mtybilly/Instruct2DS huggingface dataset:
SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders
Authors: Zi-Xiang Wei, Wei-Jie Chen, Yi-Hsin Yu, Chih-Yuan Yang*, Jane Yung-jen Hsu* Sheng-Wei Li
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Existing zero-shot skeleton-based action recognition methods utilize projection networks to learn a shared latent space of skeleton features and semantic embeddings. The inherent imbalance in action recognition datasets, characterized by variable skeleton sequences yet constant class labels, presents significant challenges for alignment. To address the imbalance, we propose SA-DVAE—Semantic Alignment via Disentangled Variational Autoencoders, a method that first adopts feature disentanglement to separate skeleton features into two independent parts—one is semantic-related and another is irrelevant—to better align skeleton and semantic features. We implement this idea via a pair of modality-specific variational autoencoders coupled with a total correction penalty. We conduct experiments on three benchmark datasets: NTU RGB+D, NTU RGB+D 120 and PKU-MMD, and our experimental results show that SA-DAVE produces improved performance over existing methods. The code is available at https://github.com/pha123661/ SA-DVAE."
Bridge Past and Future: Overcoming Information Asymmetry in Incremental Object Detection
Authors: Yipeng Gao, Shenghao Fu, Junkai Yan, Ancong Wu*, Wei-Shi Zheng* Qijie Mo
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In incremental object detection, knowledge distillation has been proven to be an effective way to alleviate catastrophic forgetting. However, previous works focused on preserving the knowledge of old models, ignoring that images could simultaneously contain categories from past, present, and future stages. The co-occurrence of objects makes the optimization objectives inconsistent across different stages since the definition for foreground objects differs across various stages, which limits the model’s performance greatly. To overcome this problem, we propose a method called “Bridge Past and Future” (BPF), which aligns models across stages, ensuring consistent optimization directions. In addition, we propose a novel Distillation with Future (DwF) loss, fully leveraging the background probability to mitigate the forgetting of old classes while ensuring a high level of adaptability in learning new classes. Extensive experiments are conducted on both Pascal VOC and MS COCO benchmarks. Without memory, BPF outperforms current state-of-the-art methods under various settings. The code is available at https://github.com/iSEE-Laboratory/BPF."
Global-to-Pixel Regression for Human Mesh Recovery
Authors: Mingshu HE*, Dongdong Yu Yabo Xiao
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Existing human mesh recovery (HMR) methods commonly leverage the global or dense-annotations-based local features to produce a single prediction from the input image. However, the compressed global and local features disrupt the spatial geometry of the human body and make it hard to capture the local dynamics, resulting in visual-mesh misalignment. Moreover, dense annotations are labor-intensive and expensive. Toward the above issues, we propose a global-to-local prediction framework to preserve spatial information and obtain precise visual-mesh alignments for top-down HMR. Specifically, we present an adaptive 2D Keypoint-Guided Local Encoding Module to enable per-pixel features to capture fine-grained body part information with structure and local context maintained. The acquisition of local features relies exclusively on sparse 2D keypoint guidance without dense annotations or heuristics keypoint-based ROI (Region of Interested) pooling. The enhanced pixel features are used to predict residuals for rectifying the initial estimation produced by global features. Secondly, we introduce a Dynamic Matching Strategy that determines positive/negative pixels by only calculating the classification and 2D keypoint costs to further improve visual-mesh alignments. The comprehensive experiments demonstrate the effectiveness of network design. Our framework outperforms previous local regression methods by a large margin and achieves state-of-the-art performance on Human3.6M and 3DPW datasets."
Visible and Clear: Finding Tiny Objects in Difference Map
Authors: Haiyu Yao, Pengfei Zhu*, Qinghua Hu Bing Cao
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Tiny object detection is one of the key challenges for most generic detectors. The main difficulty lies in extracting effective features of tiny objects. Existing methods usually perform generation-based feature enhancement, which is seriously affected by spurious textures and artifacts, making it difficult to make the tiny-object-specific features visible and clear for detection. To address this issue, we propose a self-reconstructed tiny object detection (SR-TOD) framework. We for the first time introduce a self-reconstruction mechanism in the detection model, and discover the strong correlation between it and the tiny objects. Specifically, we impose a reconstruction head in-between the neck of a detector, constructing a difference map of the reconstructed image and the input, which shows high sensitivity to tiny objects. This inspires us to enhance the weak representations of tiny objects under the guidance of the difference maps. Thus, improving the visibility of tiny objects for the detectors. Building on this, we further develop a Difference Map Guided Feature Enhancement (DGFE) module to make the tiny feature representation more clear. In addition, we further propose a new multi-instance anti-UAV dataset. Extensive experiments demonstrate our effectiveness. The code is available: https://github.com/ Hiyuur/SR-TOD."
