GenAI4ELab/papercli-papers huggingface dataset:
VisFocus: Prompt-Guided Vision Encoders for OCR-Free Dense Document Understanding
Authors: Ofir Abramovich; Niv Nayman; Sharon Fogel; Inbal Lavi; Ron Litman; Shahar Tsiper; Royee Tichauer; Srikar Appalaraju; Shai Mazor; R. Manmatha
Venue: ECCV 2024 (main)

Abstract: "In recent years, notable advancements have been made in the domain of visual document understanding, with the prevailing architecture comprising a cascade of vision and language models. The text component can either be extracted explicitly with the use of external OCR models in OCR-based approaches, or alternatively, the vision model can be endowed with reading capabilities in OCR-free approaches. Typically, the queries to the model are input exclusively to the language component, necessitating the visual features to encompass the entire document. In this paper, we present VisFocus, an OCR-free method designed to better exploit the vision encoder’s capacity by coupling it directly with the language prompt. To do so, we replace the down-sampling layers with layers that receive the input prompt and allow highlighting relevant parts of the document, while disregarding others. We pair the architecture enhancements with a novel pre-training task, using language masking on a snippet of the document text fed to the visual encoder in place of the prompt, to empower the model with focusing capabilities. Consequently, VisFocus learns to allocate its attention to text patches pertinent to the provided prompt. Our experiments demonstrate that this prompt-guided visual encoding approach significantly improves performance, achieving state-of-the-art results on various benchmarks. *Work done during an internshipemployment at Amazon† Corresponding author: nivnay@amazon.com"
Masked Angle-Aware Autoencoder for Remote Sensing Images
Authors: Zhihao Li; Biao Hou; Siteng Ma; zitong wu; Xianpeng Guo; bo ren; Licheng Jiao
Venue: ECCV 2024 (main)

Abstract: "To overcome the inherent domain gap between remote sensing (RS) images and natural images, some self-supervised representation learning methods have made promising progress. However, they have overlooked the diverse angles present in RS objects. This paper proposes the Masked Angle-Aware Autoencoder (MA3E) to perceive and learn angles during pre-training. We design a scaling center crop operation to create the rotated crop with random orientation on each original image, introducing the explicit angle variation. MA3E inputs this composite image while reconstruct the original image, aiming to effectively learn rotation-invariant representations by restoring the angle variation introduced on the rotated crop. To avoid biases caused by directly reconstructing the rotated crop, we propose an Optimal Transport (OT) loss that automatically assigns similar original image patches to each rotated crop patch for reconstruction. MA3E1 demonstrates more competitive performance than existing pre-training methods on seven different RS image datasets in three downstream tasks. 1 Our code will be released at: https://github.com/benesakitam/MA3E"
Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm
Authors: Yi Wu; Ziqiang Li; Heliang Zheng; Chaoyue Wang; Bin Li
Venue: ECCV 2024 (main)

Abstract: "Drawing on recent advancements in diffusion models for text-to-image generation, identity-preserved personalization has made significant progress in accurately capturing specific identities with just a single reference image. However, existing methods primarily integrate reference images within the text embedding space, leading to a complex entanglement of image and text information, which poses challenges for preserving both identity fidelity and semantic consistency. To tackle this challenge, we propose Infinite-ID, an ID-semantics decoupling paradigm for identity-preserved personalization. Specifically, we introduce identity-enhanced training, incorporating an additional image cross-attention module to capture sufficient ID information while deactivating the original text cross-attention module of the diffusion model. This ensures that the image stream faithfully represents the identity provided by the reference image while mitigating interference from textual input. Additionally, we introduce a feature interaction mechanism that combines a mixed attention module with an AdaIN-mean operation to seamlessly merge the two streams. This mechanism not only enhances the fidelity of identity and semantic consistency but also enables convenient control over the styles of the generated images. Extensive experimental results on both raw photo generation and style image generation demonstrate the superior performance of our proposed method."
MultiGen: Zero-shot Image Generation from Multi-modal Prompts
Authors: Zhi-Fan Wu; Lianghua Huang; Wei Wang; Yanheng Wei; Yu Liu
Venue: ECCV 2024 (main)

