GenAI4ELab/papercli-papers huggingface dataset:
Rosetta Neurons: Mining the Common Units in a Model Zoo
Authors: Amil Dravid; Yossi Gandelsman; Alexei A. Efros; Assaf Shocher
Venue: ICCV 2023 (main)

Abstract: Do different neural networks, trained for various vision tasks, share some common representations? In this paper, we demonstrate the existence of common features we call "Rosetta Neurons" across a range of models with different architectures, different tasks (generative and discriminative), and different types of supervision (class-supervised, text-supervised, self-supervised). We present an algorithm for mining a dictionary of Rosetta Neurons across several popular vision models: Class Supervised-ResNet50, DINO-ResNet50, DINO-ViT, MAE, CLIP-ResNet50, BigGAN, StyleGAN-2, StyleGAN-XL. Our findings suggest that certain visual concepts and structures are inherently embedded in the natural world and can be learned by different models regardless of the specific task or architecture, and without the use of semantic labels. We can visualize shared concepts directly due to generative models included in our analysis. The Rosetta Neurons facilitate model-to-model translation enabling various inversion-based manipulations, including cross-class alignments, shifting, zooming, and more, without the need for specialized training.
Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups
Authors: Peixia Li; Pulak Purkait; Thalaiyasingam Ajanthan; Majid Abdolshah; Ravi Garg; Hisham Husain; Chenchen Xu; Stephen Gould; Wanli Ouyang; Anton van den Hengel
Venue: ICCV 2023 (main)

Abstract: Semi-supervised semantic segmentation methods use a small amount of clean pixel-level annotations to guide the interpretation of a larger quantity of unlabelled image data. The challenges of providing pixel-accurate annotations at scale mean that the labels are typically noisy, and this contaminates the final results. In this work, we propose an approach that is robust to label noise in the annotated data.
 The method uses two diverse learning groups with different network architectures to effectively handle both label noise and unlabelled images. Each learning group consists of a teacher network, a student network and a novel filter module. The filter module of each learning group utilizes pixel-level features from the teacher network to detect incorrectly labelled pixels. To reduce confirmation bias, we employ the labels cleaned by the filter module from one learning group to train the other learning group. Experimental results on two different benchmarks and settings demonstrate the superiority of our method over state-of-the-art approaches.
AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts
Authors: Tianlong Chen; Xuxi Chen; Xianzhi Du; Abdullah Rashwan; Fan Yang; Huizhong Chen; Zhangyang Wang; Yeqing Li
Venue: ICCV 2023 (main)

Abstract: Sparsely activated Mixture-of-Experts (MoE) is becoming a promising paradigm for multi-task learning (MTL). Instead of compressing multiple tasks' knowledge into a single model, MoE separates the parameter space and only utilizes the relevant model pieces given task type and its input, which provides stabilized MTL training and ultra-efficient inference. However, current MoE approaches adopt a fixed network capacity (e.g., two experts in usual) for all tasks. It potentially results in the over-fitting of simple tasks or the under-fitting of challenging scenarios, especially when tasks are significantly distinctive in their complexity. In this paper, we propose an adaptive MoE framework for multi-task vision recognition, dubbed AdaMV-MoE. Based on the training dynamics, it automatically determines the number of activated experts for each task, avoiding the laborious manual tuning of optimal model size. To validate our proposal, we benchmark it on ImageNet classification and COCO object detection & instance segmentation which are notoriously difficult to learn in concert, due to their discrepancy. Extensive experiments across a variety of vision transformers demonstrate a superior performance of AdaMV-MoE, compared to MTL with a shared backbone and the recent state-of-the-art (SoTA) MTL MoE approach. Codes are available online: https://github.com/google-research/google-research/tree/master/moe_mtl.
Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detection
Authors: Jinglun Li; Xinyu Zhou; Pinxue Guo; Yixuan Sun; Yiwen Huang; Weifeng Ge; Wenqiang Zhang
Venue: ICCV 2023 (main)

