GenAI4ELab/papercli-papers huggingface dataset:
Dual Pseudo-Labels Interactive Self-Training for Semi-Supervised Visible-Infrared Person Re-Identification
Authors: Jiangming Shi; Yachao Zhang; Xiangbo Yin; Yuan Xie; Zhizhong Zhang; Jianping Fan; Zhongchao Shi; Yanyun Qu
Venue: ICCV 2023 (main)

Abstract: Visible-infrared person re-identification (VI-ReID) aims to match a specific person from a gallery of images captured from non-overlapping visible and infrared cameras. Most works focus on fully supervised VI-ReID, which requires substantial cross-modality annotation that is more expensive than the annotation in single-modality. To reduce the extensive cost of annotation, we explore two practical semi-supervised settings: uni-semi-supervised (annotating only visible images) and bi-semi-supervised (annotating partially in both modalities). These two semi-supervised settings face two challenges due to the large cross-modality discrepancies and the lack of correspondence supervision between visible and infrared images. Thus, it is difficult to generate reliable pseudo-labels and learn modality-invariant features from noise pseudo-labels. In this paper, we propose a dual pseudo-label interactive self-training (DPIS) for these two semi-supervised VI-ReID. Our DPIS integrates two pseudo-labels generated by distinct models into a hybrid pseudo-label for unlabeled data. However, the hybrid pseudo-label still inevitably contains noise. To eliminate the negative effect of noise pseudo-labels, we introduce three modules: noise label penalty (NLP), noise correspondence calibration (NCC), and unreliable anchor learning (UAL). Specifically, NLP penalizes noise labels, NCC calibrates noisy correspondences, and UAL mines the hard-to-discriminate features. Extensive experimental results on SYSU-MM01 and RegDB demonstrate that our DPIS achieves impressive performance under these two semi-supervised settings.
Less is More: Focus Attention for Efficient DETR
Authors: Dehua Zheng; Wenhui Dong; Hailin Hu; Xinghao Chen; Yunhe Wang
Venue: ICCV 2023 (main)

Abstract: DETR-like models have significantly boosted the performance of detectors and even outperformed classical convolutional models. However, all tokens are treated equally without discrimination brings a redundant computational burden in the traditional encoder structure. The recent sparsification strategies exploit a subset of informative tokens to reduce attention
 complexity maintaining performance through the sparse encoder. But these methods tend to rely on unreliable model statistics. Moreover, simply reducing the token population hinders the detection performance to a large extent, limiting the application of these sparse models. We propose Focus-DETR, which focuses attention on more informative tokens for a better trade-off between computation efficiency and model accuracy. Specifically, we reconstruct the encoder with dual attention, which includes a token scoring mechanism that considers both localization and category semantic information of the objects from multi-scale feature maps. We efficiently abandon the background queries and enhance the semantic interaction of the fine-grained object queries based on the scores. Compared with the state-of-the-art sparse DETR-like detectors under the same setting, our Focus-DETR gets comparable complexity while achieving 50.4AP (+2.2) on COCO. The code is available at https://github.com/huawei-noah/noah-research/tree/master/Focus-DETR and https://gitee.com/mindspore/models/tree/master/research/cv/Focus-DETR.
Efficient Controllable Multi-Task Architectures
Authors: Abhishek Aich; Samuel Schulter; Amit K. Roy-Chowdhury; Manmohan Chandraker; Yumin Suh
Venue: ICCV 2023 (main)

