mtybilly/Instruct2DS huggingface dataset:
Few-Shot Dataset Distillation via Translative Pre-Training
Authors: Xinchao Wang Songhua Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Dataset distillation aims at a small synthetic dataset to mimic the training performance on neural networks of a given large dataset. Existing approaches heavily rely on an iterative optimization to update synthetic data and multiple forward-backward passes over thousands of neural network spaces, which introduce significant overhead for computation and are inconvenient in scenarios requiring high efficiency. In this paper, we focus on few-shot dataset distillation, where a distilled dataset is synthesized with only a few or even a single network. To this end, we introduce the notion of distillation space, such that synthetic data optimized only in this specific space can achieve the effect of those optimized through numerous neural networks, with dramatically accelerated training and reduced computational cost. To learn such a distillation space, we first formulate the problem as a quad-level optimization framework and propose a bi-level algorithm. Nevertheless, the algorithm in its original form has a large memory footprint in practice due to the back-propagation through an unrolled computational graph. We then convert the problem of learning the distillation space to a first-order one based on image translation. Specifically, the synthetic images are optimized in an arbitrary but fixed neural space and then translated to those in the targeted distillation space. We pre-train the translator on some large datasets like ImageNet so that it requires only a limited number of adaptation steps on the target dataset. Extensive experiments demonstrate that the translator after pre-training and a limited number of adaptation steps achieves comparable distillation performance with state of the arts, with  15x acceleration. It also exerts satisfactory generalization performance across different datasets, storage budgets, and numbers of classes.
Random Sub-Samples Generation for Self-Supervised Real Image Denoising
Authors: Xiao Liu, Xiangyu Liao, Yuanzhouhan Cao, Chao Ren Yizhong Pan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: With sufficient paired training samples, the supervised deep learning methods have attracted much attention in image denoising because of their superior performance. However, it is still very challenging to widely utilize the supervised methods in real cases due to the lack of paired noisy-clean images. Meanwhile, most self-supervised denoising methods are ineffective as well when applied to the real-world denoising tasks because of their strict assumptions in applications. For example, as a typical method for self-supervised denoising, the original blind spot network (BSN) assumes that the noise is pixel-wise independent, which is much different from the real cases. To solve this problem, we propose a novel self-supervised real image denoising framework named Sampling Difference As Perturbation (SDAP) based on Random Sub-samples Generation (RSG) with a cyclic sample difference loss. Specifically, we dig deeper into the properties of BSN to make it more suitable for real noise. Surprisingly, we find that adding an appropriate perturbation to the training images can effectively improve the performance of BSN. Further, we propose that the sampling difference can be considered as perturbation to achieve better results. Finally we propose a new BSN framework in combination with our RSG strategy. The results show that it significantly outperforms other state-of-the-art self-supervised denoising methods on real-world datasets. The code is available at https://github.com/p1y2z3/SDAP.
Waffling Around for Performance: Visual Classification with Random Words and Broad Concepts
Authors: Jae Myung Kim, A. Sophia Koepke, Oriol Vinyals, Cordelia Schmid, Zeynep Akata Karsten Roth
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: The visual classification performance of vision-language models such as CLIP has been shown to benefit from additional semantic knowledge from large language models (LLMs) such as GPT-3. In particular, averaging over LLM-generated class descriptors, e.g. "waffle, which has a round shape", can notably improve generalization performance. In this work, we critically study this behavior and propose WaffleCLIP, a framework for zero-shot visual classification which simply replaces LLM-generated descriptors with random character and word descriptors. Without querying external models, we achieve comparable performance gains on a large number of visual classification tasks. This allows WaffleCLIP to both serve as a low-cost 
 alternative, as well as a sanity check for any future LLM-based vision-language model extensions. We conduct an extensive experimental study on the impact and shortcomings of additional semantics introduced with LLM-generated descriptors, and showcase how - if available - semantic context is better leveraged by querying LLMs for high-level concepts, which we show can be done to jointly resolve potential class name ambiguities. Code is available here: https://github.com/ExplainableML/WaffleCLIP.
Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model
Authors: Naiqi Li, Jiawei Li, Tao Dai, Yong Jiang, Shu-Tao Xia Xinyi Zhang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Unsupervised surface anomaly detection aims at discovering and localizing anomalous patterns using only anomaly-free training samples. Reconstruction-based models are among the most popular and successful methods, which rely on the assumption that anomaly regions are more difficult to reconstruct. However, there are three major challenges to the practical application of this approach: 1) the reconstruction quality needs to be further improved since it has a great impact on the final result, especially for images with structural changes; 2) it is observed that for many neural networks, the anomalies can also be well reconstructed, which severely violates the underlying assumption; 3) since reconstruction is an ill-conditioned problem, a test instance may correspond to multiple normal patterns, but most current reconstruction-based methods have ignored this critical fact. In this paper, we propose DiffAD, a method for unsupervised anomaly detection based on the latent diffusion model, inspired by its ability to generate high-quality and diverse images. We further propose noisy condition embedding and interpolated channels to address the aforementioned challenges in the general reconstruction-based pipeline. Extensive experiments show that our method achieves state-of-the-art performance on the challenging MVTec dataset, especially in localization accuracy.
AutoAD II: The Sequel - Who, When, and What in Movie Audio Description
Authors: Max Bain, Arsha Nagrani, Gul Varol, Weidi Xie, Andrew Zisserman Tengda Han
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Audio Description (AD) is the task of generating descriptions of visual content, at suitable time intervals, for the benefit of visually impaired audiences. For movies, this presents notable challenges -- AD must occur only during existing pauses in dialogue, should refer to characters by name, and ought to aid understanding of the storyline as a whole.
 To this end, we develop a new model for automatically generating movie AD, given CLIP visual features of the frames, the cast list, and the temporal locations of the speech; addressing all three of the `who', `when', and `what' questions: (i) who -- we introduce a character bank consisting of the character's name, the actor that played the part, and a CLIP feature of their face, for the principal cast of each movie, and demonstrate how this can be used to improve naming in the generated AD; (ii) when -- we investigate several models for determining whether an AD should be generated for a time interval or not, based on the visual content of the interval and its neighbours; and (iii) what -- we implement a new vision-language model for this task, that can ingest the proposals from the character bank, whilst conditioning on the visual features using cross-attention, and demonstrate how this improves over previous architectures for AD text generation in an apples-to-apples comparison.
TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance
Authors: Houwen Peng, Zhenghong Zhou, Bin Xiao, Mengchen Liu, Lu Yuan, Hong Xuan, Michael Valenzuela, Xi (Stephen) Chen, Xinggang Wang, Hongyang Chao, Han Hu Kan Wu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: In this paper, we propose a novel cross-modal distillation method, called TinyCLIP, for large-scale language-image pre-trained models. The method introduces two core techniques: affinity mimicking and weight inheritance. Affinity mimicking explores the interaction between modalities during distillation, enabling student models to mimic teachers' behavior of learning cross-modal feature alignment in a visual-linguistic affinity space. Weight inheritance transmits the pre-trained weights from the teacher models to their student counterparts to improve distillation efficiency. Moreover, we extend the method into a multi-stage progressive distillation to mitigate the loss of informative weights during extreme compression. Comprehensive experiments demonstrate the efficacy of TinyCLIP, showing that it can reduce the size of the pre-trained CLIP ViT-B/32 by 50%, while maintaining comparable zero-shot performance. While aiming for comparable performance, distillation with weight inheritance can speed up the training by 1.4 - 7.8x compared to training from scratch. Moreover, our TinyCLIP ViT-8M/16, trained on YFCC-15M, achieves an impressive zero-shot top-1 accuracy of 41.1% on ImageNet, surpassing the original CLIP ViT-B/16 by 3.5% while utilizing only 8.9% parameters. Finally, we demonstrate the good transferability of TinyCLIP in various downstream tasks. Code and models will be open-sourced at aka.ms/tinyclip.
Hyperbolic Chamfer Distance for Point Cloud Completion
Authors: Yun Yue, Songlin Hou, Xuechu Yu, Yajun Xu, Kazunori D Yamada, Ziming Zhang Fangzhou Lin
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Chamfer distance (CD) is a standard metric to measure the shape dissimilarity between point clouds in point cloud completion, as well as a loss function for (deep) learning. However, it is well known that CD is vulnerable to outliers, leading to the drift towards suboptimal models. In contrast to the literature where most works address such issues in Euclidean space, we propose an extremely simple
 yet powerful metric for point cloud completion, namely Hyperbolic Chamfer Distance (HyperCD), that computes CD in hyperbolic space. In backpropagation, HyperCD consistently assigns higher weights to the matched point pairs with smaller Euclidean distances. In this way, good point matches are likely to be preserved while bad matches can be updated gradually, leading to better completion results. We demonstrate state-of-the-art performance on the benchmark datasets, i.e. PCN, ShapeNet-55, and ShapeNet-34, and show from visualization that HyperCD can significantly improve the surface smoothness. Code is available at: https://github.com/Zhang-VISLab.
