GenAI4ELab/papercli-papers huggingface dataset:
Sampling Is Matter: Point-Guided 3D Human Mesh Reconstruction
Authors: Jeonghwan Kim; Mi-Gyeong Gwon; Hyunwoo Park; Hyukmin Kwon; Gi-Mun Um; Wonjun Kim
Venue: CVPR 2023 (main)

Abstract: This paper presents a simple yet powerful method for 3D human mesh reconstruction from a single RGB image. Most recently, the non-local interactions of the whole mesh vertices have been effectively estimated in the transformer while the relationship between body parts also has begun to be handled via the graph model. Even though those approaches have shown the remarkable progress in 3D human mesh reconstruction, it is still difficult to directly infer the relationship between features, which are encoded from the 2D input image, and 3D coordinates of each vertex. To resolve this problem, we propose to design a simple feature sampling scheme. The key idea is to sample features in the embedded space by following the guide of points, which are estimated as projection results of 3D mesh vertices (i.e., ground truth). This helps the model to concentrate more on vertex-relevant features in the 2D space, thus leading to the reconstruction of the natural human pose. Furthermore, we apply progressive attention masking to precisely estimate local interactions between vertices even under severe occlusions. Experimental results on benchmark datasets show that the proposed method efficiently improves the performance of 3D human mesh reconstruction. The code and model are publicly available at: https://github.com/DCVL-3D/PointHMR_release.
Efficient Multimodal Fusion via Interactive Prompting
Authors: Yaowei Li; Ruijie Quan; Linchao Zhu; Yi Yang
Venue: CVPR 2023 (main)

Abstract: Large-scale pre-training has brought unimodal fields such as computer vision and natural language processing to a new era. Following this trend, the size of multimodal learning models constantly increases, leading to an urgent need to reduce the massive computational cost of fine-tuning these models for downstream tasks. In this paper, we propose an efficient and flexible multimodal fusion method, namely PMF, tailored for fusing unimodally pretrained transformers. Specifically, we first present a modular multimodal fusion framework that exhibits high flexibility and facilitates mutual interactions among different modalities. In addition, we disentangle vanilla prompts into three types in order to learn different optimizing objectives for multimodal learning. It is also worth noting that we propose to add prompt vectors only on the deep layers of the unimodal transformers, thus significantly reducing the training memory usage. Experiment results show that our proposed method achieves comparable performance to several other multimodal finetuning methods with less than 3% trainable parameters and up to 66% saving of training memory usage.
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning
Authors: MyeongAh Cho; Minjung Kim; Sangwon Hwang; Chaewon Park; Kyungjae Lee; Sangyoun Lee
Venue: CVPR 2023 (main)

Abstract: Weakly-supervised Video Anomaly Detection is the task of detecting frame-level anomalies using video-level labeled training data. It is difficult to explore class representative features using minimal supervision of weak labels with a single backbone branch. Furthermore, in real-world scenarios, the boundary between normal and abnormal is ambiguous and varies depending on the situation. For example, even for the same motion of running person, the abnormality varies depending on whether the surroundings are a playground or a roadway. Therefore, our aim is to extract discriminative features by widening the relative gap between classes' features from a single branch. In the proposed Class-Activate Feature Learning (CLAV), the features are extracted as per the weights that are implicitly activated depending on the class, and the gap is then enlarged through relative distance learning. Furthermore, as the relationship between context and motion is important in order to identify the anomalies in complex and diverse scenes, we propose a Context--Motion Interrelation Module (CoMo), which models the relationship between the appearance of the surroundings and motion, rather than utilizing only temporal dependencies or motion information. The proposed method shows SOTA performance on four benchmarks including large-scale real-world datasets, and we demonstrate the importance of relational information by analyzing the qualitative results and generalization ability.
Depth Estimation From Indoor Panoramas With Neural Scene Representation
Authors: Wenjie Chang; Yueyi Zhang; Zhiwei Xiong
Venue: CVPR 2023 (main)