Abstract: We aim to train a multi-task model such that users can adjust the desired compute budget and relative importance of task performances after deployment, without retraining. This enables optimizing performance for dynamically varying user needs, without heavy computational overhead to train and save models for various scenarios. To this end, we propose a multi-task model consisting of a shared encoder and task-specific decoders where both encoder and decoder channel widths are slimmable. Our key idea is to control the task importance by varying the capacities of task-specific decoders, while controlling the total computational cost by jointly adjusting the encoder capacity. This improves overall accuracy by allowing a stronger encoder for a given budget, increases control over computational cost, and delivers high-quality slimmed sub-architectures based on user's constraints. Our training strategy involves a novel `Configuration-Invariant Knowledge Distillation' loss that enforces backbone representations to be invariant under different runtime width configurations to enhance accuracy. Further, we present a simple but effective search algorithm that translates user constraints to runtime width configurations of both the shared encoder and task decoders, for sampling the sub-architectures. The key rule for the search algorithm is to provide a larger computational budget to the higher preferred task decoder, while searching a shared encoder configuration that enhances the overall MTL performance. Various experiments on three multi-task benchmarks (PASCALContext, NYUDv2, and CIFAR100-MTL) with diverse backbone architectures demonstrate the advantage of our approach. For example, our method shows a higher controllability by   33.5% in the NYUD-v2 dataset over prior methods, while incurring much less compute cost.
HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation
Authors: Xuan Ju; Ailing Zeng; Chenchen Zhao; Jianan Wang; Lei Zhang; Qiang Xu
Venue: ICCV 2023 (main)

Abstract: Controllable human image generation (HIG) has attracted significant attention from academia and industry for its numerous real-life applications. State-of-the-art solutions, such as ControlNet and T2I-Adapter, introduce an additional learnable branch on top of the frozen pre-trained stable diffusion (SD) model, which can enforce various kinds of conditions, including skeleton guidance of HIG. While such a plug-and-play approach is appealing, the inevitable and uncertain conflicts between the original images produced from the frozen SD branch and the given condition incur significant challenges for the learnable branch, which conduct the condition learning via image feature editing.
 In this work, we propose a native skeleton-guided diffusion model for controllable HIG called HumanSD. Instead of performing image editing with dual-branch diffusion, we fine-tune the original SD model using a novel heatmap-guided denoising loss. This strategy effectively and efficiently strengthens the given skeleton condition during model training while mitigating the catastrophic forgetting effects. HumanSD is fine-tuned on the assembly of three large-scale human-centric datasets with text-image-pose information, two of which are established in this work. Experimental results show that HumanSD outperforms ControlNet in terms of pose control and image quality, particularly when the given skeleton guidance is sophisticated. Code and data are available at: https://idearesearch.github.io/HumanSD/.
Lens Parameter Estimation for Realistic Depth of Field Modeling
Authors: Dominique Piché-Meunier; Yannick Hold-Geoffroy; Jianming Zhang; Jean-François Lalonde
Venue: ICCV 2023 (main)

Abstract: We present a method to estimate the depth of field effect from a single image. Most existing methods related to this task provide either a per-pixel estimation of blur and/or depth. Instead, we go further and propose to use a lens-based representation that models the depth of field using two parameters: the blur factor and focus disparity. Those two parameters, along with the signed defocus representation, result in a more intuitive and linear representation which we solve using a novel weighting network. Furthermore, our method explicitly enforces consistency between the estimated defocus blur, the lens parameters, and the depth map. Finally, we train our deep-learning-based model on a mix of real images with synthetic depth of field and fully synthetic images. These improvements result in a more robust and accurate method, as demonstrated by our state-of-the-art results. In particular, our lens parametrization enables several applications, such as 3D staging for AR environments and seamless object compositing.
Learned Compressive Representations for Single-Photon 3D Imaging
Authors: Felipe Gutierrez-Barragan; Fangzhou Mu; Andrei Ardelean; Atul Ingle; Claudio Bruschini; Edoardo Charbon; Yin Li; Mohit Gupta; Andreas Velten
Venue: ICCV 2023 (main)

