mtybilly/Instruct2DS huggingface dataset:
Feature Separation and Recalibration for Adversarial Robustness
Authors: Yoonki Cho, Junsik Jung, Sung-Eui Yoon Woo Jae Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Deep neural networks are susceptible to adversarial attacks due to the accumulation of perturbations in the feature level, and numerous works have boosted model robustness by deactivating the non-robust feature activations that cause model mispredictions. However, we claim that these malicious activations still contain discriminative cues and that with recalibration, they can capture additional useful information for correct model predictions. To this end, we propose a novel, easy-to-plugin approach named Feature Separation and Recalibration (FSR) that recalibrates the malicious, non-robust activations for more robust feature maps through Separation and Recalibration. The Separation part disentangles the input feature map into the robust feature with activations that help the model make correct predictions and the non-robust feature with activations that are responsible for model mispredictions upon adversarial attack. The Recalibration part then adjusts the non-robust activations to restore the potentially useful cues for model predictions. Extensive experiments verify the superiority of FSR compared to traditional deactivation techniques and demonstrate that it improves the robustness of existing adversarial training methods by up to 8.57% with small computational overhead. Codes are available at https://github.com/wkim97/FSR.
Nerflets: Local Radiance Fields for Efficient Structure-Aware 3D Scene Representation From 2D Supervision
Authors: Abhijit Kundu, Thomas Funkhouser, Leonidas Guibas, Hao Su, Kyle Genova Xiaoshuai Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: We address efficient and structure-aware 3D scene representation from images. Nerflets are our key contribution-- a set of local neural radiance fields that together represent a scene. Each nerflet maintains its own spatial position, orientation, and extent, within which it contributes to panoptic, density, and radiance reconstructions. By leveraging only photometric and inferred panoptic image supervision, we can directly and jointly optimize the parameters of a set of nerflets so as to form a decomposed representation of the scene, where each object instance is represented by a group of nerflets. During experiments with indoor and outdoor environments, we find that nerflets: (1) fit and approximate the scene more efficiently than traditional global NeRFs, (2) allow the extraction of panoptic and photometric renderings from arbitrary views, and (3) enable tasks rare for NeRFs, such as 3D panoptic segmentation and interactive editing.
CLIP Is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation
Authors: Minghao Chen, Wenxiao Wang, Boxi Wu, Ke Li, Binbin Lin, Haifeng Liu, Xiaofei He Yuqi Lin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task. Mainstream approaches follow a multi-stage framework and suffer from high training costs. In this paper, we explore the potential of Contrastive Language-Image Pre-training models (CLIP) to localize different categories with only image-level labels and without further training. To efficiently generate high-quality segmentation masks from CLIP, we propose a novel WSSS framework called CLIP-ES. Our framework improves all three stages of WSSS with special designs for CLIP: 1) We introduce the softmax function into GradCAM and exploit the zero-shot ability of CLIP to suppress the confusion caused by non-target classes and backgrounds. Meanwhile, to take full advantage of CLIP, we re-explore text inputs under the WSSS setting and customize two text-driven strategies: sharpness-based prompt selection and synonym fusion. 2) To simplify the stage of CAM refinement, we propose a real-time class-aware attention-based affinity (CAA) module based on the inherent multi-head self-attention (MHSA) in CLIP-ViTs. 3) When training the final segmentation model with the masks generated by CLIP, we introduced a confidence-guided loss (CGL) focus on confident regions. Our CLIP-ES achieves SOTA performance on Pascal VOC 2012 and MS COCO 2014 while only taking 10% time of previous methods for the pseudo mask generation. Code is available at https://github.com/linyq2117/CLIP-ES.
Slimmable Dataset Condensation
Authors: Jingwen Ye, Runpeng Yu, Xinchao Wang Songhua Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Dataset distillation, also known as dataset condensation, aims to compress a large dataset into a compact synthetic one. Existing methods perform dataset condensation by assuming a fixed storage or transmission budget. When the budget changes, however, they have to repeat the synthesizing process with access to original datasets, which is highly cumbersome if not infeasible at all. In this paper, we explore the problem of slimmable dataset condensation, to extract a smaller synthetic dataset given only previous condensation results. We first study the limitations of existing dataset condensation algorithms on such a successive compression setting and identify two key factors: (1) the inconsistency of neural networks over different compression times and (2) the underdetermined solution space for synthetic data. Accordingly, we propose a novel training objective for slimmable dataset condensation to explicitly account for both factors. Moreover, synthetic datasets in our method adopt an significance-aware parameterization. Theoretical derivation indicates that an upper-bounded error can be achieved by discarding the minor components without training. Alternatively, if training is allowed, this strategy can serve as a strong initialization that enables a fast convergence. Extensive comparisons and ablations demonstrate the superiority of the proposed solution over existing methods on multiple benchmarks.
Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising
Authors: Zhilu Zhang, Xiaoyu Liu, Chaoyu Feng, Xiaotao Wang, Lei Lei, Wangmeng Zuo Junyi Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Significant progress has been made in self-supervised image denoising (SSID) in the recent few years. However, most methods focus on dealing with spatially independent noise, and they have little practicality on real-world sRGB images with spatially correlated noise. Although pixel-shuffle downsampling has been suggested for breaking the noise correlation, it breaks the original information of images, which limits the denoising performance. In this paper, we propose a novel perspective to solve this problem, i.e., seeking for spatially adaptive supervision for real-world sRGB image denoising. Specifically, we take into account the respective characteristics of flat and textured regions in noisy images, and construct supervisions for them separately. For flat areas, the supervision can be safely derived from non-adjacent pixels, which are much far from the current pixel for excluding the influence of the noise-correlated ones. And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas. For textured regions, the supervision has to be closely related to the content of adjacent pixels. And we present a locally aware network (LAN) to meet the requirement, while LAN itself is selectively supervised with the output of BNN. Combining these two supervisions, a denoising network (e.g., U-Net) can be well-trained. Extensive experiments show that our method performs favorably against state-of-the-art SSID methods on real-world sRGB photographs. The code is available at https://github.com/nagejacob/SpatiallyAdaptiveSSID.
Data-Free Knowledge Distillation via Feature Exchange and Activation Region Constraint
Authors: Jiachen Chen, Hu Han, Shuqiang Jiang Shikang Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Despite the tremendous progress on data-free knowledge distillation (DFKD) based on synthetic data generation, there are still limitations in diverse and efficient data synthesis. It is naive to expect that a simple combination of generative network-based data synthesis and data augmentation will solve these issues. Therefore, this paper proposes a novel data-free knowledge distillation method (SpaceshipNet) based on channel-wise feature exchange (CFE) and multi-scale spatial activation region consistency (mSARC) constraint. Specifically, CFE allows our generative network to better sample from the feature space and efficiently synthesize diverse images for learning the student network. However, using CFE alone can severely amplify the unwanted noises in the synthesized images, which may result in failure to improve distillation learning and even have negative effects. Therefore, we propose mSARC to assure the student network can imitate not only the logit output but also the spatial activation region of the teacher network in order to alleviate the influence of unwanted noises in diverse synthetic images on distillation learning. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet, Imagenette, and ImageNet100 show that our method can work well with different backbone networks, and outperform the state-of-the-art DFKD methods. Code will be available at: https://github.com/skgyu/SpaceshipNet.
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes From Natural Language
Authors: Rao Fu, Vivian Liu, Karl D.D. Willis, Hooman Shayani, Amir H. Khasahmadi, Srinath Sridhar, Daniel Ritchie Aditya Sanghi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We introduce CLIP-Sculptor, a method to address these constraints by producing high-fidelity and diverse 3D shapes without the need for (text, shape) pairs during training. CLIP-Sculptor achieves this in a multi-resolution approach that first generates in a low-dimensional latent space and then upscales to a higher resolution for improved shape fidelity. For improved shape diversity, we use a discrete latent space which is modeled using a transformer conditioned on CLIP's image-text embedding space. We also present a novel variant of classifier-free guidance, which improves the accuracy-diversity trade-off. Finally, we perform extensive experiments demonstrating that CLIP-Sculptor outperforms state-of-the-art baselines.
Mask-Free Video Instance Segmentation
Authors: Martin Danelljan, Henghui Ding, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu Lei Ke
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: The recent advancement in Video Instance Segmentation (VIS) has largely been driven by the use of deeper and increasingly data-hungry transformer-based models. However, video masks are tedious and expensive to annotate, limiting the scale and diversity of existing VIS datasets. In this work, we aim to remove the mask-annotation requirement. We propose MaskFreeVIS, achieving highly competitive VIS performance, while only using bounding box annotations for the object state. We leverage the rich temporal mask consistency constraints in videos by introducing the Temporal KNN-patch Loss (TK-Loss), providing strong mask supervision without any labels. Our TK-Loss finds one-to-many matches across frames, through an efficient patch-matching step followed by a K-nearest neighbor selection. A consistency loss is then enforced on the found matches. Our mask-free objective is simple to implement, has no trainable parameters, is computationally efficient, yet outperforms baselines employing, e.g., state-of-the-art optical flow to enforce temporal mask consistency. We validate MaskFreeVIS on the YouTube-VIS 2019/2021, OVIS and BDD100K MOTS benchmarks. The results clearly demonstrate the efficacy of our method by drastically narrowing the gap between fully and weakly-supervised VIS performance. Our code and trained models are available at http://vis.xyz/pub/maskfreevis.
