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ParamISP: Learned Forward and Inverse ISPs using Camera Parameters

ParamISP: Learned Forward and Inverse ISPs using Camera Para...

引用

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

作者： Kim, Woohyeok Kim, Geonu Lee, Junyong Lee, Seungyong Baek, Seung-Hwan Cho, Sunghyun POSTECH Pohang South Korea Samsung AI Ctr Toronto Toronto ON Canada Samsung Toronto ON Canada

ISBN: (纸本)9798350353006

RAW images are rarely shared mainly due to its excessive data size compared to their sRGB counterparts obtained by camera ISPs. Learning the forward and inverse processes of camera ISPs has been recently demonstrated, enabling physically-meaningful RAW-level image processing on input sRGB images. However, existing learning-based ISP methods fail to handle the large variations in the ISP processes with respect to camera parameters such as ISO and exposure time, and have limitations when used for various applications. In this paper, we propose ParamISP, a learning-based method for forward and inverse conversion between sRGB and RAW images, that adopts a novel neural-network module to utilize camera parameters, which is dubbed as ParamNet. Given the camera parameters provided in the EXIF data, ParamNet converts them into a feature vector to control the ISP networks. Extensive experiments demonstrate that ParamISP achieve superior RAWand sRGB reconstruction results compared to previous methods and it can be effectively used for a variety of applications such as deblurring dataset synthesis, raw deblurring, HDR reconstruction, and camera-to-camera transfer.

关键词： Low-level vision

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Discovering and Mitigating Visual Biases through Keyword Explanation

Discovering and Mitigating Visual Biases through Keyword Exp...

引用

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

作者： Kim, Younghyun Mo, Sangwoo Kim, Minkyu Lee, Kyungmin Lee, Jaeho Shin, Jinwoo Korea Adv Inst Sci & Technol Daejeon South Korea Univ Michigan Ann Arbor MI 48109 USA KRAFTON Seongnam South Korea POSTECH Pohang South Korea

ISBN: (纸本)9798350353006

Addressing biases in computer vision models is crucial for real-world AI deployments. However, mitigating visual biases is challenging due to their unexplainable nature, often identified indirectly through visualization or sample statistics, which necessitates additional human supervision for interpretation. To tackle this issue, we propose the Bias-to-Text (B2T) framework, which interprets visual biases as keywords. Specifically, we extract common keywords from the captions of mispredicted images to identify potential biases in the model. We then validate these keywords by measuring their similarity to the mispredicted images using a vision-language scoring model. The keyword explanation form of visual bias offers several advantages, such as a clear group naming for bias discovery and a natural extension for debiasing using these group names. Our experiments demonstrate that B2T can identify known biases, such as gender bias in CelebA, background bias in Waterbirds, and distribution shifts in ImageNet-R/C. Additionally, B2T uncovers novel biases in larger datasets, such as Dollar Street and ImageNet. For example, we discovered a contextual bias between "bee" and "flower" in ImageNet. We also highlight various applications of B2T keywords, including debiased training, CLIP prompting, and model comparison.(1)

关键词： bias and fairness explainable AI vision-language model

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DeCoTR: Enhancing Depth Completion with 2D and 3D Attentions

DeCoTR: Enhancing Depth Completion with 2D and 3D Attentions

引用

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

作者： Shi, Yunxiao Singh, Manish Kumar Cai, Hong Porikli, Fatih Qualcomm AI Res San Diego CA 92121 USA

