mtybilly/Instruct2DS huggingface dataset:
ENTL: Embodied Navigation Trajectory Learner
Authors: Aaron Walsman, Roozbeh Mottaghi Klemen Kotar
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We propose Embodied Navigation Trajectory Learner (ENTL), a method for extracting long sequence representations for embodied navigation. Our approach unifies world modeling, localization and imitation learning into a single sequence prediction task. We train our model using vector-quantized predictions of future states conditioned on current states and actions. ENTL's generic architecture enables the sharing of the the spatio-temporal sequence encoder for multiple challenging embodied tasks. We achieve competitive performance on navigation tasks using significantly less data than strong baselines while performing auxiliary tasks such as localization and future frame prediction (a proxy for world modeling). A key property of our approach is that the model is pre-trained without any explicit reward signal, which makes the resulting model generalizable to multiple tasks and environments.
AGG-Net: Attention Guided Gated-Convolutional Network for Depth Image Completion
Authors: Tingxuan Huang, Zhimin Song, Shizhuo Deng, Tong Jia Dongyue Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Recently, stereo vision based on lightweight RGBD cameras has been widely used in various fields. However, limited by the imaging principles, the commonly used RGB-D cameras based on TOF, structured light, or binocular vision acquire some invalid data inevitably, such as weak reflection, boundary shadows, and artifacts, which may bring adverse impacts to the follow-up work. In this paper, we propose a new model for depth image completion based on the Attention Guided Gated-convolutional Network (AGG-Net), through which more accurate and reliable depth images can be obtained based on the raw depth maps and the corresponding RGB images. Our model employs a UNet-like architecture which consists of two parallel branches of depth and color features. In the encoding stage, an Attention Guided Gated Convolution (AG-GConv) module is proposed to realize the fusion of depth and color features at different scales, which can effectively reduce the negative impacts of invalid depth data on the reconstruction. In the decoding stage, an Attention Guided Skip Connection (AG-SC) module is presented to avoid introducing too many depth-irrelevant features to the reconstruction. The experimental results demonstrate that our method outperforms the state-of-the-art methods on the popular benchmarks NYU-Depth V2, DIML, and SUN RGB-D.
Learning Global-aware Kernel for Image Harmonization
Authors: Jiangning Zhang, Jun Chen, Shipeng Bai, Yue Han, Yabiao Wang, Chengjie Wang, Yong Liu Xintian Shen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Image harmonization aims to solve the visual inconsistency problem in composited images by adaptively adjusting the foreground pixels with the background as references. Existing methods employ local color transformation or region matching between foreground and background, which neglects powerful proximity prior and independently distinguishes fore-/back-ground as a whole part for harmonization. As a result, they still show a limited performance across varied foreground objects and scenes. To address this issue, we propose a novel Global-aware Kernel Network (GKNet) to harmonize local regions with comprehensive consideration of long-distance background references.
 Specifically, GKNet includes two parts, i.e., harmony kernel prediction and harmony kernel modulation branches. The former includes a Long-distance Reference Extractor (LRE) to obtain long-distance context and Kernel Prediction Blocks (KPB) to predict multi-level harmony kernels by fusing global information with local features. To achieve this goal, a novel Selective Correlation Fusion (SCF) module is proposed to better select relevant long-distance background references for local harmonization. The latter employs the predicted kernels to harmonize foreground regions with both local and global awareness. Abundant experiments demonstrate the superiority of our method for image harmonization over state-of-the-art methods, e.g., achieving 39.53dB PSNR that surpasses the best counterpart by +0.78dB; decreasing fMSE by 11.5% and MSE by 6.7% compared with the SoTA method.
Real-Time Neural Rasterization for Large Scenes
Authors: Yun Chen, Ze Yang, Jingkang Wang, Sivabalan Manivasagam, Raquel Urtasun Jeffrey Yunfan Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We propose a new method for realistic real-time novel-view synthesis (NVS) of large scenes. Existing fast neural rendering methods generate realistic results, but primarily work for small scale scenes (<50 square meter) and have difficulty at large scale (>10000 square meter). Traditional graphics-based rasterization rendering is fast for large scenes but lacks realism and requires expensive manually created assets. Our approach combines the best of both worlds by taking a moderate-quality scaffold mesh as input and learning a neural texture field and shader to model view-dependant effects to enhance realism, while still using the standard graphics pipeline for real-time rendering. Our method outperforms existing neural rendering methods, providing at least 30x faster rendering with comparable or better realism for large self-driving and drone scenes. Our work is the first to enable real-time visualization of large real-world scenes.
ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer
Authors: Jiaxin Zhang, Dezhi Peng, Hao Lu, Can Huang, Yuliang Liu, Xiang Bai, Lianwen Jin Mingxin Huang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: In recent years, end-to-end scene text spotting approaches are evolving to the Transformer-based framework. While previous studies have shown the crucial importance of the intrinsic synergy between text detection and recognition, recent advances in Transformer-based methods usually adopt an implicit synergy strategy with shared query, which can not fully realize the potential of these two interactive tasks. In this paper, we argue that the explicit synergy considering distinct characteristics of text detection and recognition can significantly improve the performance text spotting. To this end, we introduce a new model named Explicit Synergy-based Text Spotting Transformer framework (ESTextSpotter), which achieves explicit synergy by modeling discriminative and interactive features for text detection and recognition within a single decoder. Specifically, we decompose the conventional shared query into task-aware queries for text polygon and content, respectively. Through the decoder with the proposed vision-language communication module, the queries interact with each other in an explicit manner while preserving discriminative patterns of text detection and recognition, thus improving performance significantly. Additionally, we propose a task-aware query initialization scheme to ensure stable training. Experimental results demonstrate that our model significantly outperforms previous state-of-the-art methods. Code is available at https://github.com/mxin262/ESTextSpotter.
UGC: Unified GAN Compression for Efficient Image-to-Image Translation
Authors: Jie Wu, Peng Zhang, Manlin Zhang, Xuefeng Xiao, Qian He, Rui Wang, Min Zheng, Xin Pan Yuxi Ren
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation. However, the success of these GAN models hinges on ponderous computational costs and labor-expensive training data. Current efficient GAN learning techniques often fall into two orthogonal aspects: i) model slimming via reduced calculation costs; ii)data/label-efficient learning with fewer training data/labels. To combine the best of both worlds, we propose a new learning paradigm, Unified GAN Compression (UGC), with a unified optimization objective to seamlessly prompt the synergy of model-efficient and label-efficient learning. UGC sets up semi-supervised-driven network architecture search and adaptive online semi-supervised distillation stages sequentially, which formulates a heterogeneous mutual learning scheme to obtain an architecture-flexible, label-efficient, and performance-excellent model.
 Extensive experiments demonstrate that UGC obtains state-of-the-art lightweight models even with less than 50% labels. UGC that compresses 40X MACs can achieve 21.43 FID on edges-shoes with 25% labels, which even outperforms the original model with 100% labels by 2.75 FID.
Efficient View Synthesis with Neural Radiance Distribution Field
Authors: Xiao Li, Jinglu Wang, Xiaoguang Han, Shuguang Cui, Yan Lu Yushuang Wu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Recent work on Neural Radiance Fields (NeRF) has demonstrated significant advances in high-quality view synthesis. A major limitation of NeRF is its low rendering efficiency due to the need for multiple network forwardings to render a single pixel. Existing methods to improve NeRF either reduce the number of required samples or optimize the implementation to accelerate the network forwarding. Despite these efforts, the problem of multiple sampling persists due to the intrinsic representation of radiance fields. In contrast, Neural Light Fields (NeLF) reduce the computation cost of NeRF by querying only one single network forwarding per pixel. To achieve a close visual quality to NeRF, existing NeLF methods require significantly larger network capacities which limits their rendering efficiency in practice. In this work, we propose a new representation called Neural Radiance Distribution Field (NeRDF) that targets efficient view synthesis in real-time. Specifically, we use a small network similar to NeRF while preserving the rendering speed with a single network forwarding per pixel as in NeLF. The key is to model the radiance distribution along each ray with frequency basis and predict frequency weights using the network. Pixel values are then computed via volume rendering on radiance distributions. Experiments show that our proposed method offers a better trade-off among speed, quality, and network size than existing methods: we achieve a  254x speed-up over NeRF with similar network size, with only a marginal performance decline. Our project page is at yushuang-wu.github.io/NeRDF.
MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and Recognition
Authors: Tao Jin, Rongjie Huang, Linjun Li, Wang Lin, Zehan Wang, Ye Wang, Huadai Liu, Aoxiong Yin, Zhou Zhao Xize Cheng
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Multi-media communications facilitate global interaction among people. However, despite researchers exploring cross-lingual translation techniques such as machine translation and audio speech translation to overcome language barriers, there is still a shortage of cross-lingual studies on visual speech. This lack of research is mainly due to the absence of datasets containing visual speech and translated text pairs. In this paper, we present AVMuST-TED, the first dataset for Audio-Visual Multilingual Speech Translation, derived from TED talks. Nonetheless, visual speech is not as distinguishable as audio speech, making it difficult to develop a mapping from source speech phonemes to the target language text. To address this issue, we propose MixSpeech, a cross-modality self-learning framework that utilizes audio speech to regularize the training of visual speech tasks. To further minimize the cross-modality gap and its impact on knowledge transfer, we suggest adopting mixed speech, which is created by interpolating audio and visual streams, along with a curriculum learning strategy to adjust the mixing ratio as needed. MixSpeech enhances speech translation in noisy environments, improving BLEU scores for four languages on AVMuST-TED by +1.4 to +4.2. Moreover, it achieves state-of-the-art performance in lip reading on CMLR (11.1%), LRS2 (25.5%), and LRS3 (28.0%).
