GenAI4ELab/papercli-papers huggingface dataset:
Navigating to Objects Specified by Images
Authors: Jacob Krantz; Theophile Gervet; Karmesh Yadav; Austin Wang; Chris Paxton; Roozbeh Mottaghi; Dhruv Batra; Jitendra Malik; Stefan Lee; Devendra Singh Chaplot
Venue: ICCV 2023 (main)

Abstract: Images are a convenient way to specify which particular object instance an embodied agent should navigate to. Solving this task requires semantic visual reasoning and exploration of unknown environments. We present a system that can perform this task in both simulation and the real world. Our modular method solves sub-tasks of exploration, goal instance re-identification, goal localization, and local navigation. We re-identify the goal instance in egocentric vision using feature-matching and localize the goal instance by projecting matched features to a map. Each sub-task is solved using off-the-shelf components requiring zero fine-tuning. On the HM3D InstanceImageNav benchmark, this system outperforms a baseline end-to-end RL policy 7x and outperforms a state-of-the-art ImageNav model 2.3x (56% vs. 25% success). We deploy this system to a mobile robot platform and demonstrate effective performance in the real world, achieving an 88% success rate across a home and an office environment.
TRM-UAP: Enhancing the Transferability of Data-Free Universal Adversarial Perturbation via Truncated Ratio Maximization
Authors: Yiran Liu; Xin Feng; Yunlong Wang; Wu Yang; Di Ming
Venue: ICCV 2023 (main)

Abstract: Aiming at crafting a single universal adversarial perturbation (UAP) to fool CNN models for various data samples, universal attack enables a more efficient and accurate evaluation for the robustness of CNN models. Early universal attacks craft UAPs depending on data priors. For more practical applications, the data-free universal attacks that make UAPs from random noises have aroused much attention recently. However, existing data-free UAP methods perturb all the CNN feature layers equally via the maximization of the CNN activation, leading to poor transferability. In this paper, we propose a novel data-free universal attack without depending on any real data samples through truncated ratio maximization, which we term as TRM-UAP. Specifically, different from the maximization of the positive activation in convolution layers, we propose to optimize the UAP generation from the ratio of positive and negative activations. To further enhance the transferability of universal attack, TRM-UAP not only performs the ratio maximization merely on low-level generic features via the truncation strategy, but also incorporates a curriculum optimization algorithm that can effectively learn the diversity of artificial images. Extensive experiments on the ImageNet dataset verify that TRM-UAP achieves a state-of-the-art average fooling rate and excellent transferability on different CNN models as compared to other data-free UAP methods. Code is available at https://github.com/RandolphCarter0/TRMUAP.
LATR: 3D Lane Detection from Monocular Images with Transformer
Authors: Yueru Luo; Chaoda Zheng; Xu Yan; Tang Kun; Chao Zheng; Shuguang Cui; Zhen Li
Venue: ICCV 2023 (main)

Abstract: 3D lane detection from monocular images is a fundamental yet challenging task in autonomous driving. Recent advances primarily rely on structural 3D surrogates (e.g., bird's eye view) built from front-view image features and camera parameters. However, the depth ambiguity in monocular images inevitably causes misalignment between the constructed surrogate feature map and the original image, posing a great challenge for accurate lane detection. To address the above issue, we present a novel LATR model, an end-to-end 3D lane detector that uses 3D-aware front-view features without transformed view representation. Specifically, LATR detects 3D lanes via cross-attention based on query and key-value pairs, constructed using our lane-aware query generator and dynamic 3D ground positional embedding. On the one hand, each query is generated based on 2D lane-aware features and adopts a hybrid embedding to enhance the lane information. On the other hand, 3D space information is injected as positional embedding from an iteratively-updated 3D ground plane. LATR outperforms previous state-of-the-art methods on both synthetic Apollo and realistic OpenLane, ONCE-3DLanes datasets by large margins (e.g., 11.4 gain in terms of F1 score on OpenLane). Code will be released at https://github.com/JMoonr/LATR.
Scratching Visual Transformer's Back with Uniform Attention
Authors: Nam Hyeon-Woo; Kim Yu-Ji; Byeongho Heo; Dongyoon Han; Seong Joon Oh; Tae-Hyun Oh
Venue: ICCV 2023 (main)

