mtybilly/Instruct2DS huggingface dataset:
Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering
Authors: Jiangbei Hu, Fei Hou, Kwan-Yee Lin, Wayne Wu, Chen Qian, Ying He* Baixin Xu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The advancements in neural rendering have increased the need for techniques that enable intuitive editing of 3D objects represented as neural implicit surfaces. This paper introduces a novel neural algorithm for parameterizing neural implicit surfaces to simple parametric domains like spheres and polycubes. Our method allows users to specify the number of cubes in the parametric domain, learning a configuration that closely resembles the target 3D object’s geometry. It computes bi-directional deformation between the object and the domain using a forward mapping from the object’s zero level set and an inverse deformation for backward mapping. We ensure nearly bijective mapping with a cycle loss and optimize deformation smoothness. The parameterization quality, assessed by angle and area distortions, is guaranteed using a Laplacian regularizer and an optimized learned parametric domain. Our framework integrates with existing neural rendering pipelines, using multi-view images of a single object or multiple objects of similar geometries to reconstruct 3D geometry and compute texture maps automatically, eliminating the need for any prior information. We demonstrate the method’s effectiveness on images of human heads and man-made objects. The source code is available at https://xubaixinxbx.github.io/neuparam."
DomainFusion: Generalizing To Unseen Domains with Latent Diffusion Models
Authors: Yabo Chen, Yuchen Liu, xiaopeng zhang*, Wenrui Dai*, Hongkai Xiong, Qi Tian Yuyang Huang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Latent Diffusion Models (LDMs) are powerful and potential tools for facilitating generation-based methods for domain generalization. However, existing diffusion-based DG methods are restricted to offline augmentation using LDM and suffer from degraded performance and prohibitive computational costs. To address these challenges, we propose DomainFusion to simultaneously achieve knowledge extraction in the latent space and augmentation in the pixel space of the Latent Diffusion Model (LDM) for efficiently and sufficiently exploiting LDM. We develop a Latent Distillation module that distills gradient priors from LDM to guide the optimization of DG models. Moreover, we design an online lightweight augmentation method by decomposing candidate images into styles and contents for using LDM in a fast and online fashion. Experimental results demonstrate that DomainFusion outperforms diffusion-based methods by a large margin and achieves SOTA performance on existing DG benchmark datasets. Remarkably, DomainFusion can significantly reduce the number of generated images (e.g. by more than 97% on DomainNet) without finetuning LDM."
Open-Set Recognition in the Age of Vision-Language Models
Authors: Niko Suenderhauf, Alex Kenna, Keita Mason Dimity Miller*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Are vision-language models (VLMs) for open-vocabulary perception inherently open-set models because they are trained on internet-scale datasets? We answer this question with a clear no – VLMs introduce closed-set assumptions via their finite query set, making them vulnerable to open-set conditions. We systematically evaluate VLMs for open-set recognition and find they frequently misclassify objects not contained in their query set, leading to alarmingly low precision when tuned for high recall and vice versa. We show that naively increasing the size of the query set to contain more and more classes does not mitigate this problem, but instead causes diminishing task performance and open-set performance. We establish a revised definition of the open-set problem for the age of VLMs, define a new benchmark and evaluation protocol to facilitate standardised evaluation and research in this important area, and evaluate promising baseline approaches based on predictive uncertainty and dedicated negative embeddings on a range of open-vocabulary VLM classifiers and object detectors."
Unsqueeze [CLS] Bottleneck to Learn Rich Representations
Authors: Shihao Ji Qing Su*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Distillation-based self-supervised learning typically leads to more compressed representations due to its radical clustering process and the implementation of a sharper target distribution. To overcome this limitation and preserve more information from input, we introduce UDI, conceptualized as Unsqueezed Distillation-based self-supervised learning (SSL). UDI enriches the learned representation by encouraging multimodal prediction distilled from a consolidated profile of local predictions that are derived via stratified sampling. Our evaluations show that UDI not only promotes semantically meaningful representations at instance level, delivering superior or competitive results to state-of-the-art SSL methods in image classification, but also effectively preserves the nuisance of input, which yields significant improvement in dense prediction tasks, including object detection and segmentation. Additionally, UDI performs competitively in low-shot image classification, improving the scalability of joint-embedding pipelines. Various visualizations and ablation studies are presented to further elucidate the mechanisms behind UDI. Our source code is available at https: //github.com/ISL-CV/udi."
