mtybilly/Instruct2DS huggingface dataset:
SMFANet: A Lightweight Self-Modulation Feature Aggregation Network for Efficient Image Super-Resolution
Authors: Long Sun, Jiangxin Dong, Jinshan Pan* mingjun zheng
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Transformer-based restoration methods achieve significant performance as the self-attention (SA) of the Transformer can explore non-local information for better high-resolution image reconstruction. However, the key dot-product SA requires substantial computational resources, which limits its application in low-power devices. Moreover, the low-pass nature of the SA mechanism limits its capacity for capturing local details, consequently leading to smooth reconstruction results. To address these issues, we propose a self-modulation feature aggregation (SMFA) module to collaboratively exploit both local and non-local feature interactions for a more accurate reconstruction. Specifically, the SMFA module employs an efficient approximation of self-attention (EASA) branch to model non-local information and uses a local detail estimation (LDE) branch to capture local details. Additionally, we further introduce a partial convolution-based feed-forward network (PCFN) to refine the representative features derived from the SMFA. Extensive experiments show that the proposed SMFANet family achieve a better trade-off between reconstruction performance and computational efficiency on public benchmark datasets. In particular, compared to the ×4 SwinIR-light, SMFANet+ achieves 0.14dB higher performance over five public testsets on average, and ×10 times faster runtime, with only about 43% of the model complexity (e.g., FLOPs). Our source codes and pre-trained models are available at: https://github.com/Zheng-MJ/SMFANet."
HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view Cameras
Authors: ZhiWei Lin, Xinhao Wang, Yongtao Wang*, Yun Xing, Shengxiang Qi, Nan Dong, Ming-Hsuan Yang Zhongyu Xia
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Three-dimensional perception from multi-view cameras is a crucial component in autonomous driving systems, which involves multiple tasks like 3D object detection and bird’s-eye-view (BEV) semantic segmentation. To improve perception precision, large image encoders, high-resolution images, and long-term temporal inputs have been adopted in recent 3D perception models, bringing remarkable performance gains. However, these techniques are often incompatible in training and inference scenarios due to computational resource constraints. Besides, modern autonomous driving systems prefer to adopt an end-to-end framework for multi-task 3D perception, which can simplify the overall system architecture and reduce the implementation complexity. However, conflict between tasks often arises when optimizing multiple tasks jointly within an end-to-end 3D perception model. To alleviate these issues, we present an end-to-end framework named HENet for multi-task 3D perception in this paper. Specifically, we propose a hybrid image encoding network, using a large image encoder for short-term frames and a small image encoder for long-term temporal frames. Then, we introduce a temporal feature integration module based on the attention mechanism to fuse the features of different frames extracted by the two aforementioned hybrid image encoders. Finally, according to the characteristics of each perception task, we utilize BEV features of different grid sizes, independent BEV encoders, and task decoders for different tasks. Experimental results show that HENet achieves state-of-the-art end-to-end multi-task 3D perception results on the nuScenes benchmark, including 3D object detection and BEV semantic segmentation."
Hierarchical Unsupervised Relation Distillation for Source Free Domain Adaptation
Authors: Xianghua Ying, Ruibin Wang, Ruohao Guo, Ji Shi, Wenzhen Yue Bowei Xing*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Source free domain adaptation (SFDA) aims to transfer the model trained on labeled source domain to unlabeled target domain without accessing source data. Recent SFDA methods predominantly rely on self-training, which supervise the model with pseudo labels generated from individual data samples. However, they often ignore the crucial data structure information and sample relationships that are beneficial for adaptive training. In this paper, we propose a novel hierarchical relation distillation framework, establishing multi-level relations across samples in an unsupervised manner, which fully exploits inherent data structure to guide sample training instead of using isolated pseudo labels. We first distinguish source-like samples based on prediction reliability during training, followed by an effort on distilling knowledge to those target-specific ones by transferring both local clustering relation and global semantic relation. Specifically, we leverage the affinity with nearest neighborhood samples for local relation and consider the similarity to category-wise Gaussian Mixtures for global relation, offering complementary supervision to facilitate student learning. To validate our approach’s effectiveness, we conduct extensive experiments on diverse benchmarks, achieving better performance compared to previous methods."