Abstract: Detecting out-of-distribution inputs for visual recognition models has become critical in safe deep learning. This paper proposes a novel hierarchical visual category modeling scheme to separate out-of-distribution data from in-distribution data through joint representation learning and statistical modeling. We learn a mixture of Gaussian models for each in-distribution category. There are many Gaussian mixture models to model different visual categories. With these Gaussian models, we design an in-distribution score function by aggregating multiple Mahalanobis-based metrics. We don't use any auxiliary outlier data as training samples, which may hurt the generalization ability of out-of-distribution detection algorithms. We split the ImageNet1k dataset into ten folds randomly. We use one fold as the in-distribution dataset and the others as out-of-distribution datasets to evaluate the proposed method. We also conduct experiments on seven popular benchmarks, including CIFAR, iNaturalist, SUN, Places, Textures, ImageNet-O, and OpenImage-O. Extensive experiments indicate that the proposed method outperforms state-of-the-art algorithms clearly. Meanwhile, we find that our visual representation has a competitive performance when compared with features learned by classical methods. These results demonstrate that the proposed method hasn't weakened the discriminative ability of visual recognition models and keeps high efficiency in detecting out-of-distribution samples.
Diffuse3D: Wide-Angle 3D Photography via Bilateral Diffusion
Authors: Yutao Jiang; Yang Zhou; Yuan Liang; Wenxi Liu; Jianbo Jiao; Yuhui Quan; Shengfeng He
Venue: ICCV 2023 (main)

Abstract: This paper aims to resolve the challenging problem of wide-angle novel view synthesis from a single image, a.k.a. wide-angle 3D photography. Existing approaches rely on local context and treat them equally to inpaint occluded RGB and depth regions, which fail to deal with large-region occlusion (i.e., observing from an extreme angle) and foreground layers might blend into background inpainting. To address the above issues, we propose Diffuse3D which employs a pre-trained diffusion model for global synthesis, while amending the model to activate depth-aware inference. Our key insight is to alter the convolution mechanism in the denoising process. We inject depth information into the denoising convolution operation with bilateral kernels, i.e., a depth kernel and a spatial kernel, to consider layered correlations among pixels. In this way, foreground regions are overlooked in background inpainting and only pixels close in depth are leveraged. On the other hand, we propose a global-local balancing approach to maximize both contextual understandings. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in novel view synthesis, especially in wide-angle scenarios. More importantly, our method does not require any training and is a plug-and-play module that can be integrated with any diffusion model. Our code can be found at https://github.com/yutaojiang1/Diffuse3D.
ReNeRF: Relightable Neural Radiance Fields with Nearfield Lighting
Authors: Yingyan Xu; Gaspard Zoss; Prashanth Chandran; Markus Gross; Derek Bradley; Paulo Gotardo
Venue: ICCV 2023 (main)

Abstract: Recent work on radiance fields and volumetric inverse rendering (e.g., NeRFs) has provided excellent results in building data-driven models of real scenes for novel view synthesis with high photorealism. While full control over viewpoint is achieved, scene lighting is typically "baked" into the model and cannot be changed; other methods only capture limited variation in lighting or make restrictive assumptions about the captured scene. These limitations prevent the application on arbitrary materials and novel 3D environments with complex, distinct lighting. In this paper, we target the application scenario of capturing high-fidelity assets for neural relighting in controlled studio conditions, but without requiring a dense light stage. Instead, we leverage a small number of area lights commonly used in photogrammetry. We propose ReNeRF, a relightable radiance field model based on the intuitive and powerful approach of image-based relighting, which implicitly captures global light transport (for arbitrary objects) without complex, error-prone simulations. Thus, our new method is simple and provides full control over viewpoint and lighting, without simplistic assumptions about how light interacts with the scene. In addition, ReNeRF does not rely on the usual assumption of distant lighting - during training, we explicitly account for the distance between 3D points in the volume and point samples on the light sources. Thus, at test time, we achieve better generalization to novel, continuous lighting directions, including nearfield lighting effects.
Segment Anything
Authors: Alexander Kirillov; Eric Mintun; Nikhila Ravi; Hanzi Mao; Chloe Rolland; Laura Gustafson; Tete Xiao; Spencer Whitehead; Alexander C. Berg; Wan-Yen Lo; Piotr Dollar; Ross Girshick
Venue: ICCV 2023 (main)

