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HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution

HMANet: Hybrid Multi-Axis Aggregation Network for Image Supe...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Shu-Chuan Chu Zhi-Chao Dou Jeng-Shyang Pan Shaowei Weng Junbao Li College of Computer Science and Engineering Shandong University of Science and Technology School of Artificial Intelligence Nanjing University of Information Science and Technology School of Information Engineering Guangdong University of Technology School of Electronic and Information Engineering Harbin Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Transformer-based methods have demonstrated excellent performance on super-resolution visual tasks, surpassing conventional convolutional neural networks. However, existing work typically restricts self-attention computation to non-overlapping windows to save computational costs. This means that Transformer-based networks can only use input information from a limited spatial range. Therefore, a novel Hybrid Multi-Axis Aggregation network (HMA) is proposed in this paper to exploit feature potential information better. HMA is constructed by stacking Residual Hybrid Transformer Blocks(RHTB) and Grid Attention Blocks(GAB). On the one side, RHTB combines channel attention and self-attention to enhance nonlocal feature fusion and produce more attractive visual results. Conversely, GAB is used in cross-domain information interaction to jointly model similar features and obtain a larger perceptual field. For the super-resolution task in the training phase, a novel pre-training method is designed to enhance the model representation capabilities further and validate the proposed model’s effectiveness through many experiments. The experimental results show that HMA outperforms the state-of-the-art methods on the benchmark dataset. We provide code and models at https://***/korouuuuu/HMA.

关键词： Training Visualization Convolution Superresolution Stacking Benchmark testing Transformers

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DiCo-NeRF: Difference of Cosine Similarity for Neural Rendering of Fisheye Driving Scenes

DiCo-NeRF: Difference of Cosine Similarity for Neural Render...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jiho Choi Gyutae Hwang Sang Jun Lee Jeonbuk National University Republic of Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural radiance fields have emerged in the field of autonomous driving, which contributes to improve perception of the complex 3D environment through the reconstruction of geometry and appearance. Moving objects and sky for outdoor environment is challenging to optimize the NeRF model. Previous work addresses these challenges through preprocessing such as masking; however, the masking process requires additional ground-truth data and a segmentation network. We propose DiCo-NeRF, an approach for driving scenes by leveraging cosine similarity map differences of vision-language aligned model. DiCo-NeRF investigates the correlation between rendered patches and predefined text and adjusts the loss of challenging patches, such as moving objects and the sky. Our neural radiance field utilizes embedding vectors from a pre-trained CLIP to obtain the cosine similarity maps. We introduce SimLoss, a loss function aimed at regulating the color field of NeRF based on the quantified distribution differences between ground-truth and rendered similarity maps. Unlike previous NeRF models that used driving datasets, our approach does not require additional input, such as sensor data, to the model. Experimental results demonstrate that the incorporation of language semantic cues improves the performance of the novel view synthesis task, particularly in complex driving environments. We conducted experiments that included fisheye driving scenes from the KITTI-360 and real-world datasets. Our code is available at https://***/ziiho08/DiCoNeRF.

关键词： Three-dimensional displays Image color analysis Computational modeling Semantics Neural radiance field Rendering (computer graphics) Vectors

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Do More With What You Have: Transferring Depth-Scale from Labeled to Unlabeled Domains

Do More With What You Have: Transferring Depth-Scale from La...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Alexandra Dana Nadav Carmel Amit Shomer Ofer Manela Tomer Peleg Samsung Israel R&D Center Tel Aviv Israel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Transferring the absolute depth prediction capabilities of an estimator to a new domain is a task with significant real-world applications. This task is specifically challenging when images from the new domain are collected without ground-truth depth measurements, and possibly with sensors of different intrinsics. To overcome such limitations, a recent zero-shot solution was trained on an extensive training dataset and encoded the various camera intrinsics. Other solutions generated synthetic data with depth labels that matched the intrinsics of the new target data to enable depth-scale transfer between the *** this work we present an alternative solution that can utilize any existing synthetic or real dataset, that has a small number of images annotated with ground truth depth labels. Specifically, we show that self-supervised depth estimators result in up-to-scale predictions that are linearly correlated to their absolute depth values across the domain, a property that we model in this work using a single scalar. In addition, aligning the field-of-view of two datasets prior to training, results in a common linear relationship for both domains. We use this observed property to transfer the depth-scale from source datasets that have absolute depth labels to new target datasets that lack these measurements, enabling absolute depth predictions in the target *** suggested method was successfully demonstrated on the KITTI, DDAD and nuScenes datasets, while using other existing real or synthetic source datasets, that have a different field-of-view, other image style or structural content, achieving comparable or better accuracy than other existing methods that do not use target ground-truth depths.

