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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11890 条记录，以下是451-460 订阅

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Leveraging Pre-trained Multi-task Deep Models for Trustworthy Facial Analysis in Affective Behaviour Analysis in-the-Wild

Leveraging Pre-trained Multi-task Deep Models for Trustworth...

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

作者： Saychenko, Andrey V. r Sber AI Lab Moscow Russia HSE Univ Lab Algorithms & Technol Network Anal Nizhnii Novgorod Russia

ISBN: (纸本)9798350365474

This article presents our results for the sixth Affective Behavior Analysis in-the-wild (ABAW) competition. To improve the trustworthiness of facial analysis, we study the possibility of using pre-trained deep models that extract reliable emotional features without the need to fine-tune the neural networks for a downstream task. In particular, we introduce several lightweight models based on MobileViT, MobileFaceNet, EfficientNet, and DDAMFN architectures trained in multi-task scenarios to recognize facial expressions, valence, and arousal on static photos. These neural networks extract frame-level features fed into a simple classifier, e.g., linear feed-forward neural network, to predict emotion intensity, compound expressions, and valence/arousal. Experimental results for three tasks from the sixth ABAW challenge demonstrate that our approach lets us significantly improve quality metrics on validation sets compared to existing non-ensemble techniques. As a result, our solutions took second place in the compound expression recognition competition.

关键词： Compound facial expressition recognition Emotional Mimicry Intensity estimation HSEmotion library lightweight neural networks Valence-Arousal prediction

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FIQA-FAS: Face Image Quality Assessment Based Face Anti-Spoofing

FIQA-FAS: Face Image Quality Assessment Based Face Anti-Spoo...

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

作者： Liang, Ya-Chi Qiu, Min-Xuan Lai, Shang-Hong Natl Tsing Hua Univ Hsinchu Taiwan

ISBN: (纸本)9798350365474

Face anti-spoofing (FAS) is to protect facial recognition systems against presentation attacks. However, recent research on FAS often neglects real-world conditions, such as changing illumination, varying angles of face, and motion blur within a video. These conditions lead to inconsistent feature quality across face images, where low-quality features can cause the model to learn unreliable information during training. Moreover, frames with low feature quality within videos result in inaccurate decisions. To address this issue, we propose the Face Image Quality Assessment Based Face Anti-Spoofing System (FIQA-FAS), which integrates a face image quality assessment module with a face anti-spoofing module. FIQA-FAS assesses the feature quality extracted from each face image and uses the quality score to compute a weighted prediction for deciding if the face in a video is live or spoof. We demonstrate the effectiveness of FIQA-FAS through experiments on the SIW and SIW-M datasets. To further demonstrate our model's capabilities, we introduce a novel simulated scenario that mimics the real world, where our model outperforms other SOTA.

关键词： Face recognition

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Anchor-based Robust Finetuning of vision-Language Models

Anchor-based Robust Finetuning of Vision-Language Models

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

作者： Han, Jinwei Lin, Zhiwen Sun, Zhongyisun Gao, Yingguo Yan, Ke Ding, Shouhong Gao, Yuan Xia, Gui-Song Wuhan Univ Sch Comp Sci Wuhan Peoples R China Tencent YouTu Lab Shenzhen Peoples R China Wuhan Univ Elect Informat Sch Wuhan Peoples R China

ISBN: (纸本)9798350353006

We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization, i.e., i) domain shift such as natural to sketch images, and ii) zero-shot capability to recognize the category that was not contained in the finetune data. Arguably, the diminished OOD generalization after finetuning stems from the excessively simplified finetuning target, which only provides the class information, such as "a photo of a [CLASS]". This is distinct from the process in that CLIP was pretrained, where there is abundant text supervision with rich semantic information. Therefore, we propose to compensate for the finetune process using auxiliary supervision with rich semantic information, which acts as anchors to preserve the OOD generalization. Specifically, two types of anchors are elaborated in our method, including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner, ii) image-text-pair anchor which is retrieved from the dataset similar to pre-training data of CLIP according to the downstream task, associating with the original CLIP text with rich semantics. Those anchors are utilized as auxiliary semantic information to maintain the original feature space of CLIP, thereby preserving the OOD generalization capabilities. Comprehensive experiments demonstrate that our method achieves in-distribution performance akin to conventional finetuning while attaining new state-of-the-art results on domain shift and zero-shot learning benchmarks.

关键词： Anchor Robust Finetuning

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The STVchrono Dataset: Towards Continuous Change recognition in Time

The STVchrono Dataset: Towards Continuous Change Recognition...

