mtybilly/Instruct2DS huggingface dataset:
Self-Supervised Audio-Visual Soundscape Stylization
Authors: Tingle Li*, Renhao Wang, Po-Yao Huang, Andrew Owens, Gopala Krishna Anumanchipalli
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Speech sounds convey a great deal of information about the scenes, resulting in a variety of effects ranging from reverberation to additional ambient sounds. In this paper, we manipulate input speech to sound as though it was recorded within a different scene, given an audio-visual conditional example recorded from that scene. Our model learns through self-supervision, taking advantage of the fact that natural video contains recurring sound events and textures. We extract an audio clip from a video and apply speech enhancement. We then train a latent diffusion model to recover the original speech, using another audio-visual clip taken from elsewhere in the video as a conditional hint. Through this process, the model learns to transfer the conditional example’s sound properties to the input speech. We show that our model can be successfully trained using unlabeled, in-the-wild videos, and that an additional visual signal can improve its sound prediction abilities."
SAVE: Protagonist Diversification with Structure Agnostic Video Editing
Authors: Yeji Song*, Wonsik Shin, Junsoo Lee, Jeesoo Kim, Nojun Kwak*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Driven by the upsurge progress in text-to-image (T2I) generation models, text-to-video (T2V) generation has experienced a significant advance as well. Accordingly, tasks such as modifying the object or changing the style in a video have been possible. However, previous works usually work well on trivial and consistent shapes, and easily collapse on a difficult target that has a largely different body shape from the original one. In this paper, we spot the bias problem in the existing video editing method that restricts the range of choices for the new protagonist and attempt to address this issue using the conventional image-level personalization method. We adopt motion personalization that isolates the motion from a single source video and then modifies the protagonist accordingly. To deal with the natural discrepancy between image and video, we propose a motion word with an inflated textual embedding to properly represent the motion in a source video. We also regulate the motion word to attend to proper motion-related areas by introducing a novel pseudo optical flow, efficiently computed from the pre-calculated attention maps. Finally, we decouple the motion from the appearance of the source video with an additional pseudo word. Extensive experiments demonstrate the editing capability of our method, taking a step toward more diverse and extensive video editing. Our project page: https://ldynx.github.io/SAVE/"
VideoAgent: Long-form Video Understanding with Large Language Model as Agent
Authors: Xiaohan Wang*, Yuhui Zhang, Orr Zohar, Serena Yeung-Levy
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Long-form video understanding represents a significant challenge within computer vision, demanding a model capable of reasoning over long multi-modal sequences. Motivated by the human cognitive process for long-form video understanding, we emphasize interactive reasoning and planning over the ability to process lengthy visual inputs. We introduce a novel agent-based system, VideoAgent, that employs a large language model as a central agent to iteratively identify and compile crucial information to answer a question, with vision-language foundation models serving as tools to translate and retrieve visual information. Evaluated on the challenging EgoSchema and NExT-QA benchmarks, VideoAgent achieves 54.1% and 71.3% zero-shot accuracy with only 8.4 and 8.2 frames used on average. These results demonstrate superior effectiveness and efficiency of our method over the current state-of-the-art methods, highlighting the potential of agent-based approaches in advancing long-form video understanding."
Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning
Authors: Thong Thanh Nguyen*, Yi Bin, Xiaobao Wu, Xinshuai Dong, Zhiyuan Hu, Khoi M Le, Cong-Duy Nguyen, See Kiong Ng, Anh Tuan Luu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Data quality stands at the forefront of deciding the effectiveness of video-language representation learning. However, video-text pairs in previous data typically do not align perfectly with each other, which might lead to video-language representations that do not accurately reflect cross-modal semantics. Moreover, previous data also possess an uneven distribution of concepts, thereby hampering the downstream performance across unpopular subjects. To address these problems, we propose a contrastive objective with a subtractive angular margin to regularize cross-modal representations in their effort to reach perfect similarity. Furthermore, to adapt to the non-uniform concept distribution, we propose a multi-layer perceptron (MLP)-parameterized weighting function that maps loss values to sample weights which enable dynamic adjustment of the model’s focus throughout the training. With the training guided by a small amount of unbiased meta-data and augmented by video-text data generated by large vision-language model, we improve video-language representations and achieve superior performances on commonly used video question answering and text-video retrieval datasets."