关键词： Training Image sensors computer vision Accuracy conferences Predictive models Cameras

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Multi-Objective Hardware Aware Neural Architecture Search using Hardware Cost Diversity

Multi-Objective Hardware Aware Neural Architecture Search us...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nilotpal Sinha Peyman Rostami Abd El Rahman Shabayek Anis Kacem Djamila Aouada SnT University of Luxembourg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hardware-aware Neural Architecture Search approaches (HW-NAS) automate the design of deep learning architectures, tailored specifically to a given target hardware platform. Yet, these techniques demand substantial computational resources, primarily due to the expensive process of assessing the performance of identified architectures. To alleviate this problem, a recent direction in the literature has employed representation similarity metric for efficiently evaluating architecture performance. Nonetheless, since it is inherently a single objective method, it requires multiple runs to identify the optimal architecture set satisfying the diverse hardware cost constraints, thereby increasing the search cost. Furthermore, simply converting the single objective into a multi-objective approach results in an under-explored architectural search space. In this study, we propose a Multi-Objective method to address the HW-NAS problem, called MO-HDNAS, to identify the trade-off set of architectures in a single run with low computational cost. This is achieved by optimizing three objectives: maximizing the representation similarity metric, minimizing hardware cost, and maximizing the hardware cost diversity. The third objective, i.e. hardware cost diversity, is used to facilitate a better exploration of the architecture search space. Experimental results demonstrate the effectiveness of our proposed method in efficiently addressing the HW-NAS problem across six edge devices for the image classification task.

关键词： Performance evaluation Costs Image edge detection computer architecture Search problems Hardware Computational efficiency

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Speech2UnifiedExpressions: Synchronous Synthesis of Co-Speech Affective Face and Body Expressions from Affordable Inputs

Speech2UnifiedExpressions: Synchronous Synthesis of Co-Speec...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Uttaran Bhattacharya Aniket Bera Dinesh Manocha Adobe Inc. San Jose CA USA Purdue University West Lafayette IN USA University of Maryland College Park MD USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a multimodal learning-based method to simultaneously synthesize co-speech facial expressions and upper-body gestures for digital characters using RGB video data captured using commodity cameras. Our approach learns from sparse face landmarks and upper-body joints, estimated directly from video data, to generate plausible emotive character motions. Given a speech audio waveform and a token sequence of the speaker’s face landmark motion and body-joint motion computed from a video, our method synthesizes the motion sequences for the speaker’s face landmarks and body joints to match the content and the affect of the speech. We design a generator consisting of a set of encoders to transform all the inputs into a multimodal embedding space capturing their correlations, followed by a pair of decoders to synthesize the desired face and pose motions. To enhance the plausibility of synthesis, we use an adversarial discriminator that learns to differentiate between the face and pose motions computed from the original videos and our synthesized motions based on their affective expressions. To evaluate our approach, we extend the TED Gesture Dataset to include view-normalized, co-speech face landmarks in addition to body gestures. We demonstrate the performance of our method through thorough quantitative and qualitative experiments on multiple evaluation metrics and via a user study. We observe that our method results in low reconstruction error and produces synthesized samples with diverse facial expressions and body gestures for digital characters. The relevant source code and dataset are available at https://***/UttaranB127/speech2unified_expressions.