Robust Multimodal Learning via Representation Decoupling
Authors: Yang Luo, Yuji Wang, Chunbo Luo* Shicai Wei
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Multimodal learning robust to missing modality has attracted increasing attention due to its practicality. Existing methods tend to address it by learning a common subspace representation for different modality combinations. However, we reveal that they are sub-optimal due to their implicit constraint on intra-class representation. Specifically, the sample with different modalities within the same class will be forced to learn representations in the same direction. This hinders the model from capturing modality-specific information, resulting in insufficient learning. To this end, we propose a novel Decoupled Multimodal Representation Network (DMRNet) to assist robust multimodal learning. Specifically, DMRNet models the input from different modality combinations as a probabilistic distribution instead of a fixed point in the latent space, and samples embeddings from the distribution for the prediction module to calculate the task loss. As a result, the direction constraint from the loss minimization is blocked by the sampled representation. This relaxes the constraint on the inference representation and enables the model to capture the specific information for different modality combinations. Furthermore, we introduce a hard combination regularizer to prevent DMRNet from unbalanced training by guiding it to pay more attention to hard modality combinations. Finally, extensive experiments on multimodal classification and segmentation tasks demonstrate that the proposed DMRNet outperforms the state-of-the-art significantly. Code is available at https://github.com/shicaiwei123/ECCV2024-"
Object-Conditioned Energy-Based Attention Map Alignment in Text-to-Image Diffusion Models
Authors: Peiyu Yu, Ying Nian Wu Yasi Zhang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Text-to-image diffusion models have shown great success in generating high-quality text-guided images. Yet, these models may still fail to semantically align generated images with the provided text prompts, leading to problems like incorrect attribute binding and/or catastrophic object neglect. Given the pervasive object-oriented structure underlying text prompts, we introduce a novel object-conditioned Energy-Based Attention Map Alignment (EBAMA) method to address the aforementioned problems. We show that an object-centric attribute binding loss naturally emerges by approximately maximizing the log-likelihood of a z-parameterized energy-based model with the help of the negative sampling technique. We further propose an object-centric intensity regularizer to prevent excessive shifts of objects attention towards their attributes. Extensive qualitative and quantitative experiments, including human evaluation, on several challenging benchmarks demonstrate the superior performance of our method over previous strong counterparts. With better aligned attention maps, our approach shows great promise in further enhancing the text-controlled image editing ability of diffusion models. The code is available at https://github.com/YasminZhang/ EBAMA."
WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing
Authors: Kaihan Li, Di You, Yichong Chen, Arvin Lin, Siying Liu, Xiaohui Li, Julie A. McCann* Shuokang Huang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "WiFi-based human sensing has exhibited remarkable potential to analyze user behaviors in a non-intrusive and device-free manner, benefiting applications as diverse as smart homes and healthcare. However, most previous works focus on single-user sensing, which has limited practicability in scenarios involving multiple users. Although recent studies have begun to investigate WiFi-based multi-user sensing, there remains a lack of benchmark datasets to facilitate reproducible and comparable research. To bridge this gap, we present WiMANS, to our knowledge, the first dataset for multi-user sensing based on WiFi. WiMANS contains over 9.4 hours of dual-band WiFi Channel State Information (CSI), as well as synchronized videos, monitoring the simultaneous activities of multiple users. We exploit WiMANS to benchmark the performance of state-of-the-art WiFi-based human sensing models and video-based models, posing new challenges and opportunities for future work. We believe WiMANS can push the boundaries of current studies and catalyze the research on WiFi-based multi-user sensing."
Embedding-Free Transformer with Inference Spatial Reduction for Efficient Semantic Segmentation
Authors: Yubin Cho, Beoungwoo Kang, Seunghun Moon, Kyeongbo Kong, Suk-Ju Kang* Hyunwoo Yu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We present an Encoder-Decoder Attention Transformer, ED-AFormer, which consists of the Embedding-Free Transformer (EFT) encoder and the all-attention decoder leveraging our Embedding-Free Attention (EFA) structure. The proposed EFA is a novel global context modeling mechanism that focuses on functioning the global non-linearity, not the specific roles of the query, key and value. For the decoder, we explore the optimized structure for considering the globality, which can improve the semantic segmentation performance. In addition, we propose a novel Inference Spatial Reduction (ISR) method for the computational efficiency. Different from the previous spatial reduction attention methods, our ISR method further reduces the key-value resolution at the inference phase, which can mitigate the computation-performance trade-off gap for the efficient semantic segmentation. Our EDAFormer shows the state-of-the-art performance with the efficient computation compared to the existing transformer-based semantic segmentation models in three public benchmarks, including ADE20K, Cityscapes and COCO-Stuff. Furthermore, our ISR method reduces the computational cost by up to 61% with minimal mIoU performance degradation on Cityscapes dataset. The code is available at https://github.com/hyunwoo137/EDAFormer."