Chordal Averaging on Flag Manifolds and Its Applications
Authors: Tolga Birdal Nathan Mankovich
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: This paper presents a new, provably-convergent algorithm for computing the flag-mean and flag-median of a set of points on a flag manifold under the chordal metric. The flag manifold is a mathematical space consisting of flags, which are sequences of nested subspaces of a vector space that increase in dimension. The flag manifold is a superset of a wide range of known matrix spaces, including Stiefel and Grassmanians, making it a general object that is useful in a wide variety computer vision problems.
 
 To tackle the challenge of computing first order flag statistics, we first transform the problem into one that involves auxiliary variables constrained to the Stiefel manifold. The Stiefel manifold is a space of orthogonal frames, and leveraging the numerical stability and efficiency of Stiefel-manifold optimization enables us to compute the flag-mean effectively. Through a series of experiments, we show the competence of our method in Grassmann and rotation averaging, as well as principal component analysis.
Towards Building More Robust Models with Frequency Bias
Authors: Dong Huang, Heming Cui Qingwen Bu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: The vulnerability of deep neural networks to adversarial samples has been a major impediment to their broad applications, despite their success in various fields. Recently, some works suggested that adversarially-trained models emphasize the importance of low-frequency information to achieve higher robustness. While several attempts have been made to leverage this frequency characteristic, they have all faced the issue that applying low-pass filters directly to input images leads to irreversible loss of discriminative information and poor generalizability to datasets with distinct frequency features. This paper presents a plug-and-play module called the Frequency Preference Control Module that adaptively reconfigures the low- and high-frequency components of intermediate feature representations, providing better utilization of frequency in robust learning. Empirical studies show that our proposed module can be easily incorporated into any adversarial training framework, further improving model robustness across different architectures and datasets. Additionally, experiments were conducted to examine how the frequency bias of robust models impacts the adversarial training process and its final robustness, revealing interesting insights.
SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videos
Authors: Yao Teng, Tao Lu, Haiguang Wang, Limin Wang Haisong Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Camera-based 3D object detection in BEV (Bird's Eye View) space has drawn great attention over the past few years. Dense detectors typically follow a two-stage pipeline by first constructing a dense BEV feature and then performing object detection in BEV space, which suffers from complex view transformations and high computation cost. On the other side, sparse detectors follow a query-based paradigm without explicit dense BEV feature construction, but achieve worse performance than the dense counterparts. In this paper, we find that the key to mitigate this performance gap is the adaptability of the detector in both BEV and image space. To achieve this goal, we propose SparseBEV, a fully sparse 3D object detector that outperforms the dense counterparts. SparseBEV contains three key designs, which are (1) scale-adaptive self attention to aggregate features with adaptive receptive field in BEV space, (2) adaptive spatio-temporal sampling to generate sampling locations under the guidance of queries, and (3) adaptive mixing to decode the sampled features with dynamic weights from the queries. On the test split of nuScenes, SparseBEV achieves the state-of-the-art performance of 67.5 NDS. On the val split, SparseBEV achieves 55.8 NDS while maintaining a real-time inference speed of 23.5 FPS. Code is available at https://github.com/MCG-NJU/SparseBEV.
Boosting Whole Slide Image Classification from the Perspectives of Distribution, Correlation and Magnification
Authors: Zhiwei Yang, Minghong Duan, Yingfan Ma, Shuo Wang, Manning Wang, Zhijian Song Linhao Qu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Bag-based multiple instance learning (MIL) methods have become the mainstream for Whole Slide Image (WSI) classification. However, there are still three important issues that have not been fully addressed: (1) positive bags with a low positive instance ratio are prone to the influence of a large number of negative instances; (2) the correlation between local and global features of pathology images has not been fully modeled; and (3) there is a lack of effective information interaction between different magnifications. In this paper, we propose MILBooster, a powerful dual-scale multi-stage MIL framework to address these issues from the perspectives of distribution, correlation, and magnification. Specifically, to address issue (1), we propose a plug-and-play bag filter that effectively increases the positive instance ratio of positive bags. For issue (2), we propose a novel window-based Transformer architecture called PiceBlock to model the correlation between local and global features of pathology images. For issue (3), we propose a dual-branch architecture to process different magnifications and design an information interaction module called Scale Mixer for efficient information interaction between them. We conducted extensive experiments on four clinical WSI classification tasks using three datasets. MILBooster achieved new state-of-the-art performance on all these tasks. Codes will be available.