Customized Generation Reimagined: Fidelity and Editability Harmonized
Authors: Yang Shen, Zhenyong Fu*, Jian Yang* Jian Jin
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Customized generation aims to incorporate a novel concept into a pre-trained text-to-image model, enabling new generations of the concept in novel contexts guided by textual prompts. However, customized generation suffers from an inherent trade-off between concept fidelity and editability, i.e., between precisely modeling the concept and faithfully adhering to the prompts. Previous methods reluctantly seek a compromise and struggle to achieve both high concept fidelity and ideal prompt alignment simultaneously. In this paper, we propose a “Divide, Conquer, then Integrate” (DCI) framework, which performs a surgical adjustment in the early stage of denoising to liberate the fine-tuned model from the fidelity-editability trade-off at inference. The two conflicting components in the trade-off are decoupled and individually conquered by two collaborative branches, which are then selectively integrated to preserve high concept fidelity while achieving faithful prompt adherence. To obtain a better fine-tuned model, we introduce an Image-specific Context Optimization (ICO) strategy for model customization. ICO replaces manual prompt templates with learnable image-specific contexts, providing an adaptive and precise fine-tuning direction to promote the overall performance. Extensive experiments demonstrate the effectiveness of our method in reconciling the fidelity-editability trade-off. Code is available at https://github.com/jinjianRick/DCI ICO."
AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit Detectors
Authors: Zitong Yu*, Xin Liu*, Weicheng Xie, Huanjing Yue, Jingyu Yang Kaishen Yuan
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Facial Action Units (AU) is a vital concept in the realm of affective computing, and AU detection has always been a hot research topic. Existing methods suffer from overfitting issues due to the utilization of a large number of learnable parameters on scarce AU-annotated datasets or heavy reliance on substantial additional relevant data. Parameter-Efficient Transfer Learning (PETL) provides a promising paradigm to address these challenges, whereas its existing methods lack design for AU characteristics. Therefore, we innovatively investigate PETL paradigm to AU detection, introducing AUFormer and proposing a novel Mixture-of-Knowledge Expert (MoKE) collaboration mechanism. An individual MoKE specific to a certain AU with minimal learnable parameters first integrates personalized multi-scale and correlation knowledge. Then the MoKE collaborates with other MoKEs in the expert group to obtain aggregated information and inject it into the frozen Vision Transformer (ViT) to achieve parameter-efficient AU detection. Additionally, we design a Margin-truncated Difficulty-aware Weighted Asymmetric Loss (MDWA-Loss), which can encourage the model to focus more on activated AUs, differentiate the difficulty of unactivated AUs, and discard potential mislabeled samples. Extensive experiments from various perspectives, including within-domain, cross-domain, data efficiency, and micro-expression domain, demonstrate AUFormer’s state-of-the-art performance and robust generalization abilities without relying on additional relevant data. The code for AUFormer is available at https://github.com/yuankaishen2001/AUFormer."
Improving Video Segmentation via Dynamic Anchor Queries
Authors: Tao Zhang*, Xiangtai Li*, Shunping Ji*, Shuicheng Yan Yikang Zhou
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Modern video segmentation methods adopt feature transitions between anchor and target queries to perform cross-frame object association. The smooth feature transitions between anchor and target queries enable these methods to achieve satisfactory performance when tracking continuously appearing objects. However, the emergence and disappearance of objects interrupt the smooth feature transition, and even widen this feature transition gap between anchor and target queries, which causes these methods to all underperform on newly emerging and disappearing objects that are common in the real world. We introduce Dynamic Anchor Queries (DAQ) to shorten the transition gap by dynamically generating anchor queries based on the features of potential newly emerging and disappearing candidates. Furthermore, we introduce a query-level object Emergence and Disappearance Simulation (EDS) strategy, which unleashes DAQ’s potential without any additional cost. Finally, we combine our proposed DAQ and EDS with the previous method, DVIS, to obtain DVIS-DAQ. Extensive experiments demonstrate that DVIS-DAQ achieves a new state-of-the-art (SOTA) performance on five mainstream video segmentation benchmarks."
Controllable Contextualized Image Captioning: Directing the Visual Narrative through User-Defined Highlights
Authors: Chaoyi Zhang, Hang Su, Hwanjun Song, Igor Shalyminov, Weidong Cai Shunqi Mao*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "(CIC) evolves traditional image captioning into a more complex domain, necessitating the ability for multimodal reasoning. It aims to generate image captions given specific contextual information. This paper further introduces a novel domain of (). Unlike CIC, which solely relies on broad context, accentuates a user-defined highlight, compelling the model to tailor captions that resonate with the highlighted aspects of the context. We present two approaches, Prompting-based Controller () and Recalibration-based Controller (), to generate focused captions. conditions the model generation on highlight by prepending captions with highlight-driven prefixes, whereas tunes the model to selectively recalibrate the encoder embeddings for highlighted tokens. Additionally, we design a GPT-4V empowered evaluator to assess the quality of the controlled captions alongside standard assessment methods. Extensive experimental results demonstrate the efficient and effective controllability of our method, charting a new direction in achieving user-adaptive image captioning. Code is avaliable at https://github.com/ShunqiM/Ctrl-CIC."