Abstract: The favorable performance of Vision Transformers (ViTs) is often attributed to the multi-head self-attention (MSA), which enables global interactions at each layer of
 a ViT model. Previous works acknowledge the property of long-range dependency for the effectiveness in MSA. In this work, we study the role of MSA in terms of the different axis, density. Our preliminary analyses suggest that the spatial interactions of learned attention maps are close to dense interactions rather than sparse ones. This is a curious phenomenon because dense attention maps are harder for the model to learn due to softmax. We interpret this opposite behavior against softmax as a strong preference for the ViT models to include dense interaction. We thus manually insert the dense uniform attention to each layer of the ViT models to supply the much-needed dense interactions. We call this method Context Broadcasting, CB. Our study demonstrates the inclusion of CB takes the role of dense attention, and thereby reduces the degree of density in the original attention maps by complying softmax in MSA. We also show that, with negligible costs of CB (1 line in your model code and no additional parameters), both the capacity and generalizability of the ViT models are increased.
Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
Authors: Jay Zhangjie Wu; Yixiao Ge; Xintao Wang; Stan Weixian Lei; Yuchao Gu; Yufei Shi; Wynne Hsu; Ying Shan; Xiaohu Qie; Mike Zheng Shou
Venue: ICCV 2023 (main)

Abstract: To replicate the success of text-to-image (T2I) generation, recent works employ large-scale video datasets to train a text-to-video (T2V) generator. Despite their promising results, such paradigm is computationally expensive. In this work, we propose a new T2V generation setting--One-Shot Video Tuning, where only one text-video pair is presented. Our model is built on state-of-the-art T2I diffusion models pre-trained on massive image data. We make two key observations: 1) T2I models can generate still images that represent verb terms; 2) extending T2I models to generate multiple images concurrently exhibits surprisingly good content consistency. To further learn continuous motion, we introduce Tune-A-Video, which involves a tailored spatio-temporal attention mechanism and an efficient one-shot tuning strategy. At inference, we employ DDIM inversion to provide structure guidance for sampling. Extensive qualitative and numerical experiments demonstrate the remarkable ability of our method across various applications.
Anchor-Intermediate Detector: Decoupling and Coupling Bounding Boxes for Accurate Object Detection
Authors: Yilong Lv; Min Li; Yujie He; Shaopeng Li; Zhuzhen He; Aitao Yang
Venue: ICCV 2023 (main)

Abstract: Anchor-based detectors have been continuously developed for object detection. However, the individual anchor box makes it difficult to predict the boundary's offset accurately. Instead of taking each bounding box as a closed individual, we consider using multiple boxes together to get prediction boxes. To this end, this paper proposes the Box Decouple-Couple(BDC) strategy in the inference, which no longer discards the overlapping boxes, but decouples the corner points of these boxes. Then, according to each corner's score, we couple the corner points to select the most accurate corner pairs. To meet the BDC strategy, a simple but novel model is designed named the Anchor-Intermediate Detector(AID), which contains two head networks, i.e., an anchor-based head and an anchor-free Corner-aware head. The corner-aware head is able to score the corners of each bounding box to facilitate the coupling between corner points. Extensive experiments on MS COCO show that the proposed anchor-intermediate detector respectively outperforms their baseline RetinaNet and GFL method by  2.4 and  1.2 AP on the MS COCO test-dev dataset without any bells and whistles.
Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph Generation
Authors: Yukuan Min; Aming Wu; Cheng Deng
Venue: ICCV 2023 (main)