VeCLIP: Improving CLIP Training via Visual-enriched Captions
Authors: Haotian Zhang, Bowen Zhang, Wentao Wu, Haoping Bai, Aleksei Timofeev, Xianzhi Du, Zhe Gan, Jiulong Shan, Chen-Nee Chuah, Yinfei Yang, Meng Cao Zhengfeng Lai*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Large-scale web-crawled datasets are fundamental for the success of pre-training vision-language models, such as CLIP. However, the inherent noise and potential irrelevance of web-crawled AltTexts pose challenges in achieving precise image-text alignment. Existing methods utilizing large language models (LLMs) for caption rewriting have shown promise on small, curated datasets like CC3M and CC12M. This study introduces a scalable pipeline for noisy caption rewriting. Unlike recent LLM rewriting techniques, we emphasize the incorporation of visual concepts into captions, termed as Visual-enriched Captions (VeCap). To ensure data diversity, we propose a novel mixed training scheme that optimizes the utilization of AltTexts alongside newly generated VeCap. We showcase the adaptation of this method for training CLIP on large-scale web-crawled datasets, termed VeCLIP. Employing this cost-effective pipeline, we effortlessly scale our dataset up to 300 million samples named VeCap dataset. Our results show significant advantages in image-text alignment and overall model performance. For example, VeCLIP achieves up to +25.2% gain in COCO and Flickr30k retrieval tasks under the 12M setting. For data efficiency, VeCLIP achieves +3% gain while only using 14% of the data employed in the vanilla CLIP and 11% in ALIGN. We also note the VeCap data is complementary with other well curated datasets good for zero-shot classification tasks. When combining VeCap and DFN, our model can achieve strong performance on both of image-text retrieval and zero-shot classification tasks, e.g., 83.1% accuracy@1 on ImageNet zero-shot for a H/14 model. We release our codes and model weights at https: //github.com/apple/ml-veclip."
Three Things We Need to Know About Transferring Stable Diffusion to Visual Dense Prediciton Tasks
Authors: Guanglu Song, Xiaoyu Shi, Yu Liu, Hongsheng Li Manyuan Zhang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this paper, we investigate how to conduct transfer learning to adapt Stable Diffusion to downstream visual dense prediction tasks such as semantic segmentation and depth estimation. We focus on fine-tuning the Stable Diffusion model, which has demonstrated impressive abilities in modeling image details and high-level semantics. Through our experiments, we have three key insights. Firstly, we demonstrate that for dense prediction tasks, the denoiser of Stable Diffusion can serve as a stronger feature encoder compared to visual-language models pre-trained with contrastive training (e.g., CLIP). Secondly, we show that the quality of extracted features is influenced by the diffusion sampling step t, sampling layer, cross-attention map, model generation capacity, and textual input. Features from Stable Diffusion UNet’s upsampling layers and earlier denoising steps lead to more discriminative features for transfer learning to downstream tasks. Thirdly, we find that tuning Stable Diffusion to downstream tasks in a parameter-efficient way is feasible. We first extensively investigate currently popular parameter-efficient tuning methods. Then we search for the best protocol for effective tuning via reinforcement learning and achieve better tuning results with fewer tunable parameters."
Learning Representations from Foundation Models for Domain Generalized Stereo Matching
Authors: Longguang Wang, Kunhong Li, WANG Yun, Yulan Guo* Yongjian Zhang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "State-of-the-art stereo matching networks trained on in-domain data often underperform on cross-domain scenes. Intuitively, leveraging the zero-shot capacity of a foundation model can alleviate the cross-domain generalization problem. The main challenge of incorporating a foundation model into stereo matching pipeline lies in the absence of an effective forward process from single-view coarse-grained tokens to cross-view fine-grained cost representations. In this paper, we propose FormerStereo, a general framework that integrates the Vision Transformer (ViT) based foundation model into the stereo matching pipeline. Using this framework, we transfer the all-purpose features to matching-specific ones. Specifically, we propose a reconstruction-constrained decoder to retrieve fine-grained representations from coarse-grained ViT tokens. To maintain cross-view consistent representations, we propose a cosine-constrained concatenation cost (C4) space to construct cost volumes. We integrate FormerStereo with state-of-the-art (SOTA) stereo matching networks and evaluate its effectiveness on multiple benchmark datasets. Experiments show that the FormerStereo framework effectively improves the zero-shot performance of existing stereo matching networks on unseen domains and achieves SOTA performance."