Abstract: Single-photon 3D cameras can record the time-of-arrival of billions of photons per second with picosecond accuracy. One common approach to summarize the photon data stream is to build a per-pixel timestamp histogram, resulting in a 3D histogram tensor that encodes distances along the time axis. As the spatio-temporal resolution of the histogram tensor increases, the in-pixel memory requirements and output data rates can quickly become impractical. To overcome this limitation, we propose a family of linear compressive representations of histogram tensors that can be computed efficiently, in an online fashion, as a matrix operation. We design practical lightweight compressive representations that are amenable to an in-pixel implementation and consider the spatio-temporal information of each timestamp. Furthermore, we implement our proposed framework as the first layer of a neural network, which enables the joint end-to-end optimization of the compressive representations and a downstream SPAD data processing model. We find that a well-designed compressive representation can reduce in-sensor memory and data rates up to 2 orders of magnitude without significantly reducing 3D imaging quality. Finally, we analyze the power consumption implications through an on-chip implementation.
Alignment-free HDR Deghosting with Semantics Consistent Transformer
Authors: Steven Tel; Zongwei Wu; Yulun Zhang; Barthélémy Heyrman; Cédric Demonceaux; Radu Timofte; Dominique Ginhac
Venue: ICCV 2023 (main)

Abstract: High dynamic range (HDR) imaging aims to retrieve information from multiple low-dynamic range inputs to generate realistic output. The essence is to leverage the contextual information, including both dynamic and static semantics, for better image generation. Existing methods often focus on the spatial misalignment across input frames caused by the foreground and/or camera motion. However, there is no research on jointly leveraging the dynamic and static context in a simultaneous manner. To delve into this problem, we propose a novel alignment-free network with a Semantics Consistent Transformer (SCTNet) with both spatial and channel attention modules in the network. The spatial attention aims to deal with the intra-image correlation to model the dynamic motion, while the channel attention enables the inter-image intertwining to enhance the semantic consistency across frames. Aside from this, we introduce a novel realistic HDR dataset with more variations in foreground objects, environmental factors, and larger motions. Extensive comparisons on both conventional datasets and ours validate the effectiveness of our method, achieving the best trade-off on the performance and the computational cost. The source code and dataset are available at https://github.com/Zongwei97/SCTNet.
Semantic-Aware Implicit Template Learning via Part Deformation Consistency
Authors: Sihyeon Kim; Minseok Joo; Jaewon Lee; Juyeon Ko; Juhan Cha; Hyunwoo J. Kim
Venue: ICCV 2023 (main)

Abstract: Learning implicit templates as neural fields has recently shown impressive performance in unsupervised shape correspondence. Despite the success, we observe current approaches, which solely rely on geometric information, often learn suboptimal deformation across generic object shapes, which have high structural variability. In this paper, we highlight the importance of part deformation consistency and propose a semantic-aware implicit template learning framework to enable semantically plausible deformation. By leveraging semantic prior from a self-supervised feature extractor, we suggest local conditioning with novel semantic-aware deformation code and deformation consistency regularizations regarding part deformation, global deformation, and global scaling. Our extensive experiments demonstrate the superiority of the proposed method over baselines in various tasks: keypoint transfer, part label transfer, and texture transfer. More interestingly, our framework shows a larger performance gain under more challenging settings. We also provide qualitative analyses to validate the effectiveness of semantic-aware deformation. The code is available at https://github.com/mlvlab/PDC.
HRS-Bench: Holistic, Reliable and Scalable Benchmark for Text-to-Image Models
Authors: Eslam Mohamed Bakr; Pengzhan Sun; Xiaoqian Shen; Faizan Farooq Khan; Li Erran Li; Mohamed Elhoseiny
Venue: ICCV 2023 (main)

Abstract: Designing robust text-to-image (T2I) models have been extensively explored in recent years, especially with the emergence of diffusion models, which achieves state-of-the-art results on T2I synthesis tasks. Despite the significant effort and success in this direction, we observed that the existing metrics need to be more robust to measure real progress. Therefore, comparing the existing models are more complex and heavily subjective for human evaluations. In addition, we observe that the efforts in developing new architectures do not coincide with efforts in the evaluation direction. Driven by this observation, the importance of designing a concrete evaluation emerges to fill the gap between designing and evaluation efforts. Accordingly, we introduce our holistic, reliable, and scalable benchmark, termed  \papernameAbbrev , for T2I models. Unlike the existing benchmarks, which focus on limited aspects, we measure 13 skills, which could be categorized into five critical skills; accuracy, robustness, generalization, fairness, and bias. In addition,  \papernameAbbrev  covers 50 applications, e.g., fashion, animals, transportation, food, and clothes. We evaluate nine recent large-scale T2I models using metrics that cover a wide range of skills. We study 13 skills, e.g., robustness, fairness, and bias. To probe the effectiveness of our  \papernameAbbrev , a human evaluation is conducted, which is aligned with 95% with our evaluations on average across the 13 skills. We hope our findings, e.g., all the existing models can not generate visual text nor emotionally grounded images, help accelerate and direct future research. To this end, the code and data are available at https://eslambakr.github.io/hrsbench.github.io/.
Multi3DRefer: Grounding Text Description to Multiple 3D Objects
Authors: Yiming Zhang; ZeMing Gong; Angel X. Chang
Venue: ICCV 2023 (main)

