pwc-archive/papers-with-abstracts huggingface dataset:
Continuous Sign Language Recognition with Adapted Conformer via Unsupervised Pretraining
Authors: Neena Aloysius, Geetha M, Prema Nedungadi
Date: 2024-05-20 00:00:00
Tasks: Sign Language Recognition, speech-recognition, Speech Recognition

Conventional Deep Learning frameworks for continuous sign language recognition (CSLR) are comprised of a single or multi-modal feature extractor, a sequence-learning module, and a decoder for outputting the glosses. The sequence learning module is a crucial part wherein transformers have demonstrated their efficacy in the sequence-to-sequence tasks. Analyzing the research progress in the field of Natural Language Processing and Speech Recognition, a rapid introduction of various transformer variants is observed. However, in the realm of sign language, experimentation in the sequence learning component is limited. In this work, the state-of-the-art Conformer model for Speech Recognition is adapted for CSLR and the proposed model is termed ConSignformer. This marks the first instance of employing Conformer for a vision-based task. ConSignformer has bimodal pipeline of CNN as feature extractor and Conformer for sequence learning. For improved context learning we also introduce Cross-Modal Relative Attention (CMRA). By incorporating CMRA into the model, it becomes more adept at learning and utilizing complex relationships within the data. To further enhance the Conformer model, unsupervised pretraining called Regressional Feature Extraction is conducted on a curated sign language dataset. The pretrained Conformer is then fine-tuned for the downstream recognition task. The experimental results confirm the effectiveness of the adopted pretraining strategy and demonstrate how CMRA contributes to the recognition process. Remarkably, leveraging a Conformer-based backbone, our model achieves state-of-the-art performance on the benchmark datasets: PHOENIX-2014 and PHOENIX-2014T.
Depth Reconstruction with Neural Signed Distance Fields in Structured Light Systems
Authors: Rukun Qiao, Hiroshi Kawasaki, Hongbin Zha
Date: 2024-05-20 00:00:00
Tasks: Depth Estimation

We introduce a novel depth estimation technique for multi-frame structured light setups using neural implicit representations of 3D space. Our approach employs a neural signed distance field (SDF), trained through self-supervised differentiable rendering. Unlike passive vision, where joint estimation of radiance and geometry fields is necessary, we capitalize on known radiance fields from projected patterns in structured light systems. This enables isolated optimization of the geometry field, ensuring convergence and network efficacy with fixed device positioning. To enhance geometric fidelity, we incorporate an additional color loss based on object surfaces during training. Real-world experiments demonstrate our method's superiority in geometric performance for few-shot scenarios, while achieving comparable results with increased pattern availability.
Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification
Authors: Weilian Zhou, Sei-ichiro Kamata, Haipeng Wang, Man-Sing Wong, Huiying, Hou
Date: 2024-05-20 00:00:00
Tasks: Hyperspectral Image Classification, image-classification, Image Classification, Mamba

Hyperspectral image (HSI) classification is pivotal in the remote sensing (RS) field, particularly with the advancement of deep learning techniques. Sequential models, adapted from the natural language processing (NLP) field such as Recurrent Neural Networks (RNNs) and Transformers, have been tailored to this task, offering a unique viewpoint. However, several challenges persist 1) RNNs struggle with centric feature aggregation and are sensitive to interfering pixels, 2) Transformers require significant computational resources and often underperform with limited HSI training samples, and 3) Current scanning methods for converting images into sequence-data are simplistic and inefficient. In response, this study introduces the innovative Mamba-in-Mamba (MiM) architecture for HSI classification, the first attempt of deploying State Space Model (SSM) in this task. The MiM model includes 1) A novel centralized Mamba-Cross-Scan (MCS) mechanism for transforming images into sequence-data, 2) A Tokenized Mamba (T-Mamba) encoder that incorporates a Gaussian Decay Mask (GDM), a Semantic Token Learner (STL), and a Semantic Token Fuser (STF) for enhanced feature generation and concentration, and 3) A Weighted MCS Fusion (WMF) module coupled with a Multi-Scale Loss Design to improve decoding efficiency. Experimental results from three public HSI datasets with fixed and disjoint training-testing samples demonstrate that our method outperforms existing baselines and state-of-the-art approaches, highlighting its efficacy and potential in HSI applications.
GGAvatar: Geometric Adjustment of Gaussian Head Avatar
Authors: Xinyang Li, Jiaxin Wang, Yixin Xuan, Gongxin Yao, Yu Pan
Date: 2024-05-20 00:00:00
Tasks: MORPH