Democratising 2D Sketch to 3D Shape Retrieval Through Pivoting
Authors: Ayan Kumar Bhunia, Aneeshan Sain, Subhadeep Koley, Tao Xiang, Yi-Zhe Song Pinaki Nath Chowdhury
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: This paper studies the problem of 2D sketch to 3D shape retrieval, but with a focus on democratising the process. We would like this democratisation to happen on two fronts: (i) to remove the need for large-scale specifically sourced 2D sketch and 3D shape datasets, and (ii) to remove restrictions on how well the user needs to sketch and from what viewpoint. The end result is a system that is trainable using existing datasets, and once trained allows users to sketch regardless of drawing skills and without restriction on view angle. We achieve all this via a clever use of pivoting, along with novel designs that injects 3D understanding of 2D sketches into the system. We perform pivoting on two existing datasets, each from a distant research domain to the other: 2D sketch and photo pairs from the sketch-based image retrieval field (SBIR), and 3D shapes from ShapeNet. It follows that the actual feature pivoting happens on photos from the former and 2D projections from the latter. Doing this already achieves most of our democratisation challenge -- the level of 2D sketch abstraction embedded in SBIR dataset offers demoralization on drawing quality, and the whole thing works without a specifically sourced 2D sketch and 3D model pair. To further achieve democratisation on sketching viewpoint, we "lift" 2D sketches to 3D space using Blind Perspective-n-Points (BPnP) that injects 3D-aware information into the sketch encoder. Results show ours achieves competitive performance compared with fully-supervised baselines, while meeting all set democratisation goals.
Simoun: Synergizing Interactive Motion-appearance Understanding for Vision-based Reinforcement Learning
Authors: Peixi Peng, Yifan Zhao, Yunpeng Zhai, Haoran Xu, Yonghong Tian Yangru Huang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Efficient motion and appearance modeling are critical for vision-based Reinforcement Learning (RL). However, existing methods struggle to reconcile motion and appearance information within the state representations learned from a single observation encoder. To address the problem, we present Synergizing Interactive Motion-appearance Understanding (Simoun), a unified framework for vision-based RL. Given consecutive observation frames, Simoun deliberately and interactively learns both motion and appearance features through a dual-path network architecture. The learning process collaborates with a structural interactive module, which explores the latent motion-appearance structures from the two network paths to leverage their complementarity. To promote sample efficiency, we further design a consistency-guided curiosity module to encourage the exploration of under-learned observations. During training, the curiosity module provides intrinsic rewards according to the consistency of environmental temporal dynamics, which are deduced from both motion and appearance network paths. Experiments conducted on the DeepMind control suite and CARLA automatic driving benchmarks demonstrate the effectiveness of Simoun, where it performs favorably against state-of-the-art methods.
AG3D: Learning to Generate 3D Avatars from 2D Image Collections
Authors: Xu Chen, Jinlong Yang, Michael J. Black, Otmar Hilliges, Andreas Geiger Zijian Dong
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: While progress in 2D generative models of human appearance has been rapid, many applications require 3D avatars that can be animated and rendered. Unfortunately, most existing methods for learning generative models of 3D humans with diverse shape and appearance require 3D training data, which is limited and expensive to acquire. The key to progress is hence to learn generative models of 3D avatars from abundant unstructured 2D image collections. However, learning realistic and complete 3D appearance and geometry in this under-constrained setting remains challenging, especially in the presence of loose clothing such as dresses. In this paper, we propose a new adversarial generative model of realistic 3D people from 2D images. Our method captures shape and deformation of the body and loose clothing by adopting a holistic 3D generator and integrating an efficient, flexible, articulation module. To improve realism, we train our model using multiple discriminators while also integrating geometric cues in the form of predicted 2D normal maps. We experimentally find that our method outperforms previous 3D- and articulation-aware methods in terms of geometry and appearance. We validate the effectiveness of our model and the importance of each component via systematic ablation studies.