Abstract: "The field of text-to-image generation has witnessed substantial advancements in the preceding years, allowing the generation of high-quality images based solely on text prompts. However, accurately describing objects through text alone is challenging, necessitating the integration of additional modalities like coordinates and images for more precise image generation. Existing methods often require fine-tuning or only support using single object as the constraint, leaving the zero-shot image generation from multi-object multi-modal prompts as an unresolved challenge. In this paper, we propose MultiGen, a novel method designed to address this problem. Given an image-text pair, we obtain object-level text, coordinates and images, and integrate the information into an “augmented token” for each object. The augmented tokens serve as additional conditions and are trained alongside text prompts in the diffusion model, enabling our model to handle multi-object multi-modal prompts. To manage the absence of modalities during inference, we leverage a coordinate model and a feature model to generate object-level coordinates and features based on text prompts. Consequently, our method can generate images from text prompts alone or from various combinations of multi-modal prompts. Through extensive qualitative and quantitative experiments, we demonstrate that our method not only outperforms existing methods but also enables a wide range of tasks."
GazeXplain: Learning to Predict Natural Language Explanations of Visual Scanpaths
Authors: Xianyu Chen; Ming Jiang; Qi Zhao
Venue: ECCV 2024 (main)

Abstract: "While exploring visual scenes, humans’ scanpaths are driven by their underlying attention processes. Understanding visual scanpaths is essential for various applications. Traditional scanpath models predict the where and when of gaze shifts without providing explanations, creating a gap in understanding the rationale behind fixations. To bridge this gap, we introduce GazeXplain, a novel study of visual scanpath prediction and explanation. This involves annotating natural-language explanations for fixations across eye-tracking datasets and proposing a general model with an attention-language decoder that jointly predicts scanpaths and generates explanations. It integrates a unique semantic alignment mechanism to enhance the consistency between fixations and explanations, alongside a cross-dataset co-training approach for generalization. These novelties present a comprehensive and adaptable solution for explainable human visual scanpath prediction. Extensive experiments on diverse eye-tracking datasets demonstrate the effectiveness of GazeXplain in both scanpath prediction and explanation, offering valuable insights into human visual attention and cognitive processes."
Learning Chain of Counterfactual Thought for Bias-Robust Vision-Language Reasoning
Authors: Yifeng Zhang; Ming Jiang; Qi Zhao
Venue: ECCV 2024 (main)

Abstract: "Despite the remarkable success of large vision-language models (LVLMs) on various tasks, their susceptibility to knowledge bias inherited from training data hinders their ability to generalize to new scenarios and limits their real-world applicability. To address this challenge, we propose the Counterfactual Bias-Robust Reasoning (CoBRa) dataset that tackles knowledge bias by offering a novel collection of VQA examples designed to evaluate and mitigate bias in LVLMs. These examples encourage counterfactual thinking by providing edited knowledge graphs and image contents, with detailed annotations of reasoning processes to facilitate a comprehensive understanding of the examples. Based on the dataset, we introduce a Chain of Counterfactual Thought (CoCT) method that learns the bias-robust reasoning processes and provides in-context examples demonstrating how existing reasoning generalizes to counterfactual scenarios. This enables LVLMs to explicitly reason step-by-step rather than relying on biased knowledge, leading to more generalizable solutions. Our extensive evaluation demonstrates that CoCT outperforms existing approaches on tasks requiring reasoning under knowledge bias. Our work is available at https://github. com/SuperJohnZhang/CoBRa."
SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img Synthesis
Authors: Hanrong Ye; Jason Kuen; Qing Liu; Zhe Lin; Brian Price; Dan Xu
Venue: ECCV 2024 (main)

