Yuan Dong

Yunhao Du, Cheng Lei, Zhicheng Zhao et al.

Visible-infrared person re-identification (VI-ReID) aims to match persons captured by visible and infrared cameras, allowing person retrieval and tracking in 24-hour surveillance systems. Previous methods focus on learning from cross-modality person images in different cameras. However, temporal information and single-camera samples tend to be neglected. To crack this nut, in this paper, we first contribute a large-scale VI-ReID dataset named BUPTCampus. Different from most existing VI-ReID datasets, it 1) collects tracklets instead of images to introduce rich temporal information, 2) contains pixel-aligned cross-modality sample pairs for better modality-invariant learning, 3) provides one auxiliary set to help enhance the optimization, in which each identity only appears in a single camera. Based on our constructed dataset, we present a two-stream framework as baseline and apply Generative Adversarial Network (GAN) to narrow the gap between the two modalities. To exploit the advantages introduced by the auxiliary set, we propose a curriculum learning based strategy to jointly learn from both primary and auxiliary sets. Moreover, we design a novel temporal k-reciprocal re-ranking method to refine the ranking list with fine-grained temporal correlation cues. Experimental results demonstrate the effectiveness of the proposed methods. We also reproduce 9 state-of-the-art image-based and video-based VI-ReID methods on BUPTCampus and our methods show substantial superiority to them. The codes and dataset are available at: https://github.com/dyhBUPT/BUPTCampus.

Yiming Zhang, Hong Yu, Ruoyi Du et al.

Audio captioning aims at describing the content of audio clips with human language. Due to the ambiguity of audio, different people may perceive the same audio differently, resulting in caption disparities (i.e., one audio may correlate to several captions with diverse semantics). For that, general audio captioning models achieve the one-to-many training by randomly selecting a correlated caption as the ground truth for each audio. However, it leads to a significant variation in the optimization directions and weakens the model stability. To eliminate this negative effect, in this paper, we propose a two-stage framework for audio captioning: (i) in the first stage, via the contrastive learning, we construct a proxy feature space to reduce the distances between captions correlated to the same audio, and (ii) in the second stage, the proxy feature space is utilized as additional supervision to encourage the model to be optimized in the direction that benefits all the correlated captions. We conducted extensive experiments on two datasets using four commonly used encoder and decoder architectures. Experimental results demonstrate the effectiveness of the proposed method. The code is available at https://github.com/PRIS-CV/Caption-Feature-Space-Regularization.

Weichao Shen, Yuan Dong, Zonghao Chen et al.

In this paper, we propose PanoViT, a panorama vision transformer to estimate the room layout from a single panoramic image. Compared to CNN models, our PanoViT is more proficient in learning global information from the panoramic image for the estimation of complex room layouts. Considering the difference between a perspective image and an equirectangular image, we design a novel recurrent position embedding and a patch sampling method for the processing of panoramic images. In addition to extracting global information, PanoViT also includes a frequency-domain edge enhancement module and a 3D loss to extract local geometric features in a panoramic image. Experimental results on several datasets demonstrate that our method outperforms state-of-the-art solutions in room layout prediction accuracy.

Lingteng Qiu, Shenhao Zhu, Qi Zuo et al.

Generating animatable human avatars from a single image is essential for various digital human modeling applications. Existing 3D reconstruction methods often struggle to capture fine details in animatable models, while generative approaches for controllable animation, though avoiding explicit 3D modeling, suffer from viewpoint inconsistencies in extreme poses and computational inefficiencies. In this paper, we address these challenges by leveraging the power of generative models to produce detailed multi-view canonical pose images, which help resolve ambiguities in animatable human reconstruction. We then propose a robust method for 3D reconstruction of inconsistent images, enabling real-time rendering during inference. Specifically, we adapt a transformer-based video generation model to generate multi-view canonical pose images and normal maps, pretraining on a large-scale video dataset to improve generalization. To handle view inconsistencies, we recast the reconstruction problem as a 4D task and introduce an efficient 3D modeling approach using 4D Gaussian Splatting. Experiments demonstrate that our method achieves photorealistic, real-time animation of 3D human avatars from in-the-wild images, showcasing its effectiveness and generalization capability.

Lingteng Qiu, Peihao Li, Qi Zuo et al.

Reconstructing an animatable 3D human from casually captured images of an articulated subject without camera or human pose information is a practical yet challenging task due to view misalignment, occlusions, and the absence of structural priors. While optimization-based methods can produce high-fidelity results from monocular or multi-view videos, they require accurate pose estimation and slow iterative optimization, limiting scalability in unconstrained scenarios. Recent feed-forward approaches enable efficient single-image reconstruction but struggle to effectively leverage multiple input images to reduce ambiguity and improve reconstruction accuracy. To address these challenges, we propose PF-LHM, a large human reconstruction model that generates high-quality 3D avatars in seconds from one or multiple casually captured pose-free images. Our approach introduces an efficient Encoder-Decoder Point-Image Transformer architecture, which fuses hierarchical geometric point features and multi-view image features through multimodal attention. The fused features are decoded to recover detailed geometry and appearance, represented using 3D Gaussian splats. Extensive experiments on both real and synthetic datasets demonstrate that our method unifies single- and multi-image 3D human reconstruction, achieving high-fidelity and animatable 3D human avatars without requiring camera and human pose annotations. Code and models will be released to the public.

Yuan Dong, Chuan Fang, Liefeng Bo et al.

Panoramic image enables deeper understanding and more holistic perception of $360^\circ$ surrounding environment, which can naturally encode enriched scene context information compared to standard perspective image. Previous work has made lots of effort to solve the scene understanding task in a bottom-up form, thus each sub-task is processed separately and few correlations are explored in this procedure. In this paper, we propose a novel method using depth prior for holistic indoor scene understanding which recovers the objects' shapes, oriented bounding boxes and the 3D room layout simultaneously from a single panorama. In order to fully utilize the rich context information, we design a transformer-based context module to predict the representation and relationship among each component of the scene. In addition, we introduce a real-world dataset for scene understanding, including photo-realistic panoramas, high-fidelity depth images, accurately annotated room layouts, and oriented object bounding boxes and shapes. Experiments on the synthetic and real-world datasets demonstrate that our method outperforms previous panoramic scene understanding methods in terms of both layout estimation and 3D object detection.

Yurong Guo, Zhanyu Ma, Xiaoxu Li et al.

Recently, graph neural networks (GNNs) have shown powerful ability to handle few-shot classification problem, which aims at classifying unseen samples when trained with limited labeled samples per class. GNN-based few-shot learning architectures mostly replace traditional metric with a learnable GNN. In the GNN, the nodes are set as the samples embedding, and the relationship between two connected nodes can be obtained by a network, the input of which is the difference of their embedding features. We consider this method of measuring relation of samples only models the sample-to-sample relation, while neglects the specificity of different tasks. That is, this method of measuring relation does not take the task-level information into account. To this end, we propose a new relation measure method, namely the task-level relation module (TLRM), to explicitly model the task-level relation of one sample to all the others. The proposed module captures the relation representations between nodes by considering the sample-to-task instead of sample-to-sample embedding features. We conducted extensive experiments on four benchmark datasets: mini-ImageNet, tiered-ImageNet, CUB-$200$-$2011$, and CIFAR-FS. Experimental results demonstrate that the proposed module is effective for GNN-based few-shot learning.