Abstract: Depth estimation from indoor panoramas is challenging due to the equirectangular distortions of panoramas and inaccurate matching. In this paper, we propose a practical framework to improve the accuracy and efficiency of depth estimation from multi-view indoor panoramic images with the Neural Radiance Field technology. Specifically, we develop two networks to implicitly learn the Signed Distance Function for depth measurements and the radiance field from panoramas. We also introduce a novel spherical position embedding scheme to achieve high accuracy. For better convergence, we propose an initialization method for the network weights based on the Manhattan World Assumption. Furthermore, we devise a geometric consistency loss, leveraging the surface normal, to further refine the depth estimation. The experimental results demonstrate that our proposed method outperforms state-of-the-art works by a large margin in both quantitative and qualitative evaluations. Our source code is available at https://github.com/WJ-Chang-42/IndoorPanoDepth.
Task-Specific Fine-Tuning via Variational Information Bottleneck for Weakly-Supervised Pathology Whole Slide Image Classification
Authors: Honglin Li; Chenglu Zhu; Yunlong Zhang; Yuxuan Sun; Zhongyi Shui; Wenwei Kuang; Sunyi Zheng; Lin Yang
Venue: CVPR 2023 (main)

Abstract: While Multiple Instance Learning (MIL) has shown promising results in digital Pathology Whole Slide Image (WSI) analysis, such a paradigm still faces performance and generalization problems due to high computational costs and limited supervision of Gigapixel WSIs. To deal with the computation problem, previous methods utilize a frozen model pretrained from ImageNet to obtain representations, however, it may lose key information owing to the large domain gap and hinder the generalization ability without image-level training-time augmentation. Though Self-supervised Learning (SSL) proposes viable representation learning schemes, the downstream task-specific features via partial label tuning are not explored. To alleviate this problem, we propose an efficient WSI fine-tuning framework motivated by the Information Bottleneck theory. The theory enables the framework to find the minimal sufficient statistics of WSI, thus supporting us to fine-tune the backbone into a task-specific representation only depending on WSI-level weak labels. The WSI-MIL problem is further analyzed to theoretically deduce our fine-tuning method. We evaluate the method on five pathological WSI datasets on various WSI heads. The experimental results show significant improvements in both accuracy and generalization compared with previous works. Source code will be available at https://github.com/invoker-LL/WSI-finetuning.
Detecting Everything in the Open World: Towards Universal Object Detection
Authors: Zhenyu Wang; Yali Li; Xi Chen; Ser-Nam Lim; Antonio Torralba; Hengshuang Zhao; Shengjin Wang
Venue: CVPR 2023 (main)

Abstract: In this paper, we formally address universal object detection, which aims to detect every scene and predict every category. The dependence on human annotations, the limited visual information, and the novel categories in the open world severely restrict the universality of traditional detectors. We propose UniDetector, a universal object detector that has the ability to recognize enormous categories in the open world. The critical points for the universality of UniDetector are: 1) it leverages images of multiple sources and heterogeneous label spaces for training through the alignment of image and text spaces, which guarantees sufficient information for universal representations. 2) it generalizes to the open world easily while keeping the balance between seen and unseen classes, thanks to abundant information from both vision and language modalities. 3) it further promotes the generalization ability to novel categories through our proposed decoupling training manner and probability calibration. These contributions allow UniDetector to detect over 7k categories, the largest measurable category size so far, with only about 500 classes participating in training. Our UniDetector behaves the strong zero-shot generalization ability on large-vocabulary datasets like LVIS, ImageNetBoxes, and VisualGenome - it surpasses the traditional supervised baselines by more than 4% on average without seeing any corresponding images. On 13 public detection datasets with various scenes, UniDetector also achieves state-of-the-art performance with only a 3% amount of training data.
Single Image Depth Prediction Made Better: A Multivariate Gaussian Take
Authors: Ce Liu; Suryansh Kumar; Shuhang Gu; Radu Timofte; Luc Van Gool
Venue: CVPR 2023 (main)