Rethinking Image Super Resolution from Training Data Perspectives
Authors: Ryu Tadokoro, Ryosuke Yamada, Yuki M Asano, Iro Laina, Christian Rupprecht, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka, Yoshimitsu Aoki Go Ohtani*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this work, we investigate the understudied effect of the training data used for image super-resolution (SR). Most commonly, novel SR methods are developed and benchmarked on common training datasets such as DIV2K and DF2K. However, we investigate and rethink the training data from the perspectives of diversity and quality, thereby addressing the question of “How important is SR training for SR models?”. To this end, we propose an automated image evaluation pipeline. With this, we stratify existing high-resolution image datasets and larger-scale image datasets such as ImageNet and PASS to compare their performances. We find that datasets with (i) low compression artifacts, (ii) high within-image diversity as judged by the number of different objects, and (iii) a large number of images from ImageNet or PASS all positively affect SR performance. We hope that the proposed simple-yet-effective dataset curation pipeline will inform the construction of SR datasets in the future and yield overall better models. Code is available at: https://github.com/gohtanii/DiverSeg-dataset"
BlazeBVD: Make Scale-Time Equalization Great Again for Blind Video Deflickering
Authors: Congying Han, Zicheng Zhang, Bonan Li*, Tiande Guo, Pingyu Wang, Xuecheng Nie Xinmin Qiu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Developing blind video deflickering (BVD) algorithms to enhance video temporal consistency, is gaining importance amid the flourish of image processing and video generation. However, the intricate nature of video data complicates the training of deep learning methods, leading to high resource consumption and instability, notably under severe lighting flicker. This underscores the critical need for a compact representation beyond pixel values to advance BVD research and applications. Inspired by the classic scale-time equalization (STE), our work introduces the histogram-assisted solution, called BlazeBVD, for high-fidelity and rapid BVD. Compared with STE, which directly corrects pixel values by temporally smoothing color histograms, BlazeBVD leverages smoothed illumination histograms within STE filtering to ease the challenge of learning temporal data using neural networks. In technique, BlazeBVD begins by condensing pixel values into illumination histograms that precisely capture flickering and local exposure variations. These histograms are then smoothed to produce singular frames set, filtered illumination maps, and exposure maps. Resorting to these deflickering priors, BlazeBVD utilizes a 2D network to restore faithful and consistent texture impacted by lighting changes or localized exposure issues. BlazeBVD also incorporates a lightweight 3D network to amend slight temporal inconsistencies, avoiding the resource consumption issue. Comprehensive experiments on synthetic, real-world and generated videos, showcase the superior qualitative and quantitative results of BlazeBVD, achieving inference speeds up to 10× faster than state-of-the-arts."
Efficient Inference of Vision Instruction-Following Models with Elastic Cache
Authors: Benlin Liu, Jiahui Wang, Yuhao Dong, Guangyi Chen, Yongming Rao, Ranjay Krishna, Jiwen Lu* Zuyan Liu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In the field of instruction-following large vision-language models (LVLMs), the efficient deployment of these models faces challenges, notably due to the high memory demands of their key-value (KV) caches. Conventional cache management strategies for LLMs focus on cache eviction, which often fails to address the specific needs of multimodal instruction-following models. Recognizing this gap, in this paper, we introduce Elastic Cache, a novel approach that benefits from applying distinct acceleration methods for instruction encoding and output generation stages. We investigate the metrics of importance in different stages and propose an ‘importance-driven cache merging’ strategy to prune redundancy caches. Instead of discarding less important caches, our strategy identifies important key/value vectors as anchor points. Surrounding less important caches are then merged with these anchors, enhancing the preservation of contextual information in the KV caches while yielding an arbitrary acceleration ratio. For instruction encoding, we utilize the frequency to evaluate the importance of caches. Regarding output generation, we prioritize tokens based on their ‘distance’ with an offset, by which both the initial and most recent tokens are retained. Results on a range of LVLMs demonstrate that Elastic Cache not only boosts efficiency but also notably outperforms existing pruning methods in language generation across various tasks. Code is available at https://github. com/liuzuyan/ElasticCache"
FreeCompose: Generic Zero-Shot Image Composition with Diffusion Prior
Authors: Wen Wang, Zhen Yang, Zeqing Yuan, Hao Chen*, Chunhua Shen* Zhekai Chen