Abstract: The scene graph generation (SGG) task is designed to identify the predicates based on the subject-object pairs. However, existing datasets generally include two imbalance cases: one is the class imbalance from the predicted predicates and another is the context imbalance from the given subject-object pairs, which presents significant challenges for SGG. Most existing methods focus on the imbalance of the predicted predicate while ignoring the imbalance of the subject-object pairs, which could not achieve satisfactory results. To address the two imbalance cases, we propose a novel Environment Invariant Curriculum Relation learning (EICR) method, which can be applied in a plug-and-play fashion to existing SGG methods. Concretely, to remove the imbalance of the subject-object pairs, we first construct different distribution environments for the subject-object pairs and learn a model invariant to the environment changes. Then, we construct a class-balanced curriculum learning strategy to balance the different environments to remove the predicate imbalance. Comprehensive experiments conducted on VG and GQA datasets demonstrate that our EICR framework can be taken as a general strategy for various SGG models, and achieve significant improvements.
Extensible and Efficient Proxy for Neural Architecture Search
Authors: Yuhong Li; Jiajie Li; Cong Hao; Pan Li; Jinjun Xiong; Deming Chen
Venue: ICCV 2023 (main)

Abstract: Efficient or near-zero-cost proxies were proposed recently to address the demanding computational issues of Neural Architecture Search (NAS) in designing deep neural networks (DNNs), where each candidate architecture network only requires one iteration of backpropagation. The values obtained from proxies are used as predictions of architecture performance for downstream tasks. However, two significant drawbacks hinder the wide adoption of these efficient proxies: 1. they are not adaptive to various NAS search spaces and 2. they are not extensible to multi-modality downstream tasks. To address these two issues, we first propose an Extensible proxy (Eproxy) that utilizes self-supervised, few-shot training to achieve near-zero costs. A key component to our Eproxy's efficiency is the introduction of a barrier layer with randomly initialized frozen convolution parameters, which adds non-linearities to the optimization spaces so that Eproxy can discriminate the performance of architectures at an early stage. We further propose a Discrete Proxy Search (DPS) method to find the optimized training settings for Eproxy with only a handful of benchmarked architectures on the target tasks. Our extensive experiments confirm the effectiveness of both Eproxy and DPS. On the NDS-ImageNet search spaces, Eproxy+DPS achieves a higher average ranking correlation (Spearman r = 0.73) than the previous efficient proxy (Spearman r = 0.56). On the NAS-Bench-Trans-Micro search spaces with seven tasks, Eproxy+DPS delivers comparable performance with the early stopping method (146x faster). For the end-to-end task such as DARTS-ImageNet-1k, our method delivers better results than NAS performed on CIFAR-10 while only requiring one GPU hour with a single batch of CIFAR-10 images.
Zenseact Open Dataset: A Large-Scale and Diverse Multimodal Dataset for Autonomous Driving
Authors: Mina Alibeigi; William Ljungbergh; Adam Tonderski; Georg Hess; Adam Lilja; Carl Lindström; Daria Motorniuk; Junsheng Fu; Jenny Widahl; Christoffer Petersson
Venue: ICCV 2023 (main)

Abstract: Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360* perception and temporal reasoning. To address this gap, we introduce ZOD, a large-scale and diverse multimodal dataset collected over two years in various European countries, covering an area 9x that of existing datasets. ZOD boasts the highest range and resolution sensors among comparable datasets, coupled with detailed keyframe annotations for 2D and 3D objects (up to 245m), road instance/semantic segmentation, traffic sign recognition, and road classification. We believe that this unique combination will facilitate breakthroughs in long-range perception and multi-task learning. The dataset is composed of Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatio-temporal learning, sensor fusion, localization, and mapping. Frames consist of 100k curated camera images with two seconds of other supporting sensor data, while the 1473 Sequences and 29 Drives include the entire sensor suite for 20 seconds and a few minutes, respectively. ZOD is the only AD dataset released under the permissive CC BY-SA 4.0 license, allowing for both research and commercial use. More information, and an extensive devkit, can be found at zod.zenseact.com.
MAAL: Multimodality-Aware Autoencoder-Based Affordance Learning for 3D Articulated Objects
Authors: Yuanzhi Liang; Xiaohan Wang; Linchao Zhu; Yi Yang
Venue: ICCV 2023 (main)