ISBN: (纸本)9798350353006

In this paper, we introduce a novel approach that harnesses both 2D and 3D attentions to enable highly accurate depth completion without requiring iterative spatial propagations. Specifically, we first enhance a baseline convolutional depth completion model by applying attention to 2D features in the bottleneck and skip connections. This effectively improves the performance of this simple network and sets it on par with the latest, complex transformer-based models. Leveraging the initial depths and features from this network, we uplift the 2D features to form a 3D point cloud and construct a 3D point transformer to process it, allowing the model to explicitly learn and exploit 3D geometric features. In addition, we propose normalization techniques to process the point cloud, which improves learning and leads to better accuracy than directly using point transformers off the shelf. Furthermore, we incorporate global attention on downsampled point cloud features, which enables long-range context while still being computationally feasible. We evaluate our method, DeCoTR, on established depth completion benchmarks, including NYU Depth V2 and KITTI, showcasing that it sets new state-of-the-art performance. We further conduct zero-shot evaluations on ScanNet and DDAD benchmarks and demonstrate that DeCoTR has superior generalizability compared to existing approaches.

关键词： 3D computer vision depth completion point transformer

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NORPPA: NOvel Ringed seal re-identification by Pelage pattern Aggregation

NORPPA: NOvel Ringed seal re-identification by Pelage Patter...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Nepovinnykh, Ekaterina Eerola, Tuomas Kalviainen, Heikki Chelak, Ilia Lappeenranta Lahti Univ Technol LUT Sch Engn Sci Dept Computat Engn Comp Vis & Pattern Recognit Lab CVPRL Lappeenranta Finland Univ Helsinki Dept Comp Sci Fac Sci Helsinki Finland

ISBN: (纸本)9798350370287;9798350370713

We propose a method for Saimaa ringed seal (Pusa hispida saimensis) re-identification. Access to large image volumes through camera trapping and crowdsourcing provides novel possibilities for animal conservation and monitoring and calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. The proposed method NOvel Ringed seal re-identification by Pelage pattern Aggregation (NORPPA) utilizes the permanent and unique pelage pattern of Saimaa ringed seals and content-based image retrieval techniques. First, the query image is preprocessed, and each seal instance is segmented. Next, the seal's pelage pattern is extracted using a U-net encoder-decoder based method. Then, CNN-based affine invariant features are embedded and aggregated into Fisher Vectors. Finally, the cosine distance between the Fisher Vectors is used to find the best match from a database of known individuals. We perform extensive experiments of various modifications of the method on challenging Saimaa ringed seals re-identification dataset. The proposed method is shown to produce the best re-identification accuracy on our dataset in comparisons with alternative approaches.

关键词： Animals

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C²T-Net: Channel-Aware Cross-Fused Transformer-Style Networks for Pedestrian Attribute recognition

C<SUP>2</SUP>T-Net: Channel-Aware Cross-Fused Transformer-St...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Bui, Doanh C. Thinh V. Le Ngo, Ba Hung Korea Univ Sch Elect Engn Seoul South Korea Vietnam Natl Univ Univ Informat Technol Ho Chi Minh City Vietnam Chonnam Natl Univ Grad Sch Data Sci Gwangju South Korea

ISBN: (纸本)9798350370287;9798350370713

Pedestrian attribute recognition (PAR) poses a significant challenge but holds practical significance in various security applications, including surveillance. In the scope of the UPAR challenge, this paper introduces the Channel-Aware Cross-Fused Transformer-Style Networks ((CT)-T-2-Net). This network effectively integrates two powerful transformer-style networks, namely the Swin Transformer (SwinT) and a customized variant of the vanilla vision transformer (EVA ViT). The aim is to capture both local and global aspects of an individual for precise attribute recognition. To facilitate the understanding of intricate relationships among channels, a channel-aware self-attention mechanism is devised and integrated into each SwinT block. Furthermore, the fusion of features from the two transformer-style networks is accomplished through cross-fusion, enabling each network to mutually amplify and boost the textural nuances present in the other. The efficacy of the proposed model has been demonstrated through its performance on three PAR benchmarks: PA100K, PETA, and the UPAR2024 private test. With respect to the PA100K benchmark, our approach has achieved state-of-the-art results when compared to models that do not employ any pre-training techniques. Our performance on the PETA dataset remains competitive, standing on par with other cutting-edge models. Notably, our model achieved runner-up performance on the UPAR2024-track-1 test set. Source code is available at https://***/caodoanh2001/upar_challenge.