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "[width=0.985]assets/teaser.pdf Figure 1: harnesses the generative prior of pre-trained diffusion models to achieve versatile image composition, such as appearance editing (image harmonization) and semantic editing (semantic image composition). Furthermore, it can be extended to various downstream applications, including object removal and multi-character customization. We offer a novel approach to image composition, which integrates multiple input images into a single, coherent image. Rather than concentrating on specific use cases such as appearance editing (image harmonization) or semantic editing (semantic image composition), we showcase the potential of utilizing the powerful generative prior inherent in large-scale pre-trained diffusion models to accomplish generic image composition applicable to both scenarios. We observe that the pre-trained diffusion models automatically identify simple copy-paste boundary areas as low-density regions during denoising. Building on this insight, we propose to optimize the composed image towards high-density regions guided by the diffusion prior. In addition, we introduce a novel mask-guided loss to further enable flexible semantic image composition. Extensive experiments validate the superiority of our approach in achieving generic zero-shot image composition. Additionally, our approach shows promising potential in various tasks, such as object removal and multi-concept customization. Project webpage: https://github.com/aim-uofa/FreeCompose"
Learning to Robustly Reconstruct Dynamic Scenes from Low-light Spike Streams
Authors: Ziluo Ding, Mianzhi Liu, Lei Ma*, Tiejun Huang Liwen Hu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Spike camera with high temporal resolution can fire continuous binary spike streams to record per-pixel light intensity. By using reconstruction methods, the scene details in high-speed scenes can be restored from spike streams. However, existing methods struggle to perform well in low-light environments due to insufficient information in spike streams. To this end, we propose a bidirectional recurrent-based reconstruction framework to better handle such extreme conditions. In more detail, a light-robust representation (LR-Rep) is designed to aggregate temporal information in spike streams. Moreover, a fusion module is used to extract temporal features. Besides, we synthesize a reconstruction dataset for high-speed low-light scenes where light sources are carefully designed to be consistent with reality. The experiment shows the superiority of our method. Importantly, our method also generalizes well to real spike streams. Our project is: https://github.com/Acnext/Learning-to-Robustly-Reconstruct-Dynamic-"
MarvelOVD: Marrying Object Recognition and Vision-Language Models for Robust Open-Vocabulary Object Detection
Authors: Lechao Cheng*, Weikai Chen, Pingping Zhang, Liang Lin, Fan Zhou, Guanbin Li* Kuo Wang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Learning from pseudo-labels that generated with VLMs (Vision Language Models) has been shown as a promising solution to assist open vocabulary detection (OVD) in recent studies. However, due to the domain gap between VLM and vision-detection tasks, pseudo-labels produced by the VLMs are prone to be noisy, while the training design of the detector further amplifies the bias. In this work, we investigate the root cause of VLMs’ biased prediction under the OVD context. Our observations lead to a simple yet effective paradigm, coded , that generates significantly better training targets and optimizes the learning procedure in an online manner by marrying the capability of the detector with the vision-language model. Our key insight is that the detector itself can act as a strong auxiliary guidance to accommodate VLM’s inability of understanding both the “background” and the context of a proposal within the image. Based on it, we greatly purify the noisy pseudo-labels via Online Mining and propose Adaptive Reweighting to effectively suppress the biased training boxes that are not well aligned with the target object. In addition, we also identify a neglected “base-novel-conflict” problem and introduce stratified label assignments to prevent it. Extensive experiments on COCO and LVIS datasets demonstrate that our method outperforms the other state-of-the-arts by significant margins. Codes are available at https://github.com/wkfdb/ MarvelOVD."
WildVidFit: Video Virtual Try-On in the Wild via Image-Based Controlled Diffusion Models
Authors: Peixin Chen, Guangrun Wang, Guanbin Li*, Philip Torr, Liang Lin Zijian He
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Video virtual try-on aims to generate realistic sequences that maintain garment identity and adapt to a person’s pose and body shape in source videos. Traditional image-based methods, relying on warping and blending, struggle with complex human movements and occlusions, limiting their effectiveness in video try-on applications. Moreover, video-based models require extensive, high-quality data and substantial computational resources. To tackle these issues, we reconceptualize video try-on as a process of generating videos conditioned on garment descriptions and human motion. Our solution, WildVidFit, employs image-based controlled diffusion models for a streamlined, one-stage approach. This model, conditioned on specific garments and individuals, is trained on still images rather than videos. It leverages diffusion guidance from pre-trained models including a video masked autoencoder for segment smoothness improvement and a self-supervised model for feature alignment of adjacent frame in the latent space. This integration markedly boosts the model’s ability to maintain temporal coherence, enabling more effective video try-on within an image-based framework. Our experiments on the VITON-HD and DressCode datasets, along with tests on the VVT and TikTok datasets, demonstrate WildVidFit’s capability to generate fluid and coherent videos. The project page website is at wildvidfit-project.github. io."