关键词： Measurement Learning systems computer vision Correlation Face recognition Source coding conferences

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NTIRE 2024 Challenge on Blind Enhancement of Compressed Image: Methods and Results

NTIRE 2024 Challenge on Blind Enhancement of Compressed Imag...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ren Yang Radu Timofte Bingchen Li Xin Li Mengxi Guo Shijie Zhao Li Zhang Zhibo Chen Dongyang Zhang Yash Arora Aditya Arora Yuanbin Chen Hui Tang Tao Wang Longxuan Zhao Bin Chen Tong Tong Qiao Mo Jingwei Bao Jinhua Hao Yukang Ding Hantang Li Ming Sun Chao Zhou Shuyuan Zhu Zhi Jin Wei Wang Dandan Zhan Jiawei Wu Jiahao Wu Luwei Tu Hongyu An Xinfeng Zhang Woon-Ha Yeo Wang-Taek Oh Young-Il Kim Han-Cheol Ryu Long Sun Mingjun Zhen Jinshan Pan Jiangxin Dong Jinhui Tang Yapeng Du Ao Li Ziyang He Lei Luo Ce Zhu Xin Yao Sunder Ali Khowaja IK Hyun Lee Jaeho Lee Seongwan Kim Sharif S M A Nodirkhuja Khujaev Roman Tsoy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the Challenge on Blind Enhancement of Compressed Image at NTIRE 2024, which aims at enhancing the quality of JPEG images which are compressed with unknown quality factor. The challenge requires that the total size of codes and pre-trained model(s) cannot exceed 300 MB, since we encourage solutions for blind enhancement with generalized models, instead of separately training several models for each quality factor. In this report, we summarize the detailed settings of the challenge, the final results, and the solutions proposed by the participants. The challenge has 129 registered participants and received 13 valid submissions. Several teams (including all TOP 3 teams) have publicly released the codes (see Sec. 4). They gauge the state-of-the-art of blind quality enhancement of compressed image.

关键词： Q-factor Training computer vision Image coding Codes Reviews conferences

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Show, Think, and Tell: Thought-Augmented Fine-Tuning of Large Language Models for Video Captioning

Show, Think, and Tell: Thought-Augmented Fine-Tuning of Larg...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Byoungjip Kim Dasol Hwang Sungjun Cho Youngsoo Jang Honglak Lee Moontae Lee LG AI Research University of Illinois Chicago

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Large language models (LLMs) have achieved a great success in natural language processing, and have a significant potential for multi-modal applications. Despite the surprising zero-shot or few-shot ability, it is also required to effectively fine-tune pre-trained language models for specific downstream tasks. In this paper, we introduce CaptionT5, a video captioning model that fine-tunes T5 towards understanding videos and generating descriptive captions. To generate a more corespondent caption, CaptionT5 introduces thought-augmented fine-tuning for video captioning, in which a pre-trained language model is fine-tuned on thought-augmented video inputs. This resembles the process that human see a video, think of visual concepts such as objects and actions, and then tell a correct and natural sentence based on the thoughts. To automatically generate thoughts, we propose (1) CLIP-guided thought sampling that samples thoughts based on the similarity in an image-text multimodal embedding space by leveraging CLIP. We also propose (2) CLIP-guided caption ranking during decoding for further performance gains. Through experimentation on VATEX, MSRVTT, and YC2 datasets, we empirically demonstrate that CaptionT5 performs competitively against prior-art video captioning approaches without using encoders specialized for video data. Further experiments show that CaptionT5 is especially effective under small number of sampled video frames.

关键词： Visualization computer vision Large language models Computational modeling conferences Performance gain Data models

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Tri-VAE: Triplet Variational Autoencoder for Unsupervised Anomaly Detection in Brain Tumor MRI

