GenAI4ELab/papercli-papers huggingface dataset:
Studying How to Efficiently and Effectively Guide Models with Explanations
Authors: Sukrut Rao; Moritz Böhle; Amin Parchami-Araghi; Bernt Schiele
Venue: ICCV 2023 (main)

Abstract: Despite being highly performant, deep neural networks might base their decisions on features that spuriously correlate with the provided labels, thus hurting generalization. To mitigate this, 'model guidance' has recently gained popularity, i.e. the idea of regularizing the models' explanations to ensure that they are "right for the right reasons". While various techniques to achieve such model guidance have been proposed, experimental validation of these approaches has thus far been limited to relatively simple and / or synthetic datasets. To better understand the effectiveness of the various design choices that have been explored in the context of model guidance, in this work we conduct an in-depth evaluation across various loss functions, attribution methods, models, and 'guidance depths' on the PASCAL VOC 2007 and MS COCO 2014 datasets. As annotation costs for model guidance can limit its applicability, we also place a particular focus on efficiency. Specifically, we guide the models via bounding box annotations, which are much cheaper to obtain than the commonly used segmentation masks, and evaluate the robustness of model guidance under limited (e.g. with only 1% of annotated images) or overly coarse annotations. Further, we propose using the EPG score as an additional evaluation metric and loss function ('Energy loss'). We show that optimizing for the Energy loss leads to models that exhibit a distinct focus on object-specific features, despite only using bounding box annotations that also include background regions. Lastly, we show that such model guidance can improve generalization under distribution shifts. Code available at: https://github.com/sukrutrao/Model-Guidance
Most Important Person-Guided Dual-Branch Cross-Patch Attention for Group Affect Recognition
Authors: Hongxia Xie; Ming-Xian Lee; Tzu-Jui Chen; Hung-Jen Chen; Hou-I Liu; Hong-Han Shuai; Wen-Huang Cheng
Venue: ICCV 2023 (main)

Abstract: Group affect refers to the subjective emotion that is evoked by an external stimulus in a group, which is an important factor that shapes group behavior and outcomes. Recognizing group affect involves identifying important individuals and salient objects among a crowd that can evoke emotions. However, most existing methods lack attention to affective meaning in group dynamics and fail to account for the contextual relevance of faces and objects in group-level images. In this work, we propose a solution by incorporating the psychological concept of the Most Important Person (MIP), which represents the most noteworthy face in a crowd and has affective semantic meaning. We present the Dual-branch Cross-Patch Attention Transformer (DCAT) which uses global image and MIP together as inputs. Specifically, we first learn the informative facial regions produced by the MIP and the global context separately. Then, the Cross-Patch Attention module is proposed to fuse the features of MIP and global context together to complement each other. Our proposed method outperforms state-of-the-art methods on GAF 3.0, GroupEmoW, and HECO datasets. Moreover, we demonstrate the potential for broader applications by showing that our proposed model can be transferred to another group affect task, group cohesion, and achieve comparable results.
SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training
Authors: Hong Yan; Yang Liu; Yushen Wei; Zhen Li; Guanbin Li; Liang Lin
Venue: ICCV 2023 (main)

Abstract: Skeleton sequence representation learning has shown great advantages for action recognition due to its promising ability to model human joints and topology. However, the current methods usually require sufficient labeled data for training computationally expensive models. Moreover, these methods ignore how to utilize the fine-grained dependencies among different skeleton joints to pre-train an efficient skeleton sequence learning model that can generalize well across different datasets. In this paper, we propose an efficient skeleton sequence learning framework, named Skeleton Sequence Learning (SSL). To comprehensively capture the human pose and obtain discriminative skeleton sequence representation, we build an asymmetric graph-based encoder-decoder pre-training architecture named SkeletonMAE, which embeds skeleton joint sequence into graph convolutional network and reconstructs the masked skeleton joints and edges based on the prior human topology knowledge. Then, the pre-trained SkeletonMAE encoder is integrated with the Spatial-Temporal Representation Learning (STRL) module to build the SSL framework. Extensive experimental results show that our SSL generalizes well across different datasets and outperforms the state-of-the-art self-supervised skeleton-based methods on FineGym, Diving48, NTU 60 and NTU 120 datasets. Moreover, we obtain comparable performance to some fully supervised methods. The code is avaliable at https://github.com/HongYan1123/SkeletonMAE.
Achievement-Based Training Progress Balancing for Multi-Task Learning
Authors: Hayoung Yun; Hanjoo Cho
Venue: ICCV 2023 (main)