Continual Detection Transformer for Incremental Object Detection
Authors: Bernt Schiele, Andrea Vedaldi, Christian Rupprecht Yaoyao Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Incremental object detection (IOD) aims to train an object detector in phases, each with annotations for new object categories. As other incremental settings, IOD is subject to catastrophic forgetting, which is often addressed by techniques such as knowledge distillation (KD) and exemplar replay (ER). However, KD and ER do not work well if applied directly to state-of-the-art transformer-based object detectors such as Deformable DETR and UP-DETR. In this paper, we solve these issues by proposing a ContinuaL DEtection TRansformer (CL-DETR), a new method for transformer-based IOD which enables effective usage of KD and ER in this context. First, we introduce a Detector Knowledge Distillation (DKD) loss, focusing on the most informative and reliable predictions from old versions of the model, ignoring redundant background predictions, and ensuring compatibility with the available ground-truth labels. We also improve ER by proposing a calibration strategy to preserve the label distribution of the training set, therefore better matching training and testing statistics. We conduct extensive experiments on COCO 2017 and demonstrate that CL-DETR achieves state-of-the-art results in the IOD setting.
Two-Stream Networks for Weakly-Supervised Temporal Action Localization With Semantic-Aware Mechanisms
Authors: Yadong Li, Hongbin Wang Yu Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Weakly-supervised temporal action localization aims to detect action boundaries in untrimmed videos with only video-level annotations. Most existing schemes detect temporal regions that are most responsive to video-level classification, but they overlook the semantic consistency between frames. In this paper, we hypothesize that snippets with similar representations should be considered as the same action class despite the absence of supervision signals on each snippet. To this end, we devise a learnable dictionary where entries are the class centroids of the corresponding action categories. The representations of snippets identified as the same action category are induced to be close to the same class centroid, which guides the network to perceive the semantics of frames and avoid unreasonable localization. Besides, we propose a two-stream framework that integrates the attention mechanism and the multiple-instance learning strategy to extract fine-grained clues and salient features respectively. Their complementarity enables the model to refine temporal boundaries. Finally, the developed model is validated on the publicly available THUMOS-14 and ActivityNet-1.3 datasets, where substantial experiments and analyses demonstrate that our model achieves remarkable advances over existing methods.
HyperMatch: Noise-Tolerant Semi-Supervised Learning via Relaxed Contrastive Constraint
Authors: Jing Lu, Kerui Liu, Yunlu Xu, Zhanzhan Cheng, Yi Niu Beitong Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Recent developments of the application of Contrastive Learning in Semi-Supervised Learning (SSL) have demonstrated significant advancements, as a result of its exceptional ability to learn class-aware cluster representations and the full exploitation of massive unlabeled data. However, mismatched instance pairs caused by inaccurate pseudo labels would assign an unlabeled instance to the incorrect class in feature space, hence exacerbating SSL's renowned confirmation bias. To address this issue, we introduced a novel SSL approach, HyperMatch, which is a plug-in to several SSL designs enabling noise-tolerant utilization of unlabeled data. In particular, confidence predictions are combined with semantic similarities to generate a more objective class distribution, followed by a Gaussian Mixture Model to divide pseudo labels into a 'confident' and a 'less confident' subset. Then, we introduce Relaxed Contrastive Loss by assigning the 'less-confident' samples to a hyper-class, i.e. the union of top-K nearest classes, which effectively regularizes the interference of incorrect pseudo labels and even increases the probability of pulling a 'less confident' sample close to its true class. Experiments and in-depth studies demonstrate that HyperMatch delivers remarkable state-of-the-art performance, outperforming FixMatch on CIFAR100 with 400 and 2500 labeled samples by 11.86% and 4.88%, respectively.
