored for multi-view crowd localization yet. Thus, in this paper, we propose a novel Mahalanobis distance-based multi-view optimal transport (M-MVOT) loss specifically designed for multi-view crowd localization. First, we replace the Euclidean-based transport cost with the Mahalanobis distance, which defines elliptical iso-contours in the cost function whose long-axis and short-axis directions are guided by the view ray direction. Second, the object-to-camera distance in each view is used to adjust the optimal transport cost of each location further, where the wrong predictions far away from the camera are more heavily penalized. Finally, we propose a strategy to consider all the input camera views in the model loss (M-MVOT) by computing the optimal transport cost for each ground-truth point based on its closest camera. Experiments demonstrate the advantage of the proposed method over density map-based or common Euclidean distance-based optimal transport loss on several multi-view crowd localization datasets."
RAW-Adapter: Adapting Pretrained Visual Model to Camera RAW Images
Authors: Ziteng Cui; Tatsuya Harada
Venue: ECCV 2024 (main)

Abstract: "sRGB images are now the predominant choice for pre-training visual models in computer vision research, owing to their ease of acquisition and efficient storage. Meanwhile, the advantage of RAW images lies in their rich physical information under variable real-world challenging lighting conditions. For computer vision tasks directly based on camera RAW data, most existing studies adopt methods of integrating image signal processor (ISP) with backend networks, yet often overlook the interaction capabilities between the ISP stages and subsequent networks. Drawing inspiration from ongoing adapter research in NLP and CV areas, we introduce RAW-Adapter, a novel approach aimed at adapting sRGB pre-trained models to camera RAW data. RAW-Adapter comprises input-level adapters that employ learnable ISP stages to adjust RAW inputs, as well as model-level adapters to build connections between ISP stages and subsequent high-level networks. Additionally, RAW-Adapter is a general framework that could be used in various computer vision frameworks. Abundant experiments under different lighting conditions have shown our algorithm’s state-of-the-art (SOTA) performance, demonstrating its effectiveness and efficiency across a range of real-world and synthetic datasets. Code is available at this url."
SLEDGE: Synthesizing Driving Environments with Generative Models and Rule-Based Traffic
Authors: Kashyap Chitta; Daniel Dauner; Andreas Geiger
Venue: ECCV 2024 (main)

Abstract: "SLEDGE is the first generative simulator for vehicle motion planning trained on real-world driving logs. Its core component is a learned model that is able to generate agent bounding boxes and lane graphs. The model’s outputs serve as an initial state for rule-based traffic simulation. The unique properties of the entities to be generated for SLEDGE, such as their connectivity and variable count per scene, render the naive application of most modern generative models to this task non-trivial. Therefore, together with a systematic study of existing lane graph representations, we introduce a novel raster-to-vector autoencoder. It encodes agents and the lane graph into distinct channels in a rasterized latent map. This facilitates both lane-conditioned agent generation and combined generation of lanes and agents with a Diffusion Transformer. Using generated entities in SLEDGE enables greater control over the simulation, e.g. upsampling turns or increasing traffic density. Further, SLEDGE can support 500m long routes, a capability not found in existing data-driven simulators like nuPlan. It presents new challenges for planning algorithms, evidenced by failure rates of over 40% for PDM, the winner of the 2023 nuPlan challenge, when tested on hard routes and dense traffic generated by our model. Compared to nuPlan, SLEDGE requires 500× less storage to set up (¡4 GB), making it a more accessible option and helping with democratizing future research in this field."
AFreeCA: Annotation-Free Counting for All
Authors: Adriano D'Alessandro; Ali Mahdavi-Amiri; Ghassan Hamarneh
Venue: ECCV 2024 (main)

