mtybilly/Instruct2DS huggingface dataset:
Active Generation for Image Classification
Authors: Tao Huang, Jiaqi Liu, Shan You*, Chang Xu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recently, the growing capabilities of deep generative models have underscored their potential in enhancing image classification accuracy. However, existing methods often demand the generation of a disproportionately large number of images compared to the original dataset, while having only marginal improvements in accuracy. This computationally expensive and time-consuming process hampers the practicality of such approaches. In this paper, we propose to address the efficiency of image generation by focusing on the specific needs and characteristics of the model. With a central tenet of active learning, our method, named ActGen, takes a training-aware approach to image generation. It aims to create images akin to the challenging or misclassified samples encountered by the current model and incorporates these generated images into the training set to augment model performance. ActGen introduces an attentive image guidance technique, using real images as guides during the denoising process of a diffusion model. The model’s attention on class prompt is leveraged to ensure the preservation of similar foreground object while diversifying the background. Furthermore, we introduce a gradient-based generation guidance method, which employs two losses to generate more challenging samples and prevent the generated images from being too similar to previously generated ones. Experimental results on the CIFAR and ImageNet datasets demonstrate that our method achieves better performance with a significantly reduced number of generated images. Code is available at https://github.com/hunto/ActGen."
FuseTeacher: Modality-fused Encoders are Strong Vision Supervisors
Authors: Chen-Wei Xie*, Siyang Sun, Liming Zhao, Pandeng Li, Shuailei Ma, Yun Zheng
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Learning visual representation with image-text datasets attracts a lot of attention in recent years. Existing approaches primarily rely on cross-modality supervision, and incorporate intra-modality supervision if necessary. They overlook the potential benefits of modality-fused supervision. Since modality-fused representation augments the image representation with textual information, we conjecture it is more discriminative and potential to be a strong teacher for visual representation learning. In this paper, we validate this hypothesis by experiments and propose a novel method that learns visual representation by modality-fused supervision. Specifically, we introduce a fusion encoder that encodes image and text into a fusion representation. This representation can be utilized to supervise the visual representation learning in two distillation ways: (i) Classification Distillation: we cluster image-text pairs into K clusters using the fusion representation and assign each pair a soft cluster assignment, which is served as a pseudo classification label for supervising the image encoder. (ii) Retrieval Distillation: we calculate the similarities between the fusion representation and all text representations in the same batch. By using the similarity distribution as pseudo retrieval similarity between the corresponding image and all texts, we can prevent one-to-one contrastive learning from separating relevant but unpaired pairs. The is compatible with existing language supervised visual representation learning methods. Experimental results demonstrate that it is able to bring significant improvements and achieves state-of-the-art methods on various datasets. Code, datasets and pre-trained models are available at https://github.com/Eniac-Xie/FuseTeacher."
Learning Local Pattern Modularization for Point Cloud Reconstruction from Unseen Classes
Authors: Chao Chen, Yu-Shen Liu*, Zhizhong Han
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "It is challenging to reconstruct 3D point clouds in unseen classes from single 2D images. Instead of object-centered coordinate system, current methods generalized global priors learned in seen classes to reconstruct 3D shapes from unseen classes in viewer-centered coordinate system. However, the reconstruction accuracy and interpretability are still eager to get improved. To resolve this issue, we introduce to learn local pattern modularization for reconstructing 3D shapes in unseen classes, which achieves both good generalization ability and high reconstruction accuracy. Our insight is to learn a local prior which is class-agnostic and easy to generalize in object-centered coordinate system. Specifically, the local prior is learned via a process of learning and customizing local pattern modularization in seen classes. During this process, we first learn a set of patterns in local regions, which is the basis in the object-centered coordinate system to represent an arbitrary region on shapes across different classes. Then, we modularize each region on an initially reconstructed shape using the learned local patterns. Based on that, we customize the local pattern modularization using the input image by refining the reconstruction with more details. Our method enables to reconstruct high fidelity point clouds from unseen classes in object-centered coordinate system without requiring a large number of patterns or any additional information, such as segmentation supervision or camera poses. Our experimental results under widely used benchmarks show that our method achieves the state-of-the-art reconstruction accuracy for shapes from unseen classes. The code is available at https://github.com/ chenchao15/Unseen."