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
Authors: Zhuoxiao Chen, Zhen Fang, Zheng Zhang, Mahsa Baktashmotlagh, Zi Huang Yadan Luo
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Achieving a reliable LiDAR-based object detector in autonomous driving is paramount, but its success hinges on obtaining large amounts of precise 3D annotations. Active learning (AL) seeks to mitigate the annotation burden through algorithms that use fewer labels and can attain performance comparable to fully supervised learning. Although AL has shown promise, current approaches prioritize the selection of unlabeled point clouds with high aleatoric and/or epistemic uncertainty, leading to the selection of more instances for labeling and reduced computational efficiency. In this paper, we resort to a novel kernel coding rate maximization (KECOR) strategy which aims to identify the most informative point clouds to acquire labels through the lens of information theory. Greedy search is applied to seek desired point clouds that can maximize the minimal number of bits required to encode the latent features. To determine the uniqueness and informativeness of the selected samples from the model perspective, we construct a proxy network of the 3D detector head and compute the outer product of Jacobians from all proxy layers to form the empirical neural tangent kernel (NTK) matrix. To accommodate both one-stage (i.e., SECOND) and two-stage detectors (i.e., PV-RCNN), we further incorporate the classification entropy maximization and well trade-off between detection performance and the total number of bounding boxes selected for annotation. Extensive experiments conducted on two 3D benchmarks and a 2D detection dataset evidence the superiority and versatility of the proposed approach. Our results show that approximately 44% box-level annotation costs and 26% computational time are reduced compared to the state-of-the-art AL method, without compromising detection performance.
Learned Image Reasoning Prior Penetrates Deep Unfolding Network for Panchromatic and Multi-spectral Image Fusion
Authors: Jie Huang, Naishan Zheng, Chongyi Li Man Zhou
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: The success of deep neural networks for pan-sharpening is commonly in a form of black box, lacking transparency and interpretability. To alleviate this issue, we propose a novel model-driven deep unfolding framework with image reasoning prior tailored for the pan-sharpening task. Different from existing unfolding solutions that deliver the proximal operator networks as the uncertain and vague priors, our framework is motivated by the content reasoning ability of masked autoencoders (MAE) with insightful designs. Specifically, the pre-trained MAE with spatial masking strategy, acting as intrinsic reasoning prior, is embedded into unfolding architecture. Meanwhile, the pre-trained MAE with spatial-spectral masking strategy is treated as the regularization term within loss function to constrain the spatial-spectral consistency. Such designs penetrate the image reasoning prior into deep unfolding networks while improving its interpretability and representation capability. The uniqueness of our framework is that the holistic learning process is explicitly integrated with the inherent physical mechanism underlying the pan-sharpening task. Extensive experiments on multiple satellite datasets demonstrate the superiority of our method over the existing state-of-the-art approaches.
Representation Disparity-aware Distillation for 3D Object Detection
Authors: Sheng Xu, Mingbao Lin, Jihao Yin, Baochang Zhang, Xianbin Cao Yanjing Li
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: In this paper, we focus on developing knowledge distillation (KD) for compact 3D detectors. We observe that off-the-shelf KD methods manifest their efficacy only when the teacher model and student counterpart share similar intermediate feature representations. This might explain why they are less effective in building extreme-compact 3D detectors where significant representation disparity arises due primarily to the intrinsic sparsity and irregularity in 3D point clouds. This paper presents a novel representation disparity-aware distillation (RDD) method to address the representation disparity issue and reduce performance gap between compact students and over-parameterized teachers. This is accomplished by building our RDD from an innovative perspective of information bottleneck (IB), which can effectively minimize the disparity of proposal region pairs from student and teacher in features and logits. Extensive experiments are performed to demonstrate the superiority of our RDD over existing KD methods. For example, our RDD increases mAP of CP-Voxel-S to 57.1% on nuScenes dataset, which even surpasses teacher performance while taking up only 42% FLOPs.
NCHO: Unsupervised Learning for Neural 3D Composition of Humans and Objects
Authors: Shunsuke Saito, Hanbyul Joo Taeksoo Kim
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Deep generative models have been recently extended to synthesizing 3D digital humans. However, previous approaches treat clothed humans as a single chunk of geometry without considering the compositionality of clothing and accessories. As a result, individual items cannot be naturally composed into novel identities, leading to limited expressiveness and controllability of generative 3D avatars. While several methods attempt to address this by leveraging synthetic data, the interaction between humans and objects is not authentic due to the domain gap, and manual asset creation is difficult to scale for a wide variety of objects. In this work, we present a novel framework for learning a compositional generative model of humans and objects (backpacks, coats, scarves, and more) from real-world 3D scans. Our compositional model is interaction-aware, meaning the spatial relationship between humans and objects, and the mutual shape change by physical contact is fully incorporated. The key challenge is that, since humans and objects are in contact, their 3D scans are merged into a single piece. To decompose them without manual annotations, we propose to leverage two sets of 3D scans of a single person with and without objects. Our approach learns to decompose objects and naturally compose them back into a generative human model in an unsupervised manner. Despite our simple setup requiring only the capture of a single subject with objects, our experiments demonstrate the strong generalization of our model by enabling the natural composition of objects to diverse identities in various poses and the composition of multiple objects, which is unseen in training data. The project page is available at https://taeksuu.github.io/ncho.
