GenAI4ELab/papercli-papers huggingface dataset:
Mitigating and Evaluating Static Bias of Action Representations in the Background and the Foreground
Authors: Haoxin Li; Yuan Liu; Hanwang Zhang; Boyang Li
Venue: ICCV 2023 (main)

Abstract: In video action recognition, shortcut static features can interfere with the learning of motion features, resulting in poor out-of-distribution (OOD) generalization. The video background is clearly a source of static bias, but the video foreground, such as the clothing of the actor, can also provide static bias. In this paper, we empirically verify the existence of foreground static bias by creating test videos with conflicting signals from the static and moving portions of the video. To tackle this issue, we propose a simple yet effective technique, StillMix, to learn robust action representations. Specifically, StillMix identifies bias-inducing video frames using a 2D reference network and mixes them with videos for training, serving as effective bias suppression even when we cannot explicitly extract the source of bias within each video frame or enumerate types of bias. Finally, to precisely evaluate static bias, we synthesize two new benchmarks, SCUBA for static cues in the background, and SCUFO for static cues in the foreground. With extensive experiments, we demonstrate that StillMix mitigates both types of static bias and improves video representations for downstream applications.
RbA: Segmenting Unknown Regions Rejected by All
Authors: Nazir Nayal; Misra Yavuz; João F. Henriques; Fatma Güney
Venue: ICCV 2023 (main)

Abstract: Standard semantic segmentation models owe their success to curated datasets with a fixed set of semantic categories, without contemplating the possibility of identifying unknown objects from novel categories. Existing methods in outlier detection suffer from a lack of smoothness and objectness in their predictions, due to limitations of the per-pixel classification paradigm. Furthermore, additional training for detecting outliers harms the performance of known classes. In this paper, we explore another paradigm with region-level classification to better segment unknown objects. We show that the object queries in mask classification tend to behave like one vs. all classifiers. Based on this finding, we propose a novel outlier scoring function called RbA by defining the event of being an outlier as being rejected by all known classes. Our extensive experiments show that mask classification improves the performance of the existing outlier detection methods, and the best results are achieved with the proposed RbA. We also propose an objective to optimize RbA using minimal outlier supervision. Further fine-tuning with outliers improves the unknown performance, and unlike previous methods, it does not degrade the inlier performance. 
 Project page: https://kuis-ai.github.io/RbA
CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution
Authors: Zixuan Chen; Lingxiao Yang; Jian-Huang Lai; Xiaohua Xie
Venue: ICCV 2023 (main)

Abstract: Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread attention, aiming to supersample medical volumes at arbitrary scales via a single model. However, existing MIASSR methods face two major limitations: (i) reliance on high-resolution (HR) volumes and (ii) limited generalization ability, which restricts their applications in various scenarios. To overcome these limitations, we propose Cube-based Neural Radiance Field (CuNeRF), a zero-shot MIASSR framework that is able to yield medical images at arbitrary scales and free viewpoints in a continuous domain. Unlike existing MISR methods that only fit the mapping between low-resolution (LR) and HR volumes, CuNeRF focuses on building a continuous volumetric representation from each LR volume without the knowledge from the corresponding HR one. This is achieved by the proposed differentiable modules: cube-based sampling, isotropic volume rendering, and cube-based hierarchical rendering. Through extensive experiments on magnetic resource imaging (MRI) and computed tomography (CT) modalities, we demonstrate that CuNeRF can synthesize high-quality SR medical images, which outperforms state-of-the-art MISR methods, achieving better visual verisimilitude and fewer objectionable artifacts. Compared to existing MISR methods, our CuNeRF is more applicable in practice.
Beyond Object Recognition: A New Benchmark towards Object Concept Learning
Authors: Yong-Lu Li; Yue Xu; Xinyu Xu; Xiaohan Mao; Yuan Yao; Siqi Liu; Cewu Lu
Venue: ICCV 2023 (main)