Source-Free Domain-Invariant Performance Prediction
Authors: Ekaterina Khramtsova*, Mahsa Baktashmotlagh, Guido Zuccon, Xi Wang, Mathieu Salzmann
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most existing performance prediction methods heavily rely on the source data in their estimation process, limiting their applicability in a more realistic setting where only the trained model is accessible. The few methods that do not require source data exhibit considerably inferior performance. In this work, we propose a source-free approach centred on uncertainty-based estimation, using a generative model for calibration in the absence of source data. We establish connections between our approach for unsupervised calibration and temperature scaling. We then employ a gradient-based strategy to evaluate the correctness of the calibrated predictions. Our experiments on benchmark object recognition datasets reveal that existing source-based methods fall short with limited source sample availability. Furthermore, our approach significantly outperforms the current state-of-the-art source-free and source-based methods, affirming its effectiveness in domain-invariant performance estimation."
Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures
Authors: Sayanton V. Dibbo*, Adam Breuer, Juston Moore, Michael Teti
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent model inversion attack algorithms permit adversaries to reconstruct a neural network’s private and potentially sensitive training data by repeatedly querying the network. In this work, we develop a novel network architecture that leverages sparse-coding layers to obtain superior robustness to this class of attacks. Three decades of computer science research has studied sparse coding in the context of image denoising, object recognition, and adversarial misclassification settings, but to the best of our knowledge, its connection to state-of-the-art privacy vulnerabilities remains unstudied. In this work, we hypothesize that sparse coding architectures suggest an advantageous means to defend against model inversion attacks because they allow us to control the amount of irrelevant private information encoded by a network in a manner that is known to have little effect on classification accuracy. Specifically, compared to networks trained with a variety of state-of-the-art defenses, our sparse-coding architectures maintain comparable or higher classification accuracy while degrading state-of-the-art training data reconstructions by factors of 1.1 to 18.3 across a variety of reconstruction quality metrics (PSNR, SSIM, FID). This performance advantage holds across 5 datasets ranging from CelebA faces to medical images and CIFAR-10, and across various state-of-the-art SGD-based and GAN-based inversion attacks, including Plug-&-Play attacks. We provide a cluster-ready PyTorch codebase to promote research and standardize defense evaluations."
Constructing Concept-based Models to Mitigate Spurious Correlations with Minimal Human Effort
Authors: Jeeyung Kim*, Ze Wang, Qiang Qiu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Enhancing model interpretability can address spurious correlations by revealing how models draw their predictions. Concept Bottleneck Models (CBMs) can provide a principled way of disclosing and guiding model behaviors through human-understandable concepts, albeit at a high cost of human efforts in data annotation. In this paper, we leverage a synergy of multiple foundation models to construct CBMs with nearly no human effort. We discover undesirable biases in CBMs built on pre-trained models and propose a novel framework designed to exploit pre-trained models while being immune to these biases, thereby reducing vulnerability to spurious correlations. Specifically, our method offers a seamless pipeline that adopts foundation models for assessing potential spurious correlations in datasets, annotating concepts for images, and refining the annotations for improved robustness. We evaluate the proposed method on multiple datasets, and the results demonstrate its effectiveness in reducing model reliance on spurious correlations while preserving its interpretability."
Direct Distillation between Different Domains
Authors: Jialiang Tang, Shuo Chen*, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong*, Masashi Sugiyama
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Knowledge Distillation (KD) aims to learn a compact student network using knowledge from a large pre-trained teacher network, where both networks are trained on data from the same distribution. However, in practical applications, the student network may be required to perform in a new scenario (i.e., the target domain), which usually exhibits significant differences from the known scenario of the teacher network (i.e., the source domain). The traditional domain adaptation techniques can be integrated with KD in a two-stage process to bridge the domain gap, but the ultimate reliability of two-stage approaches tends to be limited due to the high computational consumption and the additional errors accumulated from both stages. To solve this problem, we propose a new one-stage method dubbed “Direct Distillation between Different Domains” (4Ds). We first design a learnable adapter based on the Fourier transform to separate the domain-invariant knowledge from the domain-specific knowledge. Then, we build a fusion-activation mechanism to transfer the valuable domain-invariant knowledge to the student network, while simultaneously encouraging the adapter within the teacher network to learn the domain-specific knowledge of the target data. As a result, the teacher network can effectively transfer categorical knowledge that aligns with the target domain of the student network. Intensive experiments on various benchmark datasets demonstrate that our proposed 4Ds method successfully produces reliable student networks and outperforms state-of-the-art approaches. Code is available at https://github.com/tangjialiang97/4Ds."
Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagery
Authors: Andy V Huynh*, Lauren Gillespie, Jael Lopez-Saucedo, Claire Tang, Rohan Sikand, Moisés Expósito-Alonso
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Multimodal image-text contrastive learning has shown that joint representations can be learned across modalities. Here, we show how leveraging multiple views of image data with contrastive learning can improve downstream fine-grained classification performance for species recognition, even when one view is absent. We propose ContRastive Image-remote Sensing Pre-training (CRISP)—a new pre-training task for ground-level and aerial image representation learning of the natural world—and introduce Nature Multi-View (NMV), a dataset of natural world imagery including > 3 million ground-level and aerial image pairs for over 6,000 plant taxa across the ecologically diverse state of California. The NMV dataset and accompanying material are available at hf.co/datasets/ andyvhuynh/NatureMultiView."
V-Trans4Style: Visual Transition Recommendation for Video Production Style Adaptation
Authors: Pooja Guhan*, Tsung-Wei Huang, Guan-Ming Su, Subhadra Gopalakrishnan, Dinesh Manocha
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We introduce V-Trans4Style, an innovative algorithm tailored for dynamic video content editing needs. It is designed to adapt videos to different production styles like documentaries, dramas, feature films, or a specific YouTube channel’s video-making technique. Our algorithm recommends optimal visual transitions to help achieve this flexibility using a more bottom-up approach. We first employ a transformer-based encoder-decoder network to learn recommending temporally consistent and visually seamless sequences of visual transitions using only the input videos. We then introduce a style conditioning module that leverages this model to iteratively adjust the visual transitions obtained from the decoder through activation maximization. We demonstrate the efficacy of our method through experiments conducted on our newly introduced AutoTransition++ dataset. It is a 6k video version of AutoTransition Dataset that additionally categorizes its videos into different production style categories. Our encoder-decoder model outperforms the state-of-the-art transition recommendation method, achieving improvements of 10% to 80% in Recall@K and mean rank values over baseline. Our style conditioning module results in visual transitions that improve the capture of the desired video production style characteristics by an average of around 12% in comparison to other methods when measured with similarity metrics. We hope that our work serves as a foundation for exploring and understanding video production styles further. [Project Website]"
GRiT: A Generative Region-to-text Transformer for Object Understanding
Authors: Jialian Wu*, Jianfeng Wang, Zhengyuan Yang, Zhe Gan, Zicheng Liu, Junsong Yuan, Lijuan Wang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This paper presents a Generative RegIon-to-Text transformer, GRiT, for object understanding. The spirit of GRiT is to formulate object understanding as  pairs, where region locates objects and text describes objects. Specifically, GRiT consists of a visual encoder to extract image features, a foreground object extractor to localize objects, and a text decoder to generate natural language for objects. With the same model architecture, GRiT describes objects via not only simple nouns, but also rich descriptive sentences. We define GRiT as open-set object understanding, as it has no limit on object description output from the model architecture perspective. Experimentally, we apply GRiT to dense captioning and object detection tasks. GRiT achieves superior dense captioning performance (15.5 mAP on Visual Genome) and competitive detection accuracy (60.4 AP on COCO test-dev). Code is available at https://github.com/JialianW/GRiT"
LRSLAM: Low-rank Representation of Signed Distance Fields in Dense Visual SLAM System
Authors: Hongbeen Park, Minjeong Park, Giljoo Nam, Jinkyu Kim*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Simultaneous Localization and Mapping (SLAM) has been crucial across various domains, including autonomous driving, mobile robotics, and mixed reality. Dense visual SLAM, leveraging RGB-D camera systems, offers advantages but faces challenges in achieving real-time performance, robustness, and scalability for large-scale scenes. Recent approaches utilizing neural implicit scene representations show promise but suffer from high computational costs and memory requirements. ESLAM introduced a plane-based tensor decomposition but still struggled with memory growth. Addressing these challenges, we propose a more efficient visual SLAM model, called LRSLAM, utilizing low-rank tensor decomposition methods. Our approach, leveraging the Six-axis and CP decompositions, achieves better convergence rates, memory efficiency, and reconstruction/localization quality than existing state-of-the-art approaches. Evaluation across diverse indoor RGB-D datasets demonstrates LRSLAM’s superior performance in terms of parameter efficiency, processing time, and accuracy, retaining reconstruction and localization quality. Our code will be publicly available upon publication."