Diffusion Models as Optimizers for Efficient Planning in Offline RL
Authors: Yunqiang Pei, Guoqing Wang*, Yangming Zhang, Yang Yang, Peng Wang, Heng Tao Shen Renming Huang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Diffusion models have shown strong competitiveness in offline reinforcement learning tasks by formulating decision-making as sequential generation. However, the practicality of these methods is limited due to the lengthy inference processes they require. In this paper, we address this problem by decomposing the sampling process of diffusion models into two decoupled subprocesses: 1) generating a feasible trajectory, which is a time-consuming process, and 2) optimizing the trajectory. With this decomposition approach, we are able to partially separate efficiency and quality factors, enabling us to simultaneously gain efficiency advantages and ensure quality assurance. We propose the Trajectory Diffuser, which utilizes a faster autoregressive model to handle the generation of feasible trajectories while retaining the trajectory optimization process of diffusion models. This allows us to achieve more efficient planning without sacrificing capability. To evaluate the effectiveness and efficiency of the Trajectory Diffuser, we conduct experiments on the D4RL benchmarks. The results demonstrate that our method achieves 3 -10 × faster inference speed compared to previous sequence modeling methods, while also outperforming them in terms of overall performance. https: //github.com/RenMing-Huang/TrajectoryDiffuser"
Enhanced Sparsification via Stimulative Training
Authors: Weihao Lin, Hancheng Ye, Peng Ye, Chong Yu, Baopu Li, Tao Chen* Shengji Tang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Sparsification-based pruning has been an important category in model compression. Existing methods commonly set sparsity-inducing penalty terms to suppress the importance of dropped weights, which is regarded as the suppressed sparsification paradigm. However, this paradigm inactivates the dropped parts of networks causing capacity damage before pruning, thereby leading to performance degradation. To alleviate this issue, we first study and reveal the relative sparsity effect in emerging stimulative training. Based on the sparsity effect, we propose a structured pruning framework, named STP. It is based on an enhanced sparsification paradigm which maintains the magnitude of dropped weights and enhances the expressivity of kept weights by self-distillation. Besides, to find an optimal architecture for the pruned network, we propose a multi-dimension architecture space and a knowledge distillation-guided exploration strategy. To reduce the huge capacity gap of distillation, we propose a subnet mutating expansion technique. Extensive experiments on various benchmarks indicate the effectiveness of STP. Specifically, without fine-tuning, our method consistently achieves superior performance at different budgets, especially under extremely aggressive pruning scenarios, e.g., remaining 95.11% Top-1 accuracy (72.43% in 76.15%) while reducing 85% FLOPs for ResNet-50 on ImageNet. Codes are at https:// github.com/tsj-001/STP."
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs
Authors: Chenhang Cui, Zijun Wang, Yiyang Zhou, Bingchen Zhao, Junlin Han, Wangchunshu Zhou, Huaxiu Yao, Cihang Xie* Haoqin Tu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This work focuses on benchmarking the capabilities of vision large language models (VLLMs) in visual reasoning. Different from prior studies, we shift our focus from evaluating standard performance to introducing a comprehensive safety evaluation suite Unicorn, covering out-of-distribution (OOD) generalization and adversarial robustness. For the OOD evaluation, we present two novel visual question-answering (VQA) datasets, each with one variant, designed to test model performance under challenging conditions. In exploring adversarial robustness, we propose a straightforward attack strategy for misleading VLLMs to produce visual-unrelated responses. Moreover, we assess the efficacy of two jailbreaking strategies, targeting either the vision or language input of VLLMs. Our evaluation of 22 diverse models, ranging from open-source VLLMs to GPT-4V and Gemini Pro, yields interesting observations: 1) Current VLLMs struggle with OOD texts but not images, unless the visual information is limited; and 2) These VLLMs can be easily misled by deceiving vision encoders only, and their vision-language training often compromise safety protocols. We release this safety evaluation suite at https://github.com/UCSC-VLAA/vllm-safety-benchmark."
NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation
Authors: Yikai Wang, Yanwei Fu*, Xuelin Qian, Chong Li, Yun Wang, Jianfeng Feng Jingyang Huo
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent fMRI-to-image approaches mainly focused on associating fMRI signals with specific conditions of pre-trained diffusion models. These approaches, while producing high-quality images, capture only a limited aspect of the complex information in fMRI signals and offer little detailed control over image creation. In contrast, this paper proposes to directly modulate the generation process of diffusion models using fMRI signals. Our approach, NeuroPictor, divides the fMRI-to-image process into three steps: i) fMRI calibrated-encoding, to tackle multi-individual pre-training for a shared latent space to minimize individual difference and enable the subsequent multi-subject training; ii) fMRI-to-image multi-subject pre-training, perceptually learning to guide diffusion model with high- and low-level conditions across different individuals; iii) fMRI-to-image single-subject refining, similar with step ii but focus on adapting to particular individual. NeuroPictor extracts high-level semantic features from fMRI signals that characterizing the visual stimulus and incrementally fine-tunes the diffusion model with a low-level manipulation network to provide precise structural instructions. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity, particularly in the within-subject setting, as evidenced in benchmark datasets. Our code and model are available at https://jingyanghuo.github.io/neuropictor/."