PolicyCleanse: Backdoor Detection and Mitigation for Competitive Reinforcement Learning
Authors: Ang Li, Lixu Wang, Cong Liu Junfeng Guo
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: While real-world applications of reinforcement learning (RL) are becoming popular, the security and robustness of RL systems are worthy of more attention and exploration. In particular, recent works have revealed that, in a multi-agent RL environment, backdoor trigger actions can be injected into a victim agent (a.k.a. Trojan agent), which can result in a catastrophic failure as soon as it sees the backdoor trigger action. To ensure the security of RL agents against malicious backdoors, in this work, we propose the problem of Backdoor Detection in multi-agent RL systems, with the objective of detecting Trojan agents as well as the corresponding potential trigger actions, and further trying to mitigate their bad impact. In order to solve this problem, we propose PolicyCleanse that is based on the property that the activated Trojan agent's accumulated rewards degrade noticeably after several timesteps. Along with PolicyCleanse, we also design a machine unlearning-based approach that can effectively mitigate the detected backdoor. Extensive experiments demonstrate that
 the proposed methods can accurately detect Trojan agents, and outperform existing backdoor mitigation baseline approaches by at least 3% in winning rate across various types of agents and environments.
Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Reflection
Authors: Tao Hu, Haoyu Zhao, Shu Liu, Ying-Cong Chen Wenhang Ge
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Neural implicit surface learning has shown significant progress in multi-view 3D reconstruction, where an object is represented by multilayer perceptrons that provide continuous implicit surface representation and view-dependent radiance. However, current methods often fail to accurately reconstruct reflective surfaces, leading to severe ambiguity. To overcome this issue, we propose Ref-NeuS, which aims to reduce ambiguity by attenuating the effect of reflective surfaces. Specifically, we utilize an anomaly detector to estimate an explicit reflection score with the guidance of multi-view context to localize reflective surfaces. Afterward, we design a reflection-aware photometric loss that adaptively reduces ambiguity by modeling rendered color as a Gaussian distribution, with the reflection score representing the variance. We show that together with a reflection direction-dependent radiance, our model achieves high-quality surface reconstruction on reflective surfaces and outperforms the state-of-the-arts by a large margin. Besides, our model is also comparable on general surfaces.
Innovating Real Fisheye Image Correction with Dual Diffusion Architecture
Authors: Chunyu Lin, Kang Liao, Yao Zhao Shangrong Yang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Fisheye image rectification is hindered by synthetic models producing poor results for real-world correction. To address this, we propose a Dual Diffusion Architecture (DDA) for fisheye rectification that offers better practicality. The DDA leverages Denoising Diffusion Probabilistic Models (DDPMs) to gradually introduce bidirectional noise, allowing the synthesized and real images to develop into a consistent noise distribution. As a result, our network can perceive the distribution of unlabelled real fisheye images without relying on a transfer network, thus improving the performance of real fisheye correction. Additionally, we design an unsupervised one-pass network that generates a plausible new condition to strengthen guidance and address the non-negligible indeterminacy between the prior condition and the target. It can significantly affect the rectification task, especially in cases where radial distortion causes significant artifacts. This network can be regarded as an alternate scheme for fast producing reliable results without iterative inference. Compared to the state-of-the-art methods, our approach achieves superior performance in both synthetic and real fisheye image corrections.
Global Perception Based Autoregressive Neural Processes
Authors: Jinyang Tai
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Increasingly, autoregressive approaches are being used to serialize observed variables based on specific criteria. The Neural Processes (NPs) model variable distribution as a continuous function and provide quick solutions for different tasks using a meta-learning framework. This paper proposes an autoregressive-based framework for NPs, based on their autoregressive properties. This framework leverages the autoregressive stacking effects of various variables to enhance the representation of the latent distribution, concurrently refining local and global relationships within the positional representation through the use of a sliding window
 mechanism. Autoregression improves function approximations in a stacked fashion, thereby raising the upper bound of the optimization. We have designated this framework as Autoregressive Neural Processes (AENPs) or Conditional Autoregressive Neural Processes (CAENPs). Traditional NP models and their variants aim to capture relationships between the context sample points, without addressing either local or global considerations. Specifically, we capture contextual relationships in the deterministic path and introduce sliding window attention and global attention to reconcile local and global relationships in the context sample points. Autoregressive constraints exist between multiple latent variables in the latent paths, thus building a complex global structure that allows our model to learn complex
 distributions. Finally, we demonstrate the effectiveness of the NPs or CFANPs models for 1D data, Bayesian optimization, and 2D data.