Understanding Multi-compositional learning in Vision and Language models via Category Theory
Authors: Sotirios Panagiotis Chytas*, Hyunwoo J Kim, Vikas Singh
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Pre-trained large language models (and multi-modal models) offer excellent performance across a wide range of tasks. Despite their effectiveness, we have limited knowledge of their internal knowledge representation. To get started, we use the classic problem of Compositional Zero-Shot Learning (CZSL) as an example, and first provide a structured view of the latent space that any general model (LLM or otherwise) should nominally respect. We obtain a practical solution to the CZSL problem that can deal with both Open and Closed-World single-attribute compositions as well as multi-attribute compositions with relative ease, where we achieve performance competitive with methods designed solely for that task (i.e., adaptations to other tasks are difficult). Then, we extend this perspective to analysis of existing LLMs and ask to what extent they satisfy our axiomatic definitions. Our analysis shows a mix of interesting and unsurprising findings, but nonetheless suggests that our criteria is meaningful and may yield a more structured approach for potential incorporation in training such models, strategies for additional data collection, and diagnostics beyond visual inspection. The code is available at https://github.com/SPChytas/CatCom."
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
Authors: Shangchao Su, Bin Li*, Xiangyang Xue
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "With the increasing availability of Foundation Models, federated tuning has garnered attention in the field of federated learning, utilizing data and computation resources from multiple clients to collaboratively fine-tune foundation models. However, in real-world federated scenarios, there often exist a multitude of heterogeneous clients with varying computation and communication resources, rendering them incapable of supporting the entire model fine-tuning process. In response to this challenge, we propose a novel federated tuning algorithm, FedRA. The implementation of FedRA is straightforward and can be seamlessly integrated into any Transformer-based model without the need for further modification to the original model. Specifically, in each communication round, FedRA randomly generates an allocation matrix. For resource-constrained clients, it reorganizes a small number of layers from the original model based on the allocation matrix and fine-tunes using adapters. Subsequently, the server aggregates the updated adapter parameters from the clients according to the current allocation matrix into the corresponding layers of the original model. It is worth noting that FedRA also supports scenarios where none of the clients can support the entire global model, which is an impressive advantage. We conduct experiments on two large-scale image datasets, DomainNet and NICO++, under various non-iid settings. The results demonstrate that FedRA outperforms the compared methods significantly."
Panel-Specific Degradation Representation for Raw Under-Display Camera Image Restoration
Authors: Youngjin Oh*, Keuntek Lee, Jooyoung Lee, Dae-Hyun Lee, Nam Ik Cho
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Under-display camera (UDC) image restoration aims to restore images distorted by the OLED display panel covering the frontal camera on a smartphone. Previous deep learning-based UDC restoration methods focused on restoring the image within the RGB domain with the collection of real or synthetic RGB datasets. However, UDC images in these datasets exhibit domain differences from real commercial smartphone UDC images while inherently constraining the problem and solution within the RGB domain. To address this issue, we collect well-aligned sensor-level real UDC images using panels from two commercial smartphones equipped with UDC. We also propose a new UDC restoration method to exploit the disparities between degradations caused by different panels, considering that UDC degradations are specific to the type of OLED panel. For this purpose, we train an encoder with an unsupervised learning scheme using triplet loss that aims to extract the inherent degradations caused by different panels from degraded UDC images as implicit representations. The learned panel-specific degradation representations are then provided as priors to our restoration network based on an efficient Transformer network. Extensive experiments show that our proposed method achieves state-of-the-art performance on our real raw image dataset and generalizes well to previous datasets. Our dataset and code is available at https://github.com/OBAKSA/ DREUDC."
Unlocking Textual and Visual Wisdom: Open-Vocabulary 3D Object Detection Enhanced by Comprehensive Guidance from Text and Image
Authors: Pengkun Jiao*, Na Zhao*, Jingjing Chen, Yu-Gang Jiang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Open-vocabulary 3D object detection (OV-3DDet) aims to localize and recognize both seen and previously unseen object categories within any new 3D scene. While language and vision foundation models have achieved success in handling various open-vocabulary tasks with abundant training data, OV-3DDet faces a significant challenge due to the limited availability of training data. Although some pioneering efforts have integrated vision-language models (VLM) knowledge into OV-3DDet learning, the full potential of these foundational models has yet to be fully exploited. In this paper, we unlock the textual and visual wisdom to tackle the open-vocabulary 3D detection task by leveraging the language and vision foundation models. We leverage a vision foundation model to provide image-wise guidance for discovering novel classes in 3D scenes. Specifically, we utilize a object detection vision foundation model to enable the zero-shot discovery of objects in images, which serves as the initial seeds and filtering guidance to identify novel 3D objects. Additionally, to align the 3D space with the powerful vision-language space, we introduce a hierarchical alignment approach, where the 3D feature space is aligned with the vision-language feature space using a pretrained VLM at the instance, category, and scene levels. Through extensive experimentation, we demonstrate significant improvements in accuracy and generalization, highlighting the potential of foundation models in advancing open-vocabulary 3D object detection in real-world scenarios."