Interactive 3D Object Detection with Prompts
Authors: Xiangru Lin, Wei Zhang, Jincheng Lu, Xuekuan Wang, Xiao Tan, Yingying Li, Errui Ding, Jingdong Wang, Guanbin Li* Ruifei Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The evolution of 3D object detection hinges not only on advanced models but also on effective and efficient annotation strategies. Despite this progress, the labor-intensive nature of 3D object annotation remains a bottleneck, hindering further development in the field. This paper introduces a novel approach, incorporated with “prompt in 2D, detect in 3D” and “detect in 3D, refine in 3D” strategies, to 3D object annotation: multi-modal interactive 3D object detection. Firstly, by allowing users to engage with simpler 2D interaction prompts (e.g., clicks or boxes on a camera image or a bird’s eye view), we bridge the complexity gap between 2D and 3D spaces, reimagining the annotation workflow. Besides, Our framework also supports flexible iterative refinement to the initial 3D annotations, further assisting annotators in achieving satisfying results. Evaluation on the nuScenes dataset demonstrates the effectiveness of our method. And thanks to the prompt-driven and interactive designs, our approach also exhibits outstanding performance in open-set scenarios. This work not only offers a potential solution to the 3D object annotation problem but also paves the way for further innovations in the 3D object detection community."
How Video Meetings Change Your Expression
Authors: Utkarsh Mall, Purva Tendulkar, Carl Vondrick Sumit Sarin*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Do our facial expressions change when we speak over video calls? Given two unpaired sets of videos of people, we seek to automatically find spatio-temporal patterns that are distinctive of each set. Existing methods use discriminative approaches and perform post-hoc explainability analysis. Such methods are insufficient as they are unable to provide insights beyond obvious dataset biases, and the explanations are useful only if humans themselves are good at the task. Instead, we tackle the problem through the lens of generative domain translation: our method generates a detailed report of learned, input-dependent spatio-temporal features and the extent to which they vary between the domains. We demonstrate that our method can discover behavioral differences between conversing face-to-face (F2F) and on video-calls (VCs). We also show the applicability of our method on discovering differences in presidential communication styles. Additionally, we are able to predict temporal change-points in videos that decouple expressions in an unsupervised way, and increase the interpretability and usefulness of our model. Finally, our method, being generative, can be used to transform a video call to appear as if it were recorded in a F2F setting. Experiments and visualizations show our approach is able to discover a range of behaviors, taking a step towards deeper understanding of human behaviors. Video results, code and data can be found at facet.cs.columbia.edu."
GRACE: Graph-Based Contextual Debiasing for Fair Visual Question Answering
Authors: Ming Jiang, Qi Zhao* Yifeng Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Large language models (LLMs) exhibit exceptional reasoning capabilities and have played significant roles in knowledge-based visual question-answering (VQA) systems. By conditioning on in-context examples and task-specific prompts, they comprehensively understand input questions and provide answers relevant to the context. However, due to the reliance on in-context examples, LLMs are susceptible to inheriting dataset biases in context descriptions and the provided examples. Innovative methods are required to ensure that LLMs can deliver unbiased yet contextually relevant responses. To tackle this challenge, we present GRAph-based Contextual DEbiasing (GRACE), a novel graph-based method for debiasing knowledge-based VQA models. This approach consists of two novel and generally applicable components. First, we propose an unsupervised context graph learning method that combats biases by explicitly creating a balanced context graph under the guidance of fairness constraints. Second, building upon the context graph, we consider both semantic features and reasoning processes to enhance prompting with more relevant and diverse in-context examples. Through extensive experimentation on both in-distribution (OK-VQA) and out-of-distribution (VQA-CP, GQA-OOD) datasets, we demonstrate the effectiveness of GRACE in mitigating biases and achieving generalization. Additionally, analyses of the model performance across gender groups demonstrate GRACE’s potential impacts on social equity. Our source code is publicly available at https://github.com/SuperJohnZhang/ContextGraphKVQA."
Neural Volumetric World Models for Autonomous Driving
Authors: Jimuyang Zhang*, Eshed Ohn-Bar* Zanming Huang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Effectively navigating a dynamic 3D world requires a comprehensive understanding of the 3D geometry and motion of surrounding objects and layouts. However, existing methods for perception and planning in autonomous driving primarily rely on a 2D spatial representation, based on a bird’s eye perspective of the scene, which is insufficient for modeling motion characteristics and decision-making in real-world 3D settings with occlusion, partial observability, subtle motions, and varying terrains. Motivated by this key insight, we present a novel framework for learning end-to-end autonomous driving based on volumetric representations. Our proposed neural volumetric world modeling approach, NeMo, can be trained in a self-supervised manner for image reconstruction and occupancy prediction tasks, benefiting scalable training and deployment paradigms such as imitation learning. Specifically, we demonstrate how the higher-fidelity modeling of 3D volumetric representations benefits vision-based motion planning. We further propose a motion flow module to model complex dynamic scenes, enabling additional robust spatiotemporal consistency supervision. Moreover, a temporal attention module is introduced to effectively integrate predicted future volumetric features for the planning task. Our proposed sensorimotor agent achieves state-of-the-art driving performance on nuScenes and CARLA, outperforming prior baseline methods by over 18%."