Learning Representation for Multitask Learning through Self-Supervised Auxiliary Learning
Authors: Seokwon Shin, Hyungrok Do, Youngdoo Son*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Multi-task learning is a popular machine learning approach that enables simultaneous learning of multiple related tasks, improving algorithmic efficiency and effectiveness. In the hard parameter sharing approach, an encoder shared through multiple tasks generates data representations passed to task-specific predictors. Therefore, it is crucial to have a shared encoder that provides decent representations for every and each task. However, despite recent advances in multi-task learning, the question of how to improve the quality of representations generated by the shared encoder remains open. To address this gap, we propose a novel approach called Dummy Gradient norm Regularization (DGR) that aims to improve the universality of the representations generated by the shared encoder. Specifically, the method decreases the norm of the gradient of the loss function with respect to dummy task-specific predictors to improve the universality of the shared encoder’s representations. Through experiments on multiple multi-task learning benchmark datasets, we demonstrate that DGR effectively improves the quality of the shared representations, leading to better multi-task prediction performances. Applied to various classifiers, the shared representations generated by DGR also show superior performance compared to existing multi-task learning methods. Moreover, our approach takes advantage of computational efficiency due to its simplicity. The simplicity also allows us to seamlessly integrate DGR with the existing multi-task learning algorithms."
Neural Poisson Solver: A Universal and Continuous Framework for Natural Signal Blending
Authors: Delong Wu, Hao Zhu, Qi Zhang, You Li, Xun Cao*, Zhan Ma*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Implicit Neural Representation (INR) has become a popular method for representing visual signals (, 2D images and 3D scenes), demonstrating promising results in various downstream applications. Given its potential as a medium for visual signals, exploring the development of a neural blending method that utilizes INRs is a natural progression. Neural blending involves merging two INRs to create a new INR that encapsulates information from both original representations. A direct approach involves applying traditional image editing methods to the INR rendering process. However, this method often results in blending distortions, artifacts, and color shifts, primarily due to the discretization of the underlying pixel grid and the introduction of boundary conditions for solving variational problems. To tackle this issue, we introduce the Neural Poisson Solver, a plug-and-play and universally applicable framework across different signal dimensions for blending visual signals represented by INRs. Our Neural Poisson Solver offers a variational problem-solving approach based on the continuous Poisson equation, demonstrating exceptional performance across various domains. Specifically, we propose a gradient-guided neural solver to represent the solution process of the variational problem, refining the target signal to achieve natural blending results. We also develop a Poisson equation-based loss and optimization scheme to train our solver, ensuring it effectively blends the input INR scenes while preserving their inherent structure and semantic content. The lack of dependence on additional prior knowledge makes our method easily adaptable to various task categories, highlighting its versatility. Comprehensive experimental results validate the robustness of our approach across multiple dimensions and blending tasks. Project: https://ep1phany05. github.io/NeuralPoissonSolver-website/"
Geometry Fidelity for Spherical Images
Authors: Anders Christensen*, Nooshin Mojab*, Khushman Patel, Karan Ahuja, Zeynep Akata, Ole Winther, Mar Gonzalez Franco, Andrea Colaco
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Spherical or omni-directional images offer an immersive visual format appealing to a wide range of computer vision applications. However, geometric properties of spherical images pose a major challenge for models and metrics designed for ordinary 2D images. Here, we show that direct application of Fréchet Inception Distance (FID) is insufficient for quantifying geometric fidelity in spherical images. We introduce two quantitative metrics accounting for geometric constraints, namely () and Discontinuity Score (DS). is an extension of FID tailored to additionally capture field-of-view requirements of the spherical format by leveraging cubemap projections. DS is a kernel-based seam alignment score of continuity across borders of 2D representations of spherical images. In experiments, and DS quantify geometry fidelity issues that are undetected by FID."
BAGS: Blur Agnostic Gaussian Splatting through Multi-Scale Kernel Modeling
Authors: Cheng Peng*, Yutao Tang, Yifan Zhou, Nengyu Wang, Xijun Liu, Deming Li, Rama Chellappa
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent efforts in using 3D Gaussians for scene reconstruction and novel view synthesis can achieve impressive results on curated benchmarks; however, images captured in real life are often blurry. In this work, we analyze the robustness of Gaussian-Splatting-based methods against various image blur, such as motion blur, defocus blur, downscaling blur, . Under these degradations, Gaussian-Splatting-based methods tend to overfit and produce worse results than Neural-Radiance-Field-based methods. To address this issue, we propose Blur Agnostic Gaussian Splatting (BAGS). BAGS introduces additional 2D modeling capacities such that a 3D-consistent and high quality scene can be reconstructed despite image-wise blur. Specifically, we model blur by estimating per-pixel convolution kernels from a Blur Proposal Network (BPN). BPN is designed to consider spatial, color, and depth variations of the scene to maximize modeling capacity. Additionally, BPN also proposes a quality-assessing mask, which indicates regions where blur occur. Finally, we introduce a coarse-to-fine kernel optimization scheme; this optimization scheme is fast and avoids sub-optimal solutions due to a sparse point cloud initialization, which often occurs when we apply Structure-from-Motion on blurry images. We demonstrate that BAGS achieves photorealistic renderings under various challenging blur conditions and imaging geometry, while significantly improving upon existing approaches."
CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning
Authors: Erum Mushtaq*, Duygu Nur Yaldiz, Yavuz Faruk Bakman, Jie Ding, Chenyang Tao, Dimitrios Dimitriadis, Salman Avestimehr
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Continual self-supervised learning (CSSL) learns a series of tasks sequentially on the unlabeled data. Two main challenges of continual learning are catastrophic forgetting and task confusion. While CSSL problem has been studied to address the catastrophic forgetting challenge, little work has been done to address the task confusion aspect. In this work, we show through extensive experiments that self-supervised learning (SSL) can make CSSL more susceptible to the task confusion problem, particularly in less diverse settings of class incremental learning because different classes belonging to different tasks are not trained concurrently. Motivated by this challenge, we present a novel cross-model feature Mixup (CroMo-Mixup) framework that addresses this issue through two key components: 1) Cross-Task data Mixup, which mixes samples across tasks to enhance negative sample diversity; and 2) Cross-Model feature Mixup, which learns similarities between embeddings obtained from current and old models of the mixed sample and the original images, facilitating cross-task class contrast learning and old knowledge retrieval. We evaluate the effectiveness of CroMo-Mixup to improve both Task-ID prediction and average linear accuracy across all tasks on three datasets, CIFAR10, CIFAR100, and tinyImageNet under different class-incremental learning settings. We validate the compatibility of CroMo-Mixup on four state-of-the-art SSL objectives. Code is available at https://github.com/ErumMushtaq/CroMo-Mixup."
WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generation
Authors: Jiachen Lu, Ze Huang, Zeyu Yang, Zhang Jiahui, Li Zhang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Generating multi-camera street-view videos is critical for augmenting autonomous driving datasets, addressing the urgent demand for extensive and varied data. Due to the limitations in diversity and challenges in handling lighting conditions, traditional rendering-based methods are increasingly being supplanted by diffusion-based methods. However, a significant challenge in diffusion-based methods is ensuring that the generated sensor data preserve both intra-world consistency and inter-sensor coherence. To address these challenges, we combine an additional explicit world volume and propose (). This system is specifically designed to leverage 4D world volume as a foundational element for video generation. Our model operates in two distinct phases: (i) envisioning the future 4D temporal world volume based on vehicle control sequences, and (ii) generating multi-camera videos, informed by this envisioned 4D temporal world volume and sensor interconnectivity. The incorporation of the 4D world volume empowers not only to generate high-quality street-view videos in response to vehicle control inputs but also to facilitate scene editing tasks."
Benchmarking Spurious Bias in Few-Shot Image Classifiers
Authors: Guangtao Zheng*, Wenqian Ye, Aidong Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Few-shot image classifiers are designed to recognize and classify new data with minimal supervision and limited data but often show reliance on spurious correlations between classes and spurious attributes, known as spurious bias. Spurious correlations commonly hold in certain samples and few-shot classifiers can suffer from spurious bias induced from them. There is an absence of an automatic benchmarking system to assess the robustness of few-shot classifiers against spurious bias. In this paper, we propose a systematic and rigorous benchmark framework, termed FewSTAB, to fairly demonstrate and quantify varied degrees of robustness of few-shot classifiers to spurious bias. FewSTAB creates few-shot evaluation tasks with biased attributes so that using them for predictions can demonstrate poor performance. To construct these tasks, we propose attribute-based sample selection strategies based on a pre-trained vision-language model, eliminating the need for manual dataset curation. This allows FewSTAB to automatically benchmark spurious bias using any existing test data. FewSTAB offers evaluation results in a new dimension along with a new design guideline for building robust classifiers. Moreover, it can benchmark spurious bias in varied degrees and enable designs for varied degrees of robustness. Its effectiveness is demonstrated through experiments on ten few-shot learning methods across three datasets. We hope our framework can inspire new designs of robust few-shot classifiers. Our code is available at https://github.com/ gtzheng/FewSTAB."
TurboEdit: Real-time text-based disentangled real image editing
Authors: Zongze Wu*, Nicholas I Kolkin, Jonathan Brandt, Richard Zhang, Eli Shechtman
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques."