Abstract: Understanding objects is a central building block of AI, especially for embodied AI. Even though object recognition excels with deep learning, current machines struggle to learn higher-level knowledge, e.g., what attributes an object has, and what we can do with it. Here, we propose a challenging Object Concept Learning (OCL) task to push the envelope of object understanding. It requires machines to reason out affordances and simultaneously give the reason: what attributes make an object possess these affordances. To support OCL, we build a densely annotated knowledge base including extensive annotations for three levels of object concept (category, attribute, affordance), and the clear causal relations of three levels. By analyzing the causal structure of OCL, we present a baseline, Object Concept Reasoning Network (OCRN). It leverages concept instantiation and causal intervention to infer the three levels. In experiments, OCRN effectively infers the object knowledge while following the causalities well. Our data and code are available at https://mvig-rhos.com/ocl.
Towards Open-Vocabulary Video Instance Segmentation
Authors: Haochen Wang; Cilin Yan; Shuai Wang; Xiaolong Jiang; Xu Tang; Yao Hu; Weidi Xie; Efstratios Gavves
Venue: ICCV 2023 (main)

Abstract: Video Instance Segmentation (VIS) aims at segmenting and categorizing objects in videos from a closed set of training categories, lacking the generalization ability to handle novel categories in real-world videos. To address this limitation, we make the following three contributions. First, we introduce the novel task of Open-Vocabulary Video Instance Segmentation, which aims to simultaneously segment, track, and classify objects in videos from open-set categories, including novel categories unseen during training. Second, to benchmark Open-Vocabulary VIS, we collect a Large-Vocabulary Video Instance Segmentation dataset (LV-VIS), that contains well-annotated objects from 1,196 diverse categories, significantly surpassing the category size of existing datasets by more than one order of magnitude. Third, we propose an efficient Memory-Induced Transformer architecture, OV2Seg, to first achieve Open-Vocabulary VIS in an end-to-end manner with near real-time inference speed. Extensive experiments on LV-VIS and four existing VIS datasets demonstrate the strong zero-shot generalization ability of OV2Seg on novel categories.
 The dataset and code are released here https://github.com/haochenheheda/LVVIS.
Unleashing the Power of Gradient Signal-to-Noise Ratio for Zero-Shot NAS
Authors: Zihao Sun; Yu Sun; Longxing Yang; Shun Lu; Jilin Mei; Wenxiao Zhao; Yu Hu
Venue: ICCV 2023 (main)

Abstract: Neural Architecture Search (NAS) aims to automatically find optimal neural network architectures in an efficient way. Zero-Shot NAS is a promising technique that leverages proxies to predict the accuracy of candidate architectures without any training. However, we have observed that most existing proxies do not consistently perform well across different search spaces, and are less concerned with generalization. Recently, the gradient signal-to-noise ratio (GSNR) was shown to be correlated with neural network generalization performance. In this paper, we not only explicitly give the probability that larger GSNR at network initialization can ensure better generalization, but also theoretically prove that GSNR can ensure better convergence. Then we design the Xi-based gradient signal-to-noise ratio (Xi-GSNR) as a Zero-Shot NAS proxy to predict the network accuracy at initialization. Extensive experiments in different search spaces demonstrate that Xi-GSNR provides superior ranking consistency compared to previous proxies. Moreover, Xi-GSNR-based Zero-Shot NAS also achieves outstanding performance when directly searching for the optimal architecture in various search spaces and datasets. The source code is available at https://github.com/Sunzh1996/Xi-GSNR.
EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object Understanding
Authors: Chenchen Zhu; Fanyi Xiao; Andres Alvarado; Yasmine Babaei; Jiabo Hu; Hichem El-Mohri; Sean Culatana; Roshan Sumbaly; Zhicheng Yan
Venue: ICCV 2023 (main)