IVTP: Instruction-guided Visual Token Pruning for Large Vision-Language Models
Authors: Hao Zou, Ye Xi, Bochen Wang, Zhen Xie, Liang Yu Kai Huang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Inspired by the remarkable achievements of Large Language Models (LLMs), Large Vision-Language Models (LVLMs) have likewise experienced significant advancements. However, the increased computational cost and token budget occupancy associated with lengthy visual tokens pose significant challenge to the practical applications. Considering that not all visual tokens are essential to the final response, selectively pruning redundant visual tokens can effectively alleviate this challenge. In this paper, we present a novel Instruction-guided Visual Token Pruning (IVTP) approach for LVLMs, which is designed to strike a better balance between computational efficiency and the performance. Specifically, a Group-wise Token Pruning (GTP) module based on attention rollout is integrated into the grouped transformer layer to achieve intra-group attention aggregation via residual connection, thereby improving the assessment of visual token importance, especially for LVLMs with a frozen visual encoder. We then extend the module to LLM in order to further filter out visual tokens that are pertinent to the current textual instructions, by introducing a semantically related pseudo CLS token to serve as a reference for token pruning. This two-stage token pruning mechanism permits a systematic and efficient reduction in the quantity of visual tokens while preserving essential visual information. We apply the proposed method to the most representative LVLM, i.e. LLaVA-1.5. Experimental results demonstrate that when the number of visual tokens is reduced by 88.9%, the computational complexity is decreased by over 46%, with only an average 1.0% accuracy drop across 12 benchmarks, and remarkably surpasses the state-of-the-art token pruning methods."
RegionDrag: Fast Region-Based Image Editing with Diffusion Models
Authors: Xinghui Li, Kai Han* Jingyi Lu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Point-drag-based image editing methods, like DragDiffusion, have attracted significant attention. However, point-drag-based approaches suffer from computational overhead and misinterpretation of user intentions, due to the sparsity of point-based editing instructions. In this paper, we propose a region-based copy-and-paste dragging method, , to overcome these limitations. allows users to express their editing instructions in the form of handle and target regions, enabling more precise control and alleviating ambiguity. In addition, region-based operations complete editing in one iteration and are much faster than point-drag-based methods. We also incorporate the attention-swapping technique for enhanced stability during editing. To validate our approach, we extend existing point-drag-based datasets with region-based dragging instructions. Experimental results demonstrate that outperforms existing point-drag-based approaches in terms of speed, accuracy, and alignment with user intentions. Remarkably, completes the edit on an image with a resolution of 512×512 in less than 2 seconds, which is more than 100× faster than DragDiffusion, while achieving better performance. Project page: https://visual-ai.github.io/regiondrag."
On the Error Analysis of 3D Gaussian Splatting and an Optimal Projection Strategy
Authors: Jiayang Bai, Jie Guo*, Yuanqi Li, Yanwen Guo Letian Huang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "3D Gaussian Splatting has garnered extensive attention and application in real-time neural rendering. Concurrently, concerns have been raised about the limitations of this technology in aspects such as point cloud storage, performance, and robustness in sparse viewpoints, leading to various improvements. However, there has been a notable lack of attention to the fundamental problem of projection errors introduced by the local affine approximation inherent in the splatting itself, and the consequential impact of these errors on the quality of photo-realistic rendering. This paper addresses the projection error function of 3D Gaussian Splatting, commencing with the residual error from the first-order Taylor expansion of the projection function. The analysis establishes a correlation between the error and the Gaussian mean position. Subsequently, leveraging function optimization theory, this paper analyzes the function’s minima to provide an optimal projection strategy for Gaussian Splatting referred to Optimal Gaussian Splatting, which can accommodate a variety of camera models. Experimental validation further confirms that this projection methodology reduces artifacts, resulting in a more convincingly realistic rendering."
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding
Authors: Shreya Pathak, Hamza Merzic, Jonathan Richard Schwarz, Ryutaro Tanno, Olivier Henaff* Talfan Evans*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Power-law scaling indicates that large-scale training with uniform sampling is prohibitively slow. Active learning methods aim to increase data efficiency by prioritizing learning on the most relevant examples. Despite their appeal, these methods have yet to be widely adopted since no one algorithm has been shown to a) generalize across models and tasks b) scale to large datasets and c) yield overall FLOP savings when accounting for the overhead of data selection. In this work we propose a method which satisfies these three properties, leveraging small, cheap proxy models to estimate “learnability” scores for datapoints, which are used to prioritize data for training much larger models. As a result, models trained using our methods – ClassAct and ActiveCLIP – require 46% and 51% fewer training updates and up to 25% less total computation to reach the same performance as uniformly-trained visual classifiers on JFT and multimodal models on ALIGN, respectively. Finally, we find our data-prioritization scheme to be complementary with recent data-curation and learning objectives, yielding a new state-of-the-art in several multimodal transfer tasks."
Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration
Authors: Qi Zhang*, Wenbo Hu, Ying Feng, Lei ZHU, Kui Jia* Zhihao Liang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "3D Gaussian Splatting (3DGS) recently gained popularity by combining the advantages of both primitive-based and volumetric 3D representations, resulting in improved quality and efficiency for 3D scene rendering. However, 3DGS is not alias-free and still produces severe blurring or jaggies when rendered at varying resolutions because the discrete sampling scheme used treats each pixel as an isolated single point, which is insensitive to changes in the footprints of pixels and is restricted in sampling bandwidth. In this paper, we use a conditioned logistic function as the analytic approximation of the cumulative distribution function (CDF) of the Gaussian signal and calculate the integral by subtracting the CDFs. We introduce this approximation to two-dimensional pixel shading and present Analytic-Splatting, which analytically approximates the Gaussian integral within the 2D-pixel window area to better capture the intensity response of each pixel. Then, we use the approximated response of the pixel window integral area to participate in the transmittance calculation of volume rendering, making Analytic-Splatting sensitive to the changes in pixel footprint at different resolutions. Extensive experiments on various datasets validate that our approach has better anti-aliasing capability that gives more details and better fidelity."
GRA: Detecting Oriented Objects through Group-wise Rotating and Attention
Authors: Yifan Pu, Yizeng Han, Jiayi Guo, Yiru Wang, Xiu Li*, Gao Huang* Jiangshan Wang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Oriented object detection, an emerging task in recent years, aims to identify and locate objects across varied orientations. This requires the detector to accurately capture the orientation information, which varies significantly within and across images. Despite the existing substantial efforts, simultaneously ensuring model effectiveness and parameter efficiency remains challenging in this scenario. In this paper, we propose a lightweight yet effective Group-wise Rotating and Attention (GRA) module to replace the convolution operations in backbone networks for oriented object detection. GRA can adaptively capture fine-grained features of objects with diverse orientations, comprising two key components: Group-wise Rotating and Group-wise Attention. Group-wise Rotating first divides the convolution kernel into groups, where each group extracts different object features by rotating at a specific angle according to the object orientation. Subsequently, Group-wise Attention is employed to adaptively enhance the object-related regions in the feature. The collaborative effort of these components enables GRA to effectively capture the various orientation information while maintaining parameter efficiency. Extensive experimental results demonstrate the superiority of our method. For example, GRA achieves a new state-of-the-art (SOTA) on the DOTA-v2.0 benchmark, while saving the parameters by nearly 50% compared to the previous SOTA method. Code is available at https://github.com/wangjiangshan0725/GRA."
Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer
Authors: Duomin Wang, Baoyuan Wang Yu Deng*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this paper, we propose a novel learning approach for feed-forward one-shot 4D head avatar synthesis. Different from existing methods that often learn from reconstructing monocular videos guided by 3DMM, we employ pseudo multi-view videos to learn a 4D head synthesizer in a data-driven manner, avoiding reliance on inaccurate 3DMM reconstruction that could be detrimental to the synthesis performance. The key idea is to first learn a 3D head synthesizer using synthetic multi-view images to convert monocular real videos into multi-view ones, and then utilize the pseudo multi-view videos to learn a 4D head synthesizer via cross-view self-reenactment. By leveraging a simple vision transformer backbone with motion-aware cross-attentions, our method exhibits superior performance compared to previous methods in terms of reconstruction fidelity, geometry consistency, and motion control accuracy. We hope our method offers novel insights into integrating 3D priors with 2D supervisions for improved 4D head avatar creation."