Abstract: "Object counting methods typically rely on manually annotated datasets. The cost of creating such datasets has restricted the versatility of these networks to count objects from specific classes (such as humans or penguins), and counting objects from diverse categories remains a challenge. The availability of robust text-to-image latent diffusion models (LDMs) raises the question of whether these models can be utilized to generate counting datasets. However, LDMs struggle to create images with an exact number of objects based solely on text prompts but they can be used to offer a dependable sorting signal by adding and removing objects within an image. Leveraging this data, we initially introduce an unsupervised sorting methodology to learn object-related features that are subsequently refined and anchored for counting purposes using counting data generated by LDMs. Further, we present a density classifier-guided method for dividing an image into patches containing objects that can be reliably counted. Consequently, we can generate counting data for any type of object and count them in an unsupervised manner. Our approach outperforms unsupervised and few-shot alternatives and is not restricted to specific object classes for which counting data is available. Code available at: github.com/adrian-dalessandro/AFreeCA."
Adversarially Robust Distillation by Reducing the Student-Teacher Variance Gap
Authors: Junhao Dong; Piotr Koniusz; Junxi Chen; Yew-Soon Ong
Venue: ECCV 2024 (main)

Abstract: "Adversarial robustness generally relies on large-scale architectures and datasets, hindering resource-efficient deployment. For scalable solutions, adversarially robust knowledge distillation has emerged as a principle strategy, facilitating the transfer of robustness from a large-scale teacher model to a lightweight student model. However, existing works focus solely on sample-to-sample alignment of features or predictions between the teacher and student models, overlooking the vital role of their statistical alignment. Thus, we propose a novel adversarially robust knowledge distillation method that integrates the alignment of feature distributions between the teacher and student backbones under adversarial and clean sample sets. To motivate our idea, for an adversarially trained model (, student or teacher), we show that the robust accuracy (evaluated on testing adversarial samples under an increasing perturbation radius) correlates negatively with the gap between the feature variance evaluated on testing adversarial samples and testing clean samples. Such a negative correlation exhibits a strong linear trend, suggesting that aligning the feature covariance of the student model toward the feature covariance of the teacher model should improve the adversarial robustness of the student model by reducing the variance gap. A similar trend is observed by reducing the variance gap between the gram matrices of the student and teacher models. Extensive evaluations highlight the state-of-the-art adversarial robustness and natural performance of our method across diverse datasets and distillation scenarios."
LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation
Authors: Yushi Lan; Fangzhou Hong; Shuai Yang; Shangchen Zhou; Xuyi Meng; Bo Dai; Xingang Pan; Chen Change Loy
Venue: ECCV 2024 (main)

Abstract: "The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework called to address this gap and enable fast, high-quality, and generic conditional 3D generation. Our approach harnesses a 3D-aware architecture and variational autoencoder (VAE) to encode the input image(s) into a structured, compact, and 3D latent space. The latent is decoded by a transformer-based decoder into a high-capacity 3D neural field. Through training a diffusion model on this 3D-aware latent space, our method achieves superior performance on Objaverse, ShapeNet and FFHQ for conditional 3D generation. Moreover, it surpasses existing 3D diffusion methods in terms of inference speed, requiring no per-instance optimization. Video demos can be found on our project webpage: https://nirvanalan.github.io/projects/ ln3diff."
Hierarchical Temporal Context Learning for Camera-based Semantic Scene Completion
Authors: Bohan Li; Jiajun Deng; Wenyao Zhang; Zhujin Liang; Dalong Du; Xin Jin; Wenjun Zeng
Venue: ECCV 2024 (main)

Abstract: "Camera-based 3D semantic scene completion (SSC) is pivotal for predicting complicated 3D layouts with limited 2D image observations. The existing mainstream solutions generally leverage temporal information by roughly stacking history frames to supplement the current frame, such straightforward temporal modeling inevitably diminishes valid clues and increases learning difficulty. To address this problem, we present HTCL, a novel Hierarchical Temporal Context Learning paradigm for improving camera-based semantic scene completion. The primary innovation of this work involves decomposing temporal context learning into two hierarchical steps: (a) cross-frame affinity measurement and (b) affinity-based dynamic refinement. Firstly, to separate critical relevant context from redundant information, we introduce the pattern affinity with scale-aware isolation and multiple independent learners for fine-grained contextual correspondence modeling. Subsequently, to dynamically compensate for incomplete observations, we adaptively refine the feature sampling locations based on initially identified locations with high affinity and their neighboring relevant regions. Our method ranks 1st on the SemanticKITTI benchmark and even surpasses LiDAR-based methods in terms of mIoU on the OpenOccupancy benchmark. Our code is available on https://github.com/Arlo0o/HTCL."
Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration
Authors: Xueyang Kang; Zhaoliang Luan; Kourosh Khoshelham; Bing WANG
Venue: ECCV 2024 (main)