Diffusion-Guided Weakly Supervised Semantic Segmentation
Authors: Sung-Hoon Yoon, Hoyong Kwon, Jaeseok Jeong, Daehee Park, Kuk-Jin Yoon*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Weakly Supervised Semantic Segmentation (WSSS) with classification labels typically uses Class Activation Maps to localize the object based on Convolutional Neural Networks (CNN). With limited receptive fields, CNN-based CAMs often fail to localize the whole object. The emergence of a Vision Transformer (ViT) alleviates the problem with superior performance, but the lack of locality in ViT introduces a new challenge. Inspired by the ability of Denoising Diffusion Probabilistic Models (DDPM) to capture high-level semantic information, we bring diffusion models to WSSS to resolve the problem. Firstly, to fuse and semantically align the information between DDPM and ViT, we design the Locality Fusion Cross Attention (LFCA) module. Using the aggregated features from the denoising process of the pretrained DDPM, LFCA generates CAMs (Diffusion-CAMs) that provide locality information to CAMs from ViT (ViT-CAMs). Secondly, by adding noise to the original image and denoising it with DDPM, we obtain a denoised image that can be leveraged as an augmented sample. To effectively guide ViT in excavating the relation between the patches, we devise the Patch Affinity Consistency (PAC) between the outputs of the original image and the denoised image. Extensive ablation studies support the superiority of the proposed method. Our method achieves new state-of-the-art performance on two widely used datasets in WSSS; PASCAL VOC 2012 and MS-COCO 2014. The code is available at https://github.com/yoon307/DiG."
Weakly-Supervised Spatio-Temporal Video Grounding with Variational Cross-Modal Alignment
Authors: Yang Jin*, Yadong Mu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This paper explores the spatio-temporal video grounding (STVG) task, which aims at localizing a particular object corresponding to a given textual description in an untrimmed video. Existing approaches mainly resort to object-level manual annotations as the supervision for addressing this challenging task. Such a paradigm heavily constrains the scalability of processing large-scale unlabeled data. To this end, we present a novel framework that is capable of grounding the target object relying only on the video-sentence correspondence. Specifically, our model re-formulates the original STVG task as two cross-modal alignment sub-problems: region-phrase and frame-sentence. Since the absence of ground-truth alignments during the training stage, we treat them as latent variables and learn to model the joint conditional distribution by reconstructing the interactions of entities in the video. The entire framework can be effectively optimized by the variational Expectation-Maximization (EM) algorithm, which alternates between two updating steps for progressively maximizing the likelihood of query sentence, thereby approximating the real cross-modal assignment. Extensive experiments on two video benchmarks (VidSTG and HC-STVG) further show the effectiveness of the proposed method."
When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset
Authors: Yi Zhang, Wang Zeng, Sheng Jin, Chen Qian*, Ping Luo, Wentao Liu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent years have witnessed increasing research attention towards pedestrian detection by taking the advantages of different sensor modalities (RGB, IR, Depth, LiDAR and Event). However, designing a unified generalist model that can effectively process diverse sensor modalities remains a challenge. This paper introduces MMPedestron, a novel generalist model for multimodal perception. Unlike previous specialist models that only process one or a pair of specific modality inputs, MMPedestron is able to process multiple modal inputs and their dynamic combinations. The proposed approach comprises a unified encoder for modal representation and fusion and a general head for pedestrian detection. We introduce two extra learnable tokens, MAA and MAF, for adaptive multi-modal feature fusion. In addition, we construct the MMPD dataset, the first large-scale benchmark for multi-modal pedestrian detection. This benchmark incorporates existing public datasets and a newly collected dataset called EventPed, covering a wide range of sensor modalities including RGB, IR, Depth, LiDAR, and Event data. With multi-modal joint training, our model achieves state-of-the-art performance on a wide range of pedestrian detection benchmarks, surpassing leading models tailored for specific sensor modality. For example, it achieves 71.1 AP on COCO-Persons and 72.6 AP on LLVIP. Notably, our model achieves comparable performance to the InternImage-H model on CrowdHuman with 30× smaller parameters. Codes and data are available at https://github. com/BubblyYi/MMPedestron. : Corresponding author."
NVS-Adapter: Plug-and-Play Novel View Synthesis from a Single Image
Authors: Yoonwoo Jeong, Jinwoo Lee, Chiheon Kim, Minsu Cho*, Doyup Lee*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent advancements in Novel View Synthesis (NVS) from a single image have produced impressive results by leveraging the generation capabilities of pre-trained Text-to-Image (T2I) models. However, previous NVS approaches require extra optimization to use other plug-and-play image generation modules such as ControlNet and LoRA, as they fine-tune the T2I parameters. In this study, we propose an efficient plug-and-play adaptation module, NVS-Adapter, that is compatible with existing plug-and-play modules without extensive fine-tuning. We introduce target views and reference view alignment to improve the geometric consistency of multi-view predictions. Experimental results demonstrate the compatibility of our NVS-Adapter with existing plug-and-play modules. Moreover, our NVS-Adapter shows superior performance over state-of-the-art methods on NVS benchmarks although it does not fine-tune billions of parameters of the pre-trained T2I models. The code and data are publicly available at postech-cvlab.github.io/nvsadapter/ 1 1 This work was done during the summer internship program at KakaoBrain."
