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NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2024 Challenge on Stereo Image Super-Resolution: Metho...

引用

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

作者： Wang, Longguang Guo, Yulan Li, Juncheng Liu, Hongda Zhao, Yang Wang, Yingqian Jin, Zhi Gu, Shuhang Timofte, Radu Aviation University of Air Force Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China National University of Defense Technology China Shanghai University China University of Electronic Science and Technology of China China Computer Vision Lab University of Würzburg Germany

ISBN: (纸本)9798350365474

This paper summarizes the 3rd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of this challenge is to super-resolve a low-resolution stereo image pair to a high-resolution one with a magnification factor of x4 under a limited computational budget. Compared with single image SR, the major challenge of this challenge lies in how to exploit additional information in another viewpoint and how to maintain stereo consistency in the results. This challenge has 2 tracks, including one track on bicubic degradation and one track on real degradations. In total, 108 and 70 participants were successfully registered for each track, respectively. In the test phase, 14 and 13 teams successfully submitted valid results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR.

关键词： Stereocenters

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TimeBalance: Temporally-Invariant and Temporally-Distinctive Video Representations for Semi-Supervised Action recognition

TimeBalance: Temporally-Invariant and Temporally-Distinctive...

引用

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

作者： Dave, Ishan Rajendrakumar Rizve, Mamshad Nayeem Chen, Chen Shah, Mubarak Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9798350301298

Semi-Supervised Learning can be more beneficial for the video domain compared to images because of its higher annotation cost and dimensionality. Besides, any video understanding task requires reasoning over both spatial and temporal dimensions. In order to learn both the static and motion related features for the semi-supervised action recognition task, existing methods rely on hard input inductive biases like using two-modalities (RGB and Optical-flow) or two-stream of different playback rates. Instead of utilizing unlabeled videos through diverse input streams, we rely on self-supervised video representations, particularly, we utilize temporally-invariant and temporally-distinctive representations. We observe that these representations complement each other depending on the nature of the action. Based on this observation, we propose a student-teacher semi-supervised learning framework, TimeBalance, where we distill the knowledge from a temporally-invariant and a temporally-distinctive teacher. Depending on the nature of the unlabeled video, we dynamically combine the knowledge of these two teachers based on a novel temporal similarity-based reweighting scheme. Our method achieves state-of-the-art performance on three action recognition benchmarks: UCF101, HMDB51, and Kinetics400. Code: https://***/DAVEISHAN/TimeBalance.

关键词： Video: Action and event understanding

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Bidirectional Cross-Modal Knowledge Exploration for Video recognition with Pre-trained vision-Language Models

Bidirectional Cross-Modal Knowledge Exploration for Video Re...

引用

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

作者： Wu, Wenhao Wang, Xiaohan Luo, Haipeng Wang, Jingdong Yang, Yi Ouyang, Wanli Univ Sydney Camperdown NSW Australia Baidu Inc Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability on various visual tasks. Transferring knowledge from such powerful VLMs is a promising direction for building effective video recognition models. However, current exploration in this field is still limited. We believe that the greatest value of pre-trained VLMs lies in building a bridge between visual and textual domains. In this paper, we propose a novel framework called BIKE, which utilizes the cross-modal bridge to explore bidirectional knowledge: i) We introduce the Video Attribute Association mechanism, which leverages the Video-to-Text knowledge to generate textual auxiliary attributes for complementing video recognition. ii) We also present a Temporal Concept Spotting mechanism that uses the Text-to-Video expertise to capture temporal saliency in a parameter-free manner, leading to enhanced video representation. Extensive studies on six popular video datasets, including Kinetics-400 & 600, UCF-101, HMDB-51, ActivityNet and Charades, show that our method achieves state-of-the-art performance in various recognition scenarios, such as general, zero-shot, and few-shot video recognition. Our best model achieves a state-of-the-art accuracy of 88.6% on the challenging Kinetics-400 using the released CLIP model. The code is available at https://***/whwu95/BIKE.

关键词： Video: Action and event understanding

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Disentangled Representation Learning for Unsupervised Neural Quantization

Disentangled Representation Learning for Unsupervised Neural...