Abstract: Neural-network-based single image depth prediction (SIDP) is a challenging task where the goal is to predict the scene's per-pixel depth at test time. Since the problem, by definition, is ill-posed, the fundamental goal is to come up with an approach that can reliably model the scene depth from a set of training examples. In the pursuit of perfect depth estimation, most existing state-of-the-art learning techniques predict a single scalar depth value per-pixel. Yet, it is well-known that the trained model has accuracy limits and can predict imprecise depth. Therefore, an SIDP approach must be mindful of the expected depth variations in the model's prediction at test time. Accordingly, we introduce an approach that performs continuous modeling of per-pixel depth, where we can predict and reason about the per-pixel depth and its distribution. To this end, we model per-pixel scene depth using a multivariate Gaussian distribution. Moreover, contrary to the existing uncertainty modeling methods---in the same spirit, where per-pixel depth is assumed to be independent, we introduce per-pixel covariance modeling that encodes its depth dependency w.r.t. all the scene points. Unfortunately, per-pixel depth covariance modeling leads to a computationally expensive continuous loss function, which we solve efficiently using the learned low-rank approximation of the overall covariance matrix. Notably, when tested on benchmark datasets such as KITTI, NYU, and SUN-RGB-D, the SIDP model obtained by optimizing our loss function shows state-of-the-art results. Our method's accuracy (named MG) is among the top on the KITTI depth-prediction benchmark leaderboard.
NUWA-LIP: Language-Guided Image Inpainting With Defect-Free VQGAN
Authors: Minheng Ni; Xiaoming Li; Wangmeng Zuo
Venue: CVPR 2023 (main)

Abstract: Language-guided image inpainting aims to fill the defective regions of an image under the guidance of text while keeping the non-defective regions unchanged. However, directly encoding the defective images is prone to have an adverse effect on the non-defective regions, giving rise to distorted structures on non-defective parts. To better adapt the text guidance to the inpainting task, this paper proposes NUWA-LIP, which involves defect-free VQGAN (DF-VQGAN) and a multi-perspective sequence-to-sequence module (MP-S2S). To be specific, DF-VQGAN introduces relative estimation to carefully control the receptive spreading, as well as symmetrical connections to protect structure details unchanged. For harmoniously embedding text guidance into the locally defective regions, MP-S2S is employed by aggregating the complementary perspectives from low-level pixels, high-level tokens as well as the text description. Experiments show that our DF-VQGAN effectively aids the inpainting process while avoiding unexpected changes in non-defective regions. Results on three open-domain benchmarks demonstrate the superior performance of our method against state-of-the-arts. Our code, datasets, and model will be made publicly available.
One-Shot Model for Mixed-Precision Quantization
Authors: Ivan Koryakovskiy; Alexandra Yakovleva; Valentin Buchnev; Temur Isaev; Gleb Odinokikh
Venue: CVPR 2023 (main)

Abstract: Neural network quantization is a popular approach for model compression. Modern hardware supports quantization in mixed-precision mode, which allows for greater compression rates but adds the challenging task of searching for the optimal bit width. The majority of existing searchers find a single mixed-precision architecture. To select an architecture that is suitable in terms of performance and resource consumption, one has to restart searching multiple times. We focus on a specific class of methods that find tensor bit width using gradient-based optimization. First, we theoretically derive several methods that were empirically proposed earlier. Second, we present a novel One-Shot method that finds a diverse set of Pareto-front architectures in O(1) time. For large models, the proposed method is 5 times more efficient than existing methods. We verify the method on two classification and super-resolution models and show above 0.93 correlation score between the predicted and actual model performance. The Pareto-front architecture selection is straightforward and takes only 20 to 40 supernet evaluations, which is the new state-of-the-art result to the best of our knowledge.
MARLIN: Masked Autoencoder for Facial Video Representation LearnINg
Authors: Zhixi Cai; Shreya Ghosh; Kalin Stefanov; Abhinav Dhall; Jianfei Cai; Hamid Rezatofighi; Reza Haffari; Munawar Hayat
Venue: CVPR 2023 (main)