Spike-Temporal Latent Representation for Energy-Efficient Event-to-Video Reconstruction
Authors: Jian-Huang Lai*, Lingxiao Yang, Xiaohua Xie Jianxiong Tang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Event-to-Video (E2V) reconstruction aims to recover grayscale video from neuromorphic event streams, with Spiking Neural Networks (SNNs) being promising energy-efficient models for this task. Event voxels effectively compress event streams for E2V reconstruction, yet their temporal latent representation is rarely considered in SNN-based approaches. In this paper, we propose a spike-temporal latent representation (STLR) model for SNN-based E2V reconstruction. The STLR solves the temporal latent coding of event voxels for video frame reconstruction. It is composed of two cascaded SNNs: a) Spike-based Voxel Temporal Encoder (SVT) and b) U-shape SNN Decoder. The SVT is a spike-driven spatial unfolding network with a specially designed coding dynamic. It encodes the event voxel into the layer-wise spiking features for latent coding, approximating the fixed point of the Iterative Shrinkage-Thresholding Algorithm. Then, the U-shape SNN decoder reconstructs the video based on the encoded spikes. Experimental results demonstrate that the STLR achieves performance comparable to popular SNNs on IJRR, HQF, and MVSEC datasets while significantly enhancing energy efficiency."
Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer
Authors: Yirui Wang*, Ke Yan, Haoshen Li, Dazhou Guo, Li Zhang, Na Shen, Qifeng Wang, Xiaowei Ding, Le Lu, Xianghua Ye*, Dakai Jin* Qinji Yu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Lymph node (LN) assessment is a critical yet very challenging task in the routine clinical workflow of radiology and oncology. Accurate LN analysis is essential for cancer diagnosis, staging and treatment planning. Finding scatteredly distributed, low-contrast clinically relevant LNs in 3D CT is difficult even for experienced physicians under high inter-observer variations. Previous automatic LN detection typically yields limited recall and high false positives (FPs) due to adjacent anatomies with similar image intensities, shapes or textures (vessels, muscles, esophagus, etc). In this work, we propose a new LN DEtection TRansformer, named LN-DETR, with location debiased query selection and contrastive query learning to enhance the representation ability of LN queries, important to increase the detection sensitivity and reduce FPs or duplicates. We also enhance LN-DETR by adapting an efficient multi-scale 2.5D fusion scheme to incorporate the 3D context. Trained and tested on 3D CT scans of 1067 patients (with 10, 000+ labeled LNs) via combining seven LN datasets from different body parts (neck, chest, and abdomen) and pathologies/cancers, our method significantly improves the performance of previous leading methods by > 4 ∼ 5% average recall at the same FP rates in both internal and external testing. We further evaluate on the universal lesion detection task using DeepLesion benchmark, and our method achieves the top performance of 88.46% averaged recall, compared with other leading reported results."
Chat-Edit-3D: Interactive 3D Scene Editing via Text Prompts
Authors: Yufeng Wang*, Yi-Hsuan Tsai, Yi Yang, Wenrui Ding, Shuchang Zhou, Ming-Hsuan Yang shuangkang fang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Recent work on image content manipulation based on vision-language pre-training models has been effectively extended to text-driven 3D scene editing. However, existing schemes for 3D scene editing still exhibit certain shortcomings, hindering their further interactive design. Such schemes typically adhere to fixed input patterns, limiting users’ flexibility in text input. Moreover, their editing capabilities are constrained by a single or a few 2D visual models and require intricate pipeline design to integrate these models into 3D reconstruction processes. To address the aforementioned issues, we propose a dialogue-based 3D scene editing approach, termed , which is centered around a large language model that allows for arbitrary textual input from users and interprets their intentions, subsequently facilitating the autonomous invocation of the corresponding visual expert models. Furthermore, we design a scheme utilizing Hash-Atlas to represent 3D scene views, which transfers the editing of 3D scenes onto 2D atlas images. This design achieves complete decoupling between the 2D editing and 3D reconstruction processes, enabling to flexibly integrate a wide range of existing 2D or 3D visual models without necessitating intricate fusion designs. Experimental results demonstrate that effectively integrates multiple visual models to achieve diverse editing visual effects, possessing strong scene comprehension and multi-round dialog capabilities. The source codes can be made available at https://sk-fun. fun/CE3D."
Event-Adapted Video Super-Resolution
Authors: Dachun Kai, Yueyi Zhang, Zheng-Jun Zha, Xiaoyan Sun, Zhiwei Xiong* Zeyu Xiao
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Introducing event cameras into video super-resolution (VSR) shows great promise. In practice, however, integrating event data as a new modality necessitates a laborious model architecture design. This not only consumes substantial time and effort but also disregards valuable insights from successful existing VSR models. Furthermore, the resource-intensive process of retraining these newly designed models exacerbates the challenge. In this paper, inspired by the recent success of parameter-efficient tuning in reducing the number of trainable parameters of a pre-trained model for downstream tasks, we introduce the Event AdapTER (EATER) for VSR. EATER efficiently utilizes knowledge of VSR models at the feature level through two lightweight and trainable components: the event-adapted alignment (EAA) unit and the event-adapted fusion (EAF) unit. The EAA unit aligns multiple frames based on the event stream in a coarse-to-fine manner, while the EAF unit efficiently fuses frames with the event stream through a multi-scale design. Thanks to both units, EATER outperforms the full fine-tuning approach with parameter efficiency, as demonstrated by comprehensive experiments. Z. Xiao and D. Kai — Equal contribution."