Abstract: We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at https://segment-anything.com to foster research into foundation models for computer vision. We recommend reading the full paper at: https://arxiv.org/abs/2304.02643.
Unsupervised Prompt Tuning for Text-Driven Object Detection
Authors: Weizhen He; Weijie Chen; Binbin Chen; Shicai Yang; Di Xie; Luojun Lin; Donglian Qi; Yueting Zhuang
Venue: ICCV 2023 (main)

Abstract: Grounded language-image pre-trained models have shown strong zero-shot generalization to various downstream object detection tasks. Despite their promising performance, the models rely heavily on the laborious prompt engineering. Existing works typically address this problem by tuning text prompts using downstream training data in a few-shot or fully supervised manner. However, a rarely studied problem is to optimize text prompts without using any annotations. In this paper, we delve into this problem and propose an Unsupervised Prompt Tuning framework for text-driven object detection, which is composed of two novel mean teaching mechanisms. In conventional mean teaching, the quality of pseudo boxes is expected to optimize better as the training goes on, but there is still a risk of overfitting noisy pseudo boxes. To mitigate this problem, 1) we propose Nested Mean Teaching, which adopts nested-annotation to supervise teacher-student mutual learning in a bi-level optimization manner; 2) we propose Dual Complementary Teaching, which employs an offline pre-trained teacher and an online mean teacher via data-augmentation-based complementary labeling so as to ensure learning without accumulating confirmation bias. By integrating these two mechanisms, the proposed unsupervised prompt tuning framework achieves significant performance improvement on extensive object detection datasets.
Tubelet-Contrastive Self-Supervision for Video-Efficient Generalization
Authors: Fida Mohammad Thoker; Hazel Doughty; Cees G. M. Snoek
Venue: ICCV 2023 (main)

Abstract: We propose a self-supervised method for learning motion-focused video representations. Existing approaches minimize distances between temporally augmented videos, which maintain high spatial similarity. We instead propose to learn similarities between videos with identical local motion dynamics but an otherwise different appearance. We do so by adding synthetic motion trajectories to videos which we refer to as tubelets. 
 By simulating different tubelet motions and applying transformations, such as scaling and rotation, we introduce motion patterns beyond what is present in the pretraining data. This allows us to learn a video representation that is remarkably data efficient: our approach maintains performance when using only 25% of the pretraining videos. Experiments on 10 diverse downstream settings 
 demonstrate our competitive performance and generalizability to new domains and fine-grained actions.
Re-ReND: Real-Time Rendering of NeRFs across Devices
Authors: Sara Rojas; Jesus Zarzar; Juan C. Pérez; Artsiom Sanakoyeu; Ali Thabet; Albert Pumarola; Bernard Ghanem
Venue: ICCV 2023 (main)

Abstract: This paper proposes a novel approach for rendering a pre-trained Neural Radiance Field (NeRF) in real-time on resource-constrained devices. We introduce Re-ReND, a method enabling Real-time Rendering of NeRFs across Devices. Re-ReND is designed to achieve real-time performance by converting the NeRF into a representation that can be efficiently processed by standard graphics pipelines. The proposed method distills the NeRF by extracting the learned density into a mesh, while the learned color information is factorized into a set of matrices that represent the scene's light field. Factorization implies the field is queried via inexpensive MLP-free matrix multiplications, while using a light field allows rendering a pixel by querying the field a single time--as opposed to hundreds of queries when employing a radiance field. Since the proposed representation can be implemented using a fragment shader, it can be directly integrated with standard rasterization frameworks. Our flexible implementation can render a NeRF in real-time with low memory requirements and on a wide range of resource-constrained devices, including mobiles and AR/VR headsets. Notably, we find that Re-ReND can achieve over a 2.6-fold increase in rendering speed versus the state-of-the-art without perceptible losses in quality.
360VOT: A New Benchmark Dataset for Omnidirectional Visual Object Tracking
Authors: Huajian Huang; Yinzhe Xu; Yingshu Chen; Sai-Kit Yeung
Venue: ICCV 2023 (main)