Soft Shadow Diffusion (SSD): Physics-inspired Learning for 3D Computational Periscopy
Authors: Fadlullah A Raji*, John Murray-Bruce*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Conventional imaging requires a line of sight to create accurate visual representations of a scene. In certain circumstances, however, obtaining a suitable line of sight may be impractical, dangerous, or even impossible. Non-line-of-sight (NLOS) imaging addresses this challenge by reconstructing the scene from indirect measurements. Recently, passive NLOS methods that use an ordinary photograph of the subtle shadow cast onto a visible wall by the hidden scene have gained interest. These methods are currently limited to 1D or low-resolution 2D color imaging or to localizing a hidden object whose shape is approximately known. Here, we generalize this class of methods and demonstrate a 3D reconstruction of a hidden scene from an ordinary NLOS photograph. To achieve this, we propose a novel reformulation of the light transport model that conveniently decomposes the hidden scene into light-occluding and non-light-occluding components to yield a separable non-linear least squares (SNLLS) inverse problem. We develop two solutions: A gradient-based optimization method and a physics-inspired neural network approach, which we call Soft Shadow diffusion (SSD). Despite the challenging ill-conditioned inverse problem encountered here, our approaches are effective on numerous 3D scenes in real experimental scenarios. Moreover, SSD is trained in simulation but generalizes well to unseen classes in simulation and real-world NLOS scenes. SSD also shows surprising robustness to noise and ambient illumination."
Augmented Neural Fine-tuning for Efficient Backdoor Purification
Authors: Nazmul Karim*, Abdullah Al Arafat, Umar Khalid, Zhishan Guo, Nazanin Rahnavard
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent studies have revealed the vulnerability of deep neural networks (DNNs) to various backdoor attacks, where the behavior of DNNs can be compromised by utilizing certain types of triggers or poisoning mechanisms. State-of-the-art (SOTA) defenses employ too-sophisticated mechanisms that require either a computationally expensive adversarial search module for reverse-engineering the trigger distribution or an over-sensitive hyper-parameter selection module. Moreover, they offer sub-par performance in challenging scenarios, , limited validation data and strong attacks. In this paper, we propose—Neural mask Fine-Tuning (NFT)—with an aim to optimally re-organize the neuron activities in a way that the effect of the backdoor is removed. Utilizing a simple data augmentation like MixUp, NFT relaxes the trigger synthesis process and eliminates the requirement of the adversarial search module. Our study further reveals that direct weight fine-tuning under limited validation data results in poor post-purification clean test accuracy, primarily due to overfitting issue. To overcome this, we propose to fine-tune neural masks instead of model weights. In addition, a mask regularizer has been devised to further mitigate the model drift during the purification process. The distinct characteristics of NFT render it highly efficient in both runtime and sample usage, as it can remove the backdoor even when a single sample is available from each class. We validate the effectiveness of NFT through extensive experiments covering the tasks of image classification, object detection, video action recognition, 3D point cloud, and natural language processing. We evaluate our method against 14 different attacks (LIRA, WaNet, ) on 11 benchmark data sets (ImageNet, UCF101, Pascal VOC, ModelNet, OpenSubtitles2012, ). Our code is available online in this GitHub Repository."
REDIR: Refocus-free Event-based De-occlusion Image Reconstruction
Authors: Qi Guo, Hailong Shi*, Huan Li, Jinsheng Xiao, Xingyu Gao*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The employment of the event-based synthetic aperture imaging (E-SAI) technique, which has the capability to capture high-frequency light intensity variations, has facilitated its extensive application on scene de-occlusion reconstruction tasks. However, existing methods usually require prior information and have strict restriction of camera motion on SAI acquisition methods. This paper proposes a novel end-to-end refocus-free variable E-SAI de-occlusion image reconstruction approach REDIR, which can align the global and local features of the variable event data and effectively achieve high-resolution imaging of pure event streams. To further improve the reconstruction of the occluded target, we propose a perceptual mask-gated connection module to interlink information between modules, and incorporate a spatial-temporal attention mechanism into the SNN block to enhance target extraction ability of the model. Through extensive experiments, our model achieves state-of-the-art reconstruction quality on the traditional E-SAI dataset without prior information, while verifying the effectiveness of the variable event data feature registration method on our newly introduced V-ESAI dataset, which obviates the reliance on prior knowledge and extends the applicability of SAI acquisition methods by incorporating focus changes, lens rotations, and non-uniform motion."
