pwc-archive/papers-with-abstracts huggingface dataset:
Improving CLIP Fine-tuning Performance
Authors: Yixuan Wei, Han Hu, Zhenda Xie, Ze Liu, Zheng Zhang, Yue Cao, Jianmin Bao, Dong Chen, Baining Guo
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Diagnostic, object-detection, Object Detection, Zero-Shot Learning

    CLIP models have demonstrated impressively high zero-shot recognition accuracy, however, their fine-tuning performance on downstream vision tasks is sub-optimal. Contrarily, masked image modeling (MIM) performs exceptionally for fine-tuning on downstream tasks, despite the absence of semantic labels during training. We note that the two tasks have different ingredients: image-level targets versus token-level targets, a cross-entropy loss versus a regression loss, and full-image inputs versus partial-image inputs. To mitigate the differences, we introduce a classical feature map distillation framework, which can simultaneously inherit the semantic capability of CLIP models while constructing a task incorporated key ingredients of MIM. Experiments suggest that the feature map distillation approach significantly boosts the fine-tuning performance of CLIP models on several typical downstream vision tasks. We also observe that the approach yields new CLIP representations which share some diagnostic properties with those of MIM. Furthermore, the feature map distillation approach generalizes to other pre-training models, such as DINO, DeiT and SwinV2-G, reaching a new record of 64.2 mAP on COCO object detection with +1.1 improvement. The code and mod- els are publicly available at https://github.com/ SwinTransformer/Feature-Distillation.
Locating Noise is Halfway Denoising for Semi-Supervised Segmentation
Authors: Yan Fang, Feng Zhu, Bowen Cheng, Luoqi Liu, Yao Zhao, Yunchao Wei
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Denoising, Semantic Segmentation, Semi-Supervised Semantic Segmentation

    We investigate semi-supervised semantic segmentation with self-training, where a teacher model generates pseudo masks to exploit the benefits of a large amount of unlabeled images. We notice that the noisy label from the generated pseudo masks is the major obstacle to achieving good performance. Previous works all treat the noise in pixel level and ignore the contextual information of the noise. This work shows that locating the patch-wise noisy region is a better way to deal with noise. To be specific, our method, named Uncertainty-aware Patch CutMix (UPC), first estimates the uncertainty of per-pixel prediction for pseudo masks of unlabeled images. Then UPC splits the uncertainty map into patches and calculates patch-wise uncertainty. UPC selects top-k most uncertain patches to generate the uncertain regions. Finally, uncertain regions are replaced with reliable ones from labeled images. We conduct extensive experiments using standard semi-supervised settings on Pascal VOC and Cityscapes. Experiment results show that UPC can significantly boost the performance of the state-of-the-art methods. In addition, we further demonstrate that our UPC is robust to out-of-distribution unlabeled images, eg, MSCOCO.
Learning Neural Implicit Surfaces with Object-Aware Radiance Fields
Authors: Yiheng Zhang, Zhaofan Qiu, Yingwei Pan, Ting Yao, Tao Mei
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Object Reconstruction, Object, Object Reconstruction

    Recent progress on multi-view 3D object reconstruction has featured neural implicit surfaces via learning high-fidelity radiance fields. However, most approaches hinge on the visual hull derived from cost-expensive silhouette masks to obtain object surfaces. In this paper, we propose a novel Object-aware Radiance Fields (ORF) to automatically learn an object-aware geometry reconstruction. The geometric correspondences between multi-view 2D object regions and 3D implicit/explicit object surfaces are additionally exploited to boost the learning of object surfaces. Technically, a critical transparency discriminator is designed to distinguish the object-intersected and object-bypassed rays based on the estimated 2D object regions, leading to 3D implicit object surfaces. Such implicit surfaces can be directly converted into explicit object surfaces (e.g., meshes) via marching cubes. Then, we build the geometric correspondence between 2D planes and 3D meshes by rasterization, and project the estimated object regions into 3D explicit object surfaces by aggregating the object information across multiple views. The aggregated object information in 3D explicit object surfaces is further reprojected back to 2D planes, aiming to update 2D object regions and enforce them to be multi-view consistent. Extensive experiments on DTU and BlendedMVS verify the capability of ORF to produce comparable surfaces against the state-of-the-art models that demand silhouette masks.
Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's-Eye View
Authors: Jiayu Yang, Enze Xie, Miaomiao Liu, Jose M. Alvarez
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Autonomous Driving, Hallucination, object-detection, Object Detection, Representation Learning, Segmentation, Semantic Segmentation