Look Hear: Gaze Prediction for Speech-directed Human Attention
Authors: Seoyoung Ahn, Zhibo Yang, Niranjan Balasubramanian, Dimitris Samaras, Gregory Zelinsky, Minh Hoai Sounak Mondal*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "For computer systems to effectively interact with humans using spoken language, they need to understand how the words being generated affect the users’ moment-by-moment attention. Our study focuses on the incremental prediction of attention as a person is seeing an image and hearing a referring expression defining the object in the scene that should be fixated by gaze. To predict the gaze scanpaths in this incremental object referral task, we developed the Attention in Referral Transformer model or ART, which predicts the human fixations spurred by each word in a referring expression. ART uses a multimodal transformer encoder to jointly learn gaze behavior and its underlying grounding tasks, and an autoregressive transformer decoder to predict, for each word, a variable number of fixations based on fixation history. To train ART, we created RefCOCO-Gaze, a large-scale dataset of 19,738 human gaze scanpaths, corresponding to 2,094 unique image-expression pairs, from 220 participants performing our referral task. In our quantitative and qualitative analyses, ART not only outperforms existing methods in scanpath prediction, but also appears to capture several human attention patterns, such as waiting, scanning, and verification. Code and dataset are available at: https://github.com/ cvlab-stonybrook/ART."
Raising the Ceiling: Conflict-Free Local Feature Matching with Dynamic View Switching
Authors: Songlin Du* Xiaoyong Lu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Current feature matching methods prioritize improving modeling capabilities to better align outputs with ground-truth matches, which are the theoretical upper bound on matching results, metaphorically depicted as the “ceiling”. However, these enhancements fail to address the underlying issues that directly hinder ground-truth matches, including the scarcity of matchable points in small scale images, matching conflicts in dense methods, and the keypoint-repeatability reliance in sparse methods. We propose a novel feature matching method named RCM, which Raises the Ceiling of Matching from three aspects. 1) RCM introduces a dynamic view switching mechanism to address the scarcity of matchable points in source images by strategically switching image pairs. 2) RCM proposes a conflict-free coarse matching module, addressing matching conflicts in the target image through a many-to-one matching strategy. 3) By integrating the semi-sparse paradigm and the coarse-to-fine architecture, RCM preserves the benefits of both high efficiency and global search, mitigating the reliance on keypoint repeatability. As a result, RCM enables more matchable points in the source image to be matched in an exhaustive and conflict-free manner in the target image, leading to a substantial 260% increase in ground-truth matches. Comprehensive experiments show that RCM exhibits remarkable performance and efficiency in comparison to state-of-the-art methods."
Q&A Prompts: Discovering Rich Visual Clues through Mining Question-Answer Prompts for VQA requiring Diverse World Knowledge
Authors: Weifeng Ge* Haibo Wang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "With the breakthrough of multi-modal large language models (MLLMs), answering complex visual questions that demand advanced reasoning abilities and world knowledge has become a much more important testbed for developing AI models than ever. However, equipping MLLMs with robust cross-modality reasoning ability remains challenging since the cognition scheme of humans has not been understood systematically. In this paper, we believe that if we can collect rich visual clues, we will recognize the image more accurately, understand the question better, recall relevant knowledge more easily, and finally reason out the answer. We discover these rich visual clues by mining question-answer pairs in images and sending them into multi-modal large language models as prompts. We call the proposed method Q&A Prompts. Specifically, we first use the image-answer pairs and the corresponding questions in the training set as inputs and outputs to train a visual question generation (VQG) model. Then, we use an image tagging model to identify various instances and send packaged image-tag pairs into the VQG model to generate relevant questions with the extracted image tags as answers. Finally, we encode these generated question-answer pairs as prompts with a visual-aware prompting module and send them into pre-trained MLLMs to reason out the final answers. Experimental results show that, compared with state-of-the-art methods, our Q&A Prompts achieves substantial improvements on the challenging visual question answering datasets requiring reasoning over diverse world knowledge, such as OK-VQA and A-OKVQA. Codes will be avaliable at link."