Class-incremental Continual Learning for Instance Segmentation with Image-level Weak Supervision
Authors: Guan-Sheng Chen, Shun-Xian Cai, Ting-Yun Wei, Huei-Fang Yang, Chu-Song Chen Yu-Hsing Hsieh
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Instance segmentation requires labor-intensive manual labeling of the contours of complex objects in images for training. The labels can also be provided incrementally in practice to balance the human labor in different time steps. However, research on incremental learning for instance segmentation with only weak labels is still lacking. In this paper, we propose a continual-learning method to segment object instances from image-level labels. Unlike most weakly-supervised instance segmentation (WSIS) which relies on traditional object proposals, we transfer the semantic knowledge from weakly-supervised semantic segmentation (WSSS) to WSIS to generate instance cues. To address the background shift problem in continual learning, we employ the old class segmentation results generated by the previous model to provide more reliable semantic and peak hypotheses. To our knowledge, this is the first work on weakly-supervised continual learning for instance segmentation of images. Experimental results show that our method can achieve better performance on Pascal VOC and COCO datasets under various incremental settings.
When Prompt-based Incremental Learning Does Not Meet Strong Pretraining
Authors: Yi-Xing Peng, Wei-Shi Zheng Yu-Ming Tang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Incremental learning aims to overcome catastrophic forgetting when learning deep networks from sequential tasks. With impressive learning efficiency and performance, prompt-based methods adopt a fixed backbone to sequential tasks by learning task-specific prompts. However, existing prompt-based methods heavily rely on strong pretraining (typically trained on ImageNet-21k), and we find that their models could be trapped if the potential gap between the pretraining task and unknown future tasks is large. In this work, we develop a learnable Adaptive Prompt Generator (APG). The key is to unify the prompt retrieval and prompt learning processes into a learnable prompt generator. Hence, the whole prompting process can be optimized to reduce the negative effects of the gap between tasks effectively. To make our APG avoid learning ineffective knowledge, we maintain a knowledge pool to regularize APG with the feature distribution of each class. Extensive experiments show that our method significantly outperforms advanced methods in exemplar-free incremental learning without (strong) pretraining. Besides, under strong retraining, our method also has comparable performance to existing prompt-based models, showing that our method can still benefit from pretraining. Codes can be found at https://github.com/TOM-tym/APG
Multimodal High-order Relation Transformer for Scene Boundary Detection
Authors: Zhangxiang Shi, Tianzhu Zhang, Xiaoyuan Yu, Lei Xiao Xi Wei
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Scene boundary detection breaks down long videos into meaningful story-telling units and plays a crucial role in high-level video understanding. Despite significant advancements in this area, this task remains a challenging problem as it requires a comprehensive understanding of multimodal cues and high-level semantics. To tackle this issue, we propose a multimodal high-order relation transformer, which integrates a high-order encoder and an adaptive decoder in a unified framework. By modeling the multimodal cues and exploring similarities between the shots, the encoder is capable of capturing high-order relations between shots and extracting shot features with context semantics. By clustering the shots adaptively, the decoder can discover more universal switch pattern between successive scenes, thus helping scene boundary detection. Extensive experimental results on three standard benchmarks demonstrate that the proposed model performs favorably against state-of-the-art video scene detection methods.