Breaking The Limits of Text-conditioned 3D Motion Synthesis with Elaborative Descriptions
Authors: Jack Urbanek, Alexander G. Hauptmann, Jungdam Won Yijun Qian
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Given its wide applications, there is increasing focus on generating 3D human motions from textual descriptions. Differing from the majority of previous works, which regard actions as single entities and can only generate short sequences for simple motions, we propose EMS, an elaborative motion synthesis model conditioned on detailed natural language descriptions. It generates natural and smooth motion sequences for long and complicated actions by factorizing them into groups of atomic actions. Meanwhile, it understands atomic-action level attributes (e.g., motion direction, speed, and body parts) and enables users to generate sequences of unseen complex actions from unique sequences of known atomic actions with independent attribute settings and timings applied. We evaluate our method on the KIT Motion-Language and BABEL benchmarks, where it outperforms all previous state-of-the-art with noticeable margins.
VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity Control
Authors: Yanyang Li, Michael R. Lyu, Liwei Wang Zi-Yuan Hu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: As the model size of pre-trained language models (PLMs) grows rapidly, full fine-tuning becomes prohibitively expensive for model training and storage. In vision-and-language (VL), parameter-efficient tuning (PET) techniques are proposed to integrate modular modifications (e.g., Adapter) into encoder-decoder PLMs. By tuning a small set of trainable parameters, these techniques perform on par with full fine-tuning. However, excessive modular modifications and neglecting the unique abilities of the encoders and decoders can lead to performance degradation, while existing PET techniques (e.g., VL-Adapter) overlook these issues. In this paper, we propose a Vision-and-Language Parameter-Efficient Tuning (VL-PET) framework to impose effective control over modular modifications via a novel granularity-controlled mechanism. Considering different granularity-controlled matrices generated by this mechanism, a variety of model-agnostic VL-PET modules can be instantiated from our framework for better efficiency and effectiveness trade-offs. We further propose lightweight designs to enhance VL alignment and modeling for the encoders and maintain text generation for the decoders. Extensive experiments conducted on four image-text tasks and four video-text tasks demonstrate the efficiency, effectiveness, scalability and transferability of our VL-PET framework. In particular, our VL-PET-large significantly outperforms full fine-tuning by 2.39% (2.61%) and VL-Adapter by 2.92% (3.41%) with BART-base (T5-base) on image-text tasks, while utilizing fewer trainable parameters. Furthermore, we validate the enhanced effect of employing our VL-PET designs (e.g., granularity-controlled mechanism and lightweight designs) on existing PET techniques, enabling them to achieve significant performance improvements.
ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation
Authors: Xiangxiang Chu, Yuda Fan, Zhexi Zhang, Bo Zhang, Xiaokang Yang, Junchi Yan Xiaoxing Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Albeit being a prevalent architecture searching approach, differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet resides in the memory. This is where the single-path DARTS comes in, which only chooses a single-path submodel at each step. While being memory-friendly, it also comes with low computational costs. Nonetheless, we discover a critical issue of single-path DARTS that has not been primarily noticed. Namely, it also suffers from severe performance collapse since too many parameter-free operations like skip connections are derived, just like DARTS does. In this paper, we propose a new algorithm called RObustifying Memory-Efficient NAS (ROME) to give a cure. First, we disentangle the topology search from the operation search to make searching and evaluation consistent. We then adopt Gumbel-Top2 reparameterization and gradient accumulation to robustify the unwieldy bi-level optimization. We verify ROME extensively across 15 benchmarks to demonstrate its effectiveness and robustness.
Toward Multi-Granularity Decision-Making: Explicit Visual Reasoning with Hierarchical Knowledge
Authors: Shi Chen, Qi Zhao Yifeng Zhang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Answering visual questions requires the ability to parse visual observations and correlate them with a variety of knowledge. Existing visual question answering (VQA) models either pay little attention to the role of knowledge or do not take into account the granularity of knowledge, e.g., attaching the color of "grassland" to "ground"). They have yet to develop the capability of modeling knowledge of multiple granularity, and are also vulnerable to spurious data biases. To fill the gap, this paper makes progresses from two distinct perspectives: (1) It presents a Hierarchical Concept Graph (HCG) that discriminates and associates multi-granularity concepts with a multi-layered hierarchical structure, aligning visual observations with knowledge across different levels to alleviate data biases. (2) To facilitate a comprehensive understanding of how knowledge contributes throughout the decision-making process, we further propose an interpretable Hierarchical Concept Neural Module Network (HCNMN) It explicitly propagates multi-granularity knowledge across the hierarchical structure and incorporates them with a sequence of reasoning steps, providing a transparent interface to elaborate on the integration of observations and knowledge. Through extensive experiments on multiple challenging datasets (i.e., GQA,VQA,FVQA,OK-VQA) , we demonstrate the effectiveness of our method in answering questions in different scenarios. Our code is available at https://github.com/SuperJohnZhang/HCNMN.