Abstract: Inferring affordance for 3D articulated objects is a challenging and practical problem. It is a primary problem for applying robots to real-world scenarios. The exploration can be summarized as figuring out where to act and how to act. Correspondingly, the task mainly requires producing actionability scores, action proposals, and success likelihood scores according to the given 3D object information and robotic information. Current works usually directly process multi-modal inputs with early fusion and apply critic networks to produce scores, which leads to insufficient multi-modal learning ability and inefficiently iterative training in multiple stages. This paper proposes a novel Multimodality-Aware Autoencoder-based affordance Learning (MAAL) for the 3D object affordance problem. It is an efficient pipeline, trained in one go, and only requires a few positive samples in training data. More importantly, MAAL contains a MultiModal Energized Encoder (MME) for better multi-modal learning. It comprehensively models all multi-modal inputs from 3D objects and robotic actions. Jointly considering information from multiple modalities, the encoder further learns interactions between robots and objects. MME empowers the better multi-modal learning ability for understanding object affordance. Experimental results and visualizations, based on a large-scale dataset PartNet-Mobility, show the effectiveness of MAAL in learning multi-modal data and solving the 3D articulated object affordance problem.
Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain Generalization
Authors: Xiran Wang; Jian Zhang; Lei Qi; Yinghuan Shi
Venue: ICCV 2023 (main)

Abstract: Domain generalization (DG) is proposed to deal with the issue of domain shift, which occurs when statistical differences exist between source and target domains. However, most current methods do not account for a common realistic scenario where the source and target domains have different classes. To overcome this deficiency, open set domain generalization (OSDG) then emerges as a more practical setting to recognize unseen classes in unseen domains. An intuitive approach is to use multiple one-vs-all classifiers to define decision boundaries for each class and reject the outliers as unknown. However, the significant class imbalance between positive and negative samples often causes the boundaries biased towards positive ones, resulting in misclassification for known samples in the unseen target domain. In this paper, we propose a novel meta-learning-based framework called dualistic MEta-learning with joint DomaIn-Class matching (MEDIC), which considers gradient matching towards inter-domain and inter-class splits simultaneously to find a generalizable boundary balanced for all tasks. Experimental results demonstrate that MEDIC not only outperforms previous methods in open set scenarios, but also maintains competitive close set generalization ability at the same time. Our code is available at https://github.com/zzwdx/MEDIC.
Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples
Authors: Qiufan Ji; Lin Wang; Cong Shi; Shengshan Hu; Yingying Chen; Lichao Sun
Venue: ICCV 2023 (main)

Abstract: Deep Neural Networks (DNNs) for 3D point cloud recognition are vulnerable to adversarial examples, threatening their practical deployment. Despite the many research endeavors have been made to tackle this issue in recent years, the diversity of adversarial examples on 3D point clouds makes them more challenging to defend against than those on 2D images. For examples, attackers can generate adversarial examples by adding, shifting, or removing points. Consequently, existing defense strategies are hard to counter unseen point cloud adversarial examples. In this paper, we first establish a comprehensive, and rigorous point cloud adversarial robustness benchmark to evaluate adversarial robustness, which can provide a detailed understanding of the effects of the defense and attack methods. We then collect existing defense tricks in point cloud adversarial defenses and then perform extensive and systematic experiments to identify an effective combination of these tricks. Furthermore, we propose a hybrid training augmentation methods that consider various types of point cloud adversarial examples to adversarial training, significantly improving the adversarial robustness. By combining these tricks, we construct a more robust defense framework achieving an average accuracy of 83.45% against various attacks, demonstrating its capability to enabling robust learners. Our codebase are open-sourced on https://github.com/qiufan319/benchmark_pc_attack.git
Weakly Supervised Referring Image Segmentation with Intra-Chunk and Inter-Chunk Consistency
Authors: Jungbeom Lee; Sungjin Lee; Jinseok Nam; Seunghak Yu; Jaeyoung Do; Tara Taghavi
Venue: ICCV 2023 (main)