Tri-MipRF: Tri-Mip Representation for Efficient Anti-Aliasing Neural Radiance Fields
Authors: Yuling Wang, Lin Ma, Bangbang Yang, Lin Gao, Xiao Liu, Yuewen Ma Wenbo Hu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Despite the tremendous progress in neural radiance fields (NeRF), we still face a dilemma of the trade-off between quality and efficiency, e.g., MipNeRF presents fine-detailed and anti-aliased renderings but takes days for training, while Instant-ngp can accomplish the reconstruction in a few minutes but suffers from blurring or aliasing when rendering at various distances or resolutions due to ignoring the sampling area. To this end, we propose a novel Tri-Mip encoding (a la "mipmap") that enables both instant reconstruction and anti-aliased high-fidelity rendering for neural radiance fields. The key is to factorize the pre-filtered 3D feature spaces in three orthogonal mipmaps. In this way, we can efficiently perform 3D area sampling by taking advantage of 2D pre-filtered feature maps, which significantly elevates the rendering quality without sacrificing efficiency. To cope with the novel Tri-Mip representation, we propose a cone-casting rendering technique to efficiently sample anti-aliased 3D features with the Tri-Mip encoding considering both pixel imaging and observing distance. Extensive experiments on both synthetic and real-world datasets demonstrate our method achieves state-of-the-art rendering quality and reconstruction speed while maintaining a compact representation that reduces 25% model size compared against Instant-ngp. Code is available at the project webpage: https: //wbhu.github.io/projects/Tri-MipRF
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark
Authors: Janez Perš, Matej Kristan Lojze Žust
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: The progress in maritime obstacle detection is hindered by the lack of a diverse dataset that adequately captures the complexity of general maritime environments. We present the first maritime panoptic obstacle detection benchmark LaRS, featuring scenes from Lakes, Rivers and Seas. Our major contribution is the new dataset, which boasts the largest diversity in recording locations, scene types, obstacle classes, and acquisition conditions among the related datasets. LaRS is composed of over 4000 per-pixel labeled key frames with nine preceding frames to allow utilization of the temporal texture, amounting to over 40k frames. Each key frame is annotated with 8 thing, 3 stuff classes and 19 global scene attributes. We report the results of 27 semantic and panoptic segmentation methods, along with several performance insights and future research directions. To enable objective evaluation, we have implemented an online evaluation server. The LaRS dataset, evaluation toolkit and benchmark are publicly available at: https://lojzezust.github.io/lars-dataset
Exploring Transformers for Open-world Instance Segmentation
Authors: Yi Jiang, Bin Yan, Huchuan Lu, Zehuan Yuan, Ping Luo Jiannan Wu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Open-world instance segmentation is a rising task, which aims to segment all objects in the image by learning from a limited number of base-category objects. This task is challenging, as the number of unseen categories could be hundreds of times larger than that of seen categories. Recently, the DETR-like models have been extensively studied in the closed world while stay unexplored in the open world. In this paper, we utilize the Transformer for open-world instance segmentation and present SWORD. Firstly, we introduce to attach the stop-gradient operation before classification head and further add IoU heads for discovering novel objects. We demonstrate that a simple stop-gradient operation not only prevents the novel objects from being suppressed as background, but also allows the network to enjoy the merit of heuristic label assignment. Secondly, we propose a novel contrastive learning framework to enlarge the representations between objects and background. Specifically, we maintain a universal object queue to obtain the object center, and dynamically select positive and negative samples from the object queries for contrastive learning. While the previous works only focus on pursuing average recall and neglect average precision, we show the prominence of SWORD by giving consideration to both criteria. Our models achieve state-of-the-art performance in various open-world cross-category and cross-dataset generalizations. Particularly, in VOC to non-VOC setup, our method sets new state-of-the-art results of 40.0% on ARb100 and 34.9% on ARm100. For COCO to UVO generalization, SWORD significantly outperforms the previous best open-world model by 5.9% on APm and 8.1% on ARm100, respectively.
VQA Therapy: Exploring Answer Differences by Visually Grounding Answers
Authors: Samreen Anjum, Danna Gurari Chongyan Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Visual question answering is a task of predicting the answer to a question about an image. Given that different people can provide different answers to a visual question, we aim to better understand why with answer groundings. We introduce the first dataset that visually grounds each unique answer to each visual question, which we call VQAAnswerTherapy. We then propose two novel problems of predicting whether a visual question has a single answer grounding and localizing all answer groundings. We benchmark modern algorithms for these novel problems to show where they succeed and struggle. The dataset and evaluation server can be found publicly at https://vizwiz.org/tasks-and-datasets/vqa-answer-therapy/.
Energy-based Self-Training and Normalization for Unsupervised Domain Adaptation
Authors: Basura Fernando, Ehsan Abbasnejad, Munawar Hayat, Shahram Khadivi, Mehrtash Harandi, Hamid Rezatofighi, Gholamreza Haffari Samitha Herath
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We propose an Unsupervised Domain Adaptation (UDA) method by making use of Energy-Based Learning (EBL) and demonstrate 1. EBL can be used to improve the instance selection for a self-training task on the unlabelled target domain, and 2. alignment and normalizing energy scores can learn domain-invariant representations. For the former, we show that an energy-based selection criterion can be used to model instance selections by mimicking the joint distribution between data and predictions in the target domain. As per learning domain invariant representations, we show that stable domain alignment can be achieved by a combined energy alignment and an energy normalization process.
 We implement our method in consistent with the vision-transformer (ViT) backbone and empirically show that our proposed method can outperform state-of-the-art ViT based UDA methods on diverse benchmarks (DomainNet, OfficeHome, and VISDA2017).