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

作者： Sun, Yanjun Qiu, Yue Khan, Mariia Matsuzawa, Fumiya Iwata, Kenji Natl Inst Adv Ind Sci & Technol Tokyo Japan Keio Univ Tokyo Japan Edith Cowan Univ Joondalup Australia

ISBN: (纸本)9798350353006

Recognizing continuous changes offers valuable insights into past historical events, supports current trend analysis, and facilitates future planning. This knowledge is crucial for a variety of fields, such as meteorology and agriculture, environmental science, urban planning and construction, tourism, and cultural preservation. Currently available datasets in the field of scene change understanding primarily concentrate on two main tasks: the detection of changed regions within a scene and the linguistic description of the change content. Existing datasets focus on recognizing discrete changes, such as adding or deleting an object from two images, and largely rely on artificially generated images. Consequently, the existing change understanding methods primarily focus on identifying distinct object differences, overlooking the importance of continuous, gradual changes occurring over extended time intervals. To address the above issues, we propose a novel bench-mark dataset, STVchrono, targeting the localization and description of long-term continuous changes in real-world scenes. The dataset consists of 71,900 photographs from Google Street View API taken over an 18-year span across 50 cities all over the world. Our STVchrono dataset is designed to support real-world continuous change recognition and description in both image pairs and extended image sequences, while also enabling the segmentation of changed regions. We conduct experiments to evaluate state-of-the-art methods on continuous change description and segmentation, as well as multimodal Large Language Models for describing changes. Our findings reveal that even the most advanced methods lag human performance, emphasizing the need to adapt them to continuously changing real-world scenarios. We hope that our benchmark dataset will further facilitate the research of temporal change recognition in a dynamic world. The STVchrono dataset is available at STVchrono Dataset.

关键词： Image segmentation

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An End-to-End vision Transformer Approach for Image Copy Detection

An End-to-End Vision Transformer Approach for Image Copy Det...

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

作者： Lee, Jiahe Steven Hsu, Wynne Lee, Mong Li Natl Univ Singapore Inst Data Sci Singapore Singapore Natl Univ Singapore Ctr Trusted Internet & Community Singapore Singapore

ISBN: (纸本)9798350365474

Image copy detection is one of the pivotal tools to safeguard online information integrity. The challenge lies in determining whether a query image is an edited copy, which necessitates the identification of candidate source images through a retrieval process. The process requires discriminative features comprising of both global descriptors that are designed to be augmentation-invariant and local descriptors that can capture salient foreground objects to assess whether a query image is an edited copy of some source reference image. This work describes an end-to-end solution that leverage a vision Transformer model to learn such discriminative features and perform implicit matching between the query image and the reference image. Experimental results on two benchmark datasets demonstrate that the proposed solution outperforms state-of-the-art methods. Case studies illustrate the effectiveness of our approach in matching reference images from which the query images have been copy-edited.

关键词： image copy detection image retrieval

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COTR: Compact Occupancy TRansformer for vision-based 3D Occupancy Prediction

COTR: Compact Occupancy TRansformer for Vision-based 3D Occu...

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

作者： Ma, Qihang Tan, Xin Qu, Yanyun Ma, Lizhuang Zhang, Zhizhong Xie, Yuan East China Normal Univ Shanghai Peoples R China East China Normal Univ Chongqing Inst Chongqing Peoples R China Xiamen Univ Fujian Peoples R China

ISBN: (纸本)9798350353006

The autonomous driving community has shown significant interest in 3D occupancy prediction, driven by its exceptional geometric perception and general object recognition capabilities. To achieve this, current works try to construct a Tri-Perspective View (TPV) or Occupancy (OCC) representation extending from the Bird-Eye-View perception. However, compressed views like TPV representation lose 3D geometry information while raw and sparse OCC representation requires heavy but redundant computational costs. To address the above limitations, we propose Compact Occupancy TRansformer (COTR), with a geometry-aware occupancy encoder and a semantic-aware group decoder to reconstruct a compact 3D OCC representation. The occupancy encoder first generates a compact geometrical OCC feature through efficient explicit-implicit view transformation. Then, the occupancy decoder further enhances the semantic discriminability of the compact OCC representation by a coarse-to-fine semantic grouping strategy. Empirical experiments show that there are evident performance gains across multiple baselines, e.g., COTR outperforms baselines with a relative improvement of 8%-15%, demonstrating the superiority of our method. The code is available at https://***/NotACracker/COTR.

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Low-Rank Rescaled vision Transformer Fine-Tuning: A Residual Design Approach

Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual...

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

作者： Dong, Wei Zhang, Xing Chen, Bihui Yang, Dawei Lin, Zhijun Yang, Qingsen Wang, Peng Yang, Yang Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China Xian Univ Architecture & Technol Coll Informat & Control Engn Xian Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9798350353006

Parameter-efficient fine-tuning for pre-trained vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features poses a key challenge. Currently, there is a lack of focus on guiding this delicate trade-off. In this study, we approach the problem from the perspective of Singular Value Decomposition (SVD) of pre-trained parameter matrices, providing insights into the tuning dynamics of existing methods. Building upon this understanding, we propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy. This strategy not only enhances flexibility in parameter tuning but also ensures that new parameters do not deviate excessively from the pre-trained model through a residual design. Extensive experiments demonstrate that our method achieves competitive performance across various downstream image classification tasks, all while maintaining comparable new parameters. We believe this work takes a step forward in offering a unified perspective for interpreting existing methods and serves as motivation for the development of new approaches that move closer to effectively considering the crucial trade-off mentioned above. Our code is available at https://***/zstarN70/***.