We propose GGAvatar, a novel 3D avatar representation designed to robustly model dynamic head avatars with complex identities and deformations. GGAvatar employs a coarse-to-fine structure, featuring two core modules: Neutral Gaussian Initialization Module and Geometry Morph Adjuster. Neutral Gaussian Initialization Module pairs Gaussian primitives with deformable triangular meshes, employing an adaptive density control strategy to model the geometric structure of the target subject with neutral expressions. Geometry Morph Adjuster introduces deformation bases for each Gaussian in global space, creating fine-grained low-dimensional representations of deformation behaviors to address the Linear Blend Skinning formula's limitations effectively. Extensive experiments show that GGAvatar can produce high-fidelity renderings, outperforming state-of-the-art methods in visual quality and quantitative metrics.
MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering
Authors: Jingqun Tang, Qi Liu, YongJie Ye, Jinghui Lu, Shu Wei, Chunhui Lin, Wanqing Li, Mohamad Fitri Faiz Bin Mahmood, Hao Feng, Zhen Zhao, Yanjie Wang, Yuliang Liu, Hao liu, Xiang Bai, Can Huang
Date: 2024-05-20 00:00:00
Tasks: Benchmarking, Question Answering, Reading Comprehension, Scene Understanding, Translation, Visual Question Answering, Visual Question Answering (VQA)

Text-Centric Visual Question Answering (TEC-VQA) in its proper format not only facilitates human-machine interaction in text-centric visual environments but also serves as a de facto gold proxy to evaluate AI models in the domain of text-centric scene understanding. Nonetheless, most existing TEC-VQA benchmarks have focused on high-resource languages like English and Chinese. Despite pioneering works to expand multilingual QA pairs in non-text-centric VQA datasets through translation engines, the translation-based protocol encounters a substantial "visual-textual misalignment" problem when applied to TEC-VQA. Specifically, it prioritizes the text in question-answer pairs while disregarding the visual text present in images. Moreover, it fails to address complexities related to nuanced meaning, contextual distortion, language bias, and question-type diversity. In this work, we tackle multilingual TEC-VQA by introducing MTVQA, the first benchmark featuring high-quality human expert annotations across 9 diverse languages, consisting of 6,778 question-answer pairs across 2,116 images. Further, by comprehensively evaluating numerous state-of-the-art Multimodal Large Language Models~(MLLMs), including Qwen2-VL, GPT-4o, GPT-4V, Claude3, and Gemini, on the MTVQA benchmark, it is evident that there is still a large room for performance improvement (Qwen2-VL scoring 30.9 versus 79.7 for human performance), underscoring the value of MTVQA. Additionally, we supply multilingual training data within the MTVQA dataset, demonstrating that straightforward fine-tuning with this data can substantially enhance multilingual TEC-VQA performance. We aspire that MTVQA will offer the research community fresh insights and stimulate further exploration in multilingual visual text comprehension. The project homepage is available at https://bytedance.github.io/MTVQA/.
GuidedRec: Guiding Ill-Posed Unsupervised Volumetric Recovery
Authors: Alexandre Cafaro, Amaury Leroy, Guillaume Beldjoudi, Pauline Maury, Charlotte Robert, Eric Deutsch, Vincent Grégoire, Vincent Lepetit, Nikos Paragios
Date: 2024-05-20 00:00:00