Abstract: "We present , a new data generation approach that pushes the performance boundaries of state-of-the-art image segmentation models. One major bottleneck of previous data synthesis methods for segmentation is the design of “segmentation labeler module”, which is used to synthesize segmentation masks for images [?]. The segmentation labeler modules, which are segmentation models by themselves, bound the performance of downstream segmentation models trained on the synthetic masks. These methods encounter a “chicken or egg dilemma” and thus fail to outperform existing segmentation models. To address this issue, we propose a novel method that reverses the traditional data generation process: we first (i) generate highly diverse segmentation masks that match real-world distribution from text prompts, and then (ii) synthesize realistic images conditioned on the segmentation masks. In this way, we avoid the need for any segmentation labeler module. integrates two data generation strategies, namely MaskSyn and ImgSyn, to largely improve data diversity in synthetic masks and images. Notably, the high quality of our synthetic data enables our method to outperform the previous data synthesis method [?] by +25.2 mIoU on ADE20K when trained with pure synthetic data. On the highly competitive ADE20K and COCO benchmarks, our data generation method markedly improves the performance of state-of-the-art segmentation models in semantic segmentation, panoptic segmentation, and instance segmentation. Moreover, experiments show that training with our synthetic data makes the segmentation models more robust towards unseen data domains, including real-world and AI-generated images."
Sync from the Sea: Retrieving Alignable Videos from Large-Scale Datasets
Authors: Ishan Rajendrakumar Dave; Fabian Caba; Mubarak Shah; Simon Jenni
Venue: ECCV 2024 (main)

Abstract: "Temporal video alignment aims to synchronize the key events like object interactions or action phase transitions in two videos. Such methods could benefit various video editing, processing, and understanding tasks. However, existing approaches operate under the restrictive assumption that a suitable video pair for alignment is given, significantly limiting their broader applicability. To address this, we re-pose temporal alignment as a search problem and introduce the task of Alignable Video Retrieval (AVR). Given a query video, our approach can identify well-alignable videos from a large collection of clips and temporally synchronize them to the query. To achieve this, we make three key contributions: 1) we introduce DRAQ, a video alignability indicator to identify and re-rank the best alignable video from a set of candidates; 2) we propose an effective and generalizable frame-level video feature design to improve the alignment performance of several off-the-shelf feature representations, and 3) we propose a novel benchmark and evaluation protocol for AVR using cycle-consistency metrics. Our experiments on 3 datasets, including large-scale Kinetics700, demonstrate the effectiveness of our approach in identifying alignable video pairs from diverse datasets."
FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition
Authors: Ishan Rajendrakumar Dave; Mamshad Nayeem Rizve; Mubarak Shah
Venue: ECCV 2024 (main)

Abstract: "Real-life applications of action recognition often require a fine-grained understanding of subtle movements, e.g., in sports analytics, user interactions in AR/VR, and surgical videos. Although fine-grained actions are more costly to annotate, existing semi-supervised action recognition has mainly focused on coarse-grained action recognition. Since fine-grained actions are more challenging due to the absence of scene bias, classifying these actions requires an understanding of action-phases. Hence, existing coarse-grained semi-supervised methods do not work effectively. In this work, we for the first time thoroughly investigate semi-supervised fine-grained action recognition (FGAR). We observe that alignment distances like dynamic time warping (DTW) provide a suitable action-phase-aware measure for comparing fine-grained actions, a concept previously unexploited in FGAR. However, since regular DTW distance is pairwise and assumes strict alignment between pairs, it is not directly suitable for classifying fine-grained actions. To utilize such alignment distances in a limited-label setting, we propose an Alignability-Verification-based Metric learning technique to effectively discriminate between fine-grained action pairs. Our learnable alignability score provides a better phase-aware measure, which we use to refine the pseudo-labels of the primary video encoder. Our collaborative pseudo-labeling-based framework ‘FinePseudo’ significantly outperforms prior methods on four fine-grained action recognition datasets: Diving48, FineGym99, FineGym288, and FineDiving, and shows improvement on existing coarse-grained datasets: Kinetics400 and Something-SomethingV2. We also demonstrate the robustness of our collaborative pseudo-labeling in handling novel unlabeled classes in open-world semi-supervised setups."
Elegantly Written: Disentangling Writer and Character Styles for Enhancing Online Chinese Handwriting
Authors: Yu Liu; Fatimah binti Khalid; Lei Wang; Youxi Zhang; Cunrui Wang
Venue: ECCV 2024 (main)