Abstract: We introduce the task of localizing a flexible number of objects in real-world 3D scenes using natural language descriptions. Existing 3D visual grounding tasks focus on localizing a unique object given a text description. However, such a strict setting is unnatural as localizing potentially multiple objects is a common need in real-world scenarios and robotic tasks (e.g., visual navigation and object rearrangement). To address this setting we propose Multi3DRefer, generalizing the ScanRefer dataset and task. Our dataset contains 61926 descriptions of 11609 objects, where zero, single or multiple target objects are referenced by each description. We also introduce a new evaluation metric and benchmark methods from prior work to enable further investigation of multi-modal 3D scene understanding. Furthermore, we develop a better baseline leveraging 2D features from CLIP by rendering object proposals online with contrastive learning, which outperforms the state of the art on the ScanRefer benchmark.
Examining Autoexposure for Challenging Scenes
Authors: SaiKiran Tedla; Beixuan Yang; Michael S. Brown
Venue: ICCV 2023 (main)

Abstract: Autoexposure (AE) is a critical step applied by camera systems to ensure properly exposed images. While current AE algorithms are effective in well-lit environments with constant illumination, these algorithms still struggle in environments with bright light sources or scenes with abrupt changes in lighting. A significant hurdle in developing new AE algorithms for challenging environments, especially those with time-varying lighting, is the lack of suitable image datasets. To address this issue, we have captured a new 4D exposure dataset that provides a large solution space (i.e., shutter speed range from 1/500 to 15 seconds) over a temporal sequence with moving objects, bright lights, and varying lighting. In addition, we have designed a software platform to allow AE algorithms to be used in a plug-and-play manner with the dataset. Our dataset and associate platform enable repeatable evaluation of different AE algorithms and provide a much-needed starting point to develop better AE methods. We examine several existing AE strategies using our dataset and show that most users prefer a simple saliency method for challenging lighting conditions.
DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment
Authors: Xujie Zhang; Binbin Yang; Michael C. Kampffmeyer; Wenqing Zhang; Shiyue Zhang; Guansong Lu; Liang Lin; Hang Xu; Xiaodan Liang
Venue: ICCV 2023 (main)

Abstract: Cross-modal garment synthesis and manipulation will significantly benefit the way fashion designers generate garments and modify their designs via flexible linguistic interfaces. However, despite the significant progress that has been made in generic image synthesis using diffusion models, producing garment images with garment part level semantics that are well aligned with input text prompts and then flexibly manipulating the generated results still remains a problem. Current approaches follow the general text-to-image paradigm and mine cross-modal relations via simple cross-attention modules, neglecting the structural correspondence between visual and textual representations in the fashion design domain. In this work, we instead introduce DiffCloth, a diffusion-based pipeline for cross-modal garment synthesis and manipulation, which empowers diffusion models with flexible compositionality in the fashion domain by structurally aligning the cross-modal semantics. Specifically, we formulate the part-level cross-modal alignment as a bipartite matching problem between the linguistic Attribute-Phrases (AP) and the visual garment parts which are obtained via constituency parsing and semantic segmentation, respectively. To mitigate the issue of attribute confusion, we further propose a semantic-bundled cross-attention to preserve the spatial structure similarities between the attention maps of attribute adjectives and part nouns in each AP. Moreover, DiffCloth allows for manipulation of the generated results by simply replacing APs in the text prompts. The manipulation-irrelevant regions are recognized by blended masks obtained from the bundled attention maps of the APs and kept unchanged. Extensive experiments on the CM-Fashion benchmark demonstrate that DiffCloth both yields state-of-the-art garment synthesis results by leveraging the inherent structural information and supports flexible manipulation with region consistency.
Improved Visual Fine-tuning with Natural Language Supervision
Authors: Junyang Wang; Yuanhong Xu; Juhua Hu; Ming Yan; Jitao Sang; Qi Qian
Venue: ICCV 2023 (main)