Abstract: Object understanding in egocentric visual data is arguably a fundamental research topic in egocentric vision. However, existing object datasets are either non-egocentric or have limitations in object categories, visual content, and annotation granularities. In this work, we introduce EgoObjects, a large-scale egocentric dataset for fine-grained object understanding. Its Pilot version contains over 9K videos collected by 250 participants from 50+ countries using 4 wearable devices, and over 650K object annotations from 368 object categories. Unlike prior datasets containing only object category labels, EgoObjects also annotates each object with an instance-level identifier, and includes over 14K unique object instances. EgoObjects was designed to capture the same object under diverse background complexities, surrounding objects, distance, lighting and camera motion. In parallel to the data collection, we conducted data annotation by developing a multi-stage federated annotation process to accommodate the growing nature of the dataset. To bootstrap the research on EgoObjects, we present a suite of 4 benchmark tasks around the egocentric object understanding, including a novel instance level- and the classical category level object detection. Moreover, we also introduce 2 novel continual learning object detection tasks. The dataset and API are available at https://github.com/facebookresearch/EgoObjects.
What Can Simple Arithmetic Operations Do for Temporal Modeling?
Authors: Wenhao Wu; Yuxin Song; Zhun Sun; Jingdong Wang; Chang Xu; Wanli Ouyang
Venue: ICCV 2023 (main)

Abstract: Temporal modeling plays a crucial role in understanding video content. To tackle this problem, previous studies built complicated temporal relations through time sequence thanks to the development of computationally powerful devices. In this work, we explore the potential of four simple arithmetic operations for temporal modeling. Specifically, we first capture auxiliary temporal cues by computing addition, subtraction, multiplication, and division between pairs of extracted frame features. Then, we extract corresponding features from these cues to benefit the original temporal-irrespective domain. We term such a simple pipeline as an Arithmetic Temporal Module (ATM), which operates on the stem of a visual backbone with a plug-and-play style. We conduct comprehensive ablation studies on the instantiation of ATMs and demonstrate that this module provides powerful temporal modeling capability at a low computational cost. Moreover, the ATM is compatible with both CNNs- and ViTs-based architectures. Our results show that ATM achieves superior performance over several popular video benchmarks. Specifically, on Something-Something V1, V2 and Kinetics-400, we reach top-1 accuracy of 65.6%, 74.6%, and 89.4% respectively. The code is available at https://github.com/whwu95/ATM.
Pixel Adaptive Deep Unfolding Transformer for Hyperspectral Image Reconstruction
Authors: Miaoyu Li; Ying Fu; Ji Liu; Yulun Zhang
Venue: ICCV 2023 (main)

Abstract: Hyperspectral Image (HSI) reconstruction has made gratifying progress with the deep unfolding framework by formulating the problem into a data module and a prior module. Nevertheless, existing methods still face the problem of insufficient matching with HSI data. The issues lie in three aspects: 1) fixed gradient descent step in the data module while the degradation of HSI is agnostic in the pixel-level. 2) inadequate prior module for 3D HSI cube. 3) stage interaction ignoring the differences in features at different stages. To address these issues, in this work, we propose a Pixel Adaptive Deep Unfolding Transformer (PADUT) for HSI reconstruction. In the data module, a pixel adaptive descent step is employed to focus on pixel-level agnostic degradation. In the prior module, we introduce the Non-local Spectral Transformer (NST) to emphasize the 3D characteristics of HSI for recovering. Moreover, inspired by the diverse expression of features in different stages and depths, the stage interaction is improved by the Fast Fourier Transform (FFT). Experimental results on both simulated and real scenes exhibit the superior performance of our method compared to state-of-the-art HSI reconstruction methods. The code is released at: https://github.com/MyuLi/PADUT
BiViT: Extremely Compressed Binary Vision Transformers
Authors: Yefei He; Zhenyu Lou; Luoming Zhang; Jing Liu; Weijia Wu; Hong Zhou; Bohan Zhuang
Venue: ICCV 2023 (main)