Segment and Recognize Anything at Any Granularity
Authors: Feng Li*, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Chunyuan Li, Jianwei Yang, Lei Zhang*, Jianfeng Gao*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this work, we introduce , an augmented image segmentation foundation for segmenting and recognizing anything at desired granularities. Compared to the foundational segmentation model SAM [?], our model has two unique advantages: (i) granularity-controllability in that the model can produce segmentation masks at any desired granularities, from objects to parts to both; (ii) semantic-awareness in that the model simultaneously predicts semantic labels for masks at different granularities. To enable multi-granularity capabilities, we propose a multi-choice learning scheme, where each click point generates a set of masks at multiple levels of granularity, corresponding to a set of ground-truth masks. To achieve semantic awareness, we consolidate multiple datasets of different levels of granularity and train our model using decoupled object- and part-based tasks to facilitate knowledge sharing and transfer among different tasks. To the best of our knowledge, this work is the first attempt to jointly train a model on SA-1B, instance-level, and part-level segmentation datasets. Experimental results and visualizations demonstrate that our model successfully achieves the desired goals. Furthermore, we show that multi-task training using the segmentation task defined on SA-1B and other segmentation tasks (e.g., panoptic and part segmentation) leads to performance gains on all segmentation tasks. In particular, we achieve a new state-of-the-art in COCO panoptic segmentation 60.2 PQ by adding SAM data."
Real-time Holistic Robot Pose Estimation with Unknown States
Authors: Shikun Ban, Juling Fan, Xiaoxuan Ma, Wentao Zhu*, Yu QIAO*, Yizhou Wang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Estimating robot pose from RGB images is a crucial problem in computer vision and robotics. While previous methods have achieved promising performance, most of them presume full knowledge of robot internal states, ground-truth robot joint angles. However, this assumption is not always valid in practical situations. In real-world applications such as multi-robot collaboration or human-robot interaction, the robot joint states might not be shared or could be unreliable. On the other hand, existing approaches that estimate robot pose without joint state priors suffer from heavy computation burdens and thus cannot support real-time applications. This work introduces an efficient framework for real-time robot pose estimation from RGB images without requiring known robot states. Our method estimates camera-to-robot rotation, robot state parameters, keypoint locations, and root depth, employing a neural network module for each task to facilitate learning and sim-to-real transfer. Notably, it achieves inference in a single feed-forward pass without iterative optimization. Our approach offers a 12× speed increase with state-of-the-art accuracy, enabling real-time holistic robot pose estimation for the first time. Code and models are available at https://github.com/Oliverbansk/Holistic-Robot-Pose-Estimation."
CLOSER: Towards Better Representation Learning for Few-Shot Class-Incremental Learning
Authors: Junghun Oh, Sungyong Baik, Kyoung Mu Lee*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Aiming to incrementally learn new classes with only few samples while preserving the knowledge of base (old) classes, few-shot class-incremental learning (FSCIL) faces several challenges, such as overfitting and catastrophic forgetting. Such a challenging problem is often tackled by fixing a feature extractor trained on base classes to reduce the adverse effects of overfitting and forgetting. Under such formulation, our primary focus is representation learning on base classes to tackle the unique challenge of FSCIL: simultaneously achieving the transferability and discriminability of the learned representation. Building upon the recent efforts for enhancing the transferability, such as promoting the spread of features, we find that trying to secure the spread of features within a more confined feature space enables the learned representation to strike a better balance between the transferability and discriminability. Thus, in stark contrast to prior beliefs that the inter-class distance should be maximized, we claim that the CLOSER different classes are, the better for FSCIL. The empirical results and analysis from the perspective of information bottleneck theory justify our simple yet seemingly counter-intuitive representation learning method, raising research questions and suggesting alternative research directions. The code is available here."