引用

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

作者： Noh, Haechan Hyun, Sangeek Jeong, Woojin Lim, Hanshin Heo, Jae-Pil Sungkyunkwan Univ Seoul South Korea Elect & Telecommun Res Inst Daejeon South Korea

ISBN: (纸本)9798350301298

The inverted index is a widely used data structure to avoid the infeasible exhaustive search. It accelerates retrieval significantly by splitting the database into multiple disjoint sets and restricts distance computation to a small fraction of the database. Moreover, it even improves search quality by allowing quantizers to exploit the compact distribution of residual vector space. However, we firstly point out a problem that an existing deep learning-based quantizer hardly benefits from the residual vector space, unlike conventional shallow quantizers. To cope with this problem, we introduce a novel disentangled representation learning for unsupervised neural quantization. Similar to the concept of residual vector space, the proposed method enables more compact latent space by disentangling information of the inverted index from the vectors. Experimental results on large-scale datasets confirm that our method outperforms the state-of-the-art retrieval systems by a large margin.

关键词： detection recognition: Categorization retrieval

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Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models

Learning a Practical SDR-to-HDRTV Up-conversion using New Da...

引用

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

作者： Guo, Cheng Fan, Leidong Xue, Ziyu Jiang, Xiuhua Commun Univ China State Key Lab Media Convergence & Commun Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Peking Univ Beijing Peoples R China Natl Radio & Televis Adm Acad Broadcasting Sci Beijing Peoples R China

ISBN: (纸本)9798350301298

In media industry, the demand of SDR-to-HDRTV up-conversion arises when users possess HDR-WCG (high dynamic range-wide color gamut) TVs while most off-the-shelf footage is still in SDR (standard dynamic range). The research community has started tackling this low-level vision task by learning-based approaches. When applied to real SDR, yet, current methods tend to produce dim and desaturated result, making nearly no improvement on viewing experience. Different from other network-oriented methods, we attribute such deficiency to training set (HDR-SDR pair). Consequently, we propose new HDRTV dataset (dubbed HDRTV4K) and new HDR-to-SDR degradation models. Then, it's used to train a luminance-segmented network (LSN) consisting of a global mapping trunk, and two Transformer branches on bright and dark luminance range. We also update assessment criteria by tailored metrics and subjective experiment. Finally, ablation studies are conducted to prove the effectiveness. Our work is available at: https://***/AndreGuo/HDRTVDM.

关键词： Low-level vision

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Hierarchical Prompt Learning for Multi-Task Learning

Hierarchical Prompt Learning for Multi-Task Learning

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

作者： Liu, Yajing Lu, Yuning Liu, Hao An, Yaozu Xu, Zhuoran Yao, Zhuokun Zhang, Baofeng Xiong, Zhiwei Gui, Chenguang JD Logist Beijing Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

vision-language models (VLMs) can effectively transfer to various vision tasks via prompt learning. Real-world scenarios often require adapting a model to multiple similar yet distinct tasks. Existing methods focus on learning a specific prompt for each task, limiting the ability to exploit potentially shared information from other tasks. Naively training a task-shared prompt using a combination of all tasks ignores fine-grained task correlations. Significant discrepancies across tasks could cause negative transferring. Considering this, we present Hierarchical Prompt (HiPro) learning, a simple and effective method for jointly adapting a pre-trained VLM to multiple downstream tasks. Our method quantifies inter-task affinity and subsequently constructs a hierarchical task tree. Task-shared prompts learned by internal nodes explore the information within the corresponding task group, while task-individual prompts learned by leaf nodes obtain fine-grained information targeted at each task. The combination of hierarchical prompts provides high-quality content of different granularity. We evaluate HiPro on four multi-task learning datasets. The results demonstrate the effectiveness of our method.

关键词： language reasoning vision

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Open Set Action recognition via Multi-Label Evidential Learning

Open Set Action Recognition via Multi-Label Evidential Learn...

引用

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

作者： Zhao, Chen Du, Dawei Hoogs, Anthony Funk, Christopher Kitware Clifton Pk NY 12065 USA

ISBN: (纸本)9798350301298

Existing methods for open set action recognition focus on novelty detection that assumes video clips show a single action, which is unrealistic in the real world. We propose a new method for open set action recognition and novelty detection via MUlti-Label Evidential learning (MULE), that goes beyond previous novel action detection methods by addressing the more general problems of single or multiple actors in the same scene, with simultaneous action(s) by any actor. Our Beta Evidential Neural Network estimates multi-action uncertainty with Beta densities based on actor-context-object relation representations. An evidence debiasing constraint is added to the objective function for optimization to reduce the static bias of video representations, which can incorrectly correlate predictions and static cues. We develop a primal-dual average scheme update-based learning algorithm to optimize the proposed problem and provide corresponding theoretical analysis. Besides, uncertainty and belief-based novelty estimation mechanisms are formulated to detect novel actions. Extensive experiments on two real-world video datasets show that our proposed approach achieves promising performance in single/multi-actor, single/multi-action settings. Our code and models are released at https://***/charliezhaoyinpeng/mule.