Abstract: 360deg images can provide an omnidirectional field of view which is important for stable and long-term scene perception. In this paper, we explore 360deg images for visual object tracking and perceive new challenges caused by large distortion, stitching artifacts, and other unique attributes of 360deg images. To alleviate these problems, we take advantage of novel representations of target localization, i.e., bounding field-of-view, and then introduce a general 360 tracking framework that can adopt typical trackers for omnidirectional tracking. More importantly, we propose a new large-scale omnidirectional tracking benchmark dataset, 360VOT, in order to facilitate future research. 360VOT contains 120 sequences with up to 113K high-resolution frames in equirectangular projection. And the tracking targets cover 32 categories in diverse scenarios. Moreover, we provide 4 types of unbiased ground truth, including (rotated) bounding boxes and (rotated) bounding field-of-views, as well as new metrics tailored for 360deg images which allow accurate evaluation of omnidirectional tracking performance. Finally, we extensively evaluated 20 state-of-the-art visual trackers and provided a new baseline for future comparisons. Homepage: https://360vot.hkustvgd.com
Is Imitation All You Need? Generalized Decision-Making with Dual-Phase Training
Authors: Yao Wei; Yanchao Sun; Ruijie Zheng; Sai Vemprala; Rogerio Bonatti; Shuhang Chen; Ratnesh Madaan; Zhongjie Ba; Ashish Kapoor; Shuang Ma
Venue: ICCV 2023 (main)

Abstract: We introduce DualMind, a generalist agent designed to tackle various decision-making tasks that addresses challenges posed by current methods, such as overfitting behaviors and dependence on task-specific fine-tuning. DualMind uses a novel "Dual-phase" training strategy that emulates how humans learn to act in the world. The model first learns fundamental common knowledge through a self-supervised objective tailored for control tasks and then learns how to make decisions based on different contexts through imitating behaviors conditioned on given prompts. DualMind can handle tasks across domains, scenes, and embodiments using just a single set of model weights and can execute zero-shot prompting without requiring task-specific fine-tuning. We evaluate DualMind on MetaWorld and Habitat through extensive experiments and demonstrate its superior generalizability compared to previous techniques, outperforming other generalist agents by over 50% and 70% on Habitat and MetaWorld, respectively. On the 45 tasks in MetaWorld, DualMind achieves over 30 tasks at a 90% success rate. Our source code is available at https://github.com/yunyikristy/DualMind.
Generalizing Event-Based Motion Deblurring in Real-World Scenarios
Authors: Xiang Zhang; Lei Yu; Wen Yang; Jianzhuang Liu; Gui-Song Xia
Venue: ICCV 2023 (main)

Abstract: Event-based motion deblurring has shown promising results by exploiting low-latency events. However, current approaches are limited in their practical usage, as they assume the same spatial resolution of inputs and specific blurriness distributions. This work addresses these limitations and aims to generalize the performance of event-based deblurring in real-world scenarios. We propose a scale-aware network that allows flexible input spatial scales and enables learning from different temporal scales of motion blur. A two-stage self-supervised learning scheme is then developed to fit real-world data distribution. By utilizing the relativity of blurriness, our approach efficiently ensures the restored brightness and structure of latent images and further generalizes deblurring performance to handle varying spatial and temporal scales of motion blur in a self-distillation manner. Our method is extensively evaluated, demonstrating remarkable performance, and we also introduce a real-world dataset consisting of multi-scale blurry frames and events to facilitate research in event-based deblurring.
Handwritten and Printed Text Segmentation: A Signature Case Study
Authors: Sina Gholamian; Ali Vahdat
Venue: ICCV 2023 (main)