Coarse-to-Fine Implicit Representation Learning for 3D Hand-Object Reconstruction from a Single RGB-D Image
Authors: Pengfei Ren, Jingyu Wang*, Qi Qi, Haifeng Sun, Zirui Zhuang*, Jianxin Liao Xingyu Liu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent research has explored implicit representations, such as signed distance function (SDF), for interacting hand-object reconstruction. SDF enables modeling hand-held objects with arbitrary topology and overcomes the resolution limitations of parametric models, allowing for finer-grained reconstruction. However, directly modeling detailed SDFs from visual clues presents challenges due to depth ambiguity and appearance similarity, especially in cluttered real-world scenes. In this paper, we propose a coarse-to-fine SDF framework for 3D hand-object reconstruction, which leverages the perceptual advantages of RGB-D modality in visual and geometric aspects, to progressively model the implicit field. Specifically, we model a coarse SDF for visual perception of overall scenes. Then, we propose a 3D Point-Aligned Implicit Function (3D PIFu) for fine-level SDF learning, which leverages both local geometric clues and the coarse-level visual priors to capture intricate details. Additionally, we propose a surface-aware efficient reconstruction strategy that sparsely performs SDF query based on the hand-object semantic priors. Experiments on two challenging hand-object datasets show that our method outperforms existing methods by a large margin."
Efficient Snapshot Spectral Imaging: Calibration-Free Parallel Structure with Aperture Diffraction Fusion
Authors: Lihao Hu, Shiqiao Li, Chenglong Huang, Xun Cao Tao Lv*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Aiming to address the repetitive need for complex calibration in existing snapshot spectral imaging methods, while better trading off system complexity and spectral reconstruction accuracy, we demonstrate a novel Parallel Coded Calibration-free Aperture Diffraction Imaging Spectrometer (PCCADIS) with simplest parallel architecture and enhanced light throughput. The system integrates monochromatic acquisition of diffraction-induced blurred spatial-spectral projections with uncalibrated chromatic filtering for guidance, enabling portable and lightweight implementation. In the inverse process of PCCADIS, aperture diffraction produces blurred patterns through global replication and band-by-band integration, posing challenges in registration with clearly structured RGB information by feature matching. Therefore, the Self-aware Spectral Fusion Cascaded Transformer (SSFCT) is proposed to realize the fusion and reconstruction of uncalibrated inputs, which demonstrates the potential to substantially improve the accuracy of snapshot spectral imaging while concurrently reducing the associated costs. Our methodology is rigorously evaluated through comprehensive simulation experiments and real reconstruction experiments with a prototype, confirming the high accuracy and user-friendliness of PCCADIS."
Enhancing Recipe Retrieval with Foundation Models: A Data Augmentation Perspective
Authors: Bin Zhu, Yanbin Hao*, Shuo Wang Fangzhou Song
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Learning recipe and food image representation in common embedding space is non-trivial but crucial for cross-modal recipe retrieval. In this paper, we propose a new perspective for this problem by utilizing foundation models for data augmentation. Leveraging on the remarkable capabilities of foundation models (i.e., Llama2 and SAM), we propose to augment recipe and food image by extracting alignable information related to the counterpart. Specifically, Llama2 is employed to generate a textual description from the recipe, aiming to capture the visual cues of a food image, and SAM is used to produce image segments that correspond to key ingredients in the recipe. To make full use of the augmented data, we introduce Data Augmented Retrieval framework (DAR) to enhance recipe and image representation learning for cross-modal retrieval. We first inject adapter layers to pre-trained CLIP model to reduce computation cost rather than fully fine-tuning all the parameters. In addition, multi-level circle loss is proposed to align the original and augmented data pairs, which assigns different penalties for positive and negative pairs. On the Recipe1M dataset, our DAR outperforms all existing methods by a large margin. Extensive ablation studies validate the effectiveness of each component of DAR. Code is available at magentahttps://github.com/Noah888/DAR."