From Images to Textual Prompts: Zero-Shot Visual Question Answering With Frozen Large Language Models
Authors: Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Boyang Li, Dacheng Tao, Steven Hoi Jiaxian Guo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnection and task disconnection between LLM and VQA task. End-to-end training on vision and language data may bridge the disconnections, but is inflexible and computationally expensive. To address this issue, we propose Img2Prompt, a plug-and-play module that provides the prompts that can bridge the aforementioned modality and task disconnections, so that LLMs can perform zero-shot VQA tasks without end-to-end training. In order to provide such prompts, we further employ LLM-agnostic models to provide prompts that can describe image content and self-constructed question-answer pairs, which can effectively guide LLM to perform zero-shot VQA tasks. Img2Prompt offers the following benefits: 1) It can flexibly work with various LLMs to perform VQA. 2) Without the needing of end-to-end training, it significantly reduces the cost of deploying LLM for zero-shot VQA tasks. 3) It achieves comparable or better performance than methods relying on end-to-end training. For example, we outperform Flamingo by 5.6% on VQAv2. On the challenging A-OKVQA dataset, our method even outperforms few-shot methods by as much as 20%.
LEGO-Net: Learning Regular Rearrangements of Objects in Rooms
Authors: Sijie Ding, Jeong Joon Park, Rahul Sajnani, Adrien Poulenard, Srinath Sridhar, Leonidas Guibas Qiuhong Anna Wei
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Humans universally dislike the task of cleaning up a messy room. If machines were to help us with this task, they must understand human criteria for regular arrangements, such as several types of symmetry, co-linearity or co-circularity, spacing uniformity in linear or circular patterns, and further inter-object relationships that relate to style and functionality. Previous approaches for this task relied on human input to explicitly specify goal state, or synthesized scenes from scratch--but such methods do not address the rearrangement of existing messy scenes without providing a goal state. In this paper, we present LEGO-Net, a data-driven transformer-based iterative method for LEarning reGular rearrangement of Objects in messy rooms. LEGO-Net is partly inspired by diffusion models--it starts with an initial messy state and iteratively "de-noises" the position and orientation of objects to a regular state while reducing distance traveled. Given randomly perturbed object positions and orientations in an existing dataset of professionally-arranged scenes, our method is trained to recover a regular re-arrangement. Results demonstrate that our method is able to reliably rearrange room scenes and outperform other methods. We additionally propose a metric for evaluating regularity in room arrangements using number-theoretic machinery.
FastInst: A Simple Query-Based Model for Real-Time Instance Segmentation
Authors: Pengyu Li, Yifeng Geng, Xuansong Xie Junjie He
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Recent attention in instance segmentation has focused on query-based models. Despite being non-maximum suppression (NMS)-free and end-to-end, the superiority of these models on high-accuracy real-time benchmarks has not been well demonstrated. In this paper, we show the strong potential of query-based models on efficient instance segmentation algorithm designs. We present FastInst, a simple, effective query-based framework for real-time instance segmentation. FastInst can execute at a real-time speed (i.e., 32.5 FPS) while yielding an AP of more than 40 (i.e., 40.5 AP) on COCO test-dev without bells and whistles. Specifically, FastInst follows the meta-architecture of recently introduced Mask2Former. Its key designs include instance activation-guided queries, dual-path update strategy, and ground truth mask-guided learning, which enable us to use lighter pixel decoders, fewer Transformer decoder layers, while achieving better performance. The experiments show that FastInst outperforms most state-of-the-art real-time counterparts, including strong fully convolutional baselines, in both speed and accuracy. Code can be found at https://github.com/junjiehe96/FastInst.
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
Authors: Jiangmiao Pang, Xinshuo Weng, Rawal Khirodkar, Kris Kitani Jinkun Cao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Kalman filter (KF) based methods for multi-object tracking (MOT) make an assumption that objects move linearly. While this assumption is acceptable for very short periods of occlusion, linear estimates of motion for prolonged time can be highly inaccurate. Moreover, when there is no measurement available to update Kalman filter parameters, the standard convention is to trust the priori state estimations for posteriori update. This leads to the accumulation of errors during a period of occlusion. The error causes significant motion direction variance in practice. In this work, we show that a basic Kalman filter can still obtain state-of-the-art tracking performance if proper care is taken to fix the noise accumulated during occlusion. Instead of relying only on the linear state estimate (i.e., estimation-centric approach), we use object observations (i.e., the measurements by object detector) to compute a virtual trajectory over the occlusion period to fix the error accumulation of filter parameters. This allows more time steps to correct errors accumulated during occlusion. We name our method Observation-Centric SORT (OC-SORT). It remains Simple, Online, and Real-Time but improves robustness during occlusion and non-linear motion. Given off-the-shelf detections as input, OC-SORT runs at 700+ FPS on a single CPU. It achieves state-of-the-art on multiple datasets, including MOT17, MOT20, KITTI, head tracking, and especially DanceTrack where the object motion is highly non-linear. The code and models are available at https://github.com/noahcao/OC_SORT.
