GenAI4ELab/papercli-papers huggingface dataset:
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
Authors: Hongwu Peng; Shaoyi Huang; Tong Zhou; Yukui Luo; Chenghong Wang; Zigeng Wang; Jiahui Zhao; Xi Xie; Ang Li; Tony Geng; Kaleel Mahmood; Wujie Wen; Xiaolin Xu; Caiwen Ding
Venue: ICCV 2023 (main)

Abstract: The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues. Private inference (PI) techniques using cryptographic primitives offer a solution but often have high computation and communication costs, particularly with non-linear operators like ReLU. Many attempts to reduce ReLU operations exist, but they may need heuristic threshold selection or cause substantial accuracy loss. This work introduces AutoReP, a gradient-based approach to lessen non-linear operators and alleviate these issues. It automates the selection of ReLU and polynomial functions to speed up PI applications and introduces distribution-aware polynomial approximation (DaPa) to maintain model expressivity while accurately approximating ReLUs. Our experimental results demonstrate significant accuracy improvements of 6.12% (94.31%, 12.9K ReLU budget, CIFAR-10), 8.39% (74.92%, 12.9K ReLU budget, CIFAR-100), and 9.45% (63.69%, 55K ReLU budget, Tiny-ImageNet) over current state-of-the-art methods, e.g., SNL. Morever, AutoReP is applied to EfficientNet-B2 on ImageNet dataset, and achieved 75.55% accuracy with 176.1 xReLU budget reduction.
MotionLM: Multi-Agent Motion Forecasting as Language Modeling
Authors: Ari Seff; Brian Cera; Dian Chen; Mason Ng; Aurick Zhou; Nigamaa Nayakanti; Khaled S. Refaat; Rami Al-Rfou; Benjamin Sapp
Venue: ICCV 2023 (main)

Abstract: Reliable forecasting of the future behavior of road agents is a critical component to safe planning in autonomous vehicles. Here, we represent continuous trajectories as sequences of discrete motion tokens and cast multi-agent motion prediction as a language modeling task over this domain. Our model, MotionLM, provides several advantages: First, it does not require anchors or explicit latent variable optimization to learn multimodal distributions. Instead, we leverage a single standard language modeling objective, maximizing the average log probability over sequence tokens. Second, our approach bypasses post-hoc interaction heuristics where individual agent trajectory generation is conducted prior to interactive scoring. Instead, MotionLM produces joint distributions over interactive agent futures in a single autoregressive decoding process. In addition, the model's sequential factorization enables temporally causal conditional rollouts. The proposed approach establishes new state-of-the-art performance for multi-agent motion prediction on the Waymo Open Motion Dataset, ranking 1st on the interactive challenge leaderboard.
Black Box Few-Shot Adaptation for Vision-Language Models
Authors: Yassine Ouali; Adrian Bulat; Brais Matinez; Georgios Tzimiropoulos
Venue: ICCV 2023 (main)

Abstract: Vision-Language (V-L) models trained with contrastive learning to align the visual and language modalities have been shown to be strong few-shot learners. Soft prompt learning is the method of choice for few-shot downstream adaption aiming to bridge the modality gap caused by the distribution shift induced by the new domain. While parameter-efficient, prompt learning still requires access to the model weights and can be computationally infeasible for large models with billions of parameters. To address these shortcomings, in this work, we describe a black-box method for V-L few-shot adaptation that (a) operates on pre-computed image and text features and hence works without access to the model's weights, (b) it is orders of magnitude faster at training time, (c) it is amenable to both supervised and unsupervised training, and (d) it can be even used to align image and text features computed from uni-modal models. To achieve this, we propose Linear Feature Alignment (LFA), a simple linear approach for V-L re-alignment in the target domain. LFA is initialized from a closed-form solution to a least-squares problem and then it is iteratively updated by minimizing a re-ranking loss. Despite its simplicity, our approach can even surpass soft-prompt learning methods as shown by extensive experiments on 11 image and 2 video datasets.
Center-Based Decoupled Point-cloud Registration for 6D Object Pose Estimation
Authors: Haobo Jiang; Zheng Dang; Shuo Gu; Jin Xie; Mathieu Salzmann; Jian Yang
Venue: ICCV 2023 (main)