PapMOT: Exploring Adversarial Patch Attack against Multiple Object Tracking
Authors: Tingsong Jiang*, Wen Yao*, Shuai Jia*, Weijia Zhang*, Weien Zhou*, Chao Ma*, Xiaoqian Chen* Jiahuan Long*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Tracking multiple objects in a continuous video stream is crucial for many computer vision tasks. It involves detecting and associating objects with their respective identities across successive frames. Despite significant progress made in multiple object tracking (MOT), recent studies have revealed the vulnerability of existing MOT methods to adversarial attacks. Nevertheless, all of these attacks belong to digital attacks that inject pixel-level noise into input images, and are therefore ineffective in physical scenarios. To fill this gap, we propose PapMOT, which can generate physical adversarial patches against MOT for both digital and physical scenarios. Besides attacking the detection mechanism, PapMOT also optimizes a printable patch that can be detected as new targets to mislead the identity association process. Moreover, we introduce a patch enhancement strategy to further degrade the temporal consistency of tracking results across video frames, resulting in more aggressive attacks. We further develop new evaluation metrics to assess the robustness of MOT against such attacks. Extensive evaluations on multiple datasets demonstrate that our PapMOT can successfully attack various architectures of MOT trackers in digital scenarios. We also validate the effectiveness of PapMOT for physical attacks by deploying printed adversarial patches in the real world."
HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models
Authors: Zhaowei CHEN, Zhenyu Zhao, Yuhao Chen, Yao Tang, Jiajun Liang* Shen Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Diffusion models have become a mainstream approach for high-resolution image synthesis. However, directly generating higher-resolution images from pretrained diffusion models will encounter unreasonable object duplication and exponentially increase the generation time. In this paper, we discover that object duplication arises from feature duplication in the deep blocks of the U-Net. Concurrently, We pinpoint the extended generation times to self-attention redundancy in U-Net’s top blocks. To address these issues, we propose a tuning-free higher-resolution framework named HiDiffusion. Specifically, HiDiffusion contains Resolution-Aware U-Net (RAU-Net) that dynamically adjusts the feature map size to resolve object duplication and engages Modified Shifted Window Multi-head Self-Attention (MSW-MSA) that utilizes optimized window attention to reduce computations. we can integrate HiDiffusion into various pretrained diffusion models to scale image generation resolutions even to 4096×4096 at 1.5-6× the inference speed of previous methods. Extensive experiments demonstrate that our approach can address object duplication and heavy computation issues, achieving state-of-the-art performance on higher-resolution image synthesis tasks."
On the Approximation Risk of Few-Shot Class-Incremental Learning
Authors: Zhong Ji*, Xiyao Liu, Yanwei Pang, Jungong Han Xuan Wang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Few-Shot Class-Incremental Learning (FSCIL) aims to learn new concepts with few training samples while preserving previously acquired knowledge. Although promising performance has been achieved, there remains an underexplored aspect regarding the basic statistical principles underlying FSCIL. Therefore, we thoroughly explore the approximation risk of FSCIL, encompassing both transfer and consistency risks. By tightening the upper bounds of these risks, we derive practical guidelines for designing and training FSCIL models. These guidelines include (1) expanding training datasets for base classes, (2) preventing excessive focus on specific features, (3) optimizing classification margin discrepancy, and (4) ensuring unbiased classification across both base and novel classes. Leveraging these insights, we conduct comprehensive experiments to validate our principles, achieving state-of-the-art performance on three FSCIL benchmark datasets. Code is available at https://github.com/xwangrs/Approximation_FSCIL-ECCV2024. git."
Syn-to-Real Domain Adaptation for Point Cloud Completion via Part-based Approach
Authors: Jihun Kim, Kuk-Jin Yoon* Yunseo Yang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Acquiring complete point clouds for real-world scenarios is labor-intensive, making it impractical for conventional learning-based approaches. Numerous methods have been proposed to overcome this limitation by leveraging synthetic complete point clouds. While access to complete point clouds offers a notable advantage, they often struggle to bridge domain gaps, leading to sub-optimal performance. As a remedy, we propose a novel part-based framework for synthetic-to-real domain adaptation in point cloud completion. Our approach starts on the observation that domain gaps inherent in part information are relatively small, as parts are shared properties across categories regardless of domains. To employ part-based approach to point cloud completion, we introduce Part-Based Decomposition (PBD) module to generate part input point clouds. Subsequently, we design a Part-Aware Completion (PAC) module, which operates in a part-wise manner to produce complete point clouds. Within PAC, we devise a novel part-aware transformer to learn relationships between parts and utilize this information to infer missing parts in incomplete point clouds. Extensive experiments demonstrate that our part-based framework significantly outperforms existing studies on real-world point cloud datasets. The code is available at https: //github.com/yun-seo/PPCC"
Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain Generalization
Authors: Jian Zhang, Lei Qi*, Yinghuan Shi*, Yang Gao Jiajun Hu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Domain generalization (DG) aims to avoid the performance degradation of the model when the distribution shift between the limited training data and unseen test data occurs. Recently, foundation models with enormous parameters have been pre-trained with huge datasets, demonstrating strong generalization ability and showing promising direction for solving the DG problem. However, fully Fine-Tuning (FT) the foundation models results in unsatisfactory out-of-distribution accuracy due to the destroyed pre-trained generalized features. Recently, Parameter-Efficient Fine-Tuning (PEFT) alleviates the above problem by fine-tuning a small portion of the model parameters while keeping the rest frozen, which achieves better generalization performance compared to FT. Nevertheless, PEFT still suffers from the issue of overfitting to the training domains. To address the above issue, we propose Parameter-Efficient Group with Orthogonal regularization (PEGO) for vision transformers, which effectively preserves the generalization ability of the pre-trained network and learns more diverse knowledge compared with conventional PEFT. Specifically, we inject a group of trainable Low-Rank Adaptation (LoRA) modules into the pre-trained model and propose an orthogonal regularization loss to enhance the generalization ability of the model. Our framework achieves SOTA performance on five DG benchmarks, while only requiring training a small number of parameters without adding additional testing cost."
SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning
Authors: Liuyu Xiang, Lang Huang, Jiafeng Mao, Ling Xiao, Toshihiko Yamasaki Zerun Wang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Open-set semi-supervised learning (OSSL) leverages practical open-set unlabeled data, comprising both in-distribution (ID) samples from seen classes and out-of-distribution (OOD) samples from unseen classes, for semi-supervised learning (SSL). Prior OSSL methods initially learned the decision boundary between ID and OOD with labeled ID data, subsequently employing self-training to refine this boundary. These methods, however, suffer from the tendency to overtrust the labeled ID data: the scarcity of labeled data caused the distribution bias between the labeled samples and the entire ID data, which misleads the decision boundary to overfit. The subsequent self-training process, based on the overfitted result, fails to rectify this problem. In this paper, we address the overtrusting issue by treating OOD samples as an additional class, forming a new SSL process. Specifically, we propose SCOMatch, a novel OSSL method that 1) selects reliable OOD samples as new labeled data with an OOD memory queue and a corresponding update strategy and 2) integrates the new SSL process into the original task through our Simultaneous Close-set and Open-set self-training. SCOMatch refines the decision boundary of ID and OOD classes across the entire dataset, thereby leading to improved results. Extensive experimental results show that SCOMatch significantly outperforms the state-of-the-art methods on various benchmarks. The effectiveness is further verified through ablation studies and visualization. Our code will be available at https://github.com/komejisatori/ SCOMatch."
Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning
Authors: Tianyu Li, Guoqing Wang*, Peng Wang, Yang Yang, Jie Zou Meixuan Li
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this study, we address the intricate challenge of multi-task dense prediction, encompassing tasks such as semantic segmentation, depth estimation, and surface normal estimation, particularly when dealing with partially annotated data (MTPSL). The complexity arises from the absence of complete task labels for each training image. Given the inter-related nature of these pixel-wise dense tasks, our focus is on mining and capturing cross-task relationships. Existing solutions typically rely on learning global image representations for global cross-task image matching, imposing constraints that, unfortunately, sacrifice the finer structures within the images. Attempting local matching as a remedy faces hurdles due to the lack of precise region supervision, making local alignment a challenging endeavor. The introduction of Segment Anything Model (SAM) sheds light on addressing local alignment challenges by providing free and high-quality solutions for region detection. Leveraging SAM-detected regions, the subsequent challenge lies in aligning the representations within these regions. Diverging from conventional methods that directly learn a monolithic image representation, our proposal involves modeling region-wise representations using Gaussian Distributions. Aligning these distributions between corresponding regions from different tasks imparts higher flexibility and capacity to capture intra-region structures, accommodating a broader range of tasks. This innovative approach significantly enhances our ability to effectively capture cross-task relationships, resulting in improved overall performance in partially supervised multi-task dense prediction scenarios. Extensive experiments conducted on two widely used benchmarks underscore the superior effectiveness of our proposed method, showcasing state-of-the-art performance even when compared to fully supervised methods. https://github.com/HereNowL/Region-aware-Distribution-Contrast"
MasterWeaver: Taming Editability and Face Identity for Personalized Text-to-Image Generation
Authors: Zhilong Ji, Jinfeng Bai, Hongzhi Zhang, Lei Zhang*, Wangmeng Zuo* Yuxiang Wei
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Text-to-image (T2I) diffusion models have shown significant success in personalized text-to-image generation, which aims to generate novel images with human identities indicated by the reference images. Despite promising identity fidelity has been achieved by several tuning-free methods, they often suffer from overfitting issues. The learned identity tends to entangle with irrelevant information, resulting in unsatisfied text controllability, especially on faces. In this work, we present MasterWeaver, a test-time tuning-free method designed to generate personalized images with both high identity fidelity and flexible editability. Specifically, MasterWeaver adopts an encoder to extract identity features and steers the image generation through additionally introduced cross attention. To improve editability while maintaining identity fidelity, we propose an editing direction loss for training, which aligns the editing directions of our MasterWeaver with those of the original T2I model. Additionally, a face-augmented dataset is constructed to facilitate disentangled identity learning, and further improve the editability. Extensive experiments demonstrate that our MasterWeaver can not only generate personalized images with faithful identity, but also exhibit superiority in text controllability. Our code can be found at https://github. com/csyxwei/MasterWeaver."