Tri-VAE: Triplet Variational Autoencoder for Unsupervised An...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hansen Wijanarko Evelyne Calista Li-Fen Chen Yong-Sheng Chen National Yang Ming Chiao Tung University Taiwan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The intricate manifestations of pathological brain lesions in imaging data pose challenges for supervised detection methods due to the scarcity of annotated samples. To overcome this difficulty, our focus shifts to unsupervised anomaly detection. In this work, we exclusively train the proposed model using healthy data to identify unseen anomalies during testing. This study entails investigating the triplet-based variational autoencoder to simultaneously learn the distribution of healthy brain data and denoising capabilities. Importantly, we rectify a misconception inherent in prior projection-based approaches which relies on the presumption that healthy regions within images would persist unaltered in the reconstructed output. This inadvertently implied a substantial likeness in latent space representations between lesion and lesion-free images. However, this assumption might not hold true, particularly due to the potential significant impact of lesion area intensities on the projection process notably for autoencoders with single information bottleneck. To overcome this limitation, we disentangled metric learning from latent sampling. This approach ensures that both lesion and lesion-free input images are projected into the same distribution, specifically the lesion-free projection. Moreover, we introduce a semantic-guided gated cross skip module to enhance spatial detail retrieval while suppressing anomalies, leveraging robust healthy brain representation semantics exist in the deeper levels of the decoder. We also discovered that incorporating structure similarity index measure as an extra training objective bolsters the capability of anomaly detection for the proposed model.

关键词： Training Measurement Pathology Semantics Noise reduction Brain modeling pattern recognition

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CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery

CDAD-Net: Bridging Domain Gaps in Generalized Category Disco...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sai Bhargav Rongali Sarthak Mehrotra Ankit Jha Mohamad Hassan N C Shirsha Bose Tanisha Gupta Mainak Singha Biplab Banerjee Indian Institute of Technology Bombay India INRIA Grenoble France Technical University of Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In Generalized Category Discovery (GCD), we cluster unlabeled samples of known and novel classes, leveraging a training dataset of known classes. A salient challenge arises due to domain shifts between these datasets. To address this, we present a novel setting: Across Domain Generalized Category Discovery (AD-GCD) and bring forth CDAD-Net (Class Discoverer Across Domains) as a remedy. CDAD-Net is architected to synchronize potential known class samples across both the labeled (source) and unlabeled (target) datasets, while emphasizing the distinct categorization of the target data. To facilitate this, we propose an entropy-driven adversarial learning strategy that accounts for the distance distributions of target samples relative to source-domain class prototypes. Parallelly, the discriminative nature of the shared space is upheld through a fusion of three metric learning objectives. In the source domain, our focus is on refining the proximity between samples and their affiliated class prototypes, while in the target domain, we integrate a neighborhood-centric contrastive learning mechanism, enriched with an adept neighbors-mining approach. To further accentuate the nuanced feature interrelation among semantically aligned images, we champion the concept of conditional image inpainting, underscoring the premise that semantically analogous images prove more efficacious to the task than their disjointed counterparts. Experimentally, CDAD-Net eclipses existing literature with a performance increment of 8-15% on three AD-GCD benchmarks we present.

关键词： Training Measurement computer vision conferences Refining Prototypes Contrastive learning

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End-to-End Neural Network Compression via l1/l2 Regularized Latency Surrogates

End-to-End Neural Network Compression via l1/l2 Regularized ...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Anshul Nasery Hardik Shah Arun Sai Suggala Prateek Jain University of Washington ETH Zurich Google Research India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural network (NN) compression via techniques such as pruning, quantization requires setting compression hyperparameters (e.g., number of channels to be pruned, bitwidths for quantization) for each layer either manually or via neural architecture search (NAS) which can be computationally expensive. We address this problem by providing an end-to-end technique that optimizes for model’s Floating Point Operations (FLOPs) via a novel $\frac{{{\ell _1}}}{{{\ell _2}}}$ latency surrogate. Our algorithm is versatile and can be used with many popular compression methods including pruning, low-rank factorization, and quantization, and can optimize for on-device latency. Crucially, it is fast and runs in almost the same amount of time as a single model training run; which is a significant training speed-up over standard NAS methods. For BERT compression on GLUE fine-tuning tasks, we achieve 50% reduction in FLOPs with only 1% drop in performance. For compressing MobileNetV3 on ImageNet-1K, we achieve 15% reduction in FLOPs without drop in accuracy, while still requiring 3× less training compute than SOTA NAS techniques. Finally, for transfer learning on smaller datasets, our technique identifies 1.2× - 1.4× cheaper architectures than standard MobileNetV3, EfficientNet suite of architectures at almost the same training cost and accuracy.

关键词： Training Quantization (signal) Image coding Accuracy Costs Transfer learning computer architecture

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