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HRVDA: High-Resolution Visual Document Assistant

HRVDA: High-Resolution Visual Document Assistant

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liu, Chaohu Yin, Kun Cao, Haoyu Jiang, Xinghua Li, Xin Liu, Yinsong Jiang, Deqiang Sun, Xing Xu, Linli Univ Sci & Technol China Sch Comp Sci & Technol Hefei Anhui Peoples R China State Key Lab Cognit Intelligence Hefei Anhui Peoples R China Tencent YouTu Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

Leveraging vast training data, multimodal large language models (MLLMs) have demonstrated formidable general visual comprehension capabilities and achieved remarkable performance across various tasks. However, their performance in visual document understanding still leaves much room for improvement. This discrepancy is primarily attributed to the fact that visual document understanding is a fine-grained prediction task. In natural scenes, MLLMs typically use low-resolution images, leading to a substantial loss of visual information. Furthermore, general-purpose MLLMs do not excel in handling document-oriented instructions. In this paper, we propose a High-Resolution Visual Document Assistant (HRVDA), which bridges the gap between MLLMs and visual document understanding. This model employs a content filtering mechanism and an instruction filtering module to separately filter out the content-agnostic visual tokens and instruction-agnostic visual tokens, thereby achieving efficient model training and inference for high-resolution images. In addition, we construct a document-oriented visual instruction tuning dataset and apply a multi-stage training strategy to enhance the model's document modeling capabilities. Extensive experiments demonstrate that our model achieves state-of-the-art performance across multiple document understanding datasets, while maintaining training efficiency and inference speed comparable to low-resolution models.

关键词： Document understanding Multimodal vision-language model

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Aligning Bag of Regions for Open-Vocabulary Object Detection

Aligning Bag of Regions for Open-Vocabulary Object Detection

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wu, Size Zhang, Wenwei Jin, Sheng Liu, Wentao Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Univ Hong Kong Hong Kong Peoples R China SenseTime Res & Tetras AI Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Pre-trained vision-language models (VLMs) learn to align vision and language representations on large-scale datasets, where each image-text pair usually contains a bag of semantic concepts. However, existing open-vocabulary object detectors only align region embeddings individually with the corresponding features extracted from the VLMs. Such a design leaves the compositional structure of semantic concepts in a scene under-exploited, although the structure may be implicitly learned by the VLMs. In this work, we propose to align the embedding of bag of regions beyond individual regions. The proposed method groups contextually interrelated regions as a bag. The embeddings of regions in a bag are treated as embeddings of words in a sentence, and they are sent to the text encoder of a VLM to obtain the bag-of-regions embedding, which is learned to be aligned to the corresponding features extracted by a frozen VLM. Applied to the commonly used Faster R-CNN, our approach surpasses the previous best results by 4.6 box AP50 and 2.8 mask AP on novel categories of open-vocabulary COCO and LVIS benchmarks, respectively. Code and models are available at https://***/wusize/ovdet.

关键词： detection recognition: Categorization retrieval

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引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Shaharabany, Tal Wolf, Lior Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9798350301298

A phrase grounding model receives an input image and a text phrase and outputs a suitable localization map. We present an effective way to refine a phrase ground model by considering self-similarity maps extracted from the latent representation of the model's image encoder. Our main insights are that these maps resemble localization maps and that by combining such maps, one can obtain useful pseudo-labels for performing self-training. Our results surpass, by a large margin, the state of the art in weakly supervised phrase grounding. A similar gap in performance is obtained for a recently proposed downstream task called WWbL, in which only the image is input, without any text. Our code is available at https://***/talshaharabany/Similarity-Maps-forSelf-Training-Weakly-Supervised- Phrase-Grounding.

关键词： language reasoning vision

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Logarithmic Lenses: Exploring Log RGB Data for Image Classification

Logarithmic Lenses: Exploring Log RGB Data for Image Classif...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Maxwell, Bruce A. Singhania, Sumegha Patel, Avnish Kumar, Rahul Fryling, Heather Li, Sihan Sun, Haonan He, Ping Li, Zewen Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

The design of deep network architectures and training methods in computer vision has been well-explored. However, in almost all cases the images have been used as provided, with little exploration of pre-processing steps beyond normalization and data augmentation. Virtually all images posted on the web or captured by devices are processed for viewing by humans. Is the pipeline used for humans also best for use by computers and deep networks? The human visual system uses logarithmic sensors;differences and sums correspond to ratios and products. Fea-tures in log space will be invariant to intensity changes and robust to color balance changes. Log RGB space also reveals structure that is corrupted by typical pre-processing. We explore using linear and log RGB data for training standard backbone architectures on an image classification task using data derived directly from RAW images to guarantee its integrity. We found that networks trained on log RGB data exhibit improved performance on an unmodified test set and invariance to intensity and color balance modifications without additional training or data augmentation. Furthermore, we found that the gains from using high quality log data could also be partially or fully realized from data in 8-bit sRGB-JPG format by inverting the sRGB transform and taking the log. These results imply existing databases may benefit from this type of pre-processing. While working with log data, we found it was critical to retain the integrity of the log relationships and that networks using log data train best with meta-parameters different than those used for sRGB or linear data. Finally, we introduce a new 10-category 10k RAW image data set (RAW10) for image classification and other purposes to enable further the exploration of log RGB as an input format for deep networks in computer vision.