Catastrophic Overfitting: A Potential Blessing in Disguise
Authors: Lihe Zhang*, Yuqiu Kong, Baocai Yin MN Zhao
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Fast Adversarial Training (FAT) has gained increasing attention within the research community owing to its efficacy in improving adversarial robustness. Particularly noteworthy is the challenge posed by catastrophic overfitting (CO) in this field. Although existing FAT approaches have made strides in mitigating CO, the ascent of adversarial robustness occurs with a non-negligible decline in classification accuracy on clean samples. To tackle this issue, we initially employ the feature activation differences between clean and adversarial examples to analyze the underlying causes of CO. Intriguingly, our findings reveal that CO can be attributed to the feature coverage induced by a few specific pathways. By intentionally manipulating feature activation differences in these pathways with well-designed regularization terms, we can effectively mitigate and induce CO, providing further evidence for this observation. Notably, models trained stably with these terms exhibit superior performance compared to prior FAT work. On this basis, we harness CO to achieve ‘attack obfuscation’, aiming to bolster model performance. Consequently, the models suffering from CO can attain optimal classification accuracy on both clean and adversarial data when adding random noise to inputs during evaluation. We also validate their robustness against transferred adversarial examples and the necessity of inducing CO to improve robustness. Hence, CO may not be a problem that has to be solved."
Long-range Turbulence Mitigation: A Large-scale Dataset and A Coarse-to-fine Framework
Authors: Run Sun, Yi Chang*, Shuning Cao, Xueyao Xiao, Luxin Yan Shengqi Xu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Long-range imaging inevitably suffers from atmospheric turbulence with severe geometric distortions due to random refraction of light. The further the distance, the more severe the disturbance. Despite existing research has achieved great progress in tackling short-range turbulence, there is less attention paid to long-range turbulence with significant distortions. To address this dilemma and advance the field, we construct a large-scale real long-range atmospheric turbulence dataset (RLR-AT), including 1500 turbulence sequences spanning distances from 1 Km to 13 Km. The advantages of RLR-AT compared to existing ones: turbulence with longer-distances and higher-diversity, scenes with greater-variety and larger-scale. Moreover, most existing work adopts either registration-based or decomposition-based methods to address distortions through one-step mitigation. However, they fail to effectively handle long-range turbulence due to its significant pixel displacements. In this work, we propose a coarse-to-fine framework to handle severe distortions, which cooperates dynamic turbulence and static background priors (CDSP). On the one hand, we discover the pixel motion statistical prior of turbulence, and propose a frequency-aware reference frame for better large-scale distortion registration, greatly reducing the burden of refinement. On the other hand, we take advantage of the static prior of background, and propose a subspace-based low-rank tensor refinement model to eliminate the misalignments inevitably left by registration while well preserving details. The dynamic and static priors complement to each other, facilitating us to progressively mitigate long-range turbulence with severe distortions. Extensive experiments demonstrate that the proposed method outperforms SOTA methods on different datasets."
SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models
Authors: Ceyuan Yang*, Anyi Rao, Maneesh Agrawala, Dahua Lin*, Bo Dai* Yuwei Guo
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "The development of text-to-video (T2V), i.e., generating videos with a given text prompt, has been significantly advanced in recent years. However, relying solely on text prompts often results in ambiguous frame composition due to spatial uncertainty. The research community thus leverages the dense structure signals, e.g., per-frame depth/edge sequences to enhance controllability, whose collection accordingly increases the burden of inference. In this work, we present to enable flexible structure control with temporally sparse signals, requiring only one or few inputs, as shown in fig:teaser. It incorporates an additional condition encoder to process these sparse signals while leaving the pre-trained T2V model untouched. The proposed approach is compatible with various modalities, including sketches, depth, and RGB images, providing more practical control for video generation and promoting applications such as storyboarding, depth rendering, keyframe animation, and interpolation. Extensive experiments demonstrate the generalization of on both original and personalized T2V generators2 . 2 Project page: https://guoyww.github.io/projects/SparseCtrl"
Visual Alignment Pre-training for Sign Language Translation
Authors: Yuecong Min, Xilin Chen* Peiqi Jiao
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Sign Language Translation (SLT) aims to translate sign videos into text sentences. While gloss sequences, the written approximation of sign videos, provide informative alignment supervision for visual representation learning in SLT, the associated high cost of gloss annotations hampers the scalability. Recent works have yet to achieve satisfactory results without gloss annotations. In this study, we attribute the challenge to the flexible correspondence between visual and textual tokens, and aim to address it by constructing a gloss-like constraint from text sentences. Specifically, we propose a Visual Alignment Pre-training (VAP) scheme to exploit visual information by aligning visual and textual tokens in a greedy manner. The VAP scheme enhances visual encoder in capturing semantic-aware visual information and facilitates better adaptation with translation modules pre-trained on large-scale corpora. Experimental results across four SLT benchmarks demonstrate the effectiveness of VAP, which can generate reasonable alignments and significantly narrow the performance gap with gloss-based methods."