    Recent vision-only perception models for autonomous driving achieved promising results by encoding multi-view image features into Bird's-Eye-View (BEV) space. A critical step and the main bottleneck of these methods is transforming image features into the BEV coordinate frame. This paper focuses on leveraging geometry information, such as depth, to model such feature transformation. Existing works rely on non-parametric depth distribution modeling leading to significant memory consumption, or ignore the geometry information to address this problem. In contrast, we propose to use parametric depth distribution modeling for feature transformation. We first lift the 2D image features to the 3D space defined for the ego vehicle via a predicted parametric depth distribution for each pixel in each view. Then, we aggregate the 3D feature volume based on the 3D space occupancy derived from depth to the BEV frame. Finally, we use the transformed features for downstream tasks such as object detection and semantic segmentation. Existing semantic segmentation methods do also suffer from an hallucination problem as they do not take visibility information into account. This hallucination can be particularly problematic for subsequent modules such as control and planning. To mitigate the issue, our method provides depth uncertainty and reliable visibility-aware estimations. We further leverage our parametric depth modeling to present a novel visibility-aware evaluation metric that, when taken into account, can mitigate the hallucination problem. Extensive experiments on object detection and semantic segmentation on the nuScenes datasets demonstrate that our method outperforms existing methods on both tasks.
Stabilizing Visual Reinforcement Learning via Asymmetric Interactive Cooperation
Authors: Yunpeng Zhai, Peixi Peng, Yifan Zhao, Yangru Huang, Yonghong Tian
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

    Vision-based reinforcement learning (RL) depends on discriminative representation encoders to abstract the observation states. Despite the great success of increasing CNN parameters for many supervised computer vision tasks, reinforcement learning with temporal-difference (TD) losses cannot benefit from it in most complex environments. In this paper, we analyze that the training instability arises from the oscillating self-overfitting of the heavy-optimizable encoder. We argue that serious oscillation will occur to the parameters when enforced to fit the sensitive TD targets, causing uncertain drifting of the latent state space and thus transmitting these perturbations to the policy learning. To alleviate this phenomenon, we propose a novel asymmetric interactive cooperation approach with the interaction between a heavy-optimizable encoder and a supportive light-optimizable encoder, in which both their advantages are integrated including the highly discriminative capability as well as the training stability. We also present a greedy bootstrapping optimization to isolate the visual perturbations from policy learning, where representation and policy are trained sufficiently by turns. Finally, we demonstrate the effectiveness of our method in utilizing larger visual models by first-person highway driving task CARLA and Vizdoom environments.
Parallel Attention Interaction Network for Few-Shot Skeleton-Based Action Recognition
Authors: Xingyu Liu, Sanping Zhou, Le Wang, Gang Hua
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Action Recognition, Few-Shot Skeleton-Based Action Recognition, Skeleton Based Action Recognition

    Learning discriminative features from very few labeled samples to identify novel classes has received increasing attention in skeleton-based action recognition. Existing works aim to learn action-specific embeddings by exploiting either intra-skeleton or inter-skeleton spatial associations, which may lead to less discriminative representations. To address these issues, we propose a novel Parallel Attention Interaction Network (PAINet) that incorporates two complementary branches to strengthen the match by inter-skeleton and intra-skeleton correlation. Specifically, a topology encoding module utilizing topology and physical information is proposed to enhance the modeling of interactive parts and joint pairs in both branches. In the Cross Spatial Alignment branch, we employ a spatial cross-attention module to establish joint associations across sequences, and a directional Average Symmetric Surface Metric is introduced to locate the closest temporal similarity. In parallel, the Cross Temporal Alignment branch incorporates a spatial self-attention module to aggregate spatial context within sequences as well as applies the temporal cross-attention network to correct misalignment temporally and calculate similarity. Extensive experiments on three skeleton benchmarks, namely NTU-T, NTU-S, and Kinetics, demonstrate the superiority of our framework and consistently outperform state-of-the-art methods.
Lecture Presentations Multimodal Dataset: Towards Understanding Multimodality in Educational Videos
Authors: Dong Won Lee, Chaitanya Ahuja, Paul Pu Liang, Sanika Natu, Louis-Philippe Morency
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Attribute, Retrieval, Text Retrieval