Abstract: Multi-task learning faces two challenging issues: (1) the high cost of annotating labels for all tasks and (2) balancing the training progress of various tasks with different natures. To resolve the label annotation issue, we construct a large-scale "partially annotated" multi-task dataset by combining task-specific datasets. However, the numbers of annotations for individual tasks are imbalanced, which
 may escalate an imbalance in training progress. To balance the training progress, we propose an achievement-based multi-task loss to modulate training speed based on the "achievement," defined as the ratio of current accuracy to single-task accuracy. Then, we formulate the multitask loss as a weighted geometric mean of individual task losses instead of a weighted sum to prevent any task from dominating the loss. In experiments, we evaluated the accuracy and training speed of the proposed multi-task loss on the large-scale multi-task dataset against recent multitask losses. The proposed loss achieved the best multi-task accuracy without incurring training time overhead. Compared to single-task models, the proposed one achieved 1.28%, 1.65%, and 1.18% accuracy improvement in object detection, semantic segmentation, and depth estimation, respectively, while reducing computations to 33.73%. Source code is available at https://github.com/
 samsung/Achievement-based-MTL.
Pose-Free Neural Radiance Fields via Implicit Pose Regularization
Authors: Jiahui Zhang; Fangneng Zhan; Yingchen Yu; Kunhao Liu; Rongliang Wu; Xiaoqin Zhang; Ling Shao; Shijian Lu
Venue: ICCV 2023 (main)

Abstract: Pose-free neural radiance fields (NeRF) aim to train NeRF with unposed multi-view images and it has achieved very impressive success in recent years. Most existing works share the pipeline of training a coarse pose estimator with rendered images at first, followed by a joint optimization of estimated poses and neural radiance field. However, as the pose estimator is trained with only rendered images, the pose estimation is usually biased or inaccurate for real images due to the domain gap between real images and rendered images, leading to poor robustness for the pose estimation of real images and further local min- ima in joint optimization. We design IR-NeRF, an innovative pose-free NeRF that introduces implicit pose regularization to refine pose estimator with unposed real images and improve the robustness of the pose estimation for real images. With a collection of 2D images of a specific scene, IR-NeRF constructs a scene codebook that stores scene features and captures the scene-specific pose distribution implicitly as priors. Thus, the robustness of pose estimation can be promoted with the scene priors according to the rationale that a 2D real image can be well reconstructed from the scene codebook only when its estimated pose lies within the pose distribution. Extensive experiments show that IR-NeRF achieves superior novel view synthesis and outperforms the state-of-the-art consistently across multiple synthetic and real datasets.
Self-supervised Learning to Bring Dual Reversed Rolling Shutter Images Alive
Authors: Wei Shang; Dongwei Ren; Chaoyu Feng; Xiaotao Wang; Lei Lei; Wangmeng Zuo
Venue: ICCV 2023 (main)

Abstract: Modern consumer cameras usually employ the rolling shutter (RS) mechanism, where images are captured by scanning scenes row-by-row, yielding RS distortions for dynamic scenes. To correct RS distortions, existing methods adopt a fully supervised learning manner, where high framerate global shutter (GS) images should be collected as ground-truth supervision. In this paper, we propose a Self-supervised learning framework for Dual reversed RS distortions Correction (SelfDRSC), where a DRSC network can be learned to generate a high framerate GS video only based on dual RS images with reversed distortions. In particular, a bidirectional distortion warping module is proposed for reconstructing dual reversed RS images, and then a self-supervised loss can be deployed to train DRSC network by enhancing the cycle consistency between input and reconstructed dual reversed RS images. Besides start and end RS scanning time, GS images at arbitrary intermediate scanning time can also be supervised in SelfDRSC, thus enabling the learned DRSC network to generate a high framerate GS video. Moreover, a simple yet effective self-distillation strategy is introduced in self-supervised loss for mitigating boundary artifacts in generated GS images. On synthetic dataset, SelfDRSC achieves better or comparable quantitative metrics in comparison to state-of-the-art methods trained in the full supervision manner. On real-world RS cases, our SelfDRSC can produce high framerate GS videos with finer correction textures and better temporary consistency. The source code and trained models are made publicly available at https://github.com/ shangwei5/SelfDRSC.
Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentation
Authors: Chen Liang; Wenguan Wang; Jiaxu Miao; Yi Yang
Venue: ICCV 2023 (main)