A Simple Baseline for Spoken Language to Sign Language Translation with 3D Avatars
Authors: Ronglai Zuo, Fangyun Wei*, Zenggui Chen, Brian Mak, Jiaolong Yang, Xin Tong
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The objective of this paper is to develop a functional system for translating spoken languages into sign languages, referred to as Spoken2Sign translation. The Spoken2Sign task is orthogonal and complementary to traditional sign language to spoken language (Sign2Spoken) translation. To enable Spoken2Sign translation, we present a simple baseline consisting of three steps: 1) creating a gloss-video dictionary using existing Sign2Spoken benchmarks; 2) estimating a 3D sign for each sign video in the dictionary; 3) training a Spoken2Sign model, which is composed of a Text2Gloss translator, a sign connector, and a rendering module, with the aid of the yielded gloss-3D sign dictionary. The translation results are then displayed through a sign avatar. As far as we know, we are the first to present the Spoken2Sign task in an output format of 3D signs. In addition to its capability of Spoken2Sign translation, we also demonstrate that two by-products of our approach—3D keypoint augmentation and multi-view understanding—can assist in keypoint-based sign language understanding. Code and models are available at https://github.com/FangyunWei/SLRT."
An accurate detection is not all you need to combat label noise in web-noisy datasets
Authors: Paul Albert*, Kevin McGuinness, Eric Arazo, Tarun Krishna, Noel O Connor, Jack Valmadre
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Training a classifier on web-crawled data demands learning algorithms that are robust to annotation errors and irrelevant examples. This paper builds upon the recent empirical observation that applying unsupervised contrastive learning to noisy, web-crawled datasets yields a feature representation under which the in-distribution (ID) and out-of-distribution (OOD) samples are linearly separable [?]. We show that direct estimation of the separating hyperplane can indeed offer an accurate detection of OOD samples, and yet, surprisingly, this detection does not translate into gains in classification accuracy. Digging deeper into this phenomenon, we discover that the near-perfect detection misses a type of clean examples that are valuable for supervised learning. These examples often represent visually simple images, which are relatively easy to identify as clean examples using standard loss- or distance-based methods despite being poorly separated from the OOD distribution using unsupervised learning. Because we further observe a low correlation with SOTA metrics, this urges us to propose a hybrid solution that alternates between noise detection using linear separation and a state-of-the-art (SOTA) small-loss approach. When combined with the SOTA algorithm PLS, we substantially improve SOTA results for real-world image classification in the presence of web noise https://github.com/PaulAlbert31/ LSA"
Online Vectorized HD Map Construction using Geometry
Authors: Zhixin Zhang, Yiyuan Zhang, Xiaohan Ding, Fusheng Jin*, Xiangyu Yue
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Online vectorized High-Definition (HD) map construction is critical for downstream prediction and planning. Recent efforts have built strong baselines for this task, however, geometric shapes and relations of instances in road systems are still under-explored, such as parallelism, perpendicular, rectangle-shape, . In our work, we propose GeMap (Geometry Map), which end-to-end learns Euclidean shapes and relations of map instances beyond fundamental perception. Specifically, we design a geometric loss based on angle and magnitude clues, robust to rigid transformations of driving scenarios. To address the limitations of the vanilla attention mechanism in learning geometry, we propose to decouple self-attention to handle Euclidean shapes and relations independently. GeMap achieves new state-of-the-art performance on the nuScenes and Argoverse 2 datasets. Remarkably, it reaches a 71.8% mAP on the large-scale Argoverse 2 dataset, outperforming MapTRv2 by +4.4% and surpassing the 70% mAP threshold for the first time. Code is available at https://github.com/cnzzx/GeMap."
Image-adaptive 3D Lookup Tables for Real-time Image Enhancement with Bilateral Grids
Authors: Wontae Kim*, Nam Ik Cho*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Image enhancement and restoration methods using adaptive 3D lookup tables (3D LUTs) have shown promising results with real-time inferencing. These methods directly transform input pixel values into enhanced ones by using interpolation operations with predicted 3D LUT values. However, it is still challenging to deal with locally different properties of images since most 3D LUT methods are simple color-to-color transforms. Although including spatial information in this transform can be a good solution, it can significantly increase the number of parameters and inference time. To address this issue, we propose an efficient spatial-aware image enhancement model that combines bilateral grids and 3D LUTs. Specifically, we transform bilateral grids into a spatial feature domain to incorporate spatial information in our 3D LUT model. To reduce inference time and save parameters, we use slicing operations in our network architecture instead of the long decoding path of the U-Net architecture used in most existing studies. Our model achieves state-of-the-art performance without increasing parameters and further reduces inference time, as demonstrated by extensive results. Codes are available at https://github.com/WontaeaeKim/ LUTwithBGrid"
Learned HDR Image Compression for Perceptually Optimal Storage and Display
Authors: Peibei Cao, HAOYU CHEN, Jingzhe Ma, Yu-Chieh Yuan, Zhiyong Xie, Xin Xie, Haiqing Bai, Kede Ma*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "High dynamic range (HDR) capture and display have seen significant growth in popularity driven by the advancements in technology and increasing consumer demand for superior image quality. As a result, HDR image compression is crucial to fully realize the benefits of HDR imaging without suffering from large file sizes and inefficient data handling. Conventionally, this is achieved by introducing a residual/gain map as additional metadata to bridge the gap between HDR and low dynamic range (LDR) images, making the former compatible with LDR image codecs but offering suboptimal rate-distortion performance. In this work, we initiate efforts towards end-to-end optimized HDR image compression for perceptually optimal storage and display. Specifically, we learn to compress an HDR image into two bitstreams: one for generating an LDR image to ensure compatibility with legacy LDR displays, and another as side information to aid HDR image reconstruction from the output LDR image. To measure the perceptual quality of output HDR and LDR images, we use two recently proposed image distortion metrics, both validated against human perceptual data of image quality and with reference to the uncompressed HDR image. Through end-to-end optimization for rate-distortion performance, our method dramatically improves HDR and LDR image quality at all bit rates. The code is available at https://github.com/cpb68/EPIC-HDR/."
Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completion
Authors: Huadong Li, Minhao Jing, Jin Wang, Shichao Dong, Jiajun Liang, Haoqiang Fan, Renhe Ji*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "It is widely believed that sparse supervision is worse than dense supervision in the field of depth completion, but the underlying reasons for this are rarely discussed. To this end, we revisit the task of radar-camera depth completion and present a new method with sparse LiDAR supervision to outperform previous dense LiDAR supervision methods in both accuracy and speed. Specifically, when trained by sparse LiDAR supervision, depth completion models usually output depth maps containing significant stripe-like artifacts. We find that such a phenomenon is caused by the implicitly learned positional distribution pattern from sparse LiDAR supervision, termed as LiDAR Distribution Leakage (LDL) in this paper. Based on such understanding, we present a novel Disruption-Compensation radar-camera depth completion framework to address this issue. The Disruption part aims to deliberately disrupt the learning of LiDAR distribution from sparse supervision, while the Compensation part aims to leverage 3D spatial and 2D semantic information to compensate for the information loss of previous disruptions. Extensive experimental results demonstrate that by reducing the impact of LDL, our framework with sparse supervision outperforms the state-of-the-art dense supervision methods with 11.6% improvement in Mean Absolute Error (MAE) and 1.6× speedup in Frame Per Second (FPS). The code is available at https://github.com/megvii-research/Sparse-Beats-Dense."
Non-Exemplar Domain Incremental Learning via Cross-Domain Concept Integration
Authors: Qiang Wang*, Yuhang He, Songlin Dong, Xinyuan Gao, Shaokun Wang, Yihong Gong
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Existing approaches to Domain Incremental Learning (DIL) address catastrophic forgetting by storing and rehearsing exemplars from old domains. However, exemplar-based solutions are not always viable due to data privacy concerns or storage limitations. Therefore, Non-Exemplar Domain Incremental Learning (NEDIL) has emerged as a significant paradigm for resolving DIL challenges. Current NEDIL solutions extend the classifier incrementally for new domains to learn new knowledge, but unrestricted extension within the same feature space leads to inter-class confusion. To tackle this issue, we propose a simple yet effective method through cross-domain concePt INtegrAtion (PINA). We train a Unified Classifier (UC) as a concept container across all domains. Then, a Domain Specific Alignment (DSA) module is proposed for each incremental domain, aligning the feature distribution to the base domain. During inference, we introduce a Patch Shuffle Selector (PSS) to select appropriate parameters of DSA for test images. Our developed patch shuffling technique disrupts class-dependent information, outperforming the domain selectors based on K-Nearest Neighbors or Nearest Mean Classifier. Extensive experiments demonstrate that our method achieves state-of-the-art performance while reducing the number of additional parameters. The source code will be released in https://github.com/qwangcv/PINA."
Free-VSC: Free Semantics from Visual Foundation Models for Unsupervised Video Semantic Compression
Authors: Yuan Tian*, Guo Lu*, Guangtao Zhai*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Unsupervised video semantic compression (UVSC), i.e., compressing videos to better support various analysis tasks, has recently garnered attention. However, the semantic richness of previous methods remains limited, due to the single semantic learning objective, limited training data, etc. To address this, we propose to boost the UVSC task by absorbing the off-the-shelf rich semantics from VFMs. Specifically, we introduce a VFMs-shared semantic alignment layer, complemented by VFM-specific prompts, to flexibly align semantics between the compressed video and various VFMs. This allows different VFMs to collaboratively build a mutually-enhanced semantic space, guiding the learning of the compression model. Moreover, we introduce a dynamic trajectory-based inter-frame compression scheme, which first estimates the semantic trajectory based on the historical content, and then traverses along the trajectory to predict the future semantics as the coding context. This reduces the overall bitcost of the system, further improving the compression efficiency. Our approach outperforms previous coding methods on three mainstream tasks and six datasets."