PointRegGPT: Boosting 3D Point Cloud Registration using Generative Point-Cloud Pairs for Training
Authors: Hao Xu, Haipeng Li, Kunming Luo, Guanghui Liu, Chi-Wing Fu, Ping Tan, Shuaicheng Liu* Suyi Chen
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Data plays a crucial role in training learning-based methods for 3D point cloud registration. However, the real-world dataset is expensive to build, while rendering-based synthetic data suffers from domain gaps. In this work, we present , boosting 3D Point cloud Registration using Generative Point-cloud pairs for Training. Given a single depth map, we first apply a random camera motion to re-project it into a target depth map. Converting them to point clouds gives a training pair. To enhance the data realism, we formulate a generative model as a depth inpainting diffusion to process the target depth map with the re-projected source depth map as the condition. Also, we design a depth correction module to alleviate artifacts caused by point penetration during the re-projection. To our knowledge, this is the first generative approach that explores realistic data generation for indoor 3D point cloud registration. When equipped with our approach, several recent algorithms can improve their performance significantly and achieve SOTA consistently on two common benchmarks. The code and dataset will be released on https://github.com/Chen-Suyi/PointRegGPT."
General Geometry-aware Weakly Supervised 3D Object Detection
Authors: Junsong Fan, Liyi Chen, Zhaoxiang Zhang, Zhen Lei, Lei Zhang Guowen Zhang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "3D object detection is an indispensable component for scene understanding. However, the annotation of large-scale 3D datasets requires significant human effort. To tackle this problem, many methods adopt weakly supervised 3D object detection that estimates 3D boxes by leveraging 2D boxes and scene/class-specific priors. However, these approaches generally depend on sophisticated manual priors, which is hard to generalize to novel categories and scenes. In this paper, we are motivated to propose a general approach, which can be easily adapted to new scenes and/or classes. A unified framework is developed for learning 3D object detectors from RGB images and associated 2D boxes. In specific, we propose three general components: prior injection module to obtain general object geometric priors from LLM model, 2D space projection constraint to minimize the discrepancy between the boundaries of projected 3D boxes and their corresponding 2D boxes on the image plane, and 3D space geometry constraint to build a Point-to-Box alignment loss to further refine the pose of estimated 3D boxes. Experiments on KITTI and SUN-RGBD datasets demonstrate that our method yields surprisingly high-quality 3D bounding boxes with only 2D annotation. The source code is available at https://github.com/gwenzhang/GGA."
Long-CLIP: Unlocking the Long-Text Capability of CLIP
Authors: Pan Zhang, Xiaoyi Dong*, Yuhang Zang, Jiaqi Wang* Beichen Zhang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for zero-shot classification, text-image retrieval, and text-image generation by aligning image and text modalities. Despite its widespread adoption, a significant limitation of CLIP lies in the inadequate length of text input. The length of the text token is restricted to 77, and an empirical study shows the actual effective length is even less than 20. This prevents CLIP from handling detailed descriptions, limiting its applications for image retrieval and text-to-image generation with extensive prerequisites. To this end, we propose Long-CLIP as a plug-and-play alternative to CLIP that supports long-text input, retains or even surpasses its zero-shot generalizability, and aligns the CLIP latent space, making it readily replace CLIP without any further adaptation in downstream frameworks. Nevertheless, achieving this goal is far from straightforward, as simplistic fine-tuning can result in a significant degradation of CLIP’s performance. Moreover, substituting the text encoder with a language model supporting longer contexts necessitates pretraining with vast amounts of data, incurring significant expenses. Accordingly, Long-CLIP introduces an efficient fine-tuning solution on CLIP with two novel strategies designed to maintain the original capabilities, including (1) a knowledge-preserved stretching of positional embedding and (2) a primary component matching of CLIP features. With leveraging just one million extra long text-image pairs, Long-CLIP has shown the superiority to CLIP for about 20% in long caption text-image retrieval and 6% in traditional text-image retrieval tasks, , COCO and Flickr30k. Furthermore, Long-CLIP offers enhanced capabilities for generating images from detailed text descriptions by replacing CLIP in a plug-and-play manner. Codes and models are released at https://github.com/beichenzbc/ Long-CLIP."