关键词： Low-Rank Adaptation Parameter-efficient fine-tuning Singular Value Decomposition

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Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding

Chat-UniVi: Unified Visual Representation Empowers Large Lan...

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

作者： Jin, Peng Takanobu, Ryuichi Zhang, Wancai Cao, Xiaochun Yuan, Li Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Peking Univ Ai Sci AI4S Preferred Program Shenzhen Grad Sch Shenzhen Peoples R China Nari Technol Co Ltd Beijing Peoples R China Sun Yat Sen Univ Sch Cyber Sci & Tech Shenzhen Campus Shenzhen Peoples R China

ISBN: (纸本)9798350353006

Large language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multi-modal conversations. However, existing methods encounter challenges in effectively handling both image and video understanding, particularly with limited visual tokens. In this work, we introduce Chat-UniVi, a Unified vision-language model capable of comprehending and engaging in conversations involving images and videos through a unified visual representation. Specifically, we employ a set of dynamic visual tokens to uniformly represent images and videos. This representation framework empowers the model to efficiently utilize a limited number of visual tokens to simultaneously capture the spatial details necessary for images and the comprehensive temporal relationship required for videos. Moreover, we leverage a multi-scale representation, enabling the model to perceive both high-level semantic concepts and low-level visual details. Notably, ChatUniVi is trained on a mixed dataset containing both images and videos, allowing direct application to tasks involving both mediums without requiring any modifications. Extensive experimental results demonstrate that Chat-UniVi consistently outperforms even existing methods exclusively designed for either images or videos. Code is available at https://***/PKU-YuanGroup/Chat-UniVi.

关键词： Image Understanding Large Language Models Video Understanding vision Language Model

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Prompt Learning with One-Shot Setting based Feature Space Analysis in vision-and-Language Models

Prompt Learning with One-Shot Setting based Feature Space An...

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

作者： Hirohashi, Yuki Hirakawa, Tsubasa Yamashita, Takayoshi Fujiyoshi, Hironobu OMRON Corp Kyoto Japan Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9798350365474

By using few-shot data and labels, prompt learning obtains optimal prompts that are capable of achieving high performance on downstream tasks. Existing prompt learning methods generate high-quality prompts that are suitable for downstream tasks but tend to perform poorly in scenarios where only very limited data (e.g., one-shot) is available. We address on this challenging one-shot scenario and propose a novel architecture for prompt learning, called Image-Text Feature Alignment Branch (ITFAB). ITFAB aligns text features closer to the centroids of image features and separates text features with different classes to resolve misalignment in the feature space, thereby facilitating the acquisition of high-quality prompts with very limited data. In one-shot setting, our method outperforms the existing CoOp and CoCoOp methods and in some cases even surpasses CoCoOp's 16-shot performance. Testing on different datasets and domain, show that ITFAB almost matches CoCoOp's effectiveness. It also works with current prompt learning methods like MapLe and PromptSRC, improving their performance in one-shot setting.

关键词： Prompt Learning vision-and-Language Model

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Taming Self-Training for Open-Vocabulary Object Detection

Taming Self-Training for Open-Vocabulary Object Detection

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

作者： Zhao, Shiyu Schulter, Samuel Zhao, Long Zhang, Zhixing Kumar, Vijay B. G. Suh, Yumin Chandraker, Manmohan Metaxas, Dimitris N. Rutgers State Univ New Brunswick NJ 08901 USA NEC Labs Amer Princeton NJ USA Google Res Mountain View CA USA Univ Calif San Diego San Diego CA USA

ISBN: (纸本)9798350353006

Recent studies have shown promising performance in open-vocabulary object detection (OVD) by utilizing pseudo labels (PLs) from pretrained vision and language models (VLMs). However, teacher-student self-training, a powerful and widely used paradigm to leverage PLs, is rarely explored for OVD. This work identifies two challenges of using self-training in OVD: noisy PLs from VLMs and frequent distribution changes of PLs. To address these challenges, we propose SAS-Det that tames self-training for OVD from two key perspectives. First, we present a split-and-fusion (SAF) head that splits a standard detection into an open-branch and a closed-branch. This design can reduce noisy supervision from pseudo boxes. Moreover, the two branches learn complementary knowledge from different training data, significantly enhancing performance when fused together. Second, in our view, unlike in closed-set tasks, the PL distributions in OVD are solely determined by the teacher model. We introduce a periodic update strategy to decrease the number of updates to the teacher, thereby decreasing the frequency of changes in PL distributions, which stabilizes the training process. Extensive experiments demonstrate SAS-Det is both efficient and effective. SAS-Det outperforms recent models of the same scale by a clear margin and achieves 37.4 AP50 and 29.1 APr on novel categories of the COCO and LVIS benchmarks, respectively. Code is available at https://***/xiaofeng94/SAS-Det.

关键词： open-vocabulary object detection pseudo label self-training vision and language

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