3D-aware Image Generation using 2D Diffusion Models
Authors: Jiaolong Yang, Binbin Huang, Xin Tong Jianfeng Xiang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: In this paper, we introduce a novel 3D-aware image generation method that leverages 2D diffusion models. We formulate the 3D-aware image generation task as multiview 2D image set generation, and further to a sequential unconditional-conditional multiview image generation process. This allows us to utilize 2D diffusion models to boost the generative modelling power of the method. Additionally, we incorporate depth information from monocular depth estimators to construct the training data for the conditional diffusion model using only still images. We train our method on a large-scale unstructured dataset, i.e., ImageNet, which is not addressed by previous methods. It produces high-quality images that significantly outperform prior methods. Furthermore, our approach showcases its capability to generate instances with large view angles, even though the training images are diverse and unaligned, gathered from "in-the-wild" real-world environments.
Locating Noise is Halfway Denoising for Semi-Supervised Segmentation
Authors: Feng Zhu, Bowen Cheng, Luoqi Liu, Yao Zhao, Yunchao Wei Yan Fang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We investigate semi-supervised semantic segmentation with self-training, where a teacher model generates pseudo masks to exploit the benefits of a large amount of unlabeled images. We notice that the noisy label from the generated pseudo masks is the major obstacle to achieving good performance. Previous works all treat the noise in pixel level and ignore the contextual information of the noise. This work shows that locating the patch-wise noisy region is a better way to deal with noise. To be specific, our method, named Uncertainty-aware Patch CutMix (UPC), first estimates the uncertainty of per-pixel prediction for pseudo masks of unlabeled images. Then UPC splits the uncertainty map into patches and calculates patch-wise uncertainty. UPC selects top-k most uncertain patches to generate the uncertain regions. Finally, uncertain regions are replaced with reliable ones from labeled images. We conduct extensive experiments using standard semi-supervised settings on Pascal VOC and Cityscapes. Experiment results show that UPC can significantly boost the performance of the state-of-the-art methods. In addition, we further demonstrate that our UPC is robust to out-of-distribution unlabeled images, eg, MSCOCO.
Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution
Authors: Yingqian Wang, Longguang Wang, Jungang Yang, Shilin Zhou, Yulan Guo Zhengyu Liang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution (SR), but is highly challenging due to its non-local property caused by the disparities among LF images. Although many deep neural networks (DNNs) have been developed for LF image SR and achieved continuously improved performance, existing methods cannot well leverage the long-range spatial-angular correlation and thus suffer a significant performance drop when handling scenes with large disparity variations. In this paper, we propose a simple yet effective method to learn the non-local spatial-angular correlation for LF image SR. In our method, we adopt the epipolar plane image (EPI) representation to project the 4D spatial-angular correlation onto multiple 2D EPI planes, and then develop a Transformer network with repetitive self-attention operations to learn the spatial-angular correlation by modeling the dependencies between each pair of EPI pixels. Our method can fully incorporate the information from all angular views while achieving a global receptive field along the epipolar line. We conduct extensive experiments with insightful visualizations to validate the effectiveness of our method. Comparative results on five public datasets show that our method not only achieves state-of-the-art SR performance, but also performs robust to disparity variations.
ICE-NeRF: Interactive Color Editing of NeRFs via Decomposition-Aware Weight Optimization
Authors: Dae-Shik Kim Jae-Hyeok Lee
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Neural Radiance Fields (NeRFs) have gained considerable attention for their high-quality results in 3D scene reconstruction and rendering. Recently, there have been active studies on various tasks such as novel view synthesis and scene editing. However, editing NeRFs is challenging as accurately decomposing the desired area of 3D space and ensuring the consistency of edited results from different angles is difficult. In this paper, we propose ICE-NeRF, an Interactive Color Editing framework that performs color editing by taking a pre-trained NeRF and a rough user mask as input. Our proposed method performs the entire color editing process in only under a minute using a partial fine-tuning approach. To perform effective color editing, we address two issues: (1) the entanglement of the implicit representation that causes unwanted color changes in undesired areas when learning weights, and (2) the loss of multi-view consistency when fine-tuning for a single or a few views. To address these issues, we introduce two techniques: Activation Field-based Regularization (AFR) and Single-mask Multi-view Rendering (SMR). The AFR performs weight regularization during fine-tuning based on the assumption that not all weights have an equal impact on the desired area. The SMR maps the 2D mask to 3D space through inverse projection and renders it from other views to generate multi-view masks. ICE-NeRF not only enables well-decomposed, multi-view consistent color editing but also significantly reduces processing time compared to existing methods.
