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Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation

Boosting Object Detection with Zero-Shot Day-Night Domain Ad...

引用

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

作者： Du, Zhipeng Shi, Miaojing Deng, Jiankang Kings Coll London Dept Informat London England Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China Imperial Coll London Dept Comp London England Huawei London Res London England

ISBN: (纸本)9798350353006

Detecting objects in low-light scenarios presents a persistent challenge, as detectors trained on well-lit data exhibit significant performance degradation on low-light data due to low visibility. Previous methods mitigate this issue by exploring image enhancement or object detection techniques with real low-light image datasets. However, the progress is impeded by the inherent difficulties about collecting and annotating low-light images. To address this challenge, we propose to boost low-light object detection with zero-shot day-night domain adaptation, which aims to generalize a detector from well-lit scenarios to low-light ones without requiring real low-light data. Revisiting Retinex theory in the low-level vision, we first design a reflectance representation learning module to learn Retinex-based illumination invariance in images with a carefully designed illumination invariance reinforcement strategy. Next, an interchange-redecomposition-coherence procedure is introduced to improve over the vanilla Retinex image decomposition process by performing two sequential image decompositions and introducing a redecomposition cohering loss. Extensive experiments on ExDark, DARK FACE, and CODaN datasets show strong low-light generalizability of our method. Our code is available at https://***/ZPDu/DAI-Net.

关键词： Low-light vision Object Detction Zero-shot Domain Adaptation

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Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning

Troika: Multi-Path Cross-Modal Traction for Compositional Ze...

引用

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

作者： Hu, Siteng Gong, Biao Feng, Yutong Zhang, Min Lv, Yiliang Wang, Donglin Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China Westlake Univ Sch Engn AI Div Machine Intelligence Lab MiLAB Hangzhou Peoples R China

ISBN: (纸本)9798350353006

Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs. Relying on learning the joint representation of seen compositions, these methods ignore the explicit modeling of the state and object, thus limiting the exploitation of pre-trained knowledge and generalization to unseen compositions. With a particular focus on the universality of the solution, in this work, we propose a novel paradigm for CZSL models that establishes three identification branches (i.e., Multi-Path) to jointly model the state, object, and composition. The presented Troika is an outstanding implementation that aligns the branch-specific prompt representations with decomposed visual features. To calibrate the bias between semantically similar multi-modal representations, we further devise a Cross-Modal Traction module into Troika that shifts the prompt representation towards the current visual content. We conduct extensive experiments on three popular benchmarks, where our method significantly outperforms existing methods in both closed-world and open-world settings. The code will be available at https://***/bighuang624/Troika.

关键词： compositional zero-shot learning parameter-efficient transfer learning Troika vision-language models

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GenZI: Zero-Shot 3D Human-Scene Interaction Generation

GenZI: Zero-Shot 3D Human-Scene Interaction Generation

引用

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

作者： Li, Lei Dai, Angela Tech Univ Munich Munich Germany

ISBN: (纸本)9798350353006

Can we synthesize 3D humans interacting with scenes without learning from any 3D human-scene interaction data? We propose GenZI(1), the first zero-shot approach to generating 3D human-scene interactions. Key to GenZI is our distillation of interaction priors from large vision-language models (VLMs), which have learned a rich semantic space of 2D human-scene compositions. Given a natural language description and a coarse point location of the desired interaction in a 3D scene, we first leverage VLMs to imagine plausible 2D human interactions inpainted into multiple rendered views of the scene. We then formulate a robust iterative optimization to synthesize the pose and shape of a 3D human model in the scene, guided by consistency with the 2D interaction hypotheses. In contrast to existing learning-based approaches, GenZI circumvents the conventional need for captured 3D interaction data, and allows for flexible control of the 3D interaction synthesis with easy-to-use text prompts. Extensive experiments show that our zero-shot approach has high flexibility and generality, making it applicable to diverse scene types, including both indoor and outdoor environments.

关键词： Human-Scene Interaction vision-Language Models Zero-Shot

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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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Fooling Polarization-based vision using Locally Controllable Polarizing Projection

Fooling Polarization-based Vision using Locally Controllable...

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

作者： Li, Zhuoxiao Zhong, Zhihang Nobuhara, Shohei Nishino, Ko Zheng, Yinqiang Univ Tokyo Tokyo Japan Shanghai Artificial Intelligence Lab Shanghai Peoples R China Kyoto Univ Kyoto Japan

ISBN: (纸本)9798350353006

Polarization is a fundamental property of light that encodes abundant information regarding surface shape, material, illumination and viewing geometry. The computer vision community has witnessed a blossom of polarization-based vision applications, such as reflection removal, shape-from-polarization (SfP), transparent object segmentation and color constancy, partially due to the emergence of single-chip mono/color polarization sensors that make polarization data acquisition easier than ever. However, is polarization-based vision vulnerable to adversarial attacks? If so, is that possible to realize these adversarial attacks in the physical world, without being perceived by human eyes? In this paper, we warn the community of the vulnerability of polarization-based vision, which can be more serious than RGB-based vision. By adapting a commercial LCD projector, we achieve locally controllable polarizing projection, which is successfully utilized to fool state-of-the-art polarization-based vision algorithms for glass segmentation and SfP. Compared with existing physical attacks on RGB-based vision, which always suffer from the trade-off between attack efficacy and eye conceivability, the adversarial attackers based on polarizing projection are contact-free and visually imperceptible, since naked human eyes can rarely perceive the difference of viciously manipulated polarizing light and ordinary illumination. This poses unprecedented risks on polarization-based vision, for which due attentions should be paid and counter measures be considered.