Abstract: In this paper, we propose a novel center-based decoupled point cloud registration framework for robust 6D object pose estimation in real-world scenarios. Our method decouples the translation from the entire transformation by predicting the object center and estimating the rotation in a center-aware manner. This center offset-based translation estimation is correspondence-free, freeing us from the difficulty of constructing correspondences in challenging scenarios, thus improving robustness. To obtain reliable center predictions, we use a multi-view (bird's eye view and front view) object shape description of the source-point features, with both views jointly voting for the object center. Additionally, we propose an effective shape embedding module to augment the source features, largely completing the missing shape information due to partial scanning, thus facilitating the center prediction. With the center-aligned source and model point clouds, the rotation predictor utilizes feature similarity to establish putative correspondences for SVD-based rotation estimation. In particular, we introduce a center-aware hybrid feature descriptor with a normal correction technique to extract discriminative, part-aware features for high-quality correspondence construction. Our experiments show that our method outperforms the state-of-the-art methods by a large margin on real-world datasets such as TUD-L, LINEMOD, and Occluded-LINEMOD.
Self-Ordering Point Clouds
Authors: Pengwan Yang; Cees G. M. Snoek; Yuki M. Asano
Venue: ICCV 2023 (main)

Abstract: In this paper we address the task of finding representative subsets of points in a 3D point cloud by means of a point-wise ordering. Only a few works have tried to address this challenging vision problem, all with the help of hard to obtain point and cloud labels. Different from these works, we introduce the task of point-wise ordering in 3D point clouds through self-supervision, which we call self-ordering. We further contribute the first end-to-end trainable network that learns a point-wise ordering in a self-supervised fashion. It utilizes a novel differentiable point scoring-sorting strategy and it constructs an hierarchical contrastive scheme to obtain self-supervision signals. We extensively ablate the method and show its superior performance even compared to supervised ordering methods on multiple datasets and tasks including zero-shot ordering of point clouds from unseen categories.
Continual Segment: Towards a Single, Unified and Non-forgetting Continual Segmentation Model of 143 Whole-body Organs in CT Scans
Authors: Zhanghexuan Ji; Dazhou Guo; Puyang Wang; Ke Yan; Le Lu; Minfeng Xu; Qifeng Wang; Jia Ge; Mingchen Gao; Xianghua Ye; Dakai Jin
Venue: ICCV 2023 (main)

Abstract: Deep learning empowers the mainstream medical image segmentation methods. Nevertheless, current deep segmentation approaches are not capable of efficiently and effectively adapting and updating the trained models when new segmentation classes are incrementally added. In the real clinical environment, it can be preferred that segmentation models could be dynamically extended to segment new organs/tumors without the (re-)access to previous training datasets due to obstacles of patient privacy and data storage. This process can be viewed as a continual semantic segmentation (CSS) problem, being understudied for multi-organ segmentation. In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs. Using the encoder/decoder network structure, we demonstrate that a continually trained then frozen encoder coupled with incrementally-added decoders can extract sufficiently representative image features for new classes to be subsequently and validly segmented, while avoiding the catastrophic forgetting in CSS. To maintain a single network model complexity, each decoder is progressively pruned using neural architecture search and teacher-student based knowledge distillation. Finally, we propose a body-part and anomaly-aware output merging module to combine organ predictions originating from different decoders and incorporate both healthy and pathological organs appearing in different datasets. Trained and validated on 3D CT scans of 2500+ patients from four datasets, our single network can segment a total of 143 whole-body organs with very high accuracy, closely reaching the upper bound performance level by training four separate segmentation models (i.e., one model per dataset/task).
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation
Authors: Aishik Konwer; Xiaoling Hu; Joseph Bae; Xuan Xu; Chao Chen; Prateek Prasanna
Venue: ICCV 2023 (main)