    Many educational videos use slide presentations, a sequence of visual pages that contain text and figures accompanied by spoken language, which are constructed and presented carefully in order to optimally transfer knowledge to students. Previous studies in multimedia and psychology attribute the effectiveness of lecture presentations to their multimodal nature. As a step toward developing vision-language models to aid in student learning as intelligent teacher assistants, we introduce the Lecture Presentations Multimodal (LPM) Dataset as a large-scale benchmark testing the capabilities of vision-and-language models in multimodal understanding of educational videos. Our dataset contains aligned slides and spoken language, for 180+ hours of video and 9000+ slides, with 10 lecturers from various subjects (e.g., computer science, dentistry, biology). We introduce three research tasks, (1) figure-to-text retrieval, (2) text-to-figure retrieval, and (3) generation of slide explanations, which are grounded in multimedia learning and psychology principles to test a vision-language model's understanding of multimodal content. We provide manual annotations to help implement these tasks and establish baselines on them. Comparing baselines and human student performances, we find that state-of-the-art vision-language models (zero-shot and fine-tuned) struggle in (1) weak crossmodal alignment between slides and spoken text, (2) learning novel visual mediums, (3) technical language, and (4) long-range sequences. We introduce PolyViLT, a novel multimodal transformer trained with a multi-instance learning loss that is more effective than current approaches for retrieval. We conclude by shedding light on the challenges and opportunities in multimodal understanding of educational presentation videos.
Rethinking Video Frame Interpolation from Shutter Mode Induced Degradation
Authors: Xiang Ji, Zhixiang Wang, Zhihang Zhong, Yinqiang Zheng
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Disentanglement, Image Restoration, Video Frame Interpolation

    Image restoration from various motion-related degradations, like blurry effects recorded by a global shutter (GS) and jello effects caused by a rolling shutter (RS), has been extensively studied. It has been recently recognized that such degradations encode temporal information, which can be exploited for video frame interpolation (VFI), a more challenging task than pure restoration. However, these VFI researches are mainly grounded on experiments with synthetic data, rather than real data. More fundamentally, under the same imaging condition, it remains unknown which degradation will be more effective toward VFI. In this paper, we present the first real-world dataset for learning and benchmark degraded video frame interpolation, named RD-VFI, and further explore the performance differences of three types of degradations, including GS blur, RS distortion, and an in-between effect caused by the rolling shutter with global reset (RSGR), thanks to our novel quad-axis imaging system. Moreover, we propose a unified Progressive Mutual Boosting Network (PMBNet) model to interpolate middle frames at arbitrary times for all shutter modes. Its disentanglement strategy and dual-stream correction enable us to adaptively deal with different degradations for VFI. Experimental results demonstrate that our PMBNet is superior to the respective state-of-the-art methods on all shutter modes.
PIDRo: Parallel Isomeric Attention with Dynamic Routing for Text-Video Retrieval
Authors: Peiyan Guan, Renjing Pei, Bin Shao, Jianzhuang Liu, Weimian Li, Jiaxi Gu, Hang Xu, Songcen Xu, Youliang Yan, Edmund Y. Lam
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Representation Learning, Retrieval, Sentence, Video Alignment, Video Retrieval, Video Understanding

    Text-video retrieval is a fundamental task with high practical value in multi-modal research. Inspired by the great success of pre-trained image-text models with large-scale data, such as CLIP, many methods are proposed to transfer the strong representation learning capability of CLIP to text-video retrieval. However, due to the modality difference between videos and images, how to effectively adapt CLIP to the video domain is still underexplored. In this paper, we investigate this problem from two aspects. First, we enhance the transferred image encoder of CLIP for fine-grained video understanding in a seamless fashion. Second, we conduct fine-grained contrast between videos and texts from both model improvement and loss design. Particularly, we propose a fine-grained contrastive model equipped with parallel isomeric attention and dynamic routing, namely PIDRo, for text-video retrieval. The parallel isomeric attention module is used as the video encoder, which consists of two parallel branches modeling the spatial-temporal information of videos from both patch and frame levels. The dynamic routing module is constructed to enhance the text encoder of CLIP, generating informative word representations by distributing the fine-grained information to the related word tokens within a sentence. Such model design provides us with informative patch, frame and word representations. We then conduct token-wise interaction upon them. With the enhanced encoders and the token-wise loss, we are able to achieve finer-grained text-video alignment and more accurate retrieval. PIDRo obtains state-of-the-art performance over various text-video retrieval benchmarks, including MSR-VTT, MSVD, LSMDC, DiDeMo and ActivityNet.
Homeomorphism Alignment for Unsupervised Domain Adaptation
Authors: Lihua Zhou, Mao Ye, Xiatian Zhu, Siying Xiao, Xu-Qian Fan, Ferrante Neri
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Domain Adaptation, Pseudo Label, Self-Supervised Learning, Unsupervised Domain Adaptation