Self-Evolved Dynamic Expansion Model for Task-Free Continual Learning
Authors: Adrian G. Bors Fei Ye
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Task-Free Continual Learning (TFCL) aims to learn new concepts from a stream of data without any task information. The Dynamic Expansion Model (DEM) has shown promising results in TFCL by dynamically expanding the model's capacity to deal with shifts in the data distribution. However, existing approaches only consider the recognition of the input shift as the expansion signal and ignore the correlation between the newly incoming data and previously learned knowledge, resulting in adding and training unnecessary parameters. In this paper, we propose a novel and effective framework for TFCL, which dynamically expands the architecture of a DEM model through a self-assessment mechanism evaluating the diversity of knowledge among existing experts as expansion signals. This mechanism ensures learning additional underlying data distributions with a compact model structure. A novelty-aware sample selection approach is proposed to manage the memory buffer that forces the newly added expert to learn novel information from a data stream, which further promotes the diversity among experts. Moreover, we also propose to reuse previously learned representation information for learning new incoming data by using knowledge transfer in TFCL, which has not been explored before. The DEM expansion and training are regularized through a gradient updating mechanism to gradually explore the positive forward transfer, further improving the performance. Empirical results on TFCL benchmarks show that the proposed framework outperforms the state-of-the-art while using a reasonable number of parameters. The code is available at https://github.com/dtuzi123/SEDEM/.
Adaptive Template Transformer for Mitochondria Segmentation in Electron Microscopy Images
Authors: Naisong Luo, Rui Sun, Meng Meng, Tianzhu Zhang, Zhiwei Xiong, Yongdong Zhang Yuwen Pan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Mitochondria, as tiny structures within the cell, are of significant importance to study cell functions for biological and clinical analysis. And exploring how to automatically segment mitochondria in electron microscopy (EM) images has attracted increasing attention. However, most of existing methods struggle to adapt to different scales and appearances of the input due to the inherent limitations of the traditional CNN architecture. To mitigate these limitations, we propose a novel adaptive template transformer (ATFormer) for mitochondria segmentation. The proposed ATFormer model enjoys several merits. First, the designed structural template learning module can acquire appearance-adaptive templates of background, foreground and contour to sense the characteristics of different shapes of mitochondria. And we further adopt an optimal transport algorithm to enlarge the discrepancy among diverse templates to fully activate corresponding regions. Second, we introduce a hierarchical attention learning mechanism to absorb multi-level information for templates to be adaptive scale-aware classifiers for dense prediction. Extensive experimental results on three challenging benchmarks including MitoEM, Lucchi and NucMM-Z datasets demonstrate that our ATFormer performs favorably against state-of-the-art mitochondria segmentation methods.
Collaborative Tracking Learning for Frame-Rate-Insensitive Multi-Object Tracking
Authors: Junta Wu, Yi Fu Yiheng Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Multi-object tracking (MOT) at low frame rates can reduce computational, storage and power overhead to better meet the constraints of edge devices. Many existing MOT methods suffer from significant performance degradation in low-frame-rate videos due to significant location and appearance changes between adjacent frames. To this end, we propose to explore collaborative tracking learning (CoTracker) for frame-rate-insensitive MOT in a query-based end-to-end manner. Multiple historical queries of the same target jointly track it with richer temporal descriptions. Meanwhile, we insert an information refinement module between every two temporal blocking decoders to better fuse temporal clues and refine features. Moreover, a tracking object consistency loss is proposed to guide the interaction between historical queries. Extensive experimental results demonstrate that in high-frame-rate videos, CoTracker obtains higher performance than state-of-the-art methods on large-scale datasets Dancetrack and BDD100K, and outperforms the existing end-to-end methods on MOT17. More importantly, CoTracker has a significant advantage over state-of-the-art methods in low-frame-rate videos, which allows it to obtain faster processing speeds by reducing frame-rate requirements while maintaining higher performance. Code will be released at https://github.com/yolomax/ColTrack
Tangent Model Composition for Ensembling and Continual Fine-tuning
Authors: Stefano Soatto Tian Yu Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Tangent Model Composition (TMC) is a method to combine component models independently fine-tuned around a pre-trained point. Component models are tangent vectors to the pre-trained model that can be added, scaled, or subtracted to support incremental learning, ensembling, or unlearning. Component models are composed at inference time via scalar combination, reducing the cost of ensembling to that of a single model. TMC improves accuracy by 4.2% compared to ensembling non-linearly fine-tuned models at a 2.5x to 10x reduction of inference cost, growing linearly with the number of component models. Each component model can be forgotten at zero cost, with no residual effect on the resulting inference. When used for continual fine-tuning, TMC is not constrained by sequential bias and can be executed in parallel on federated data. TMC outperforms recently published continual fine-tuning methods almost uniformly on each setting -- task-incremental, class-incremental, and data-incremental -- on a total of 13 experiments across 3 benchmark datasets, despite not using any replay buffer. TMC is designed for composing models that are local to a pre-trained embedding, but could be extended to more general settings.