Multi-View Azimuth Stereo via Tangent Space Consistency
Authors: Hiroaki Santo, Fumio Okura, Yasuyuki Matsushita Xu Cao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: We present a method for 3D reconstruction only using calibrated multi-view surface azimuth maps. Our method, multi-view azimuth stereo, is effective for textureless or specular surfaces, which are difficult for conventional multi-view stereo methods. We introduce the concept of tangent space consistency: Multi-view azimuth observations of a surface point should be lifted to the same tangent space. Leveraging this consistency, we recover the shape by optimizing a neural implicit surface representation. Our method harnesses the robust azimuth estimation capabilities of photometric stereo methods or polarization imaging while bypassing potentially complex zenith angle estimation. Experiments using azimuth maps from various sources validate the accurate shape recovery with our method, even without zenith angles.
VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models
Authors: Amber Xie, Pieter Abbeel Ajay Jain
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Diffusion models have shown impressive results in text-to-image synthesis. Using massive datasets of captioned images, diffusion models learn to generate raster images of highly diverse objects and scenes. However, designers frequently use vector representations of images like Scalable Vector Graphics (SVGs) for digital icons, graphics and stickers. Vector graphics can be scaled to any size, and are compact. In this work, we show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics. We do so without access to large datasets of captioned SVGs. Instead, inspired by recent work on text-to-3D synthesis, we vectorize a text-to-image diffusion sample and fine-tune with a Score Distillation Sampling loss. By optimizing a differentiable vector graphics rasterizer, our method distills abstract semantic knowledge out of a pretrained diffusion model. By constraining the vector representation, we can also generate coherent pixel art and sketches. Our approach, VectorFusion, produces more coherent graphics than prior works that optimize CLIP, a contrastive image-text model.
The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
Authors: Sungdong Kim, Jin-Hwa Kim, Donghyun Kwak, Byoung-Tak Zhang Gi-Cheon Kang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Visual dialog (VisDial) is a task of answering a sequence of questions grounded in an image, using the dialog history as context. Prior work has trained the dialog agents solely on VisDial data via supervised learning or leveraged pre-training on related vision-and-language datasets. This paper presents a semi-supervised learning approach for visually-grounded dialog, called Generative Self-Training (GST), to leverage unlabeled images on the Web. Specifically, GST first retrieves in-domain images through out-of-distribution detection and generates synthetic dialogs regarding the images via multimodal conditional text generation. GST then trains a dialog agent on the synthetic and the original VisDial data. As a result, GST scales the amount of training data up to an order of magnitude that of VisDial (1.2M to 12.9M QA data). For robust training of the synthetic dialogs, we also propose perplexity-based data selection and multimodal consistency regularization. Evaluation on VisDial v1.0 and v0.9 datasets shows that GST achieves new state-of-the-art results on both datasets. We further observe the robustness of GST against both visual and textual adversarial attacks. Finally, GST yields strong performance gains in the low-data regime. Code is available at https://github.com/gicheonkang/gst-visdial.
Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis
Authors: Ziwei Wang, Jie Zhou, Jiwen Lu Xiuwei Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: In this paper, we propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis. We empirically observe that sparse convolution operation causes larger quantization errors than standard convolution. However, conventional network quantization methods directly binarize the weights and activations in sparse convolution, resulting in performance drop due to the significant quantization loss. On the contrary, we search the optimal subset of convolution operation that activates the sparse convolution at various locations for quantization error alleviation, and the performance gap between real-valued and binary sparse convolutional networks is closed without complexity overhead. Specifically, we first present the shifted sparse convolution that fuses the information in the receptive field for the active sites that match the pre-defined positions. Then we employ the differentiable search strategies to discover the optimal opsitions for active site matching in the shifted sparse convolution, and the quantization errors are significantly alleviated for efficient point cloud analysis. For fair evaluation of the proposed method, we empirically select the recently advances that are beneficial for sparse convolution network binarization to construct a strong baseline. The experimental results on ScanNet and NYU Depth v2 show that our BSC-Net achieves significant improvement upon our srtong baseline and outperforms the state-of-the-art network binarization methods by a remarkable margin without additional computation overhead for binarizing sparse convolutional networks.