关键词： computer vision

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Generating Enhanced Negatives for Training Language-Based Object Detectors

Generating Enhanced Negatives for Training Language-Based Ob...

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

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

ISBN: (纸本)9798350353006

The recent progress in language-based open-vocabulary object detection can be largely attributed to finding better ways of leveraging large-scale data with free-form text annotations. Training such models with a discriminative objective function has proven successful, but requires good positive and negative samples. However, the free-form nature and the open vocabulary of object descriptions make the space of negatives extremely large. Prior works randomly sample negatives or use rule-based techniques to build them. In contrast, we propose to leverage the vast knowledge built into modern generative models to automatically build negatives that are more relevant to the original data. Specifically, we use large-Language-models to generate negative text descriptions, and text-to-image diffusion models to also generate corresponding negative images. Our experimental analysis confirms the relevance of the generated negative data, and its use in language-based detectors improves performance on two complex benchmarks. Code is available at https://***/xiaofeng94/Gen-Enhanced-Negs.

关键词： large language model negative example mining open-vocabulary object detection vision and language

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Language-only Efficient Training of Zero-shot Composed Image Retrieval

Language-only Efficient Training of Zero-shot Composed Image...

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

作者： Gu, Geonmo Chun, Sanghyuk Kim, Wonjae Kang, Yoohoon Yun, Sangdoo NAVER Vis Seongnam South Korea NAVER AI Lab Bundangdong South Korea

ISBN: (纸本)9798350353006

Composed image retrieval (CIR) task takes a composed query of image and text, aiming to search relative images for both conditions. Conventional CIR approaches need a training dataset composed of triplets of query image, query text, and target image, which is very expensive to collect. Several recent works have worked on the zero-shot (ZS) CIR paradigm to tackle the issue without using pre-collected triplets. However, the existing ZS-CIR methods show limited backbone scalability and generalizability due to the lack of diversity of the input texts during training. We propose a novel CIR framework, only using language for its training. Our LinCIR (Language-only training for CIR) can be trained only with text datasets by a novel self-supervision named self-masking projection (SMP). We project the text latent embedding to the token embedding space and construct a new text by replacing the keyword tokens of the original text. Then, we let the new and original texts have the same latent embedding vector. With this simple strategy, LinCIR is surprisingly efficient and highly effective;LinCIR with CLIP ViT-G backbone is trained in 48 minutes and shows the best ZS-CIR performances on four different CIR benchmarks, CIRCO, GeneCIS, FashionIQ, and CIRR, even outperforming supervised method on FashionIQ. Code is available at ***/navervision/lincir

关键词： composed image retrieval language-only training vision and language zero-shot composed image retrieval zs-cir

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JRDB-PanoTrack: An Open-world Panoptic Segmentation and Tracking Robotic Dataset in Crowded Human Environments

JRDB-PanoTrack: An Open-world Panoptic Segmentation and Trac...

引用

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

作者： Lel, Duy Tho Gou, Chenhui Datta, Stavya Shi, Hengcan Reid, Ian Cai, Jianfei Rezatofighi, Hamid Monash Univ Melbourne Vic Australia MBZUAI Abu Dhabi U Arab Emirates Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9798350353006

Autonomous robot systems have attracted increasing research attention in recent years, where environment understanding is a crucial step for robot navigation, human-robot interaction, and decision. Real-world robot systems usually collect visual data from multiple sensors and are required to recognize numerous objects and their movements in complex human-crowded settings. Traditional benchmarks, with their reliance on single sensors and limited object classes and scenarios, fail to provide the comprehensive environmental understanding robots need for accurate navigation, interaction, and decision-making. As an extension of JRDB dataset, we unveil JRDB-PanoTrack, a novel open-world panoptic segmentation and tracking benchmark, towards more comprehensive environmental perception. JRDB-PanoTrack includes (1) various data involving indoor and outdoor crowded scenes, as well as comprehensive 2D and 3D synchronized data modalities;(2) high-quality 2D spatial panoptic segmentation and temporal tracking annotations, with additional 3D label projections for further spatial understanding;(3) diverse object classes for closed- and open-world recognition benchmarks, with OSPA-based metrics for evaluation. Extensive evaluation of leading methods shows significant challenges posed by our dataset.