Abstract: This paper proposes a self-supervised approach to learn universal facial representations from videos, that can transfer across a variety of facial analysis tasks such as Facial Attribute Recognition (FAR), Facial Expression Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS). Our proposed framework, named MARLIN, is a facial video masked autoencoder, that learns highly robust and generic facial embeddings from abundantly available non-annotated web crawled facial videos. As a challenging auxiliary task, MARLIN reconstructs the spatio-temporal details of the face from the densely masked facial regions which mainly include eyes, nose, mouth, lips, and skin to capture local and global aspects that in turn help in encoding generic and transferable features. Through a variety of experiments on diverse downstream tasks, we demonstrate MARLIN to be an excellent facial video encoder as well as feature extractor, that performs consistently well across a variety of downstream tasks including FAR (1.13% gain over supervised benchmark), FER (2.64% gain over unsupervised benchmark), DFD (1.86% gain over unsupervised benchmark), LS (29.36% gain for Frechet Inception Distance), and even in low data regime. Our code and models are available at https://github.com/ControlNet/MARLIN.
Language Adaptive Weight Generation for Multi-Task Visual Grounding
Authors: Wei Su; Peihan Miao; Huanzhang Dou; Gaoang Wang; Liang Qiao; Zheyang Li; Xi Li
Venue: CVPR 2023 (main)

Abstract: Although the impressive performance in visual grounding, the prevailing approaches usually exploit the visual backbone in a passive way, i.e., the visual backbone extracts features with fixed weights without expression-related hints. The passive perception may lead to mismatches (e.g., redundant and missing), limiting further performance improvement. Ideally, the visual backbone should actively extract visual features since the expressions already provide the blueprint of desired visual features. The active perception can take expressions as priors to extract relevant visual features, which can effectively alleviate the mismatches. Inspired by this, we propose an active perception Visual Grounding framework based on Language Adaptive Weights, called VG-LAW. The visual backbone serves as an expression-specific feature extractor through dynamic weights generated for various expressions. Benefiting from the specific and relevant visual features extracted from the language-aware visual backbone, VG-LAW does not require additional modules for cross-modal interaction. Along with a neat multi-task head, VG-LAW can be competent in referring expression comprehension and segmentation jointly. Extensive experiments on four representative datasets, i.e., RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame, validate the effectiveness of the proposed framework and demonstrate state-of-the-art performance.
Continuous Intermediate Token Learning With Implicit Motion Manifold for Keyframe Based Motion Interpolation
Authors: Clinton A. Mo; Kun Hu; Chengjiang Long; Zhiyong Wang
Venue: CVPR 2023 (main)

Abstract: Deriving sophisticated 3D motions from sparse keyframes is a particularly challenging problem, due to continuity and exceptionally skeletal precision. The action features are often derivable accurately from the full series of keyframes, and thus, leveraging the global context with transformers has been a promising data-driven embedding approach. However, existing methods are often with inputs of interpolated intermediate frame for continuity using basic interpolation methods with keyframes, which result in a trivial local minimum during training. In this paper, we propose a novel framework to formulate latent motion manifolds with keyframe-based constraints, from which the continuous nature of intermediate token representations is considered. Particularly, our proposed framework consists of two stages for identifying a latent motion subspace, i.e., a keyframe encoding stage and an intermediate token generation stage, and a subsequent motion synthesis stage to extrapolate and compose motion data from manifolds. Through our extensive experiments conducted on both the LaFAN1 and CMU Mocap datasets, our proposed method demonstrates both superior interpolation accuracy and high visual similarity to ground truth motions.
Dynamic Coarse-To-Fine Learning for Oriented Tiny Object Detection
Authors: Chang Xu; Jian Ding; Jinwang Wang; Wen Yang; Huai Yu; Lei Yu; Gui-Song Xia
Venue: CVPR 2023 (main)