Abstract: Recent advances in semi-supervised semantic segmentation have been heavily reliant on pseudo labeling to compensate for limited labeled data, disregarding the valuable relational knowledge among semantic concepts. To bridge this gap, we devise LogicDiag, a brand new neural-logic semi-supervised learning framework. Our key insight is that conflicts within pseudo labels, identified through symbolic knowledge, can serve as strong yet commonly ignored learning signals. LogicDiag resolves such conflicts via reasoning with logic-induced diagnoses, enabling the recovery of (potentially) erroneous pseudo labels, ultimately alleviating the notorious error accumulation problem. We showcase the practical application of LogicDiag in the data-hungry segmentation scenario, where we formalize the structured abstraction of semantic concepts as a set of logic rules. Extensive experiments on three standard semi-supervised semantic segmentation benchmarks demonstrate the effectiveness and generality of LogicDiag. Moreover, LogicDiag highlights the promising opportunities arising from the systematic integration of symbolic reasoning into the prevalent statistical, neural learning approaches.
Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Network
Authors: Wencheng Han; Junbo Yin; Jianbing Shen
Venue: ICCV 2023 (main)

Abstract: Monocular depth estimation is known as an ill-posed task that objects in a 2D image usually do not contain sufficient information to predict their depth. Thus, it acts differently from other tasks (e.g., classification and segmentation) in many ways. In this paper, we find that self-supervised monocular depth estimation shows a direction sensitivity and environmental dependency in the feature representation. But the current CNN backbones borrowed from other tasks cannot handle different types of environmental information efficiently, limiting the overall depth accuracy. To bridge this gap, we propose a new Direction-aware Cumulative Convolution Network (DaCCN), which improves the depth feature representation in two aspects. First, we propose a direction-aware module, which can learn to adjust the feature extraction in each direction, facilitating the encoding of different types of information. Secondly, we design a new cumulative convolution to improve the efficiency for aggregating important environmental information. Experiments show that our method achieves significant improvements on three widely used benchmarks and sets a new state-of-the-art performance on the popular benchmarks with all three types of self-supervision.
Encyclopedic VQA: Visual Questions About Detailed Properties of Fine-Grained Categories
Authors: Thomas Mensink; Jasper Uijlings; Lluis Castrejon; Arushi Goel; Felipe Cadar; Howard Zhou; Fei Sha; André Araujo; Vittorio Ferrari
Venue: ICCV 2023 (main)

Abstract: We propose Encyclopedic-VQA, a large scale visual question answering (VQA) dataset featuring visual questions about detailed properties of fine-grained categories and instances. It contains 221k unique question+answer pairs each matched with (up to) 5 images, resulting in a total of 1M VQA samples. Moreover, our dataset comes with a controlled knowledge base derived from Wikipedia, marking the evidence to support each answer. Empirically, we show that our dataset poses a hard challenge for large vision+language models as they perform poorly on our dataset: PaLI [9] is state-of-the-art on OK-VQA [29], yet it only achieves 13.0% accuracy on our dataset. Moreover, we experimentally show that progress on answering our encyclopedic questions can be achieved by augmenting large models with a mechanism that retrieves relevant information for the knowledge base. An oracle experiment with perfect retrieval achieves 87.0% accuracy on the single-hop portion of our dataset, and an automatic retrieval-
 augmented prototype yields 48.8%. We believe that our dataset enables future research on retrieval-augmented vision+language models.
Towards Understanding the Generalization of Deepfake Detectors from a Game-Theoretical View
Authors: Kelu Yao; Jin Wang; Boyu Diao; Chao Li
Venue: ICCV 2023 (main)