Abstract: In medical vision, different imaging modalities provide complementary information. However, in practice, not all modalities may be available during inference or even training. Previous approaches, e.g., knowledge distillation or image synthesis, often assume the availability of full modalities for all patients during training; this is unrealistic and impractical due to the variability in data collection across sites. We propose a novel approach to learn enhanced modality-agnostic representations by employing a meta-learning strategy in training, even when only limited full modality samples are available. Meta-learning enhances partial modality representations to full modality representations by meta-training on partial modality data and meta-testing on limited full modality samples. Additionally, we co-supervise this feature enrichment by introducing an auxiliary adversarial learning branch. More specifically, a missing modality detector is used as a discriminator to mimic the full modality setting. Our segmentation framework significantly outperforms state-of-the-art brain tumor segmentation techniques in missing modality scenarios.
Zero-1-to-3: Zero-shot One Image to 3D Object
Authors: Ruoshi Liu; Rundi Wu; Basile Van Hoorick; Pavel Tokmakov; Sergey Zakharov; Carl Vondrick
Venue: ICCV 2023 (main)

Abstract: We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image. To perform novel view synthesis in this underconstrained setting, we capitalize on the geometric priors that large-scale diffusion models learn about natural images. Our conditional diffusion model uses a synthetic dataset to learn controls of the relative camera viewpoint, which allow new images to be generated of the same object under a specified camera transformation. Even though it is trained on a synthetic dataset, our model retains a strong zero-shot generalization ability to out-of-distribution datasets as well as in-the-wild images, including impressionist paintings. Our viewpoint-conditioned diffusion approach can further be used for the task of 3D reconstruction from a single image. Qualitative and quantitative experiments show that our method significantly outperforms stateof- the-art single-view 3D reconstruction and novel view synthesis models by leveraging Internet-scale pre-training.
3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces
Authors: Xuepeng Shi; Georgi Dikov; Gerhard Reitmayr; Tae-Kyun Kim; Mohsen Ghafoorian
Venue: ICCV 2023 (main)

Abstract: Self-supervised monocular depth estimation (SSMDE) aims at predicting the dense depth maps of monocular images, by learning to minimize a photometric loss using spatially neighboring image pairs during training. While SSMDE offers a significant scalability advantage over supervised approaches, it performs poorly on reflective surfaces as the photometric constancy assumption of the photometric loss is violated. We note that the appearance of reflective surfaces is view-dependent and often there are views of such surfaces in the training data that are not contaminated by strong specular reflections. Thus, reflective surfaces can be accurately reconstructed by aggregating the predicted depth of these views. Motivated by this observation, we propose 3D distillation: a novel training framework that utilizes the projected depth of reconstructed reflective surfaces to generate reasonably accurate depth pseudo-labels. To identify those surfaces automatically, we employ an uncertainty-guided depth fusion method, combining the smoother and more accurate projected depth on reflective surfaces and the detailed predicted depth elsewhere. In our experiments using the ScanNet and 7-Scenes datasets, we show that 3D distillation not only significantly improves the prediction accuracy, especially on the problematic surfaces, but also that it generalizes well over various underlying network architectures and to new datasets.
GAIT: Generating Aesthetic Indoor Tours with Deep Reinforcement Learning
Authors: Desai Xie; Ping Hu; Xin Sun; Soren Pirk; Jianming Zhang; Radomir Mech; Arie E. Kaufman
Venue: ICCV 2023 (main)