Abstract: Fine-tuning a visual pre-trained model can leverage the semantic information from large-scale pre-training data and mitigate the over-fitting problem on downstream vision tasks with limited training examples. While the problem of catastrophic forgetting in pre-trained backbone has been extensively studied for fine-tuning, its potential bias from the corresponding pre-training task and data, attracts less attention. In this work, we investigate this problem by demonstrating that the obtained classifier after fine-tuning will be close to that induced by the pre-trained model. To reduce the bias in the classifier effectively, we introduce a reference distribution obtained from a fixed text classifier, which can help regularize the learned vision classifier. The proposed method, Text Supervised fine-tuning (TeS), is evaluated with diverse pre-trained vision models including ResNet and ViT, and text encoders including BERT and CLIP, on 11 downstream tasks. The consistent improvement with a clear margin over distinct scenarios confirms the effectiveness of our proposal. Code is available at https://github.com/idstcv/TeS.
Person Re-Identification without Identification via Event anonymization
Authors: Shafiq Ahmad; Pietro Morerio; Alessio Del Bue
Venue: ICCV 2023 (main)

Abstract: Wide-scale use of visual surveillance in public spaces puts individual privacy at stake while increasing resource consumption (energy, bandwidth, and computation). Neuromorphic vision sensors (event-cameras) have been recently considered a valid solution to the privacy issue because they do not capture detailed RGB visual information of the subjects in the scene. However, recent deep learning architectures have been able to reconstruct images from event cameras with high fidelity, reintroducing a potential threat to privacy for event-based vision applications. In this paper, we aim to anonymize event-streams to protect the identity of human subjects against such image reconstruction attacks. To achieve this, we propose an end-to-end network architecture jointly optimized for the twofold objective of preserving privacy and performing a downstream task such as person ReId. Our network learns to scramble events, enforcing the degradation of images recovered from the privacy attacker. In this work, we also bring to the community the first ever event-based person ReId dataset gathered to evaluate the performance of our approach. We validate our approach with extensive experiments and report results on the synthetic event data simulated from the publicly available SoftBio dataset and our proposed Event-ReId dataset.
GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds
Authors: Jianfeng Xiang; Jiaolong Yang; Yu Deng; Xin Tong
Venue: ICCV 2023 (main)

Abstract: Recent works have shown that 3D-aware GANs trained on unstructured single image collections can generate multiview images of novel instances. The key underpinnings to achieve this are a 3D radiance field generator and a volume rendering process. However, existing methods either cannot generate high-resolution images (e.g., up to 256x256) due to the high computation cost of neural volume rendering, or rely on 2D CNNs for image-space upsampling which jeopardizes the 3D consistency across different views. This paper proposes a novel 3D-aware GAN that can generate high resolution images (up to 1024x1024) while keeping strict 3D consistency as in volume rendering. Our motivation is to achieve super-resolution directly in the 3D space to preserve 3D consistency. We avoid the otherwise prohibitively-expensive computation cost by applying 2D convolutions on a set of 2D radiance manifolds defined in the recent generative radiance manifold (GRAM) approach, and apply dedicated loss functions for effective GAN training at high resolution. Experiments on FFHQ and AFHQv2 datasets show that our method can produce high-quality 3D-consistent results that significantly outperform existing methods. It makes a significant step towards closing the gap between traditional 2D image generation and 3D-consistent free-view generation.