Abstract: This paper aims to explain the generalization of deepfake detectors from the novel perspective of multi-order interactions among visual concepts. Specifically, we propose three hypotheses: 
 1. Deepfake detectors encode multi-order interactions among visual concepts, in which the low-order interactions usually have substantially negative contributions to deepfake detection. 
 2. Deepfake detectors with better generalization abilities tend to encode low-order interactions with fewer negative contributions. 
 3. Generalized deepfake detectors usually weaken the negative contributions of low-order interactions by suppressing their strength.
 Accordingly, we design several mathematical metrics to evaluate the effect of low-order interaction for deepfake detectors.
 Extensive comparative experiments are conducted, which verify the soundness of our hypotheses.
 Based on the analyses, we further propose a generic method, which directly reduces the toxic effects of low-order interactions to improve the generalization of deepfake detectors to some extent. 
 The code will be released when the paper is accepted.
Few-Shot Common Action Localization via Cross-Attentional Fusion of Context and Temporal Dynamics
Authors: Juntae Lee; Mihir Jain; Sungrack Yun
Venue: ICCV 2023 (main)

Abstract: The goal of this paper is to localize action instances in a long untrimmed query video using just meager trimmed support videos representing a common action whose class information is not given. In this task, it is crucial to mine reliable temporal cues representing a common action from handful support videos. In our work, we develop an attention mechanism using cross-correlation. Based on this cross-attention, we first transform the support videos into query video's context to emphasize query-relevant important frames, and suppress less relevant ones. Next, we summarize sub-sequences of support video frames to represent temporal dynamics in coarse temporal granularity, which is then propagated to the fine-grained support video features through the cross-attention. In each case, the cross-attentions are applied to each support video in the individual-to-all strategy to balance heterogeneity and compatibility of the support videos. In contrast, the candidate instances in the query video are lastly attended by the resulting support video features, at once. In addition, we also develop a relational classifier head based on the query and support video representations. We show the effectiveness of our work with the state-of-the-art (SOTA) performance in benchmark datasets (ActivityNet1.3 and THUMOS14), and analyze each component extensively.
Physically-Plausible Illumination Distribution Estimation
Authors: Egor Ershov; Vasily Tesalin; Ivan Ermakov; Michael S. Brown
Venue: ICCV 2023 (main)

Abstract: A camera's auto-white-balance (AWB) module operates under the assumption that there is a single dominant illumination in a captured scene. AWB methods estimate an image's dominant illumination and use it as the target "white point" for correction. However, in natural scenes, there are often many light sources present. We performed a user study that revealed that non-dominant illuminations often produce visually pleasing white-balanced images and, in some cases, are even preferred over the dominant illumination. Motivated by this observation, we revisit AWB to predict a  distribution  of plausible illuminations for use in white balance. As part of this effort, we extend the Cube++ illumination estimation dataset to provide ground truth illumination distributions per image. Using this new ground truth data, we describe how to train a lightweight neural network method to predict the scene's illumination distribution. We describe how our idea can be used with existing image formats by embedding the estimated distribution in the RAW image to enable users to generate visually plausible white-balance images.
3DPPE: 3D Point Positional Encoding for Transformer-based Multi-Camera 3D Object Detection
Authors: Changyong Shu; Jiajun Deng; Fisher Yu; Yifan Liu
Venue: ICCV 2023 (main)

Abstract: Transformer-based methods have swept the benchmarks on 2D and 3D detection on images. Because tokenization before the attention mechanism drops the spatial information, positional encoding becomes critical for those methods. Recent works found that encodings based on samples of the 3D viewing rays can significantly improve the quality of multi-camera 3D object detection. We hypothesize that 3D point locations can provide more information than rays. Therefore, we introduce 3D point positional encoding, 3DPPE, to the 3D detection Transformer decoder. Although 3D measurements are not available at the inference time of monocular 3D object detection, 3DPPE uses predicted depth to approximate the real point positions. Our hybrid-depth module combines direct and categorical depth to estimate the refined depth of each pixel. Despite the approximation, 3DPPE achieves 46.0 mAP and 51.4 NDS on the competitive nuScenes dataset, significantly outperforming encodings based on ray samples. The codes are available at https://github.com/drilistbox/3DPPE.
Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach
Authors: Qinying Liu; Zilei Wang; Shenghai Rong; Junjie Li; Yixin Zhang
Venue: ICCV 2023 (main)