Abstract: "Point cloud registration is a foundational task for 3D alignment and reconstruction applications. While both traditional and learning-based registration approaches have succeeded, leveraging the intrinsic symmetry of point cloud data, including rotation equivariance, has received insufficient attention. This prohibits the model from learning effectively, resulting in a requirement for more training data and increased model complexity. To address these challenges, we propose a graph neural network model embedded with a local Spherical Euclidean 3D equivariance property through SE(3) message passing based propagation. Our model is composed mainly of a descriptor module, equivariant graph layers, match similarity, and the final regression layers. Such modular design enables us to utilize sparsely sampled input points and initialize the descriptor by self-trained or pre-trained geometric feature descriptors easily. Experiments conducted on the 3DMatch and KITTI datasets exhibit the compelling and robust performance of our model compared to state-of-the-art approaches, while the model complexity remains relatively low at the same time."
GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation
Authors: Chenxin Li; Xinyu Liu; Cheng Wang; Yifan Liu; Weihao Yu; Jing Shao; Yixuan Yuan
Venue: ECCV 2024 (main)

Abstract: "Recent advances in learning multi-modal representation have witnessed the success in biomedical domains. While established techniques enable handling multi-modal information, the challenges are posed when extended to various clinical modalities and practical modality-missing setting due to the inherent modality gaps. To tackle these, we propose an innovative Modality-prompted Heterogeneous Graph for Omni-modal Learning (GTP-4o), which embeds the numerous disparate clinical modalities into a unified representation, completes the deficient embedding of missing modality and reformulates the cross-modal learning with a graph-based aggregation. Specially, we establish a heterogeneous graph embedding to explicitly capture the diverse semantic properties on both the modality-specific features (nodes) and the cross-modal relations (edges). Then, we design a modality-prompted completion that enables completing the inadequate graph representation of missing modality through a graph prompting mechanism, which generates hallucination graphic topologies to steer the missing embedding towards the intact representation. Through the completed graph, we meticulously develop a knowledge-guided hierarchical cross-modal aggregation consisting of a global meta-path neighbouring to uncover the potential heterogeneous neighbors along the pathways driven by domain knowledge, and a local multi-relation aggregation module for the comprehensive cross-modal interaction across various heterogeneous relations. We assess the efficacy of our methodology on rigorous benchmarking experiments against prior state-of-the-arts. In a nutshell, GTP-4o presents an initial foray into the intriguing realm of embedding, relating and perceiving the heterogeneous patterns from various clinical modalities holistically via a graph theory. Project page: https://gtp-4-o.github.io/."
PromptCCD: Learning Gaussian Mixture Prompt Pool for Continual Category Discovery
Authors: Fernando Julio Cendra; Bingchen Zhao; Kai Han
Venue: ECCV 2024 (main)

Abstract: "We tackle the problem of Continual Category Discovery (CCD), which aims to automatically discover novel categories in a continuous stream of unlabeled data while mitigating the challenge of catastrophic forgetting – an open problem that persists even in conventional, fully supervised continual learning. To address this challenge, we propose PromptCCD, a simple yet effective framework that utilizes a Gaussian Mixture Model (GMM) as a prompting method for CCD. At the core of PromptCCD lies the Gaussian Mixture Prompting (GMP) module, which acts as a dynamic pool that updates over time to facilitate representation learning and prevent forgetting during category discovery. Moreover, GMP enables on-the-fly estimation of category numbers, allowing PromptCCD to discover categories in unlabeled data without prior knowledge of the category numbers. We extend the standard evaluation metric for Generalized Category Discovery (GCD) to CCD and benchmark state-of-the-art methods on diverse public datasets. PromptCCD significantly outperforms existing methods, demonstrating its effectiveness. Project page: https://visual-ai.github.io/promptccd."