Parrot Captions Teach CLIP to Spot Text
Authors: Conghui He*, Alex Jinpeng Wang, Bin Wang, Weijia Li, Mike Zheng Shou Yiqi Lin
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Despite CLIP [?] being the foundation model in numerous vision-language applications, CLIP suffers from a severe text spotting bias. Such bias causes CLIP models to ‘Parrot’ the visual text embedded within images while disregarding the authentic visual semantics. We uncover that in the most popular image-text dataset LAION-2B [?], the captions also densely parrot (spell) the text embedded in images. Our analysis shows that around 50% of images are embedded with visual text content and around 30% of captions words are concurrently embedded in the visual content. Based on such observation, we thoroughly inspect the different released versions of CLIP models and verify that the visual text is a dominant factor in measuring the LAION-style image-text similarity for these models. To examine whether these parrot captions shape the text spotting bias, we train a series of CLIP models with LAION subsets curated by different parrot-caption-oriented criteria. We show that training with parrot captions easily shapes such bias but harms the expected visual-language representation learning in CLIP models across various vision-language downstream tasks. This suggests that it is urgent to revisit either the design of CLIP-like models or the existing image-text dataset curation pipeline built on CLIP score filtering. Project page: https://linyq17.github.io/CLIP-Parrot-Bias/"
Solving Motion Planning Tasks with a Scalable Generative Model
Authors: Siqi Chai, Zhening Yang, Jingyu Qian, Kun Li, Wenxin Shao, Haichao Zhang, Wei Xu, Qiang Liu* Yihan Hu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "As autonomous driving systems being deployed to millions of vehicles, there is a pressing need of improving the system’s scalability, safety and reducing the engineering cost. A realistic, scalable, and practical simulator of the driving world is highly desired. In this paper, we present an efficient solution based on generative models which learns the dynamics of the driving scenes. With this model, we can not only simulate the diverse futures of a given driving scenario but also generate a variety of driving scenarios conditioned on various prompts. Our innovative design allows the model to operate in both full-Autoregressive and partial-Autoregressive modes, significantly improving inference and training speed without sacrificing generative capability. This efficiency makes it ideal for being used as an online reactive environment for reinforcement learning, an evaluator for planning policies, and a high-fidelity simulator for testing. We evaluated our model against two real-world datasets: the Waymo motion dataset and the nuPlan dataset. On the simulation realism and scene generation benchmark, our model achieves the state-of-the-art performance. And in the planning benchmarks, our planner outperforms the prior arts. We conclude that the proposed generative model may serve as a foundation for a variety of motion planning tasks, including data generation, simulation, planning, and online training. Source code is public at https://github.com/HorizonRobotics/ GUMP/."
Griffon: Spelling out All Object Locations at Any Granularity with Large Language Models
Authors: Yousong Zhu*, Zhiyang Chen, Fan Yang, Ming Tang, Jinqiao Wang Yufei Zhan
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Replicating the innate human ability to detect all objects based on free-form texts at any granularity remains a formidable challenge for Large Vision Language Models (LVLMs). Current LVLMs are predominantly constrained to locate a single, pre-existing object. This limitation leads to a compromise in model design, necessitating the introduction of visual expert models or customized head structures. Beyond these constraints, our research uncovers LVLMs’ capability for basic object perception, allowing them to accurately identify and locate objects of interest. Building on this insight, we introduce a novel Language-prompted Localization Dataset to fully unleash the capabilities of LVLMs in fine-grained object perception and precise location awareness. More importantly, we present Griffon, a purely LVLM-based baseline, which does not introduce any special tokens, expert models, or additional detection modules. It simply maintains a consistent structure with popular LVLMs by unifying data formats across various localization-related scenarios and is trained end-to-end through a well-designed pipeline. Comprehensive experiments demonstrate that Griffon not only achieves state-of-the-art performance on the fine-grained RefCOCO series and Flickr30K Entities but also approaches the capabilities of the expert model Faster RCNN on the detection benchmark MSCOCO. Data, codes, and models are released at https://github.com/jefferyZhan/Griffon."
Vision-Language Action Knowledge Learning for Semantic-Aware Action Quality Assessment
Authors: Xiao Ke*, Yuezhou Li, Rui Xu, Huanqi Wu, Xiaofeng Lin, Wenzhong Guo Huangbiao Xu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Action quality assessment (AQA) is a challenging vision task that requires discerning and quantifying subtle differences in actions from the same class. While recent research has made strides in creating fine-grained annotations for more precise analysis, existing methods primarily focus on coarse action segmentation, leading to limited identification of discriminative action frames. To address this issue, we propose a Vision-Language Action Knowledge Learning approach for action quality assessment, along with a multi-grained alignment framework to understand different levels of action knowledge. In our framework, prior knowledge, such as specialized terminology, is embedded into video-level, stage-level, and frame-level representations via CLIP. We further propose a new semantic-aware collaborative attention module to prevent confusing interactions and preserve textual knowledge in cross-modal and cross-semantic spaces. Specifically, we leverage the powerful cross-modal knowledge of CLIP to embed textual semantics into image features, which then guide action spatial-temporal representations. Our approach can be plug-and-played with existing AQA methods, frame-wise annotations or not. Extensive experiments and ablation studies show that our approach achieves state-of-the-art on four public short and long-term AQA benchmarks: FineDiving, MTL-AQA, JIGSAWS, and Fis-V."