Abstract: Weakly-supervised temporal action localization aims to localize action instances in videos with only video-level action labels. Existing methods mainly embrace a localization-by-classification pipeline that optimizes the snippet-level prediction with a video classification loss. However, this formulation suffers from the discrepancy between classification and detection, resulting in inaccurate separation of foreground and background (F&B) snippets. To alleviate this problem, we propose to explore the underlying structure among the snippets by resorting to unsupervised snippet clustering, rather than heavily relying on the video classification loss. Specifically, we propose a novel clustering-based F&B separation algorithm. It comprises two core components: a snippet clustering component that groups the snippets into multiple latent clusters and a cluster classification component that further classifies the cluster as foreground or background. As there are no ground-truth labels to train these two components, we introduce a unified self-labeling mechanism based on optimal transport to produce high-quality pseudo-labels that match several plausible prior distributions. This ensures that the cluster assignments of the snippets can be accurately associated with their F&B labels, thereby boosting the F&B separation. We evaluate our method on three benchmarks: THUMOS14, ActivityNet v1.2 and v1.3. Our method achieves promising performance on all three benchmarks while being significantly more lightweight than previous methods. Code is available at https://github.com/Qinying-Liu/CASE
VertexSerum: Poisoning Graph Neural Networks for Link Inference
Authors: Ruyi Ding; Shijin Duan; Xiaolin Xu; Yunsi Fei
Venue: ICCV 2023 (main)

Abstract: Graph neural networks (GNNs) have brought superb performance to various applications utilizing graph structural data, such as social analysis and fraud detection. The graph links, e.g., social relationships and transaction history, are sensitive and valuable information, which raises privacy concerns when using GNNs. To exploit these vulnerabilities, we propose VertexSerum, a novel graph poisoning attack that increases the effectiveness of graph link stealing by amplifying the link connectivity leakage. To infer node adjacency more accurately, we propose an attention mechanism that can be embedded into the link detection network. Our experiments demonstrate that VertexSerum significantly outperforms the SOTA link inference attack, improving the AUC scores by an average of 9.8% across four real-world datasets and three different GNN structures. Furthermore, our experiments reveal the effectiveness of VertexSerum in both black-box and online learning settings, further validating its applicability in real-world scenarios.
NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection
Authors: Chenfeng Xu; Bichen Wu; Ji Hou; Sam Tsai; Ruilong Li; Jialiang Wang; Wei Zhan; Zijian He; Peter Vajda; Kurt Keutzer; Masayoshi Tomizuka
Venue: ICCV 2023 (main)

Abstract: We present NeRF-Det, a novel method for indoor 3D detection with posed RGB images as input. Unlike existing indoor 3D detection methods that struggle to model scene geometry, our method makes novel use of NeRF in an end-to-end manner to explicitly estimate 3D geometry, thereby improving 3D detection performance. Specifically, to avoid the significant extra latency associated with per-scene optimization of NeRF, we introduce sufficient geometry priors to enhance the generalizability of NeRF-MLP. Furthermore, we subtly connect the detection and NeRF branches through a shared MLP, enabling an efficient adaptation of NeRF to detection and yielding geometry-aware volumetric representations for 3D detection. Our method outperforms state-of-the-arts by 3.9 mAP and 3.1 mAP on the ScanNet and ARKITScenes benchmarks, respectively. We provide extensive analysis to shed light on how NeRF-Det works. As a result of our joint-training design, NeRF-Det is able to generalize well to unseen scenes for object detection, view synthesis, and depth estimation tasks without requiring per-scene optimization. Code is available at https://github.com/facebookresearch/NeRF-Det.