关键词： computer vision data set image classification physics-based vision

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OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video recognition

OST: Refining Text Knowledge with Optimal Spatio-Temporal De...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chen, Tongjia Yu, Hongshan Yang, Zhengeng Li, Zechuan Sun, Wei Chen, Chen Hunan Univ Changsha Peoples R China Hunan Normal Univ Changsha Peoples R China Univ Cent Florida Ctr Res Comp Vis Orlando FL USA

ISBN: (纸本)9798350353006

Due to the resource-intensive nature of training vision-language models on expansive video data, a majority of studies have centered on adapting pre-trained image-language models to the video domain. Dominant pipelines propose to tackle the visual discrepancies with additional temporal learners while overlooking the substantial discrepancy for web-scaled descriptive narratives and concise action category names, leading to less distinct semantic space and potential performance limitations. In this work, we prioritize the refinement of text knowledge to facilitate generalizable video recognition. To address the limitations of the less distinct semantic space of category names, we prompt a large language model (LLM) to augment action class names into Spatio-Temporal Descriptors thus bridging the textual discrepancy and serving as a knowledge base for general recognition. Moreover, to assign the best descriptors with different video instances, we propose Optimal Descriptor Solver, forming the video recognition problem as solving the optimal matching flow across frame-level representations and descriptors. Comprehensive evaluations in zero-shot, few-shot, and fully supervised video recognition highlight the effectiveness of our approach. Our best model achieves a state-of-the-art zero-shot accuracy of 75.1% on Kinetics-600.

关键词： Action recognition Multi-modality Video Understanding Video Reognition Video Understanding

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Single-Model and Any-Modality for Video Object Tracking

Single-Model and Any-Modality for Video Object Tracking

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wu, Zongwei Zheng, Jilai Ren, Xiangxuan Vasluianu, Florin-Alexandru Ma, Chao Paudel, Danda Pani Luc Van Gool Timofte, Radu Univ Wurzburg Comp Vision Lab CAIDAS & IFI Wurzburg Germany Shanghai Jiao Tong Univ AI Inst Shanghai Peoples R China Sofia Univ INSAIT Sofia Bulgaria Swiss Fed Inst Technol CVL Zurich Switzerland

ISBN: (纸本)9798350353006

In the realm of video object tracking, auxiliary modalities such as depth, thermal, or event data have emerged as valuable assets to complement the RGB trackers. In practice, most existing RGB trackers learn a single set of parameters to use them across datasets and applications. However, a similar single-model unification for multimodality tracking presents several challenges. These challenges stem from the inherent heterogeneity of inputs - each with modality-specific representations, the scarcity of multi-modal datasets, and the absence of all the modalities at all times. In this work, we introduce Un-Track, a Unified Tracker of a single set of parameters for any modality. To handle any modality, our method learns their common latent space through low-rank factorization and reconstruction techniques. More importantly, we use only the RGBX pairs to learn the common latent space. This unique shared representation seamlessly binds all modalities together, enabling effective unification and accommodating any missing modality, all within a single transformer-based architecture. Our Un-Track achieves +8.1 absolute F-score gain, on the DepthTrack dataset, by introducing only +2.14 (over 21.50) GFLOPs with +6.6M (over 93M) parameters, through a simple yet efficient prompting strategy. Extensive comparisons on five benchmark datasets with different modalities show that Un-Track surpasses both SOTA unified trackers and modality-specific counterparts, validating our effectiveness and practicality. The source code is publicly available at https:// github. com/ Zongwei97/ UnTrack.

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HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization

HIER: Metric Learning Beyond Class Labels via Hierarchical R...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kim, Sungyeon Jeong, Boseung Kwak, Suha POSTECH Dept CSE Pohang South Korea POSTECH Grad Sch AI Pohang South Korea

ISBN: (纸本)9798350301298

Supervision for metric learning has long been given in the form of equivalence between human-labeled classes. Although this type of supervision has been a basis of metric learning for decades, we argue that it hinders further advances in the field. In this regard, we propose a new regularization method, dubbed HIER, to discover the latent semantic hierarchy of training data, and to deploy the hierarchy to provide richer and more fine-grained supervision than inter-class separability induced by common metric learning losses. HIER achieves this goal with no annotation for the semantic hierarchy but by learning hierarchical proxies in hyperbolic spaces. The hierarchical proxies are learnable parameters, and each of them is trained to serve as an ancestor of a group of data or other proxies to approximate the semantic hierarchy among them. HIER deals with the proxies along with data in hyperbolic space since the geometric properties of the space are well-suited to represent their hierarchical structure. The efficacy of HIER is evaluated on four standard benchmarks, where it consistently improved the performance of conventional methods when integrated with them, and consequently achieved the best records, surpassing even the existing hyperbolic metric learning technique, in almost all settings.