Transformer-Based Learned Optimization
Authors: Luke Metz, Mykhaylo Andriluka, C. Daniel Freeman, Cristian Sminchisescu Erik Gärtner
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: We propose a new approach to learned optimization where we represent the computation of an optimizer's update step using a neural network. The parameters of the optimizer are then learned by training on a set of optimization tasks with the objective to perform minimization efficiently. Our innovation is a new neural network architecture, Optimus, for the learned optimizer inspired by the classic BFGS algorithm. As in BFGS, we estimate a preconditioning matrix as a sum of rank-one updates but use a Transformer-based neural network to predict these updates jointly with the step length and direction. In contrast to several recent learned optimization-based approaches, our formulation allows for conditioning across the dimensions of the parameter space of the target problem while remaining applicable to optimization tasks of variable dimensionality without retraining. We demonstrate the advantages of our approach on a benchmark composed of objective functions traditionally used for the evaluation of optimization algorithms, as well as on the real world-task of physics-based visual reconstruction of articulated 3d human motion.
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models
Authors: Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein Gowthami Somepalli
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Cutting-edge diffusion models produce images with high quality and customizability, enabling them to be used for commercial art and graphic design purposes. But do diffusion models create unique works of art, or are they replicating content directly from their training sets? In this work, we study image retrieval frameworks that enable us to compare generated images with training samples and detect when content has been replicated. Applying our frameworks to diffusion models trained on multiple datasets including Oxford flowers, Celeb-A, ImageNet, and LAION, we discuss how factors such as training set size impact rates of content replication. We also identify cases where diffusion models, including the popular Stable Diffusion model, blatantly copy from their training data.
Neuralizer: General Neuroimage Analysis Without Re-Training
Authors: Adrian V. Dalca Steffen Czolbe
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Neuroimage processing tasks like segmentation, reconstruction, and registration are central to the study of neuroscience. Robust deep learning strategies and architectures used to solve these tasks are often similar. Yet, when presented with a new task or a dataset with different visual characteristics, practitioners most often need to train a new model, or fine-tune an existing one. This is a time-consuming process that poses a substantial barrier for the thousands of neuroscientists and clinical researchers who often lack the resources or machine-learning expertise to train deep learning models. In practice, this leads to a lack of adoption of deep learning, and neuroscience tools being dominated by classical frameworks. We introduce Neuralizer, a single model that generalizes to previously unseen neuroimaging tasks and modalities without the need for re-training or fine-tuning. Tasks do not have to be known a priori, and generalization happens in a single forward pass during inference. The model can solve processing tasks across multiple image modalities, acquisition methods, and datasets, and generalize to tasks and modalities it has not been trained on. Our experiments on coronal slices show that when few annotated subjects are available, our multi-task network outperforms task-specific baselines without training on the task.
Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral Image Classification
Authors: Yongshan Zhang, Yicong Zhou Jie Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Hyperspectral image (HSI) classification aims at assigning a unique label for every pixel to identify categories of different land covers. Existing deep learning models for HSIs are usually performed in a traditional learning paradigm. Being emerging machines, quantum computers are limited in the noisy intermediate-scale quantum (NISQ) era. The quantum theory offers a new paradigm for designing deep learning models. Motivated by the quantum circuit (QC) model, we propose a quantum-inspired spectral-spatial network (QSSN) for HSI feature extraction. The proposed QSSN consists of a phase-prediction module (PPM) and a measurement-like fusion module (MFM) inspired from quantum theory to dynamically fuse spectral and spatial information. Specifically, QSSN uses a quantum representation to represent an HSI cuboid and extracts joint spectral-spatial features using MFM. An HSI cuboid and its phases predicted by PPM are used in the quantum representation. Using QSSN as the building block, we propose an end-to-end quantum-inspired spectral-spatial pyramid network (QSSPN) for HSI feature extraction and classification. In this pyramid framework, QSSPN progressively learns feature representations by cascading QSSN blocks and performs classification with a softmax classifier. It is the first attempt to introduce quantum theory in HSI processing model design. Substantial experiments are conducted on three HSI datasets to verify the superiority of the proposed QSSPN framework over the state-of-the-art methods.