Abstract: Model binarization can significantly compress model size, reduce energy consumption, and accelerate inference through efficient bit-wise operations. Although binarizing convolutional neural networks have been extensively studied, there is little work on exploring binarization of vision Transformers which underpin most recent breakthroughs in visual recognition. To this end, we propose to solve two fundamental challenges to push the horizon of Binary Vision Transformers (BiViT). First, the traditional binary method does not take the long-tailed distribution of softmax attention into consideration, bringing large binarization errors in the attention module. To solve this, we propose Softmax-aware Binarization, which dynamically adapts to the data distribution and reduces the error caused by binarization. Second, to better preserve the information of the pretrained model and restore accuracy, we propose a Cross-layer Binarization scheme that decouples the binarization of self-attention and multi-layer perceptrons (MLPs), and Parameterized Weight Scales which introduce learnable scaling factors for weight binarization. Overall, our method performs favorably against state-of-the-arts by 19.8% on the TinyImageNet dataset. On ImageNet, our BiViT achieves a competitive 75.6% Top-1 accuracy over Swin-S model. Additionally, on COCO object detection, our method achieves an mAP of 40.8 with a Swin-T backbone over Cascade Mask R-CNN framework.
Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF
Authors: Haotian Bai; Yiqi Lin; Yize Chen; Lin Wang
Venue: ICCV 2023 (main)

Abstract: The explicit neural radiance field (NeRF) has gained considerable interest for its efficient training and fast inference capabilities, making it a promising direction such as virtual reality and gaming. In particular, PlenOctree (POT), an explicit hierarchical multi-scale octree representation, has emerged as a structural and influential framework. However, POT's fixed structure for direct optimization is sub-optimal as the scene complexity evolves continuously with updates to cached color and density, necessitating refining the sampling distribution to capture signal complexity accordingly. To address this issue, we propose the dynamic PlenOctree (DOT), which adaptively refines the sample distribution to adjust to changing scene complexity. Specifically, DOT proposes a concise yet novel hierarchical feature fusion strategy during the iterative rendering process. Firstly, it identifies the regions of interest through training signals to ensure adaptive and efficient refinement. Next, rather than directly filtering out valueless nodes, DOT introduces the sampling and pruning operations for octrees to aggregate features, enabling rapid parameter learning. Compared with POT, our DOT outperforms it by enhancing visual quality, reducing over 55.15/68.84% parameters, and providing 1.7/1.9 times FPS for NeRF-synthetic and Tanks & Temples, respectively.
Scene Matters: Model-based Deep Video Compression
Authors: Lv Tang; Xinfeng Zhang; Gai Zhang; Xiaoqi Ma
Venue: ICCV 2023 (main)

Abstract: Video compression has always been a popular research area, where many traditional and deep video compression methods have been proposed. These methods typically rely on signal prediction theory to enhance compression performance by designing high efficient intra and inter prediction strategies and compressing video frames one by one. In this paper, we propose a novel model-based video compression (MVC) framework that regards scenes as the fundamental units for video sequences. Our proposed MVC directly models the intensity variation of the entire video sequence in one scene, seeking non-redundant representations instead of reducing redundancy through spatio-temporal predictions. To achieve this, we employ implicit neural representation as our basic modeling architecture. To improve the efficiency of video modeling, we first propose context-related spatial positional embedding and frequency domain supervision in spatial context enhancement. For temporal correlation capturing, we design the scene flow constrain mechanism and temporal contrastive loss. Extensive experimental results demonstrate that our method achieves up to a 20% bitrate reduction compared to the latest video coding standard H.266 and is more efficient in decoding than existing video coding strategies.
Tree-Structured Shading Decomposition
Authors: Chen Geng; Hong-Xing Yu; Sharon Zhang; Maneesh Agrawala; Jiajun Wu
Venue: ICCV 2023 (main)

Abstract: We study inferring a tree-structured representation from a single image for object shading. Prior work typically uses the parametric or measured representation to model shading, which is neither interpretable nor easily editable. We propose using the shade tree representation, which combines basic shading nodes and compositing methods to factorize object surface shading. The shade tree representation enables novice users who are unfamiliar with the physical shading process to edit object shading in an efficient and intuitive manner. A main challenge in inferring the shade tree is that the inference problem involves both the discrete tree structure and the continuous parameters of the tree nodes. We propose a hybrid approach to address this issue. We introduce an auto-regressive inference model to generate a rough estimation of the tree structure and node parameters, and then we fine-tune the inferred shade tree through an optimization algorithm. We show experiments on synthetic images, captured reflectance, real images, and non-realistic vector drawings, allowing downstream applications such as material editing, vectorized shading, and relighting. Project website: https://chen-geng.com/inv-shade-trees.
EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones
Authors: Yulin Wang; Yang Yue; Rui Lu; Tianjiao Liu; Zhao Zhong; Shiji Song; Gao Huang
Venue: ICCV 2023 (main)