Abstract: "The electronic writing tools, while enhancing convenience, sacrifice the readability and efficiency of handwritten content. Balancing high efficiency with readable handwriting poses a challenging research task. In this paper, we propose a method sequence-based models to beautify user handwritten traces. Unlike most existing methods that treat Chinese handwriting as images and cannot reflect the human writing process, we capture individual writing characteristics from a small amount of user handwriting trajectories and beautify the user’s traces by mimicking their writing style and process. We fully consider the style of radicals and components between the content and reference glyphs, assigning appropriate fine-grained styles to strokes in the content glyphs through a cross-attention mechanism module. Additionally, we find that many style features contribute minimally to the final stylized results. Therefore, we decompose the style features into the Cartesian product of single-dimensional variable sets, effectively removing redundant features with limited impact on the stylization effect while preserving key style information. Qualitative and quantitative experiments both demonstrate the superiority of our approach."
UniCode : Learning a Unified Codebook for Multimodal Large Language Models
Authors: Sipeng Zheng; Bohan Zhou; Yicheng Feng; Ye Wang; Zongqing Lu
Venue: ECCV 2024 (main)

Abstract: "In this paper, we propose UniCode, a novel approach within the domain of multimodal large language models (MLLMs) that learns a unified codebook to efficiently tokenize visual, text, and potentially other types of signals. This innovation addresses a critical limitation in existing MLLMs: their reliance on a text-only codebook, which restricts MLLMs’ ability to generate images and texts in a multimodal context. Towards this end, we propose a language-driven iterative training paradigm, coupled with an in-context pre-training task we term “image decompression”, enabling our model to interpret compressed visual data and generate high-quality images. The unified codebook empowers our model to extend visual instruction tuning to non-linguistic generation tasks. Moreover, UniCode is adaptable to diverse stacked quantization approaches in order to compress visual signals into a more compact token representation. Despite using significantly fewer parameters and less data during training, UniCode demonstrates promising capabilities in visual reconstruction and generation. It also achieves performance comparable to leading MLLMs across a spectrum of VQA benchmarks."
When Do We Not Need Larger Vision Models?
Authors: Baifeng Shi; Ziyang Wu; Maolin Mao; Xin Wang; Trevor Darrell
Venue: ECCV 2024 (main)

Abstract: "Scaling up the size of vision models has been the de facto standard to obtain more powerful visual representations. In this work, we discuss the point beyond which larger vision models are not necessary. First, we demonstrate the power of Scaling on Scales (), whereby a pre-trained and frozen smaller vision model (, ViT-B or ViT-L), run over multiple image scales, can outperform larger models (, ViT-H or ViT-G) on classification, segmentation, depth estimation, Multimodal LLM (MLLM) benchmarks, and robotic manipulation. Notably, achieves state-of-the-art performance in detailed understanding of MLLM on the V∗ benchmark, surpassing models such as GPT-4V. We examine the conditions under which is a preferred scaling approach compared to scaling on model size. While larger models have the advantage of better generalization on hard examples, we show that features of larger vision models can be well approximated by those of multi-scale smaller models. This suggests most, if not all, of the representations learned by current large pre-trained models can also be obtained from multi-scale smaller models. Our results show that a multi-scale smaller model has comparable learning capacity to a larger model, and pre-training smaller models with can match or even exceed the advantage of larger models. We release a Python package that can apply on any vision model with one line of code: https://github.com/bfshi/scaling_on_scales."
GVGEN: Text-to-3D Generation with Volumetric Representation
Authors: Xianglong He; Junyi Chen; Sida Peng; Di Huang; Yangguang Li; Xiaoshui Huang; Chun Yuan; Wanli Ouyang; Tong He
Venue: ECCV 2024 (main)