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks
Authors: Shuning Zhang, Gujun Chen, Dong Wang, Pu Feng, Jiakai Wang, Aishan Liu, Xin Yi, Xianglong Liu Simin Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Physical world adversarial attack is a highly practical and threatening attack, which fools real world deep learning systems by generating conspicuous and maliciously crafted real world artifacts. In physical world attacks, evaluating naturalness is highly emphasized since human can easily detect and remove unnatural attacks. However, current studies evaluate naturalness in a case-by-case fashion, which suffers from errors, bias and inconsistencies. In this paper, we take the first step to benchmark and assess visual naturalness of physical world attacks, taking autonomous driving scenario as the first attempt. First, to benchmark attack naturalness, we contribute the first Physical Attack Naturalness (PAN) dataset with human rating and gaze. PAN verifies several insights for the first time: naturalness is (disparately) affected by contextual features (i.e., environmental and semantic variations) and correlates with behavioral feature (i.e., gaze signal). Second, to automatically assess attack naturalness that aligns with human ratings, we further introduce Dual Prior Alignment (DPA) network, which aims to embed human knowledge into model reasoning process. Specifically, DPA imitates human reasoning in naturalness assessment by rating prior alignment and mimics human gaze behavior by attentive prior alignment. We hope our work fosters researches to improve and automatically assess naturalness of physical world attacks. Our code and exemplar data can be found at https://github.com/zhangsn-19/PAN.
Visual Prompt Multi-Modal Tracking
Authors: Simiao Lai, Xin Chen, Dong Wang, Huchuan Lu Jiawen Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Visible-modal object tracking gives rise to a series of downstream multi-modal tracking tributaries. To inherit the powerful representations of the foundation model, a natural modus operandi for multi-modal tracking is full fine-tuning on the RGB-based parameters. Albeit effective, this manner is not optimal due to the scarcity of downstream data and poor transferability, etc. In this paper, inspired by the recent success of the prompt learning in language models, we develop Visual Prompt multi-modal Tracking (ViPT), which learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to various downstream multimodal tracking tasks. ViPT finds a better way to stimulate the knowledge of the RGB-based model that is pre-trained at scale, meanwhile only introducing a few trainable parameters (less than 1% of model parameters). ViPT outperforms the full fine-tuning paradigm on multiple downstream tracking tasks including RGB+Depth, RGB+Thermal, and RGB+Event tracking. Extensive experiments show the potential of visual prompt learning for multi-modal tracking, and ViPT can achieve state-of-the-art performance while satisfying parameter efficiency. Code and models are available at https://github.com/jiawen-zhu/ViPT.
Self-Supervised Representation Learning for CAD
Authors: Michael Hu, Milin Kodnongbua, Vladimir G. Kim, Adriana Schulz Benjamin T. Jones
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Virtually every object in the modern world was created, modified, analyzed and optimized using computer aided design (CAD) tools. An active CAD research area is the use of data-driven machine learning methods to learn from the massive repositories of geometric and program representations. However, the lack of labeled data in CAD's native format, i.e., the parametric boundary representation (B-Rep), poses an obstacle at present difficult to overcome. Several datasets of mechanical parts in B-Rep format have recently been released for machine learning research. However, large-scale databases are mostly unlabeled, and labeled datasets are small. Additionally, task-specific label sets are rare and costly to annotate. This work proposes to leverage unlabeled CAD geometry on supervised learning tasks. We learn a novel, hybrid implicit/explicit surface representation for B-Rep geometry. Further, we show that this pre-training both significantly improves few-shot learning performance and achieves state-of-the-art performance on several current B-Rep benchmarks.
DETRs With Hybrid Matching
Authors: Yuhui Yuan, Haodi He, Xiaopei Wu, Haojun Yu, Weihong Lin, Lei Sun, Chao Zhang, Han Hu Ding Jia
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections. This end-to-end signature is important for the versatility of DETR, and it has been generalized to broader vision tasks. However, we note that there are few queries assigned as positive samples and the one-to-one set matching significantly reduces the training efficacy of positive samples. We propose a simple yet effective method based on a hybrid matching scheme that combines the original one-to-one matching branch with an auxiliary one-to-many matching branch during training. Our hybrid strategy has been shown to significantly improve accuracy. In inference, only the original one-to-one match branch is used, thus maintaining the end-to-end merit and the same inference efficiency of DETR. The method is named H-DETR, and it shows that a wide range of representative DETR methods can be consistently improved across a wide range of visual tasks, including Deformable-DETR, PETRv2, PETR, and TransTrack, among others.