Free-Editor: Zero-shot Text-driven 3D Scene Editing
Authors: Nazmul Karim*, Hasan Iqbal, Umar Khalid, Chen Chen, Jing Hua
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Text-to-Image (T2I) diffusion models have recently gained traction for their versatility and user-friendliness in 2D content generation and editing. However, training a diffusion model specifically for 3D scene editing is challenging due to the scarcity of large-scale datasets. Currently, editing 3D scenes necessitates either retraining the model to accommodate various 3D edits or developing specific methods tailored to each unique editing type. Moreover, state-of-the-art (SOTA) techniques require multiple synchronized edited images from the same scene to enable effective scene editing. Given the current limitations of T2I models, achieving consistent editing effects across multiple images remains difficult, leading to multi-view inconsistency in editing. This inconsistency undermines the performance of 3D scene editing 1 when these images are utilized. In this study, we introduce a novel, training-free 3D scene editing technique called Free-Editor, which enables users to edit 3D scenes without the need for model retraining during the testing phase. Our method effectively addresses the issue of multi-view style inconsistency found in state-of-the-art (SOTA) methods through the implementation of a single-view editing scheme. Specifically, we demonstrate that editing a particular 3D scene can be achieved by modifying only a single view. To facilitate this, we present an Edit Transformer that ensures intra-view consistency and inter-view style transfer using self-view and cross-view attention mechanisms, respectively. By eliminating the need for model retraining and multi-view editing, our approach significantly reduces editing time and memory resource requirements, achieving runtimes approximately 20 times faster than SOTA methods. We have performed extensive experiments on various benchmark datasets, showcasing the diverse editing capabilities of our proposed technique. Project Page: https://free-editor.github.io/ 1 Here, 3D scene editing indicates NeRF model editing. In this study, we mainly focus on NeRF-based 3D scene representation."
DPA-Net: Structured 3D Abstraction from Sparse Views via Differentiable Primitive Assembly
Authors: Fenggen Yu*, Yiming Qian, Xu Zhang, Francisca Gil-Ureta, Brian Jackson, Eric Bennett, Hao Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We present a differentiable rendering framework to learn structured 3D abstractions in the form of primitive assemblies from sparse RGB images capturing a 3D object. By leveraging differentiable volume rendering, our method does not require 3D supervision. Architecturally, our network follows the general pipeline of an image-conditioned neural radiance field (NeRF) exemplified by pixelNeRF for color prediction. As our core contribution, we introduce differential primitive assembly (DPA) into NeRF to output a 3D occupancy field in place of density prediction, where the predicted occupancies serve as opacity values for volume rendering. Our network, coined DPA-Net, produces a union of convexes, each as an intersection of convex quadric primitives, to approximate the target 3D object, subject to an abstraction loss and a masking loss, both defined in the image space upon volume rendering. With test-time adaptation and additional sampling and loss designs aimed at improving the accuracy and compactness of the obtained assemblies, our method demonstrates superior performance over state-of-the-art alternatives for 3D primitive abstraction from sparse views."
An Empirical Study and Analysis of Text-to-Image Generation Using Large Language Model-Powered Textual Representation
Authors: Zhiyu Tan, Mengping Yang, Luozheng Qin, Hao Yang, Ye Qian, Qiang Zhou, Cheng Zhang, Hao Li*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "One critical prerequisite for faithful text-to-image generation is the accurate understanding of text inputs. Existing methods leverage the text encoder of the CLIP model to represent input prompts. However, the pre-trained CLIP model can merely encode English with a maximum token length of 77. Moreover, the model capacity of the text encoder from CLIP is relatively limited compared to Large Language Models (LLMs), which offer multilingual input, accommodate longer context, and achieve superior text representation. In this paper, we investigate LLMs as the text encoder to improve the language understanding in text-to-image generation. Unfortunately, training text-to-image generative model with LLMs from scratch demands significant computational resources and data. To this end, we introduce a three-stage training pipeline, OmniDiffusion, that effectively and efficiently integrates the existing text-to-image model with LLMs. Specifically, we propose a lightweight adapter that enables fast training of the text-to-image model using the textual representations from LLMs. Extensive experiments demonstrate that our model supports not only multilingual but also longer input context with superior image generation quality. Project page: https://llm-conditioned-diffusion.github.io"
Few-shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt
Authors: Chenxi Liu*, Zhenyi Wang, Tianyi Xiong, Ruibo Chen, Yihan Wu, junfeng guo, Heng Huang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn new classes with scarce samples while preserving knowledge of old ones. Existing FSCIL methods usually fine-tune the entire backbone, leading to overfitting and hindering the potential to learn new classes. On the other hand, recent prompt-based CIL approaches alleviate forgetting by training prompts with sufficient data in each task. In this work, we propose a novel framework named Attention-aware Self-adaptive Prompt (). encourages task-invariant prompts to capture shared knowledge by reducing specific information from the attention aspect. Additionally, self-adaptive task-specific prompts in provide specific information and transfer knowledge from old classes to new classes with an Information Bottleneck learning objective. In summary, prevents overfitting on base task and does not require enormous data in few-shot incremental tasks. Extensive experiments on three benchmark datasets validate that consistently outperforms state-of-the-art FSCIL and prompt-based CIL methods in terms of both learning new classes and mitigating forgetting. Source code is available at https://github.com/DawnLIU35/FSCIL-ASP."