SPANet: Frequency-balancing Token Mixer using Spectral Pooling Aggregation Modulation
Authors: Juhan Yoo, Kijung Kim, Jeongho Lee, Dong Hwan Kim Guhnoo Yun
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Recent studies show that self-attentions behave like low-pass filters (as opposed to convolutions) and enhancing their high-pass filtering capability improves model performance. Contrary to this idea, we investigate existing convolution-based models with spectral analysis and observe that improving the low-pass filtering in convolution operations also leads to performance improvement. To account for this observation, we hypothesize that utilizing optimal token mixers that capture balanced representations of both high- and low-frequency components can enhance the performance of models. We verify this by decomposing visual features into the frequency domain and combining them in a balanced manner. To handle this, we replace the balancing problem with a mask filtering problem in the frequency domain. Then, we introduce a novel token-mixer named SPAM and leverage it to derive a MetaFormer model termed as SPANet. Experimental results show that the proposed method provides a way to achieve this balance, and
 the balanced representations of both high- and low-frequency components can improve the performance of models on multiple computer vision tasks. Our code is available at https://doranlyong.github.io/projects/spanet/.
ASAG: Building Strong One-Decoder-Layer Sparse Detectors via Adaptive Sparse Anchor Generation
Authors: Junkai Yan, Yipeng Gao, Xiaohua Xie, Wei-Shi Zheng Shenghao Fu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Recent sparse detectors with multiple, e.g. six, decoder layers achieve promising performance but much inference time due to complex heads. Previous works have explored using dense priors as initialization and built one-decoder-layer detectors. Although they gain remarkable acceleration, their performance still lags behind their six-decoder-layer counterparts by a large margin. In this work, we aim to bridge this performance gap while retaining fast speed. We find that the architecture discrepancy between dense and sparse detectors leads to feature conflict, hampering the performance of one-decoder-layer detectors. Thus we propose Adaptive Sparse Anchor Generator (ASAG) which predicts dynamic anchors on patches rather than grids in a sparse way so that it alleviates the feature conflict problem. For each image, ASAG dynamically selects which feature maps and which locations to predict, forming a fully adaptive way to generate image-specific anchors. Further, a simple and effective Query Weighting method eases the training instability from adaptiveness. Extensive experiments show that our method outperforms dense-initialized ones and achieves a better speed-accuracy trade-off. The code is available at https://github.com/iSEE-Laboratory/ASAG.
MGMAE: Motion Guided Masking for Video Masked Autoencoding
Authors: Zhiyu Zhao, Guozhen Zhang, Yu Qiao, Limin Wang Bingkun Huang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Masked autoencoding has shown excellent performance on self-supervised video representation learning. Temporal redundancy has led to a high masking ratio and customized masking strategy in VideoMAE. In this paper, we aim to further improve the performance of video masked autoencoding by introducing a motion guided masking strategy. Our key insight is that motion is a general and unique prior in video, which should be taken into account during masked pre-training. Our motion guided masking explicitly incorporates motion information to build temporal consistent masking volume. Based on this masking volume, we can track the unmasked tokens in time and sample a set of temporal consistent cubes from videos. These temporal aligned unmasked tokens will further relieve the information leakage issue in time and encourage the MGMAE to learn more useful structure information. We implement our MGMAE with an online efficient optical flow estimator and backward masking map warping strategy. We perform experiments on the datasets of Something-Something V2 and Kinetics-400, demonstrating the superior performance of our MGMAE to the original VideoMAE. In addition, we provide the visualization analysis to illustrate that our MGMAE can sample temporal consistent cubes in a motion-adaptive manner for more effective video pre-training.