Dealing With Cross-Task Class Discrimination in Online Continual Learning
Authors: Bing Liu, Dongyan Zhao Yiduo Guo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Existing continual learning (CL) research regards catastrophic forgetting (CF) as almost the only challenge. This paper argues for another challenge in class-incremental learning (CIL), which we call cross-task class discrimination (CTCD), i.e., how to establish decision boundaries between the classes of the new task and old tasks with no (or limited) access to the old task data. CTCD is implicitly and partially dealt with by replay-based methods. A replay method saves a small amount of data (replay data) from previous tasks. When a batch of current task data arrives, the system jointly trains the new data and some sampled replay data. The replay data enables the system to partially learn the decision boundaries between the new classes and the old classes as the amount of the saved data is small. However, this paper argues that the replay approach also has a dynamic training bias issue which reduces the effectiveness of the replay data in solving the CTCD problem. A novel optimization objective with a gradient-based adaptive method is proposed to dynamically deal with the problem in the online CL process. Experimental results show that the new method achieves much better results in online CL.
Angelic Patches for Improving Third-Party Object Detector Performance
Authors: Shuo Li, Sangdon Park, Insup Lee, Osbert Bastani Wenwen Si
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Deep learning models have shown extreme vulnerability to simple perturbations and spatial transformations. In this work, we explore whether we can adopt the characteristics of adversarial attack methods to help improve perturbation robustness for object detection. We study a class of realistic object detection settings wherein the target objects have control over their appearance. To this end, we propose a reversed Fast Gradient Sign Method (FGSM) to obtain these angelic patches that significantly increase the detection probability, even without pre-knowledge of the perturbations. In detail, we apply the patch to each object instance simultaneously, strengthen not only classification but also bounding box accuracy. Experiments demonstrate the efficacy of the partial-covering patch in solving the complex bounding box problem. More importantly, the performance is also transferable to different detection models even under severe affine transformations and deformable shapes. To our knowledge, we are the first (object detection) patch that achieves both cross-model and multiple-patch efficacy. We observed average accuracy improvements of 30% in the real-world experiments, which brings large social value. Our code is available at: https://github.com/averysi224/angelic_patches.
UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy
Authors: Weikang Wan, Jialiang Zhang, Haoran Liu, Zikang Shan, Hao Shen, Ruicheng Wang, Haoran Geng, Yijia Weng, Jiayi Chen, Tengyu Liu, Li Yi, He Wang Yinzhen Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up objects in high-quality and diverse ways and generalize across hundreds of categories and even the unseen. Inspired by successful pipelines used in parallel gripper grasping, we split the task into two stages: 1) grasp proposal (pose) generation and 2) goal-conditioned grasp execution. For the first stage, we propose a novel probabilistic model of grasp pose conditioned on the point cloud observation that factorizes rotation from translation and articulation. Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud. For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution. Note that it is very challenging to learn this highly generalizable grasp policy that only takes realistic inputs without oracle states. We thus propose several important innovations, including state canonicalization, object curriculum, and teacher-student distillation. Integrating the two stages, our final pipeline becomes the first to achieve universal generalization for dexterous grasping, demonstrating an average success rate of more than 60% on thousands of object instances, which significantly outperforms all baselines, meanwhile showing only a minimal generalization gap.
A Rotation-Translation-Decoupled Solution for Robust and Efficient Visual-Inertial Initialization
Authors: Bo Xu, Zhanpeng Ouyang, Hongdong Li Yijia He
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: We propose a novel visual-inertial odometry (VIO) initialization method, which decouples rotation and translation estimation, and achieves higher efficiency and better robustness. Existing loosely-coupled VIO-initialization methods suffer from poor stability of visual structure-from-motion (SfM), whereas those tightly-coupled methods often ignore the gyroscope bias in the closed-form solution, resulting in limited accuracy. Moreover, the aforementioned two classes of methods are computationally expensive, because 3D point clouds need to be reconstructed simultaneously. In contrast, our new method fully combines inertial and visual measurements for both rotational and translational initialization. First, a rotation-only solution is designed for gyroscope bias estimation, which tightly couples the gyroscope and camera observations. Second, the initial velocity and gravity vector are solved with linear translation constraints in a globally optimal fashion and without reconstructing 3D point clouds. Extensive experiments have demonstrated that our method is 8 72 times faster (w.r.t. a 10-frame set) than the state-of-the-art methods, and also presents significantly higher robustness and accuracy. The source code is available at https://github.com/boxuLibrary/drt-vio-init.