CSOT: Cross-Scan Object Transfer for Semi-Supervised LiDAR Object Detection
Authors: Tiejun Liu, Rengang Li, Zhaoxiang Zhang, Yuntao Chen* Jinglin Zhan
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Large-scale 3D bounding box annotation is crucial for LiDAR object detection but comes at a high cost. Semi-supervised object detection (SSOD) offers promising solutions to leverage unannotated data, but the predominant pseudo-labeling approach requires careful hyperparameter tuning for training on noisy teacher labels. In this work, we propose a () paradigm for LiDAR SSOD. Central to our approach is , a transformer-based network that predicts possible placement locations and the object-place fitness scores for inserting annotated objects into unlabeled scans in a semantic coherence manner. Based on , successfully enables object copy-paste in LiDAR SSOD for the first time. To train object detectors on partially annotated scans generated by , we adopt a spatial-aware classification loss throughout our partial supervision to handle false negative issues caused by treating all unlabeled objects as background. We conduct extensive experiments to verify the efficacy and generality of our method. Compared to other state-of-the-art label-efficient methods used in LiDAR detection, our approach requires the least amount of annotation while achieves the best detector. Using only 1% of the labeled data on the Waymo dataset, our semi-supervised detector achieves performance on par with the fully supervised baseline. Similarly, on the nuScenes dataset, our semi-supervised CenterPoint reaches 99% of the fully supervised model’s detection performance in terms of NDS score, while using just 5% of the labeled data. Code is released at https://github.com/JinglinZhan/CSOT"
Learning from the Web: Language Drives Weakly-Supervised Incremental Learning for Semantic Segmentation
Authors: Giulia Rizzoli, Pietro Zanuttigh, Fu Li, Yi Niu* Chang Liu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Current weakly-supervised incremental learning for semantic segmentation (WILSS) approaches only consider replacing pixel-level annotations with image-level labels, while the training images are still from well-designed datasets. In this work, we argue that widely available web images can also be considered for the learning of new classes. To achieve this, firstly we introduce a strategy to select web images which are similar to previously seen examples in the latent space using a Fourier-based domain discriminator. Then, an effective caption-driven reharsal strategy is proposed to preserve previously learnt classes. To our knowledge, this is the first work to rely solely on web images for both the learning of new concepts and the preservation of the already learned ones in WILSS. Experimental results show that the proposed approach can reach state-of-the-art performances without using manually selected and annotated data in the incremental steps."
ShareGPT4V: Improving Large Multi-Modal Models with Better Captions
Authors: Jinsong Li, Xiaoyi Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao*, Dahua Lin* Lin Chen*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Modality alignment serves as the cornerstone for large multi-modal models (LMMs). However, the impact of different attributes (e.g., data type, quality, and scale) of training data on facilitating effective alignment is still under-explored. In this paper, we delve into the influence of training data on LMMs, uncovering three pivotal findings: 1) Highly detailed captions enable more nuanced vision-language alignment, significantly boosting the performance of LMMs in diverse benchmarks, surpassing outcomes from brief captions or VQA data; 2) Cutting-edge LMMs can be close to the captioning capability of costly human annotators, and open-source LMMs could reach similar quality after lightweight fine-tuning; 3) The performance of LMMs scales with the number of detailed captions, exhibiting remarkable improvements across a range from thousands to millions of captions. Drawing from these findings, we introduce the ShareGPT4V series for advanced modality alignment. It includes ShareGPT4V, consisting of 100K high-quality captions curated from GPT4-Vision; ShareGPT4V-PT, containing 1.2M captions produced by our Share-Captioner that can be close to the captioning capabilities of GPT4-Vision; and ShareGPT4V-7B, a simple yet superior LMM excelling in most multi-modal benchmarks, which realized better alignment based on our large-scale high-quality captions. The project is available at https://sharegpt4v.github.io/."
"Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation"
Authors: Kai Chen, Zhili LIU, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung, James Kwok, Yu Zhang* Yunhao Gou*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Multimodal large language models (MLLMs) have shown impressive reasoning abilities. However, they are also more vulnerable to jailbreak attacks than their LLM predecessors. Although still capable of detecting the unsafe responses, we observe that safety mechanisms of the pre-aligned LLMs in MLLMs can be easily bypassed with the introduction of image features. To construct robust MLLMs, we propose ECSO (Eyes Closed, Safety On), a novel training-free protecting approach that exploits the inherent safety awareness of MLLMs, and generates safer responses via adaptively transforming unsafe images into texts to activate the intrinsic safety mechanism of pre-aligned LLMs in MLLMs. Experiments on five state-of-the-art (SoTA) MLLMs demonstrate that ECSO enhances model safety significantly (, 37.6% improvement on the MM-SafetyBench (SD+OCR) and 71.3% on VLSafe with LLaVA-1.5-7B), while consistently maintaining utility results on common MLLM benchmarks. Furthermore, we show that ECSO can be used as a data engine to generate supervised-finetuning (SFT) data for MLLM alignment without extra human intervention."
Invertible Neural Warp for NeRF
Authors: Ravi Garg, Hemanth Saratchandran, Simon Lucey Shin-Fang Chng*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This paper tackles the simultaneous optimization of pose and Neural Radiance Fields (NeRF). Departing from the conventional practice of using explicit global representations for camera pose, we propose a novel overparameterized representation that models camera poses as learnable rigid warp functions. We establish that modeling the rigid warps must be tightly coupled with constraints and regularization imposed. Specifically, we highlight the critical importance of enforcing invertibility when learning rigid warp functions via neural network and propose the use of an Invertible Neural Network (INN) coupled with a geometry-informed constraint for this purpose. We present results on synthetic and real-world datasets, and demonstrate that our approach outperforms existing baselines in terms of pose estimation and high-fidelity reconstruction due to enhanced optimization convergence."