Knowledge Transfer with Simulated Inter-Image Erasing for Weakly Supervised Semantic Segmentation
Authors: Xiruo Jiang, Gensheng Pei, Zeren Sun, Yucheng Wang, Yazhou Yao Tao Chen*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Though adversarial erasing has prevailed in weakly supervised semantic segmentation to help activate integral object regions, existing approaches still suffer from the dilemma of under-activation and over-expansion due to the difficulty in determining when to stop erasing. In this paper, we propose a Knowledge Transfer with Simulated Inter-Image Erasing (KTSE) approach for weakly supervised semantic segmentation to alleviate the above problem. In contrast to existing erasing-based methods that remove the discriminative part for more object discovery, we propose a simulated inter-image erasing scenario to weaken the original activation by introducing extra object information. Then, object knowledge is transferred from the anchor image to the consequent less activated localization map to strengthen network localization ability. Considering the adopted bidirectional alignment will also weaken the anchor image activation if appropriate constraints are missing, we propose a self-supervised regularization module to maintain the reliable activation in discriminative regions and improve the inter-class object boundary recognition for complex images with multiple categories of objects. In addition, we resort to intra-image erasing and propose a multi-granularity alignment module to gently enlarge the object activation to boost the object knowledge transfer. Extensive experiments and ablation studies on PASCAL VOC 2012 and COCO datasets demonstrate the superiority of our proposed approach. Codes and models are available at https://nust-machine-intelligence-laboratory. github.io/project-KTSE."
BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical Flow
Authors: Byeonghun Lee, Sunghoon Im, Kyong Hwan Jin EungGu Kang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Multi frame super-resolution (MFSR) achieves higher performance than single image super-resolution (SISR), because MFSR leverages abundant information from multiple frames. Recent MFSR approaches adapt the deformable convolution network (DCN) to align the frames. However, the existing MFSR suffers from misalignments between the reference and source frames due to the limitations of DCN, such as small receptive fields and the predefined number of kernels. From these problems, existing MFSR approaches struggle to represent high-frequency information. To this end, we propose Deep Burst Multi-scale SR using Fourier Space with Optical Flow (BurstM). The proposed method estimates the optical flow offset for accurate alignment and predicts the continuous Fourier coefficient of each frame for representing high-frequency textures. In addition, we have enhanced the network’s flexibility by supporting various super-resolution (SR) scale factors with the unimodel. We demonstrate that our method has the highest performance and flexibility than the existing MFSR methods. Our source code is available at https://github.com/Egkang-Luis/burstm"
Diffusion Reward: Learning Rewards via Conditional Video Diffusion
Authors: Guangqi Jiang, Yanjie Ze, Huazhe Xu* Tao Huang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Learning rewards from expert videos offers an affordable and effective solution to specify the intended behaviors for reinforcement learning (RL) tasks. In this work, we propose , a novel framework that learns rewards from expert videos via conditional video diffusion models for solving complex visual RL problems. Our key insight is that lower generative diversity is exhibited when conditioning diffusion on expert trajectories. is accordingly formalized by the negative of conditional entropy that encourages productive exploration of expert behaviors. We show the efficacy of our method over robotic manipulation tasks in both simulation platforms and the real world with visual input. Moreover, can even solve unseen tasks successfully and effectively, largely surpassing baseline methods. Project page and code: citecolordiffusion-reward.github.io."
Recursive Visual Programming
Authors: Sanjay Subramanian, Baifeng Shi, Roei Herzig, Trevor Darrell Jiaxin Ge*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Visual Programming (VP) has emerged as a powerful framework for Visual Question Answering (VQA). By generating and executing bespoke code for each question, these methods show advancement in leveraging Large Language Models (LLMs) for complex problem-solving. Despite their potential, existing VP methods generate all code in a single function, which does not fully utilize LLM’s reasoning capacity and the modular adaptability of code. This results in code that is suboptimal in terms of both accuracy and interpretability. Inspired by human coding practices, we propose Recursive Visual Programming (RVP), which better harnesses the reasoning capacity of LLMs, provides modular code structure between code pieces, and assigns different return types for the sub-problems elegantly. RVP approaches VQA tasks with an top-down recursive code generation approach, allowing decomposition of complicated problems into smaller parts. We show RVP’s efficacy through extensive experiments on benchmarks including VSR, COVR, GQA, and NextQA, underscoring the value of adopting human-like recursive and modular programming techniques for solving VQA tasks. Our code is available at https://github.com/para-lost/RVP."