Abstract: Placing and orienting a camera to compose aesthetically meaningful shots of a scene is not only a key objective in real-world photography and cinematography but also for virtual content creation. The framing of a camera often significantly contributes to the story telling in movies, games, and mixed reality applications. Generating single camera poses or even contiguous trajectories either requires a significant amount of manual labor or requires solving high-dimensional optimization problems, which can be computationally demanding and error-prone. In this paper, we introduce GAIT, a framework for training a Deep Reinforcement Learning (DRL) agent, that learns to automatically control a camera to generate a sequence of aesthetically meaningful views for synthetic 3D indoor scenes. To generate sequences of frames with high aesthetic value, GAIT relies on a neural aesthetics estimator, which is trained on a crowed-sourced dataset. Additionally, we introduce regularization techniques for diversity and smoothness to generate visually interesting trajectories for a 3D environment, and to constrain agent acceleration in the reward function to generate a smooth sequence of camera frames. We validated our method by comparing it to baseline algorithms, based on a perceptual user study, and through ablation studies. Code and visual results are available on the project website: https://desaixie.github.io/gait-rl
Low-Light Image Enhancement with Multi-Stage Residue Quantization and Brightness-Aware Attention
Authors: Yunlong Liu; Tao Huang; Weisheng Dong; Fangfang Wu; Xin Li; Guangming Shi
Venue: ICCV 2023 (main)

Abstract: Low-light image enhancement (LLIE) aims to recover illumination and improve the visibility of low-light images. Conventional LLIE methods often produce poor results because they neglect the effect of noise interference. Deep learning-based LLIE methods focus on learning a mapping function between low-light images and normal-light images that outperforms conventional LLIE methods. However, most deep learning-based LLIE methods cannot yet fully exploit the guidance of auxiliary priors provided by normal-light images in the training dataset. In this paper, we propose a brightness-aware network with normal-light priors based on brightness-aware attention and residual quantized codebook. To achieve a more natural and realistic enhancement, we design a query module to obtain more reliable normal-light features and fuse them with lowlight features by a fusion branch. In addition, we propose a brightness-aware attention module to further retain the color consistency between the enhanced results and the normal-light images. Extensive experimental results on both real-captured and synthetic data show that our method outperforms existing state-of-the-art methods.
Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition
Authors: Jungho Lee; Minhyeok Lee; Dogyoon Lee; Sangyoun Lee
Venue: ICCV 2023 (main)

Abstract: Graph convolutional networks (GCNs) are the most commonly used methods for skeleton-based action recognition and have achieved remarkable performance. Generating adjacency matrices with semantically meaningful edges is particularly important for this task, but extracting such edges is challenging problem. To solve this, we propose a hierarchically decomposed graph convolutional network (HD-GCN) architecture with a novel hierarchically decomposed graph (HD-Graph). The proposed HD-GCN effectively decomposes every joint node into several sets to extract major structurally adjacent and distant edges, and uses them to construct an HD-Graph containing those edges in the same semantic spaces of a human skeleton. In addition, we introduce an attention-guided hierarchy aggregation (A-HA) module to highlight the dominant hierarchical edge sets of the HD-Graph. Furthermore, we apply a new six-way ensemble method, which uses only joint and bone stream without any motion stream. The proposed model is evaluated and achieves state-of-the-art performance on four large, popular datasets. Finally, we demonstrate the effectiveness of our model with various comparative experiments.
LIST: Learning Implicitly from Spatial Transformers for Single-View 3D Reconstruction
Authors: Mohammad Samiul Arshad; William J. Beksi
Venue: ICCV 2023 (main)

Abstract: Accurate reconstruction of both the geometric and topological details of a 3D object from a single 2D image embodies a fundamental challenge in computer vision. Existing explicit/implicit solutions to this problem struggle to recover self-occluded geometry and/or faithfully reconstruct topological shape structures. To resolve this dilemma, we introduce LIST, a novel neural architecture that leverages local and global image features to accurately reconstruct the geometric and topological structure of a 3D object from a single image. We utilize global 2D features to predict a coarse shape of the target object and then use it as a base for higher-resolution reconstruction. By leveraging both local 2D features from the image and 3D features from the coarse prediction, we can predict the signed distance between an arbitrary point and the target surface via an implicit predictor with great accuracy. Furthermore, our model does not require camera estimation or pixel alignment. It provides an uninfluenced reconstruction from the input-view direction. Through qualitative and quantitative analysis, we show the superiority of our model in reconstructing 3D objects from both synthetic and real-world images against the state of the art.
Rethinking Mobile Block for Efficient Attention-based Models
Authors: Jiangning Zhang; Xiangtai Li; Jian Li; Liang Liu; Zhucun Xue; Boshen Zhang; Zhengkai Jiang; Tianxin Huang; Yabiao Wang; Chengjie Wang
Venue: ICCV 2023 (main)