Abstract: Referring image segmentation (RIS) aims to localize the object in an image referred by a natural language expression. Most previous studies learn RIS with a large-scale dataset containing segmentation labels, but they are costly. We present a weakly supervised learning method for RIS that only uses readily available image-text pairs. We first train a visual-linguistic model for image-text matching and extract a visual saliency map through Grad-CAM to identify the image regions corresponding to each word. However, we found two major problems with Grad-CAM. First, it lacks consideration of critical semantic relationships between words. We tackle this problem by modeling the relationship between words through intra-chunk and inter-chunk consistency. Second, Grad-CAM identifies only small regions of the referred object, leading to low recall. Therefore, we refine the localization maps with self-attention in Transformer and unsupervised object shape prior. On three popular benchmarks (RefCOCO, RefCOCO+, G-Ref), our method significantly outperforms recent comparable techniques. We also show that our method is applicable to various levels of supervision and obtains better performance than recent methods.
Poincare ResNet
Authors: Max van Spengler; Erwin Berkhout; Pascal Mettes
Venue: ICCV 2023 (main)

Abstract: This paper introduces an end-to-end residual network that operates entirely on the Poincare ball model of hyperbolic space. Hyperbolic learning has recently shown great potential for visual understanding, but is currently only performed in the penultimate layer(s) of deep networks. All visual representations are still learned through standard Euclidean networks. In this paper we investigate how to learn hyperbolic representations of visual data directly from the pixel-level. We propose Poincare ResNet, a hyperbolic counterpart of the celebrated residual network, starting from Poincare 2D convolutions up to Poincare residual connections. We identify three roadblocks for training convolutional networks entirely in hyperbolic space and propose a solution for each: (i) Current hyperbolic network initializations collapse to the origin, limiting their applicability in deeper networks. We provide an identity-based initialization that preserves norms over many layers. (ii) Residual networks rely heavily on batch normalization, which comes with expensive Frechet mean calculations in hyperbolic space. We introduce Poincare midpoint batch normalization as a faster and equally effective alternative. (iii) Due to the many intermediate operations in Poincare layers, the computation graphs of deep learning libraries blow up, limiting our ability to train on deep hyperbolic networks. We provide manual backward derivations of core hyperbolic operations to maintain manageable computation graphs.
Parameterized Cost Volume for Stereo Matching
Authors: Jiaxi Zeng; Chengtang Yao; Lidong Yu; Yuwei Wu; Yunde Jia
Venue: ICCV 2023 (main)

Abstract: Stereo matching becomes computationally challenging when dealing with a large disparity range. Prior methods mainly alleviate the computation through dynamic cost volume by focusing on a local disparity space, but it requires many iterations to get close to the ground truth due to the lack of a global view. We find that the dynamic cost volume approximately encodes the disparity space as a single Gaussian distribution with a fixed and small variance at each iteration, which results in an inadequate global view over disparity space and a small update step at every iteration. In this paper, we propose a parameterized cost volume to encode the entire disparity space using multi-Gaussian distribution. The disparity distribution of each pixel is parameterized by weights, means, and variances. The means and variances are used to sample disparity candidates for cost computation, while the weights and means are used to calculate the disparity output. The above parameters are computed through a JS-divergence-based optimization, which is realized as a gradient descent update in a feed-forward differential module. Experiments show that our method speeds up the runtime of RAFT-Stereo by 4 15 times, achieving real-time performance and comparable accuracy.
SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection
Authors: Samuel Wilson; Tobias Fischer; Feras Dayoub; Dimity Miller; Niko Sünderhauf
Venue: ICCV 2023 (main)

Abstract: We address the problem of out-of-distribution (OOD) detection for the task of object detection. We show that residual convolutional layers with batch normalisation produce Sensitivity-Aware FEatures (SAFE) that are consistently powerful for distinguishing in-distribution from out-of-distribution detections. We extract SAFE vectors for every detected object, and train a multilayer perceptron on the surrogate task of distinguishing adversarially perturbed from clean in-distribution examples. This circumvents the need for realistic OOD training data, computationally expensive generative models, or retraining of the base object detector. SAFE outperforms the state-of-the-art OOD object detectors on multiple benchmarks by large margins, e.g. reducing the FPR95 by an absolute 30.6% from 48.3% to 17.7% on the OpenImages dataset.