An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models
Authors: Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Junyang Lin, Chang Zhou, Baobao Chang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this study, we identify the inefficient attention phenomena in Large Vision-Language Models (LVLMs), notably within prominent models like LLaVA-1.5, QwenVL-Chat, and Video-LLaVA. We find that the attention computation over visual tokens is extremely inefficient in the deep layers of popular LVLMs, suggesting a need for a sparser approach compared to textual data handling. To this end, we introduce FastV, a versatile plug-and-play method designed to optimize computational efficiency by learning adaptive attention patterns in early layers and pruning visual tokens in subsequent ones. Our evaluations demonstrate FastV’s ability to dramatically reduce computational costs (e.g., a 45% reduction in FLOPs for LLaVA-1.5-13B) without sacrificing performance in a wide range of image and video understanding tasks. The computational efficiency and performance trade-off of FastV are highly customizable and Pareto-efficient. It can compress the FLOPs of a 13B-parameter model to achieve a lower cost than that of a 7B-parameter model while still maintaining superior performance. We believe FastV has practical value for the deployment of LVLMs in edge devices and commercial models. Code is released at github.com/pkunlp-icler/FastV. [width=0.9]figs/fastvt radeof fv 1.png Figure 1: The Efficiency/Performance trade-off curve of FastV. The x-axis stands for the theoretical FLOPs reduction ratio under different FastV configurations. The y-axis stands for performance under different settings, we report the average scores of {Nocaps (Cider), Flickr30k (Cider), A-OKVQA (Acc), MMMU (Acc)}. We can see that FastV can achieve 45% FLOPs reduction with nearly no performance loss for different models."
Generalizable Symbolic Optimizer Learning
Authors: Xiaotian Song, Peng Zeng, Yanan Sun*, Andy Song
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Existing automated symbolic optimizer design methods necessitate the use of proxies, often resulting in significant performance degradation when transferring to a target domain. In this paper, we propose a learning based model called Symbolic Optimizer Learner (SOL) that can discover high-performance symbolic optimizers directly on the target. SOL is integrated with symbols and can be directly transformed into a symbolic optimizer. In addition, an unrolled optimization approach is introduced for SOL training. SOL can be embedded into the training process of neural networks, optimizing the target directly without any proxies. Our extensive experiments demonstrate the good performance and high generalizability of SOL through diverse tasks, ranging from classifications to adversarial attacks, from GNN to NLP tasks. On image classification, SOL achieved ∼5× speedup and ∼3% accuracy gain. On adversarial attacks, SOL achieved the best attack success rate across seven SOTA defense models. On GNN training, SOL discovered optimizers can outperform Adam on three different datasets. On BERT fine-tuning, SOL also outperformed AdamW on five benchmarks. The source code is available at https://github.com/songxt3/SOL."
Online Continuous Generalized Category Discovery
Authors: Keon-Hee Park, Hakyung Lee, Kyungwoo Song*, Gyeong-Moon Park*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "With the advancement of deep neural networks in computer vision, artificial intelligence (AI) is widely employed in real-world applications. However, AI still faces limitations in mimicking high-level human capabilities, such as novel category discovery, for practical use. While some methods utilizing offline continual learning have been proposed for novel category discovery, they neglect the continuity of data streams in real-world settings. In this work, we introduce Online Continuous Generalized Category Discovery (OCGCD), which considers the dynamic nature of data streams where data can be created and deleted in real time. Additionally, we propose a novel method, DEAN, Discovery via Energy guidance and feature AugmentatioN, which can discover novel categories in an online manner through energy-guided discovery and facilitate discriminative learning via energy-based contrastive loss. Furthermore, DEAN effectively pseudo-labels unlabeled data through variance-based feature augmentation. Experimental results demonstrate that our proposed DEAN achieves outstanding performance in proposed OCGCD scenario. The implementation code is available at https://github.com/KHU-AGI/OCGCD."