关键词： Video: Action and event understanding

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Continuous Sign Language recognition with Correlation Network

Continuous Sign Language Recognition with Correlation Networ...

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

作者： Hu, Lianyu Gao, Liqing Liu, Zekang Feng, Wei Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

ISBN: (纸本)9798350301298

Human body trajectories are a salient cue to identify actions in the video. Such body trajectories are mainly conveyed by hands and face across consecutive frames in sign language. However, current methods in continuous sign language recognition (CSLR) usually process frames independently, thus failing to capture cross-frame trajectories to effectively identify a sign. To handle this limitation, we propose correlation network (CorrNet) to explicitly capture and leverage body trajectories across frames to identify signs. In specific, a correlation module is first proposed to dynamically compute correlation maps between the current frame and adjacent frames to identify trajectories of all spatial patches. An identification module is then presented to dynamically emphasize the body trajectories within these correlation maps. As a result, the generated features are able to gain an overview of local temporal movements to identify a sign. Thanks to its special attention on body trajectories, CorrNet achieves new state-of-the-art accuracy on four large-scale datasets, i.e., PHOENIX14, PHOENIX14-T, CSL-Daily, and CSL. A comprehensive comparison with previous spatial-temporal reasoning methods verifies the effectiveness of CorrNet. Visualizations demonstrate the effects of CorrNet on emphasizing human body trajectories across adjacent frames.

关键词： body gesture Humans: Face movement pose

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ViPLO: vision Transformer based Pose-Conditioned Self-Loop Graph for Human-Object Interaction Detection

ViPLO: Vision Transformer based Pose-Conditioned Self-Loop G...

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

作者： Park, Jeeseung Park, Jin-Woo Lee, Jong-Seok MAy I Inc Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350301298

Human-Object Interaction (HOI) detection, which localizes and infers relationships between human and objects, plays an important role in scene understanding. Although two-stage HOI detectors have advantages of high efficiency in training and inference, they suffer from lower performance than one-stage methods due to the old backbone networks and the lack of considerations for the HOI perception process of humans in the interaction classifiers. In this paper, we propose vision Transformer based Pose-Conditioned Self-Loop Graph (ViPLO) to resolve these problems. First, we propose a novel feature extraction method suitable for the vision Transformer backbone, called masking with overlapped area (MOA) module. The MOA module utilizes the overlapped area between each patch and the given region in the attention function, which addresses the quantization problem when using the vision Transformer backbone. In addition, we design a graph with a pose-conditioned self-loop structure, which updates the human node encoding with local features of human joints. This allows the classifier to focus on specific human joints to effectively identify the type of interaction, which is motivated by the human perception process for HOI. As a result, ViPLO achieves the state-of-the-art results on two public benchmarks, especially obtaining a +2.07 mAP performance gain on the HICO-DET dataset.

关键词： detection recognition: Categorization retrieval

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BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View recognition via Perspective Supervision

BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-...

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

作者： Yang, Chenyu Chen, Yuntao Tian, Hao Tao, Chenxin Zhu, Xizhou Zhang, Zhaoxiang Huang, Gao Li, Hongyang Qiao, Yu Lu, Lewei Zhou, Jie Dai, Jifeng Tsinghua Univ Beijing Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot HKISI Beijing Peoples R China SenseTime Res Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

We present a novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and better suits modern image backbones. Existing state-of-the-art BEV detectors are often tied to certain depth pre-trained backbones like VoVNet, hindering the synergy between booming image backbones and BEV detectors. To address this limitation, we prioritize easing the optimization of BEV detectors by introducing perspective view supervision. To this end, we propose a two-stage BEV detector, where proposals from the perspective head are fed into the bird's-eye-view head for final predictions. To evaluate the effectiveness of our model, we conduct extensive ablation studies focusing on the form of supervision and the generality of the proposed detector. The proposed method is verified with a wide spectrum of traditional and modern image backbones and achieves new SoTA results on the large-scale nuScenes dataset. The code shall be released soon.

关键词： detection recognition: Categorization retrieval

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