Abstract: While analyzing scanned documents, handwritten text can overlap with printed text. This overlap causes difficulties during the optical character recognition (OCR) and digitization process of documents, and subsequently, hurts downstream NLP tasks. Prior research either focuses solely on the binary classification of handwritten text or performs a three-class segmentation of the document, i.e., recognition of handwritten, printed, and background pixels. This approach results in the assignment of overlapping handwritten and printed pixels to only one of the classes, and thus, they are not accounted for in the other class. Thus, in this research, we develop novel approaches to address the challenges of handwritten and printed text segmentation. Our objective is to recover text from different classes in their entirety, especially enhancing the segmentation performance on overlapping sections. To support this task, we introduce a new dataset, SignaTR6K, collected from real legal documents, as well as a new model architecture for the handwritten and printed text segmentation task. Our best configuration outperforms prior work on two different datasets by 17.9% and 7.3% on IoU scores. The SignaTR6K dataset is accessible for download via the following link: https://forms.office.com/r/2a5RDg7cAY.
LERF: Language Embedded Radiance Fields
Authors: Justin Kerr; Chung Min Kim; Ken Goldberg; Angjoo Kanazawa; Matthew Tancik
Venue: ICCV 2023 (main)

Abstract: Humans describe the physical world using natural language to refer to specific 3D locations based on a vast range of properties: visual appearance, semantics, abstract associations, or actionable affordances. In this work we propose Language Embedded Radiance Fields (LERFs), a method for grounding language embeddings from off-the-shelf models like CLIP into NeRF, which enable these types of open-ended language queries in 3D. LERF learns a dense, multi-scale language field inside NeRF by volume rendering CLIP embeddings along training rays, supervising these embeddings across training views to provide multi-view consistency and smooth the underlying language field. After optimization, LERF can extract 3D relevancy maps for a broad range of language prompts interactively in real-time, which has potential use cases in robotics, understanding vision-language models, and interacting with 3D scenes. LERF enables pixel-aligned, zero-shot queries on the distilled 3D CLIP embeddings without relying on region proposals or masks, supporting long-tail open-vocabulary queries hierarchically across the volume. See the project website at: https://lerf.io
DomainAdaptor: A Novel Approach to Test-time Adaptation
Authors: Jian Zhang; Lei Qi; Yinghuan Shi; Yang Gao
Venue: ICCV 2023 (main)

Abstract: To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificity of unseen samples that are also critical during the test. In this paper, we investigate a more challenging task that aims to adapt a trained CNN model to unseen domains during the test. To maximumly mine the information in the test data, we propose a unified method called DomainAdaptor for the test-time adaptation, which consists of an AdaMixBN module and a Generalized Entropy Minimization (GEM) loss. Specifically, AdaMixBN addresses the domain shift by adaptively fusing training and test statistics in the normalization layer via a dynamic mixture coefficient and a statistic transformation operation. To further enhance the adaptation ability of AdaMixBN, we design a GEM loss that extends the Entropy Minimization loss to better exploit the information in the test data. Extensive experiments show DomainAdaptor consistently outperforms the state-of-the-art methods on four benchmarks. Furthermore, our method brings more remarkable improvement against existing methods on the few-data unseen domain. The code is available at https://github.com/koncle/DomainAdaptor.
RCA-NOC: Relative Contrastive Alignment for Novel Object Captioning
Authors: Jiashuo Fan; Yaoyuan Liang; Leyao Liu; Shaolun Huang; Lei Zhang
Venue: ICCV 2023 (main)

Abstract: In this paper, we introduce a novel approach to novel object captioning which employs relative contrastive learning to learn visual and semantic alignment. Our approach maximizes compatibility between regions and object tags in a contrastive manner. To set up a proper contrastive learning objective, for each image, we augment tags by leveraging the relative nature of positive and negative pairs obtained from foundation models such as CLIP. We then use the rank of each augmented tag in a list as a relative relevance label to contrast each top-ranked tag with a set of lower-ranked tags. This learning objective encourages the top-ranked tags to be more compatible with their image and text context than lower-ranked tags, thus improving the discriminative ability of the learned multi-modality representation. We evaluate our approach on two datasets and show that our proposed RCA-NOC approach outperforms state-of-the-art methods by a large margin, demonstrating its effectiveness in improving vision-language representation for novel object captioning.