Abstract: The superior performance of modern deep networks usually comes with a costly training procedure. This paper presents a new curriculum learning approach for the efficient training of visual backbones (e.g., vision Transformers). Our work is inspired by the inherent learning dynamics of deep networks: we experimentally show that at an earlier training stage, the model mainly learns to recognize some 'easier-to-learn' discriminative patterns within each example, e.g., the lower-frequency components of images and the original information before data augmentation. Driven by this phenomenon, we propose a curriculum where the model always leverages all the training data at each epoch, while the curriculum starts with only exposing the 'easier-to-learn' patterns of each example, and introduces gradually more difficult patterns. To implement this idea, we 1) introduce a cropping operation in the Fourier spectrum of the inputs, which enables the model to learn from only the lower-frequency components efficiently, 2) demonstrate that exposing the features of original images amounts to adopting weaker data augmentation, and 3) integrate 1) and 2) and design a curriculum learning schedule with a greedy-search algorithm. The resulting approach, EfficientTrain, is simple, general, yet surprisingly effective. As an off-the-shelf method, it reduces the wall-time training cost of a wide variety of popular models (e.g., ResNet, ConvNeXt, DeiT, PVT, Swin, and CSWin) by >1.5x on ImageNet-1K/22K without sacrificing accuracy. It is also effective for self-supervised learning (e.g., MAE). Code is available at https://github.com/LeapLabTHU/EfficientTrain.
Simulating Fluids in Real-World Still Images
Authors: Siming Fan; Jingtan Piao; Chen Qian; Hongsheng Li; Kwan-Yee Lin
Venue: ICCV 2023 (main)

Abstract: In this work, we tackle the problem of real-world fluid animation from a still image. The key of our system is a surface-based layered representation, where the scene is decoupled into a surface fluid layer and an impervious background layer with corresponding transparencies to characterize the composition of the two layers. The animated video can be produced by warping only the surface fluid layer according to the estimation of fluid motions and recombining it with the background. In addition, we introduce surface-only fluid simulation, a 2.5D fluid calculation, as a replacement for motion estimation. 
 Specifically, we leverage triangular mesh based on a monocular depth estimator to represent fluid surface layer and simulate the motion with the inspiration of classic physics theory of hybrid Lagrangian-Eulerian method, along with a learnable network so as to adapt to complex real-world image textures.Extensive experiments not only indicate our method's competitive performance for common fluid scenes but also better robustness and reasonability under complex transparent fluid scenarios. Moreover, as proposed surface-based layer representation and surface-only fluid simulation naturally disentangle the scene, interactive editing such as adding objects and texture replacing could be easily achieved with realistic results.
SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data
Authors: Mohammad Zohaib; Alessio Del Bue
Venue: ICCV 2023 (main)

Abstract: This paper proposes a new method to infer keypoints from arbitrary object categories in practical scenarios where point cloud data (PCD) are noisy, down-sampled and arbitrarily rotated. Our proposed model adheres to the following principles: i) keypoints inference is fully unsupervised (no annotation given), ii) keypoints position error should be low and resilient to PCD perturbations (robustness), iii) keypoints should not change their indexes for the intra-class objects (semantic coherence), iv) keypoints should be close to or proximal to PCD surface (compactness). We achieve these desiderata by proposing a new self-supervised training strategy for keypoints estimation that does not assume any a priori knowledge of the object class, and a model architecture with coupled auxiliary losses that promotes the desired keypoints properties. We compare the keypoints estimated by the proposed approach with those of the state-of-the-art unsupervised approaches. The experiments show that our approach outperforms by estimating keypoints with improved coverage (+9.41%) while being semantically consistent (+4.66%) that best characterizes the object's 3D shape for downstream tasks.