We introduce a novel unsupervised approach to reconstructing a 3D volume from only two planar projections that exploits a previous\-ly-captured 3D volume of the patient. Such volume is readily available in many important medical procedures and previous methods already used such a volume. Earlier methods that work by deforming this volume to match the projections typically fail when the number of projections is very low as the alignment becomes underconstrained. We show how to use a generative model of the volume structures to constrain the deformation and obtain a correct estimate. Moreover, our method is not bounded to a specific sensor calibration and can be applied to new calibrations without retraining. We evaluate our approach on a challenging dataset and show it outperforms state-of-the-art methods. As a result, our method could be used in treatment scenarios such as surgery and radiotherapy while drastically reducing patient radiation exposure.
Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays
Authors: Zhichao Sun, Yuliang Gu, Yepeng Liu, Zerui Zhang, Zhou Zhao, Yongchao Xu
Date: 2024-05-20 00:00:00
Tasks: Anomaly Detection, Position, Prompt Learning

Anomaly detection in chest X-rays is a critical task. Most methods mainly model the distribution of normal images, and then regard significant deviation from normal distribution as anomaly. Recently, CLIP-based methods, pre-trained on a large number of medical images, have shown impressive performance on zero/few-shot downstream tasks. In this paper, we aim to explore the potential of CLIP-based methods for anomaly detection in chest X-rays. Considering the discrepancy between the CLIP pre-training data and the task-specific data, we propose a position-guided prompt learning method. Specifically, inspired by the fact that experts diagnose chest X-rays by carefully examining distinct lung regions, we propose learnable position-guided text and image prompts to adapt the task data to the frozen pre-trained CLIP-based model. To enhance the model's discriminative capability, we propose a novel structure-preserving anomaly synthesis method within chest x-rays during the training process. Extensive experiments on three datasets demonstrate that our proposed method outperforms some state-of-the-art methods. The code of our implementation is available at https://github.com/sunzc-sunny/PPAD.
Data Augmentation for Text-based Person Retrieval Using Large Language Models
Authors: Zheng Li, Lijia Si, Caili Guo, Yang Yang, Qiushi Cao
Date: 2024-05-20 00:00:00
Tasks: Data Augmentation, Person Retrieval, Retrieval, Sentence, Text based Person Retrieval, Text-based Person Retrieval

Text-based Person Retrieval (TPR) aims to retrieve person images that match the description given a text query. The performance improvement of the TPR model relies on high-quality data for supervised training. However, it is difficult to construct a large-scale, high-quality TPR dataset due to expensive annotation and privacy protection. Recently, Large Language Models (LLMs) have approached or even surpassed human performance on many NLP tasks, creating the possibility to expand high-quality TPR datasets. This paper proposes an LLM-based Data Augmentation (LLM-DA) method for TPR. LLM-DA uses LLMs to rewrite the text in the current TPR dataset, achieving high-quality expansion of the dataset concisely and efficiently. These rewritten texts are able to increase the diversity of vocabulary and sentence structure while retaining the original key concepts and semantic information. In order to alleviate the hallucinations of LLMs, LLM-DA introduces a Text Faithfulness Filter (TFF) to filter out unfaithful rewritten text. To balance the contributions of original text and augmented text, a Balanced Sampling Strategy (BSS) is proposed to control the proportion of original text and augmented text used for training. LLM-DA is a plug-and-play method that can be easily integrated into various TPR models. Comprehensive experiments on three TPR benchmarks show that LLM-DA can improve the retrieval performance of current TPR models.
MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections
Authors: Jiayue Liu, Xiao Tang, Freeman Cheng, Roy Yang, Zhihao LI, Jianzhuang Liu, Yi Huang, Jiaqi Lin, Shiyong Liu, Xiaofei Wu, Songcen Xu, Chun Yuan
Date: 2024-05-20 00:00:00
Tasks: Novel View Synthesis