关键词： computer vision Open World Panoptic Segmentation Panoptic Tracking Robotic

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MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning

MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-T...

引用

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

作者： Agiza, Ahmed Neseem, Marina Reda, Sherief Brown Univ Providence RI 02912 USA

ISBN: (纸本)9798350353006

Adapting models pre-trained on large-scale datasets to a variety of downstream tasks is a common strategy in deep learning. Consequently, parameter-efficient fine-tuning methods have emerged as a promising way to adapt pre-trained models to different tasks while training only a minimal number of parameters. While most of these methods are designed for single-task adaptation, parameter-efficient training in Multi-Task Learning (MTL) architectures is still unexplored. In this paper, we introduce MTLoRA, a novel framework for parameter-efficient training of MTL models. MTLoRA employs Task-Agnostic and Task-Specific Low-Rank Adaptation modules, which effectively disentangle the parameter space in MTL fine-tuning, thereby enabling the model to adeptly handle both task specialization and interaction within MTL contexts. We applied MTLoRA to hierarchical-transformer-based MTL architectures, adapting them to multiple downstream dense prediction tasks. Our extensive experiments on the PASCAL dataset show that MTLoRA achieves higher accuracy on downstream tasks compared to fully fine-tuning the MTL model while reducing the number of trainable parameters by 3.6x. Furthermore, MTLoRA establishes a Pareto-optimal trade-off between the number of trainable parameters and the accuracy of the downstream tasks, outperforming current state-of-the-art parameter-efficient training methods in both accuracy and efficiency. Our code is publicly available.(1)

关键词： efficient multi-task learning hierarchical transformers LoRA low-rank adapatation multi-task learning parameter-efficient training PEFT vision transformers

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MAPLM: A Real-World Large-Scale vision-Language Benchmark for Map and Traffic Scene Understanding

MAPLM: A Real-World Large-Scale Vision-Language Benchmark fo...

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

作者： Cao, Xu Zhou, Tong Ma, Yunsheng Ye, Wenqian Cui, Can Tang, Kun Cao, Zhipeng Liang, Kaizhao Wang, Ziran Rehg, James M. Zheng, Chao Tencent T Lab Palo Alto CA 94306 USA Univ Illinois Champaign IL USA Purdue Univ W Lafayette IN USA Univ Virginia Charlottesville VA USA SambaNova Syst Inc Palo Alto CA USA

ISBN: (纸本)9798350353006

vision-language generative AI has demonstrated remarkable promise for empowering cross-modal scene understanding of autonomous driving and high-definition (HD) map systems. However, current benchmark datasets lack multi-modal point cloud, image, and language data pairs. Recent approaches utilize visual instruction learning and cross-modal prompt engineering to expand vision-language models into this domain. In this paper, we propose a new vision-language benchmark that can be used to finetune traffic and HD map domain-specific foundation models. Specifically, we annotate and leverage large-scale, broad-coverage traffic and map data extracted from huge HD map annotations, and use CLIP and LLaMA-2 / Vicuna to finetune a baseline model with instruction-following data. Our experimental results across various algorithms reveal that while visual instruction-tuning large language models (LLMs) can effectively learn meaningful representations from MAPLM-QA, there remains significant room for further advancements. To facilitate applying LLMs and multi-modal data into self-driving research, we will release our visual-language QA data, and the baseline models at ***/LLVM-AD/MAPLM.

关键词： High-definition (HD) Map Large Language Model Multimodal Learning vision-Language Model Visual Question Answering

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