Abstract: Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors, especially for label assignment. Despite the exploration of adaptive label assignment in recent oriented object detectors, the extreme geometry shape and limited feature of oriented tiny objects still induce severe mismatch and imbalance issues. Specifically, the position prior, positive sample feature, and instance are mismatched, and the learning of extreme-shaped objects is biased and unbalanced due to little proper feature supervision. To tackle these issues, we propose a dynamic prior along with the coarse-to-fine assigner, dubbed DCFL. For one thing, we model the prior, label assignment, and object representation all in a dynamic manner to alleviate the mismatch issue. For another, we leverage the coarse prior matching and finer posterior constraint to dynamically assign labels, providing appropriate and relatively balanced supervision for diverse instances. Extensive experiments on six datasets show substantial improvements to the baseline. Notably, we obtain the state-of-the-art performance for one-stage detectors on the DOTA-v1.5, DOTA-v2.0, and DIOR-R datasets under single-scale training and testing. Codes are available at https://github.com/Chasel-Tsui/mmrotate-dcfl.
Controllable Mesh Generation Through Sparse Latent Point Diffusion Models
Authors: Zhaoyang Lyu; Jinyi Wang; Yuwei An; Ya Zhang; Dahua Lin; Bo Dai
Venue: CVPR 2023 (main)

Abstract: Mesh generation is of great value in various applications involving computer graphics and virtual content, yet designing generative models for meshes is challenging due to their irregular data structure and inconsistent topology of meshes in the same category. In this work, we design a novel sparse latent point diffusion model for mesh generation. Our key insight is to regard point clouds as an intermediate representation of meshes, and model the distribution of point clouds instead. While meshes can be generated from point clouds via techniques like Shape as Points (SAP), the challenges of directly generating meshes can be effectively avoided. To boost the efficiency and controllability of our mesh generation method, we propose to further encode point clouds to a set of sparse latent points with point-wise semantic meaningful features, where two DDPMs are trained in the space of sparse latent points to respectively model the distribution of the latent point positions and features at these latent points. We find that sampling in this latent space is faster than directly sampling dense point clouds. Moreover, the sparse latent points also enable us to explicitly control both the overall structures and local details of the generated meshes. Extensive experiments are conducted on the ShapeNet dataset, where our proposed sparse latent point diffusion model achieves superior performance in terms of generation quality and controllability when compared to existing methods.
Query-Centric Trajectory Prediction
Authors: Zikang Zhou; Jianping Wang; Yung-Hui Li; Yu-Kai Huang
Venue: CVPR 2023 (main)

Abstract: Predicting the future trajectories of surrounding agents is essential for autonomous vehicles to operate safely. This paper presents QCNet, a modeling framework toward pushing the boundaries of trajectory prediction. First, we identify that the agent-centric modeling scheme used by existing approaches requires re-normalizing and re-encoding the input whenever the observation window slides forward, leading to redundant computations during online prediction. To overcome this limitation and achieve faster inference, we introduce a query-centric paradigm for scene encoding, which enables the reuse of past computations by learning representations independent of the global spacetime coordinate system. Sharing the invariant scene features among all target agents further allows the parallelism of multi-agent trajectory decoding. Second, even given rich encodings of the scene, existing decoding strategies struggle to capture the multimodality inherent in agents' future behavior, especially when the prediction horizon is long. To tackle this challenge, we first employ anchor-free queries to generate trajectory proposals in a recurrent fashion, which allows the model to utilize different scene contexts when decoding waypoints at different horizons. A refinement module then takes the trajectory proposals as anchors and leverages anchor-based queries to refine the trajectories further. By supplying adaptive and high-quality anchors to the refinement module, our query-based decoder can better deal with the multimodality in the output of trajectory prediction. Our approach ranks 1st on Argoverse 1 and Argoverse 2 motion forecasting benchmarks, outperforming all methods on all main metrics by a large margin. Meanwhile, our model can achieve streaming scene encoding and parallel multi-agent decoding thanks to the query-centric design ethos.