The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning
Authors: Lingjuan Lyu, Amir Houmansadr Virat Shejwalkar
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Semi-supervised learning (SSL) is gaining popularity as it reduces cost of machine learning (ML) by training high performance models using unlabeled data. In this paper, we reveal that the key feature of SSL, i.e., learning from (non-inspected) unlabeled data, exposes SSL to strong poisoning attacks that can significantly damage its security. Poisoning is a long-standing problem in conventional supervised ML, but we argue that, as SSL relies on non-inspected unlabeled data, poisoning poses a more significant threat to SSL. We demonstrate this by designing a backdoor poisoning attack on SSL that can be conducted by a weak adversary with no knowledge of the target SSL pipeline. This is unlike prior poisoning attacks on supervised ML that assume strong adversaries with impractical capabilities. We show that by poisoning only 0.2% of the unlabeled training data, our (weak) adversary can successfully cause misclassification on more than 80% of test inputs (when they contain the backdoor trigger). Our attack remains effective across different benchmark datasets and SSL algorithms, and even circumvents state-of-the-art defenses against backdoor attacks. Our work raises significant concerns about the security of SSL in real-world security critical applications.
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning
Authors: Anna Kukleva, Dengxin Dai, Bernt Schiele Yue Fan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Semi-supervised learning (SSL) methods effectively leverage unlabeled data to improve model generalization. However, SSL models often underperform in open-set scenarios, where unlabeled data contain outliers from novel categories that do not appear in the labeled set. In this paper, we study the challenging and realistic open-set SSL setting, where the goal is to both correctly classify inliers and to detect outliers. Intuitively, the inlier classifier should be trained on inlier data only. However, we find that inlier classification performance can be largely improved by incorporating high-confidence pseudo-labeled data, regardless of whether they are inliers or outliers. Also, we propose to utilize non-linear transformations to separate the features used for inlier classification and outlier detection in the multi-task learning framework, preventing adverse effects between them. Additionally, we introduce pseudo-negative mining, which further boosts outlier detection performance. The three ingredients lead to what we call Simple but Strong Baseline (SSB) for open-set SSL. In experiments, SSB greatly improves both inlier classification and outlier detection performance, outperforming existing methods by a large margin. Our code will be released at https://github.com/YUE-FAN/SSB.
StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models
Authors: Lei Zhao, Wei Xing Zhizhong Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Content and style (C-S) disentanglement is a fundamental problem and critical challenge of style transfer. Existing approaches based on explicit definitions (e.g., Gram matrix) or implicit learning (e.g., GANs) are neither interpretable nor easy to control, resulting in entangled representations and less satisfying results. In this paper, we propose a new C-S disentangled framework for style transfer without using previous assumptions. The key insight is to explicitly extract the content information and implicitly learn the complementary style information, yielding interpretable and controllable C-S disentanglement and style transfer. A simple yet effective CLIP-based style disentanglement loss coordinated with a style reconstruction prior is introduced to disentangle C-S in the CLIP image space. By further leveraging the powerful style removal and generative ability of diffusion models, our framework achieves superior results than state of the art and flexible C-S disentanglement and trade-off control. Our work provides new insights into the C-S disentanglement in style transfer and demonstrates the potential of diffusion models for learning well-disentangled C-S characteristics.
AdvDiffuser: Natural Adversarial Example Synthesis with Diffusion Models
Authors: Xitong Gao, Juanjuan Zhao, Kejiang Ye, Cheng-Zhong Xu Xinquan Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Previous work on adversarial examples typically involves a fixed norm perturbation budget, which fails to capture the way humans perceive perturbations. Recent work has shifted towards investigating natural unrestricted adversarial examples (UAEs) that breaks l_p perturbation bounds but nonetheless remain semantically plausible. Current methods use GAN or VAE to generate UAEs by perturbing latent codes. However, this leads to loss of high-level information, resulting in low-quality and unnatural UAEs. In light of this, we propose AddDiffuser, a new method for synthesizing natural UAEs using diffusion models. It can generate UAEs from scratch or conditionally based on reference images. To generate natural UAEs, we perturb predicted images to steer their latent code towards the adversarial sample space of a particular classifier. In addition, we propose adversarial inpainting based on class activation mapping to retain the salient regions of the image while perturbing less important areas. Our method achieves impressive results on CIFAR-10, CelebA and ImageNet, and we demonstrate that it can defeat the most robust models on the RobustBench leaderboard with near 100% success rates. Furthermore, The synthesized UAEs are not only more natural but also stronger compared to the current state-of-the-art attacks. Specifically, compared with GA-attack, the UAEs generated with AdvDiffuser exhibit 6xsmaller LPIPS perturbations, 2 ~ 3 xsmaller FID scores and 0.28 higher in SSIM metrics, making them perceptually stealthier. Lastly, it is capable of generating an unlimited number of natural adversarial examples. For more please visit our project page: Link to follow.