Knowledge-Spreader: Learning Semi-Supervised Facial Action Dynamics by Consistifying Knowledge Granularity
Authors: Xiang Zhang, Taoyue Wang, Lijun Yin Xiaotian Li
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Recent studies on dynamic facial action unit (AU) detection have extensively relied on dense annotations. However, manual annotations are difficult, time-consuming, and costly. The canonical semi-supervised learning (SSL) methods ignore the consistency, extensibility, and adaptability of structural knowledge across spatial-temporal domains. Furthermore, the reliance on offline design and excessive parameters hinder the efficiency of the learning process. To remedy these issues, we propose a lightweight and on-line semi-supervised framework, a so-called Knowledge-Spreader (KS), to learn AU dynamics with sparse annotations. By formulating SSL as a Progressive Knowledge Distillation (PKD) problem, we aim to infer cross-domain information, specifically from spatial to temporal domains, by consistifying knowledge granularity within Teacher-Students Network. Specifically, KS employs sparsely annotated key-frames to learn AU dependencies as the privileged knowledge. Then, the model spreads the learned knowledge to their unlabeled neighbours by jointly applying knowledge distillation and pseudo-labeling, and completes the temporal information as the expanded knowledge. We term the progressive knowledge distillation as "Knowledge Spreading", which allows our model to learn spatial-temporal knowledge from video clips with only one label allocated. Extensive experiments demonstrate that KS achieves competitive performance as compared to the state of the arts under the circumstances of using only 2% labels on BP4D and 5% labels on DISFA. In addition, we have tested it on our newly developed large-scale comprehensive emotion database BP4D++, which contains considerable samples across well-synchronized and aligned sensor modalities for alleviating the scarcity issue of annotations and identities.
SSF: Accelerating Training of Spiking Neural Networks with Stabilized Spiking Flow
Authors: Zengjie Song, Yuxi Wang, Jun Xiao, Yuran Yang, Shuqi Mei, Zhaoxiang Zhang Jingtao Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Surrogate gradient (SG) is one of the most effective approaches for training spiking neural networks (SNNs). While assisting SNNs to achieve classification performance comparable to artificial neural networks, SG suffers from the problem of time-consuming training, preventing it from efficient learning. In this paper, we formally analyze the backward process of classic SG and find that the membrane accumulation through time leads to exponential growth of training time. With this discovery, we propose Stabilized Spiking Flow (SSF), a simple yet effective approach to accelerate training of SG-based SNNs. For each spiking neuron, SSF averages its input and output activations over time to yield stabilized input and output, respectively. Then, instead of back propagating all errors that are related to current neuron and inherently entangled in time domain, the auxiliary gradient is directly propagated from the stabilized output to input through a devised relationship mapping. Additionally, SSF method is suitable to different neuron models. Extensive experiments on both static and neuromorphic datasets demonstrate that SNNs trained with SSF approach can achieve performance comparable to the original counterparts, while reducing the training time significantly. In particular, SSF speeds up the training process of state-of-the-art SNN models up to 10x when time steps equal to 80.
Manipulate by Seeing: Creating Manipulation Controllers from Pre-Trained Representations
Authors: Sudeep Dasari, Mohan Kumar Srirama, Shubham Tulsiani, Abhinav Gupta Jianren Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: The field of visual representation learning has seen explosive growth in the past years, but its benefits in robotics have been surprisingly limited so far. Prior work uses generic visual representations as a basis to learn (task-specific) robot action policies (e.g., via behavior cloning). While the visual representations do accelerate learning, they are primarily used to encode visual observations. Thus, action information has to be derived purely from robot data, which is expensive to collect! In this work, we present a scalable alternative where the visual representations can help directly infer robot actions. We observe that vision encoders express relationships between image observations as distances (e.g., via embedding dot product) that could be used to efficiently plan robot behavior. We operationalize this insight and develop a simple algorithm for acquiring a distance function and dynamics predictor, by fine-tuning a pre-trained representation on human collected video sequences. The final method is able to substantially outperform traditional robot learning baselines (e.g., 70% success v.s. 50% for behavior cloning on pick-place) on a suite of diverse real-world manipulation tasks. It can also generalize to novel objects, without using any robot demonstrations during train time. For visualizations of the learned policies please check: https://agi-labs.github.io/manipulate-by-seeing/