3D Gaussian Splatting showcases notable advancements in photo-realistic and real-time novel view synthesis. However, it faces challenges in modeling mirror reflections, which exhibit substantial appearance variations from different viewpoints. To tackle this problem, we present MirrorGaussian, the first method for mirror scene reconstruction with real-time rendering based on 3D Gaussian Splatting. The key insight is grounded on the mirror symmetry between the real-world space and the virtual mirror space. We introduce an intuitive dual-rendering strategy that enables differentiable rasterization of both the real-world 3D Gaussians and the mirrored counterpart obtained by reflecting the former about the mirror plane. All 3D Gaussians are jointly optimized with the mirror plane in an end-to-end framework. MirrorGaussian achieves high-quality and real-time rendering in scenes with mirrors, empowering scene editing like adding new mirrors and objects. Comprehensive experiments on multiple datasets demonstrate that our approach significantly outperforms existing methods, achieving state-of-the-art results. Project page: https://mirror-gaussian.github.io/.
PT43D: A Probabilistic Transformer for Generating 3D Shapes from Single Highly-Ambiguous RGB Images
Authors: Yiheng Xiong, Angela Dai
Date: 2024-05-20 00:00:00
Tasks: Diversity

Generating 3D shapes from single RGB images is essential in various applications such as robotics. Current approaches typically target images containing clear and complete visual descriptions of the object, without considering common realistic cases where observations of objects that are largely occluded or truncated. We thus propose a transformer-based autoregressive model to generate the probabilistic distribution of 3D shapes conditioned on an RGB image containing potentially highly ambiguous observations of the object. To handle realistic scenarios such as occlusion or field-of-view truncation, we create simulated image-to-shape training pairs that enable improved fine-tuning for real-world scenarios. We then adopt cross-attention to effectively identify the most relevant region of interest from the input image for shape generation. This enables inference of sampled shapes with reasonable diversity and strong alignment with the input image. We train and test our model on our synthetic data then fine-tune and test it on real-world data. Experiments demonstrate that our model outperforms state of the art in both scenarios.
A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation
Authors: Sushmita Sarker, Prithul Sarker, Gunner Stone, Ryan Gorman, Alireza Tavakkoli, George Bebis, Javad Sattarvand
Date: 2024-05-20 00:00:00
Tasks: 3D Point Cloud Classification, 3D Shape Classification, Autonomous Driving, Deep Learning, Point Cloud Classification, Semantic Segmentation

Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future research, this paper analyzes recent progress in deep learning methods employed for point cloud processing and presents challenges and potential directions to advance this field. It serves as a comprehensive review on two major tasks in 3D point cloud processing-- namely, 3D shape classification and semantic segmentation.
Refining Coded Image in Human Vision Layer Using CNN-Based Post-Processing
Authors: Takahiro Shindo, Yui Tatsumi, Taiju Watanabe, Hiroshi Watanabe
Date: 2024-05-20 00:00:00
Tasks: Image Compression

Scalable image coding for both humans and machines is a technique that has gained a lot of attention recently. This technology enables the hierarchical decoding of images for human vision and image recognition models. It is a highly effective method when images need to serve both purposes. However, no research has yet incorporated the post-processing commonly used in popular image compression schemes into scalable image coding method for humans and machines. In this paper, we propose a method to enhance the quality of decoded images for humans by integrating post-processing into scalable coding scheme. Experimental results show that the post-processing improves compression performance. Furthermore, the effectiveness of the proposed method is validated through comparisons with traditional methods.
Depth Prompting for Sensor-Agnostic Depth Estimation
Authors: Jin-Hwi Park, Chanhwi Jeong, Junoh Lee, Hae-Gon Jeon
Date: 2024-05-20 00:00:00
Proceeding: CVPR 2024 1
Tasks: Depth Estimation, Monocular Depth Estimation, Prompt Engineering

Dense depth maps have been used as a key element of visual perception tasks. There have been tremendous efforts to enhance the depth quality, ranging from optimization-based to learning-based methods. Despite the remarkable progress for a long time, their applicability in the real world is limited due to systematic measurement biases such as density, sensing pattern, and scan range. It is well-known that the biases make it difficult for these methods to achieve their generalization. We observe that learning a joint representation for input modalities (e.g., images and depth), which most recent methods adopt, is sensitive to the biases. In this work, we disentangle those modalities to mitigate the biases with prompt engineering. For this, we design a novel depth prompt module to allow the desirable feature representation according to new depth distributions from either sensor types or scene configurations. Our depth prompt can be embedded into foundation models for monocular depth estimation. Through this embedding process, our method helps the pretrained model to be free from restraint of depth scan range and to provide absolute scale depth maps. We demonstrate the effectiveness of our method through extensive evaluations. Source code is publicly available at https://github.com/JinhwiPark/DepthPrompting .
SEMv3: A Fast and Robust Approach to Table Separation Line Detection
Authors: Chunxia Qin, Zhenrong Zhang, Pengfei Hu, Chenyu Liu, Jiefeng Ma, Jun Du
Date: 2024-05-20 00:00:00
Tasks: Line Detection