Improving Virtual Try-On with Garment-focused Diffusion Models
Authors: Siqi Wan, Yehao Li, Jingwen Chen, Yingwei Pan*, Ting Yao, Yang Cao, Tao Mei
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Diffusion models have led to the revolutionizing of generative modeling in numerous image synthesis tasks. Nevertheless, it is not trivial to directly apply diffusion models for synthesizing an image of a target person wearing a given in-shop garment, i.e., image-based virtual try-on (VTON) task. The difficulty originates from the aspect that the diffusion process should not only produce holistically high-fidelity photorealistic image of the target person, but also locally preserve every appearance and texture detail of the given garment. To address this, we shape a new Diffusion model, namely GarDiff, which triggers the garment-focused diffusion process with amplified guidance of both basic visual appearance and detailed textures (i.e., high-frequency details) derived from the given garment. GarDiff first remoulds a pre-trained latent diffusion model with additional appearance priors derived from the CLIP and VAE encodings of the reference garment. Meanwhile, a novel garment-focused adapter is integrated into the UNet of diffusion model, pursuing local fine-grained alignment with the visual appearance of reference garment and human pose. We specifically design an appearance loss over the synthesized garment to enhance the crucial, high-frequency details. Extensive experiments on VITON-HD and DressCode datasets demonstrate the superiority of our GarDiff when compared to state-of-the-art VTON approaches. Code is publicly available at: https://github.com/siqi0905/GarDiff/tree/master."
Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection
Authors: Feng Liu*, Tengteng Huang, Qianjing Zhang, Haotian Yao, Chi Zhang, Fang Wan, Qixiang Ye, Yanzhao Zhou*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Multi-view 3D object detection systems often struggle with generating precise predictions due to the challenges in estimating depth from images, increasing redundant and incorrect detections. Our paper presents Ray Denoising, an innovative method that enhances detection accuracy by strategically sampling along camera rays to construct hard negative examples. These examples, visually challenging to differentiate from true positives, compel the model to learn depth-aware features, thereby improving its capacity to distinguish between true and false positives. Ray Denoising is designed as a plug-and-play module, compatible with any DETR-style multi-view 3D detectors, and it only minimally increases training computational costs without affecting inference speed. Our comprehensive experiments, including detailed ablation studies, consistently demonstrate that Ray Denoising outperforms strong baselines across multiple datasets. It achieves a 1.9% improvement in mean Average Precision (mAP) over the state-of-the-art StreamPETR method on the NuScenes dataset. It shows significant performance gains on the Argoverse 2 dataset, highlighting its generalization capability. The code is available at magentahttps://github.com/LiewFeng/RayDN."
Disentangled Generation and Aggregation for Robust Radiance Fields
Authors: Shihe Shen, Huachen Gao, Wangze Xu, Rui Peng, Luyang Tang, Kaiqiang Xiong, Jianbo Jiao, Ronggang Wang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The utilization of the triplane-based radiance fields has gained attention in recent years due to its ability to effectively disentangle 3D scenes with a high-quality representation and low computation cost. A key requirement of this method is the precise input of camera poses. However, due to the local update property of the triplane, a similar joint estimation as previous joint pose-NeRF optimization works easily results in local minima. To this end, we propose the Disentangled Triplane Generation module to introduce global feature context and smoothness into triplane learning, which mitigates errors caused by local updating. Then, we propose the Disentangled Plane Aggregation to mitigate the entanglement caused by the common triplane feature aggregation during camera pose updating. In addition, we introduce a two-stage warm-start training strategy to reduce the implicit constraints caused by the triplane generator. Quantitative and qualitative results demonstrate that our proposed method achieves state-of-the-art performance in novel view synthesis with noisy or unknown camera poses, as well as efficient convergence of optimization. Project page: https://gaohchen.github.io/DiGARR/."
UNIKD: UNcertainty-Filtered Incremental Knowledge Distillation for Neural Implicit Representation
Authors: Mengqi Guo*, Chen Li, Hanlin Chen, Gim Hee Lee
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent neural implicit representations (NIRs) have achieved great success in the tasks of 3D reconstruction and novel view synthesis. However, they require the images of a scene from different camera views to be available for one-time training. This is expensive especially for scenarios with large-scale scenes and limited data storage. In view of this, we explore the task of incremental learning for NIRs in this work. We design a student-teacher framework to mitigate the catastrophic forgetting problem. Specifically, we iterate the process of using the student as the teacher at the end of each time step and let the teacher guide the training of the student in the next step. As a result, the student network is able to learn new information from the streaming data and retain old knowledge from the teacher network simultaneously. Although intuitive, naively applying the student-teacher pipeline does not work well in our task. Not all information from the teacher network is helpful since it is only trained with the old data. To alleviate this problem, we further introduce a random inquirer and an uncertainty-based filter to filter useful information. Our proposed method is general and thus can be adapted to different implicit representations such as neural radiance field (NeRF) and neural surface field. Extensive experimental results for both 3D reconstruction and novel view synthesis demonstrate the effectiveness of our approach compared to different baselines."