    Existing unsupervised domain adaptation (UDA) methods rely on aligning the features from the source and target domains explicitly or implicitly in a common space (i.e., the domain invariant space). Explicit distribution matching ignores the discriminability of learned features, while the implicit counterpart such as self-supervised learning suffers from pseudo-label noises. With distribution alignment, it is challenging to acquire a common space which maintains fully the discriminative structure of both domains. In this work, we propose a novel HomeomorphisM Alignment (HMA) approach characterized by aligning the source and target data in two separate spaces. Specifically, an invertible neural network based homeomorphism is constructed. Distribution matching is then used as a sewing up tool for connecting this homeomorphism mapping between the source and target feature spaces. Theoretically, we show that this mapping can preserve the data topological structure (e.g., the cluster/group structure). This property allows for more discriminative model adaptation by leveraging both the original and transformed features of source data in a supervised manner, and those of target domain in an unsupervised manner (e.g., prediction consistency). Extensive experiments demonstrate that our method can achieve the state-of-the-art results. Code is released at https://github.com/buerzlh/HMA.
Hyperbolic Chamfer Distance for Point Cloud Completion
Authors: Fangzhou Lin, Yun Yue, Songlin Hou, Xuechu Yu, Yajun Xu, Kazunori D Yamada, Ziming Zhang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Point Cloud Completion

    Chamfer distance (CD) is a standard metric to measure the shape dissimilarity between point clouds in point cloud completion, as well as a loss function for (deep) learning. However, it is well known that CD is vulnerable to outliers, leading to the drift towards suboptimal models. In contrast to the literature where most works address such issues in Euclidean space, we propose an extremely simple yet powerful metric for point cloud completion, namely Hyperbolic Chamfer Distance (HyperCD), that computes CD in hyperbolic space. In backpropagation, HyperCD consistently assigns higher weights to the matched point pairs with smaller Euclidean distances. In this way, good point matches are likely to be preserved while bad matches can be updated gradually, leading to better completion results. We demonstrate state-of-the-art performance on the benchmark datasets, i.e. PCN, ShapeNet-55, and ShapeNet-34, and show from visualization that HyperCD can significantly improve the surface smoothness. Code is available at: https://github.com/Zhang-VISLab.
DETR Does Not Need Multi-Scale or Locality Design
Authors: Yutong Lin, Yuhui Yuan, Zheng Zhang, Chen Li, Nanning Zheng, Han Hu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Decoder, Object Detection

    This paper presents an improved DETR detector that maintains a "plain" nature: using a single-scale feature map and global cross-attention calculations without specific locality constraints, in contrast to previous leading DETR-based detectors that reintroduce architectural inductive biases of multi-scale and locality into the decoder. We show that two simple technologies are surprisingly effective within a plain design to compensate for the lack of multi-scale feature maps and locality constraints. The first is a box-to-pixel relative position bias (BoxRPB) term added to the cross-attention formulation, which well guides each query to attend to the corresponding object region while also providing encoding flexibility. The second is masked image modeling (MIM)-based backbone pre-training which helps learn representation with fine-grained localization ability and proves crucial for remedying dependencies on the multi-scale feature maps. By incorporating these technologies and recent advancements in training and problem formation, the improved "plain" DETR showed exceptional improvements over the original DETR detector. By leveraging the Object365 dataset for pre-training, it achieved 63.9 mAP accuracy using a Swin-L backbone, which is highly competitive with state-of-the-art detectors which all heavily rely on multi-scale feature maps and region-based feature extraction. Code will be available at https://github.com/impiga/Plain-DETR.
Scene Graph Contrastive Learning for Embodied Navigation
Authors: Kunal Pratap Singh, Jordi Salvador, Luca Weihs, Aniruddha Kembhavi
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Contrastive Learning, Representation Learning

    Training effective embodied AI agents often involves expert imitation, specialized components such as maps, or leveraging additional sensors for depth and localization. Another approach is to use neural architectures alongside self-supervised objectives which encourage better representation learning. However, in practice, there are few guarantees that these self-supervised objectives encode task-relevant information. We propose the Scene Graph Contrastive (SGC) loss, which uses scene graphs as training-only supervisory signals. The SGC loss does away with explicit graph decoding and instead uses contrastive learning to align an agent's representation with a rich graphical encoding of its environment. The SGC loss is simple to implement and encourages representations that encode objects' semantics, relationships, and history. By using the SGC loss, we attain gains on three embodied tasks: Object Navigation, Multi-Object Navigation, and Arm Point Navigation. Finally, we present studies and analyses which demonstrate the ability of our trained representation to encode semantic cues about the environment.
HSE: Hybrid Species Embedding for Deep Metric Learning
Authors: Bailin Yang, Haoqiang Sun, Frederick W. B. Li, Zheng Chen, Jianlu Cai, Chao Song
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Metric Learning

    Deep metric learning is crucial for finding an embedding function that can generalize to training and testing data, including unknown test classes. However, limited training samples restrict the model's generalization to downstream tasks. While adding new training samples is a promising solution, determining their labels remains a significant challenge. Here, we introduce Hybrid Species Embedding (HSE), which employs mixed sample data augmentations to generate hybrid species and provide additional training signals. We demonstrate that HSE outperforms multiple state-of-the-art methods in improving the metric Recall@K on the CUB-200 , CAR-196 and SOP datasets, thus offering a novel solution to deep metric learning's limitations.