关键词： detection recognition: Categorization retrieval

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Making Visual Sense of Oracle Bones for You and Me

Making Visual Sense of Oracle Bones for You and Me

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Qiao, Runqi Yang, Lan Pang, Kaiyue Zhang, Honggang Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Univ Surrey CVSSP SketchX Guildford Surrey England

ISBN: (纸本)9798350353006

Visual perception evolves over time. This is particularly the case of oracle bone scripts, where visual glyphs seem intuitive to people from distant past prove difficult to be understood in contemporary eyes. While semantic correspondence of an oracle can be found via a dictionary lookup, this proves to be not enough for public viewers to connect the dots, i.e., why does this oracle mean that? Common solution relies on a laborious curation process to collect visual guide for each oracle (Fig. 1), which hinges on the case-by-case effort and taste of curators. This paper delves into one natural follow-up question: can AI take over? Begin with a comprehensive human study, we show participants could indeed make better sense of an oracle glyph subjected to a proper visual guide and its efficacy can be approximated via a novel metric termed TransOV (Transferable Oracle Visuals). We then define a new conditional visual generation task based on an oracle glyph and its semantic meaning and importantly approach it by circumventing any form of model training in the presence of fatal lack of oracle data. At its heart is to leverage foundation model like GPT-4V to reason about the visual cues hidden inside an oracle and take advantage of an existing text-to-image model for final visual guide generation. Extensive empirical evidence shows our AI-enabled visual guides achieve significantly comparable TransOV performance compared with those collected under manual efforts. Finally, we demonstrate the versatility of our system under a more complex setting, where it is required to work alongside with an AI image denoiser to cope with raw oracle scan image inputs (cf. processed clean oracle glyphs). Code is available at https://***/RQ-Lab/OBS-Visual.

关键词： vision

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The Neglected Tails in vision-Language Models

The Neglected Tails in Vision-Language Models

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Parashar, Shubham Lin, Zhiqiu Liu, Tian Dong, Xiangjue Li, Yanan Ramanan, Deva Caverlee, James Kong, Shu Texas A&M Univ College Stn TX 77840 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Zhejiang Lab Hangzhou Peoples R China Univ Macau Taipa Macao Peoples R China

ISBN: (纸本)9798350353006

vision-language models (VLMs) excel in zero-shot recognition but their performance varies greatly across different visual concepts. For example, although CLIP achieves impressive accuracy on ImageNet (60-80%), its performance drops below 10% for more than ten concepts like night snake, presumably due to their limited presence in the pretraining data. However, measuring the frequency of concepts in VLMs' large-scale datasets is challenging. We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts. Our analysis confirms that popular datasets, such as LAION, exhibit a long-tailed concept distribution, yielding biased performance in VLMs. We also find that downstream applications of VLMs, including visual chatbots (e.g., GPT-4V) and text-to-image models (e.g., Stable Diffusion), often fail to recognize or generate images of rare concepts identified by our method. To mitigate the imbalanced performance of zero-shot VLMs, we propose REtrieval-Augmented Learning (REAL). First, instead of prompting VLMs using the original class names, REAL uses their most frequent synonyms found in pretraining texts. This simple change already outperforms costly human-engineered and LLM-enriched prompts over nine benchmark datasets. Second, REAL trains a linear classifier on a small yet balanced set of pretraining data re-trieved using concept synonyms. REAL surpasses the previous zero-shot SOTA, using 400x less storage and 10,000x less training time!

关键词： Long tailed recognition vision-Language Models Zero-Shot recognition

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Dynamic Conceptional Contrastive Learning for Generalized Category Discovery

Dynamic Conceptional Contrastive Learning for Generalized Ca...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Pu, Nan Zhong, Zhun Sebe, Nicu Univ Trento Dept Informat Engn & Comp Sci Trento Italy

ISBN: (纸本)9798350301298

Generalized category discovery (GCD) is a recently proposed open-world problem, which aims to automatically cluster partially labeled data. The main challenge is that the unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. This leads traditional novel category discovery (NCD) methods to be incapacitated for GCD, due to their assumption of unlabeled data are only from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. However, this manner largely ignores underlying relationships between instances of the same concepts (e.g., class, super-class, and sub-class), which results in inferior representation learning. In this paper, we propose a Dynamic Conceptional Contrastive Learning (DCCL) framework, which can effectively improve clustering accuracy by alternately estimating underlying visual conceptions and learning conceptional representation. In addition, we design a dynamic conception generation and update mechanism, which is able to ensure consistent conception learning and thus further facilitate the optimization of DCCL. Extensive experiments show that DCCL achieves new state-of-the-art performances on six generic and fine-grained visual recognition datasets, especially on fine-grained ones. For example, our method significantly surpasses the best competitor by 16.2% on the new classes for the CUB-200 dataset. Code is available at https: //***/TPCD/DCCL

关键词： vision applications and systems

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