Table structure recognition (TSR) aims to parse the inherent structure of a table from its input image. The `"split-and-merge" paradigm is a pivotal approach to parse table structure, where the table separation line detection is crucial. However, challenges such as wireless and deformed tables make it demanding. In this paper, we adhere to the "split-and-merge" paradigm and propose SEMv3 (SEM: Split, Embed and Merge), a method that is both fast and robust for detecting table separation lines. During the split stage, we introduce a Keypoint Offset Regression (KOR) module, which effectively detects table separation lines by directly regressing the offset of each line relative to its keypoint proposals. Moreover, in the merge stage, we define a series of merge actions to efficiently describe the table structure based on table grids. Extensive ablation studies demonstrate that our proposed KOR module can detect table separation lines quickly and accurately. Furthermore, on public datasets (e.g. WTW, ICDAR-2019 cTDaR Historical and iFLYTAB), SEMv3 achieves state-of-the-art (SOTA) performance. The code is available at https://github.com/Chunchunwumu/SEMv3.
Evolving Storytelling: Benchmarks and Methods for New Character Customization with Diffusion Models
Authors: Xiyu Wang, YuFei Wang, Satoshi Tsutsui, Weisi Lin, Bihan Wen, Alex C. Kot
Date: 2024-05-20 00:00:00
Tasks: Knowledge Distillation, Story Generation, Story Visualization

Diffusion-based models for story visualization have shown promise in generating content-coherent images for storytelling tasks. However, how to effectively integrate new characters into existing narratives while maintaining character consistency remains an open problem, particularly with limited data. Two major limitations hinder the progress: (1) the absence of a suitable benchmark due to potential character leakage and inconsistent text labeling, and (2) the challenge of distinguishing between new and old characters, leading to ambiguous results. To address these challenges, we introduce the NewEpisode benchmark, comprising refined datasets designed to evaluate generative models' adaptability in generating new stories with fresh characters using just a single example story. The refined dataset involves refined text prompts and eliminates character leakage. Additionally, to mitigate the character confusion of generated results, we propose EpicEvo, a method that customizes a diffusion-based visual story generation model with a single story featuring the new characters seamlessly integrating them into established character dynamics. EpicEvo introduces a novel adversarial character alignment module to align the generated images progressively in the diffusive process, with exemplar images of new characters, while applying knowledge distillation to prevent forgetting of characters and background details. Our evaluation quantitatively demonstrates that EpicEvo outperforms existing baselines on the NewEpisode benchmark, and qualitative studies confirm its superior customization of visual story generation in diffusion models. In summary, EpicEvo provides an effective way to incorporate new characters using only one example story, unlocking new possibilities for applications such as serialized cartoons.
Rethinking Overlooked Aspects in Vision-Language Models
Authors: YuAn Liu, Le Tian, Xiao Zhou, Jie zhou
Date: 2024-05-20 00:00:00

Recent advancements in large vision-language models (LVLMs), such as GPT4-V and LLaVA, have been substantial. LLaVA's modular architecture, in particular, offers a blend of simplicity and efficiency. Recent works mainly focus on introducing more pre-training and instruction tuning data to improve model's performance. This paper delves into the often-neglected aspects of data efficiency during pre-training and the selection process for instruction tuning datasets. Our research indicates that merely increasing the size of pre-training data does not guarantee improved performance and may, in fact, lead to its degradation. Furthermore, we have established a pipeline to pinpoint the most efficient instruction tuning (SFT) dataset, implying that not all SFT data utilized in existing studies are necessary. The primary objective of this paper is not to introduce a state-of-the-art model, but rather to serve as a roadmap for future research, aiming to optimize data usage during pre-training and fine-tuning processes to enhance the performance of vision-language models.