Abstract: "In recent years, 3D Gaussian splatting has emerged as a powerful technique for 3D reconstruction and generation, known for its fast and high-quality rendering capabilities. Nevertheless, these methods often come with limitations, either lacking the ability to produce diverse samples or requiring prolonged inference times. To address these shortcomings, this paper introduces a novel diffusion-based framework, GVGEN, designed to efficiently generate 3D Gaussian representations from text input. We propose two innovative techniques: (1) Structured Volumetric Representation. We first arrange disorganized 3D Gaussian points as a structured form GaussianVolume. This transformation allows the capture of intricate texture details within a volume composed of a fixed number of Gaussians. To better optimize the representation of these details, we propose a unique pruning and densifying method named the Candidate Pool Strategy, enhancing detail fidelity through selective optimization. (2) Coarse-to-fine Generation Pipeline. To simplify the generation of GaussianVolume and empower the model to generate instances with detailed 3D geometry, we propose a coarse-to-fine pipeline. It initially constructs a basic geometric structure, followed by the prediction of complete Gaussian attributes. Our framework, GVGEN, demonstrates superior performance in qualitative and quantitative assessments compared to existing 3D generation methods. Simultaneously, it maintains a fast generation speed (∼7 seconds), effectively striking a balance between quality and efficiency. Our project page is https://gvgen.github.io/. ∗ Equal Contribution. † Corresponding Authors."
Bidirectional Stereo Image Compression with Cross-Dimensional Entropy Model
Authors: Zhening Liu; Xinjie Zhang; Jiawei Shao; Zehong Lin; Jun Zhang
Venue: ECCV 2024 (main)

Abstract: "With the rapid advancement of stereo vision technologies, stereo image compression has emerged as a crucial field that continues to draw significant attention. Previous approaches have primarily employed a unidirectional paradigm, where the compression of one view is dependent on the other, resulting in imbalanced compression. To address this issue, we introduce a symmetric bidirectional stereo image compression architecture, named BiSIC. Specifically, we propose a 3D convolution based codec backbone to capture local features and incorporate bidirectional attention blocks to exploit global features. Moreover, we design a novel cross-dimensional entropy model that integrates various conditioning factors, including the spatial context, channel context, and stereo dependency, to effectively estimate the distribution of latent representations for entropy coding. Extensive experiments demonstrate that our proposed BiSIC outperforms conventional image/video compression standards, as well as state-of-the-art learning-based methods, in terms of both PSNR and MS-SSIM."
"UniINR: Event-guided Unified Rolling Shutter Correction, Deblurring, and Interpolation"
Authors: Yunfan Lu; Guoqiang Liang; Yusheng Wang; Lin Wang; Hui Xiong
Venue: ECCV 2024 (main)

Abstract: "Video frames captured by rolling shutter (RS) cameras during fast camera movement frequently exhibit RS distortion and blur simultaneously. Naturally, recovering high-frame-rate global shutter (GS) sharp frames from an RS blur frame must simultaneously consider RS correction, deblur, and frame interpolation. A naive way is to decompose the whole process into separate tasks and cascade existing methods; however, this results in cumulative errors and noticeable artifacts. Event cameras enjoy many advantages, , high temporal resolution, making them potential for our problem. To this end, we propose the first and novel approach, named UniINR, to recover arbitrary frame-rate sharp GS frames from an RS blur frame and paired events. Our key idea is unifying spatial-temporal implicit neural representation (INR) to directly map the position and time coordinates to color values to address the interlocking degradations. Specifically, we introduce spatial-temporal implicit encoding (STE) to convert an RS blur image and events into a spatial-temporal representation (STR). To query a specific sharp frame (GS or RS), we embed the exposure time into STR and decode the embedded features pixel-by-pixel to recover a sharp frame. Our method features a lightweight model with only 0.38M parameters, and it also enjoys high inference efficiency, achieving 2.83ms/f rame in 31× frame interpolation of an RS blur frame. Extensive experiments show that our method significantly outperforms prior methods. Code is available at https: //github.com/yunfanLu/UniINR."