Subspace Prototype Guidance for Mitigating Class Imbalance in Point Cloud Semantic Segmentation
Authors: Jiawei Han, Kaiqi Liu*, Wei Li, Guangzhi Chen
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Point cloud semantic segmentation can significantly enhance the perception of an intelligent agent. Nevertheless, the discriminative capability of the segmentation network is influenced by the quantity of samples available for different categories. To mitigate the cognitive bias induced by class imbalance, this paper introduces a novel method, namely subspace prototype guidance (SPG), to guide the training of segmentation network. Specifically, the point cloud is initially separated into independent point sets by category to provide initial conditions for the generation of feature subspaces. The auxiliary branch which consists of an encoder and a projection head maps these point sets into separate feature subspaces. Subsequently, the feature prototypes which are extracted from the current separate subspaces and then combined with prototypes of historical subspaces guide the feature space of main branch to enhance the discriminability of features of minority categories. The prototypes derived from the feature space of main branch are also employed to guide the training of the auxiliary branch, forming a supervisory loop to maintain consistent convergence of the entire network. The experiments conducted on the large public benchmarks (i.e. S3DIS, ScanNet v2, ScanNet200, Toronto-3D) and collected real-world data illustrate that the proposed method significantly improves the segmentation performance and surpasses the state-of-the-art method. The code is available at https://github.com/Javion11/PointLiBR.git."
MoAI: Mixture of All Intelligence for Large Language and Vision Models
Authors: Byung-Kwan Lee, Beomchan Park, Chae Won Kim, Yong Man Ro*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The rise of large language models (LLMs) and instruction tuning has led to the current trend of instruction-tuned large language and vision models (LLVMs). This trend involves either meticulously curating numerous instruction tuning datasets tailored to specific objectives or enlarging LLVMs to manage vast amounts of vision language (VL) data. However, current LLVMs have disregarded the detailed and comprehensive real-world scene understanding available from specialized computer vision (CV) models in visual perception tasks such as segmentation, detection, scene graph generation (SGG), and optical character recognition (OCR). Instead, the existing LLVMs rely mainly on the large capacity and emergent capabilities of their LLM backbones. Therefore, we present a new LLVM, Mixture of All Intelligence ([width=0.025]figure/moai.pdf MoAI), which leverages auxiliary visual information obtained from the outputs of external segmentation, detection, SGG, and OCR models. MoAI operates through two newly introduced modules: MoAI-Compressor and MoAI-Mixer. After verbalizing the outputs of the external CV models, the MoAI-Compressor aligns and condenses them to efficiently use relevant auxiliary visual information for VL tasks. MoAI-Mixer then blends three types of intelligence—(1) visual features, (2) auxiliary features from the external CV models, and (3) language features—utilizing the concept of Mixture of Experts. Through this integration, MoAI significantly outperforms both open-source and closed-source LLVMs in numerous zero-shot VL tasks, particularly those related to real-world scene understanding such as object existence, positions, relations, and OCR without enlarging the model size or curating extra visual instruction tuning datasets. Code is available in https://github.com/ByungKwanLee/MoA"
Semantic-guided Robustness Tuning for Few-Shot Transfer Across Extreme Domain Shift
Authors: kangyu xiao*, Zilei Wang, junjie li
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this work, we focus on the cross-domain few-shot classification (CDFSC), which is mostly challenged by the low-data problem as well as extreme domain shift between base and novel target classes. Current methods always employ a lightweight backbone and continue to use a linear-probe-like traditional fine-tuning (Trad-FT) paradigm. While for recently emerging large-scale pre-trained model (LPM), which has more parameters with considerable prior knowledge, employing Trad-FT will face significant risks of overfitting and prior knowledge damage. In this paper, we propose semantic-guided robustness tuning (SRT), a novel fine-tuning paradigm including modulus-matching-based image-text mixup (MMIT-Mixup) and robustness-invariance fine-tuning (RI-FT), to address the CDFSC challenge of LPM. Concretely, SRT focuses on achieving robust class-specific representation. It first considers textual information as a robust and domain-invariant conductor, and MMIT-Mixup injects the domain-invariant and class-specific knowledge to obtain domain-invariant prototypes. Then, RI-FT optimizes the distance between features and prototypes to enhance the robustness of visual-encoder. We consider several types of LPMs and conduct extensive experiments, which reveals that SRT is a general solution for LPM’s CDFSC challenge and outperforms the existing methods with a large margin."