Enhancing Vectorized Map Perception with Historical Rasterized Maps
Authors: Guangwei Liu, Zihao Liu, Ningyi Xu, Yunhui Liu*, Ji Zhao Xiaoyu Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In autonomous driving, there is growing interest in end-to-end online vectorized map perception in bird’s-eye-view (BEV) space, with an expectation that it could replace traditional high-cost offline high-definition (HD) maps. However, the accuracy and robustness of these methods can be easily compromised in challenging conditions, such as occlusion or adverse weather, when relying only on onboard sensors. In this paper, we propose , leveraging a low-cost Historical Rasterized Map to enhance online vectorized map perception. The historical rasterized map can be easily constructed from past predicted vectorized results and provides valuable complementary information. To fully exploit a historical map, we propose two novel modules to enhance BEV features and map element queries. For BEV features, we employ a feature aggregation module to encode features from both onboard images and the historical map. For map element queries, we design a query initialization module to endow queries with priors from the historical map. The two modules contribute to leveraging map information in online perception. Our can be integrated with most online vectorized map perception methods. We integrate it in two state-of-the-art methods, significantly improving their performance on both the nuScenes and Argoverse 2 datasets. The source code is released at https://github.com/HXMap/HRMapNet."
Efficient and Versatile Robust Fine-Tuning of Zero-shot Models
Authors: Boseung Jeong, Donghyun Kim, Suha Kwak* Sungyeon Kim*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Large-scale image-text pre-trained models enable zero-shot classification and provide consistent accuracy across various data distributions. Nonetheless, optimizing these models in downstream tasks typically requires fine-tuning, which reduces generalization to out-of-distribution (OOD) data and demands extensive computational resources. We introduce Robust Adapter (R-Adapter), a novel method for fine-tuning zero-shot models to downstream tasks while simultaneously addressing both these issues. Our method integrates lightweight modules into the pre-trained model and employs novel self-ensemble techniques to boost OOD robustness and reduce storage expenses substantially. Furthermore, we propose MPM-NCE loss designed for fine-tuning on vision-language downstream tasks. It ensures precise alignment of multiple image-text pairs and discriminative feature learning. By extending the benchmark for robust fine-tuning beyond classification to include diverse tasks such as cross-modal retrieval and open vocabulary segmentation, we demonstrate the broad applicability of R-Adapter. Our extensive experiments demonstrate that R-Adapter achieves state-of-the-art performance across a diverse set of tasks, tuning only 13% of the parameters of the CLIP encoders."
Part2Object: Hierarchical Unsupervised 3D Instance Segmentation
Authors: Yulin Zhang, Bin Yang, Jiajin Tang, Yuexin Ma, Sibei Yang* Cheng Shi
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Unsupervised 3D instance segmentation aims to segment objects from a 3D point cloud without any annotations. Existing methods face the challenge of either too loose or too tight clustering, leading to under-segmentation or over-segmentation. To address this issue, we propose Part2Object, hierarchical clustering with object guidance. Part2Object employs multi-layer clustering from points to object parts and objects, allowing objects to manifest at any layer. Additionally, it extracts and utilizes 3D objectness priors from temporally consecutive 2D RGB frames to guide the clustering process. Moreover, we propose Hi-Mask3D to support hierarchical 3D object part and instance segmentation. By training Hi-Mask3D on the objects and object parts extracted from Part2Object, we achieve consistent and superior performance compared to state-of-the-art models in various settings, including unsupervised instance segmentation, data-efficient fine-tuning, and cross-dataset generalization. Code is release at https://github. com/ChengShiest/Part2Object."
PetFace: A Large-Scale Dataset and Benchmark for Animal Identification
Authors: Kaede Shiohara Risa Shinoda*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Automated animal face identification plays a crucial role in the monitoring of behaviors, conducting of surveys, and finding of lost animals. Despite the advancements in human face identification, the lack of datasets and benchmarks in the animal domain has impeded progress. In this paper, we introduce the dataset, a comprehensive resource for animal face identification encompassing 257,484 unique individuals across 13 animal families and 319 breed categories, including both experimental and pet animals. This large-scale collection of individuals facilitates the investigation of unseen animal face verification, an area that has not been sufficiently explored in existing datasets due to the limited number of individuals. Moreover, also has fine-grained annotations such as sex, breed, color, and pattern. We provide multiple benchmarks including re-identification for seen individuals and verification for unseen individuals. The models trained on our dataset outperform those trained on prior datasets, even for detailed breed variations and unseen animal families. Our result also indicates that there is some room to improve the performance of integrated identification on multiple animal families. We hope the dataset will facilitate animal face identification and encourage the development of non-invasive animal automatic identification methods. Our dataset and code are available at https: //dahlian00.github.io/PetFacePage/."