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SCVRL: Shuffled Contrastive Video Representation Learning

SCVRL: Shuffled Contrastive Video Representation Learning

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

作者： Dorkenwald, Michael Xiao, Fanyi Brattoli, Biagio Tighe, Joseph Modolo, Davide Heidelberg Univ Heidelberg Germany AWS AI Labs Palo Alto CA USA AWS Palo Alto CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos. Differently from previous contrast learning based methods that mostly focus on learning visual semantics (e.g., CVRL), SCVRL is capable of learning both semantic and motion patterns. For that, we reformulate the popular shuffling pretext task within a modern contrastive learning paradigm. We show that our transformer-based network has a natural capacity to learn motion in self-supervised settings and achieves strong performance, outperforming CVRL on four benchmarks.

关键词： Representation learning Visualization Semantics Self-supervised learning Benchmark testing Transformers pattern recognition

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Multiscale vision Transformers meet Bipartite Matching for efficient single-stage Action Localization

Multiscale Vision Transformers meet Bipartite Matching for e...

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

作者： Ntinou, Ioanna Sanchez, Enrique Tzimiropoulos, Georgios Queen Mary Univ London London England Samsung AI Ctr Cambridge Cambridge England

ISBN: (纸本)9798350353006

Action Localization is a challenging problem that combines detection and recognition tasks, which are often addressed separately. State-of-the-art methods rely on off-the-shelf bounding box detections pre-computed at high resolution, and propose transformer models that focus on the classification task alone. Such two-stage solutions are prohibitive for real-time deployment. On the other hand, single-stage methods target both tasks by devoting part of the network (generally the backbone) to sharing the majority of the workload, compromising performance for speed. These methods build on adding a DETR head with learnable queries that after cross- and self-attention can be sent to corresponding MLPs for detecting a person's bounding box and action. However, DETR-like architectures are challenging to train and can incur in big complexity. In this paper, we observe that a straight bipartite matching loss can be applied to the output tokens of a vision transformer. This results in a backbone + MLP architecture that can do both tasks without the need of an extra encoder-decoder head and learnable queries. We show that a single MViTv2-S architecture trained with bipartite matching to perform both tasks surpasses the same MViTv2-S when trained with RoI align on pre-computed bounding boxes. With a careful design of token pooling and the proposed training pipeline, our Bipartite-Matching vision Transformer model, BMViT, achieves +3 mAP on AVA2.2. w.r.t. the two-stage MViTv2-S counterpart. Code is available at https://***/IoannaNti/BMViT

关键词： Signal encoding

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Leveraging Frame Affinity for sRGB-to-RAWVideo De-rendering

Leveraging Frame Affinity for sRGB-to-RAWVideo De-rendering

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

作者： Zhang, Chen Han, Wencheng Zhou, Yang Shen, Jianbing Xu, Cheng-zhong Liu, Wentao SenseTime Res Hong Kong Peoples R China Tetras AI Singapore Singapore Univ Macau CIS SKL IOTSC Taipa Macao Peoples R China

ISBN: (纸本)9798350353006

Unprocessed RAW video has shown distinct advantages over sRGB video in video editing and computer vision tasks. However, capturing RAW video is challenging due to limitations in bandwidth and storage. Various methods have been proposed to address similar issues in single image RAW capture through de-rendering. These methods utilize both the metadata and the sRGB image to perform sRGB-to-RAWde-rendering and recover high-quality single-frame RAW data. However, metadata-based methods always require additional computation for online metadata generation, imposing severe burden on mobile camera device for high frame rate RAW video capture. To address this issue, we propose a framework that utilizes frame affinity to achieve high-quality sRGB-to-RAW video reconstruction. Our approach consists of two main steps. The first step, temporal affinity prior extraction, uses motion information between adjacent frames to obtain a reference RAW image. The second step, spatial feature fusion and mapping, learns a pixel-level mapping function using scene-specific and position-specific features provided by the previous frame. Our method can be easily applied to current mobile camera equipment without complicated adaptations or added burden. To demonstrate the effectiveness of our approach, we introduce the first RAW Video De-rendering Benchmark. In this benchmark, our method outperforms state-of-the-art RAW image reconstruction methods, even without image-level metadata.

关键词： Rendering (computer graphics)

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

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

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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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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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