Abstract: This paper focuses on developing modern, efficient, lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Inverted Residual Block (IRB) serves as the infrastructure for lightweight CNNs, but no counterpart has been recognized by attention-based studies. This work rethinks lightweight infrastructure from efficient IRB and effective components of Transformer from a unified perspective, extending CNN-based IRB to attention-based models and abstracting a one-residual Meta Mobile Block (MMB) for lightweight model design. Following simple but effective design criterion, we deduce a modern Inverted Residual Mobile Block (iRMB) and build a ResNet-like Efficient MOdel (EMO) with only iRMB for down-stream tasks. Extensive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, e.g., EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass equal-order CNN-/Attention-based models, while trading-off the parameter, efficiency, and accuracy well: running 2.8-4.0x faster than EdgeNeXt on iPhone14.
REAP: A Large-Scale Realistic Adversarial Patch Benchmark
Authors: Nabeel Hingun; Chawin Sitawarin; Jerry Li; David Wagner
Venue: ICCV 2023 (main)

Abstract: Machine learning models are known to be susceptible to adversarial perturbation. One famous attack is the adversarial patch, a particularly crafted sticker that makes the model mispredict the object it is placed on. This attack presents a critical threat to cyber-physical systems that rely on cameras such as autonomous cars. Despite the significance of the problem, conducting research in this setting has been difficult; evaluating attacks and defenses in the real world is exceptionally costly while synthetic data are unrealistic. In this work, we propose the REAP (REalistic Adversarial Patch) benchmark, a digital benchmark that enables the evaluations on real images under real-world conditions. Built on top of the Mapillary Vistas dataset, our benchmark contains over 14,000 traffic signs. Each sign is augmented with geometric and lighting transformations for applying a digitally generated patch realistically onto the sign. Using our benchmark, we perform the first large-scale assessments of adversarial patch attacks under realistic conditions. Our experiments suggest that patch attacks may present a smaller threat than previously believed and that the success rate of an attack on simpler digital simulations is not predictive of its actual effectiveness in practice. Our benchmark is released publicly at https://github.com/wagner-group/reap-benchmark.
LRRU: Long-short Range Recurrent Updating Networks for Depth Completion
Authors: Yufei Wang; Bo Li; Ge Zhang; Qi Liu; Tao Gao; Yuchao Dai
Venue: ICCV 2023 (main)

Abstract: Existing deep learning-based depth completion methods generally employ massive stacked layers to predict the dense depth map from sparse input data. Although such approaches greatly advance this task, their accompanied huge computational complexity hinders their practical applications. To accomplish depth completion more efficiently, we propose a novel lightweight deep network framework, the Long-short Range Recurrent Updating (LRRU) network. Without learning complex feature representations, LRRU first roughly fills the sparse input to obtain an initial dense depth map, and then iteratively updates it through learned spatially-variant kernels. Our iterative update process is content-adaptive and highly flexible, where the kernel weights are learned by jointly considering the guidance RGB images and the depth map to be updated, and large-to-small kernel scopes are dynamically adjusted to capture long-to-short range dependencies. Our initial depth map has coarse but complete scene depth information, which helps relieve the burden of directly regressing the dense depth from sparse ones, while our proposed method can effectively refine it to an accurate depth map with less learnable parameters and inference time. Experimental results demonstrate that our proposed LRRU variants achieve state-of-the-art performance across different parameter regimes. In particular, the LRRU-Base model outperforms competing approaches on the NYUv2 dataset, and ranks 1st on the KITTI depth completion benchmark at the time of submission. Project page: https://npucvr.github.io/LRRU/.