Dolfin: Diffusion Layout Transformers without Autoencoder
Authors: Zeyuan Chen, Liangjun Zhong, Zheng Ding, Zhuowen Tu* Yilin Wang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this paper, we introduce a new generative model, Diffusion Layout Transformers without Autoencoder (Dolfin), that attains significantly improved modeling capability and transparency over the existing approaches. Dolfin employs a Transformer-based diffusion process to model layout generation. In addition to an efficient bi-directional (non-causal joint) sequence representation, we also design an autoregressive diffusion model (Dolfin-AR) that is especially adept at capturing neighboring objects’ rich local semantic correlations, such as alignment, size, and overlap. When evaluated on standard unconditional layout generation benchmarks, Dolfin notably outperforms previous methods across various metrics, such as FID, alignment, overlap, MaxIoU, and DocSim scores. Moreover, Dolfin’s applications extend beyond layout generation, making it suitable for modeling other types of geometric structures, such as line segments. Our experiments present both qualitative and quantitative results to demonstrate the advantages of Dolfin."
Real-time 3D-aware Portrait Editing from a Single Image
Authors: Zifan Shi, Yinghao Xu, Hao Ouyang, Qiuyu Wang, Ceyuan Yang, Xuan Wang, Gordon Wetzstein, Yujun Shen*, Qifeng Chen* Qingyan Bai*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This work presents , a practical method that can efficiently edit a face image following given prompts, like reference images or text descriptions, in a 3D-aware manner. To this end, a lightweight module is distilled from a 3D portrait generator and a text-to-image model, which provide prior knowledge of face geometry and superior editing capability, respectively. Such a design brings two compelling advantages over existing approaches. First, our method achieves real-time editing with a feedforward network (i.e., ∼0.04s per image), over 100× faster than the second competitor. Second, thanks to the powerful priors, our module could focus on the learning of editing-related variations, such that it manages to handle various types of editing simultaneously in the training phase and further supports fast adaptation to user-specified customized types of editing during inference (e.g., with ∼5min fine-tuning per style). Project page can be found here."
StructLDM: Structured Latent Diffusion for 3D Human Generation
Authors: Fangzhou Hong, Ziwei Liu* Tao Hu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent 3D human generative models have achieved remarkable progress by learning 3D-aware GANs from 2D images. However, existing 3D human generative methods model humans in a compact 1D latent space, ignoring the articulated structure and semantics of human body topology. In this paper, we explore more expressive and higher-dimensional latent space for 3D human modeling and propose , a diffusion-based unconditional 3D human generative model, which is learned from 2D images. solves the challenges imposed due to the high-dimensional growth of latent space with three key designs: 1) A semantic structured latent space defined on the dense surface manifold of a statistical human body template. 2) A structured 3D-aware auto-decoder that factorizes the global latent space into several semantic body parts parameterized by a set of conditional structured local NeRFs anchored to the body template, which embeds the properties learned from the 2D training data and can be decoded to render view-consistent humans under different poses and clothing styles. 3) A structured latent diffusion model for generative human appearance sampling. Extensive experiments validate ’s state-of-the-art generation performance and illustrate the expressiveness of the structured latent space over the well-adopted 1D latent space. Notably, enables different levels of controllable 3D human generation and editing, including pose/view/shape control, and high-level tasks including compositional generations, part-aware clothing editing, 3D virtual try-on, etc. Project page: taohuumd.github.io/projects/StructLDM."
Image Compression for Machine and Human Vision With Spatial-Frequency Adaptation
Authors: Shaohui Li*, Shuangrui Ding, Wenrui Dai*, Maida Cao, Chenglin Li, Junni Zou, Hongkai Xiong Han Li*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Image compression for machine and human vision (ICMH) has gained increasing attention in recent years. Existing ICMH methods are limited by high training and storage overheads due to heavy design of task-specific networks. To address this issue, in this paper, we develop a novel lightweight adapter-based tuning framework for ICMH, named Adapt-ICMH, that better balances task performance and bitrates with reduced overheads. We propose a spatial-frequency modulation adapter (SFMA) that simultaneously eliminates non-semantic redundancy with a spatial modulation adapter, and enhances task-relevant frequency components and suppresses task-irrelevant frequency components with a frequency modulation adapter. The proposed adapter is plug-and-play and compatible with almost all existing learned image compression models without compromising the performance of pre-trained models. Experiments demonstrate that Adapt-ICMH consistently outperforms existing ICMH frameworks on various machine vision tasks with fewer fine-tuned parameters and reduced computational complexity. Code will be released at https://github.com/qingshi9974/ECCV2024-AdpatICMH."