Alert button
Picture for Xiaohui Shen

Xiaohui Shen

Alert button

MaXTron: Mask Transformer with Trajectory Attention for Video Panoptic Segmentation

Nov 30, 2023
Ju He, Qihang Yu, Inkyu Shin, Xueqing Deng, Xiaohui Shen, Alan Yuille, Liang-Chieh Chen

Video panoptic segmentation requires consistently segmenting (for both `thing' and `stuff' classes) and tracking objects in a video over time. In this work, we present MaXTron, a general framework that exploits Mask XFormer with Trajectory Attention to tackle the task. MaXTron enriches an off-the-shelf mask transformer by leveraging trajectory attention. The deployed mask transformer takes as input a short clip consisting of only a few frames and predicts the clip-level segmentation. To enhance the temporal consistency, MaXTron employs within-clip and cross-clip tracking modules, efficiently utilizing trajectory attention. Originally designed for video classification, trajectory attention learns to model the temporal correspondences between neighboring frames and aggregates information along the estimated motion paths. However, it is nontrivial to directly extend trajectory attention to the per-pixel dense prediction tasks due to its quadratic dependency on input size. To alleviate the issue, we propose to adapt the trajectory attention for both the dense pixel features and object queries, aiming to improve the short-term and long-term tracking results, respectively. Particularly, in our within-clip tracking module, we propose axial-trajectory attention that effectively computes the trajectory attention for tracking dense pixels sequentially along the height- and width-axes. The axial decomposition significantly reduces the computational complexity for dense pixel features. In our cross-clip tracking module, since the object queries in mask transformer are learned to encode the object information, we are able to capture the long-term temporal connections by applying trajectory attention to object queries, which learns to track each object across different clips. Without bells and whistles, MaXTron demonstrates state-of-the-art performances on video segmentation benchmarks.

* Code at https://github.com/TACJu/MaXTron 
Viaarxiv icon

Towards Open-Ended Visual Recognition with Large Language Model

Nov 14, 2023
Qihang Yu, Xiaohui Shen, Liang-Chieh Chen

Localizing and recognizing objects in the open-ended physical world poses a long-standing challenge within the domain of machine perception. Recent methods have endeavored to address the issue by employing a class-agnostic mask (or box) proposal model, complemented by an open-vocabulary classifier (e.g., CLIP) using pre-extracted text embeddings. However, it is worth noting that these open-vocabulary recognition models still exhibit limitations in practical applications. On one hand, they rely on the provision of class names during testing, where the recognition performance heavily depends on this predefined set of semantic classes by users. On the other hand, when training with multiple datasets, human intervention is required to alleviate the label definition conflict between them. In this paper, we introduce the OmniScient Model (OSM), a novel Large Language Model (LLM) based mask classifier, as a straightforward and effective solution to the aforementioned challenges. Specifically, OSM predicts class labels in a generative manner, thus removing the supply of class names during both training and testing. It also enables cross-dataset training without any human interference, exhibiting robust generalization capabilities due to the world knowledge acquired from the LLM. By combining OSM with an off-the-shelf mask proposal model, we present promising results on various benchmarks, and demonstrate its effectiveness in handling novel concepts. Code/model are available at https://github.com/bytedance/OmniScient-Model.

Viaarxiv icon

Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP

Aug 04, 2023
Qihang Yu, Ju He, Xueqing Deng, Xiaohui Shen, Liang-Chieh Chen

Figure 1 for Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
Figure 2 for Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
Figure 3 for Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
Figure 4 for Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP

Open-vocabulary segmentation is a challenging task requiring segmenting and recognizing objects from an open set of categories. One way to address this challenge is to leverage multi-modal models, such as CLIP, to provide image and text features in a shared embedding space, which bridges the gap between closed-vocabulary and open-vocabulary recognition. Hence, existing methods often adopt a two-stage framework to tackle the problem, where the inputs first go through a mask generator and then through the CLIP model along with the predicted masks. This process involves extracting features from images multiple times, which can be ineffective and inefficient. By contrast, we propose to build everything into a single-stage framework using a shared Frozen Convolutional CLIP backbone, which not only significantly simplifies the current two-stage pipeline, but also remarkably yields a better accuracy-cost trade-off. The proposed FC-CLIP, benefits from the following observations: the frozen CLIP backbone maintains the ability of open-vocabulary classification and can also serve as a strong mask generator, and the convolutional CLIP generalizes well to a larger input resolution than the one used during contrastive image-text pretraining. When training on COCO panoptic data only and testing in a zero-shot manner, FC-CLIP achieve 26.8 PQ, 16.8 AP, and 34.1 mIoU on ADE20K, 18.2 PQ, 27.9 mIoU on Mapillary Vistas, 44.0 PQ, 26.8 AP, 56.2 mIoU on Cityscapes, outperforming the prior art by +4.2 PQ, +2.4 AP, +4.2 mIoU on ADE20K, +4.0 PQ on Mapillary Vistas and +20.1 PQ on Cityscapes, respectively. Additionally, the training and testing time of FC-CLIP is 7.5x and 6.6x significantly faster than the same prior art, while using 5.9x fewer parameters. FC-CLIP also sets a new state-of-the-art performance across various open-vocabulary semantic segmentation datasets. Code at https://github.com/bytedance/fc-clip

* code and model available at https://github.com/bytedance/fc-clip 
Viaarxiv icon

$R^{2}$Former: Unified $R$etrieval and $R$eranking Transformer for Place Recognition

Apr 06, 2023
Sijie Zhu, Linjie Yang, Chen Chen, Mubarak Shah, Xiaohui Shen, Heng Wang

Figure 1 for $R^{2}$Former: Unified $R$etrieval and $R$eranking Transformer for Place Recognition
Figure 2 for $R^{2}$Former: Unified $R$etrieval and $R$eranking Transformer for Place Recognition
Figure 3 for $R^{2}$Former: Unified $R$etrieval and $R$eranking Transformer for Place Recognition
Figure 4 for $R^{2}$Former: Unified $R$etrieval and $R$eranking Transformer for Place Recognition

Visual Place Recognition (VPR) estimates the location of query images by matching them with images in a reference database. Conventional methods generally adopt aggregated CNN features for global retrieval and RANSAC-based geometric verification for reranking. However, RANSAC only employs geometric information but ignores other possible information that could be useful for reranking, e.g. local feature correlations, and attention values. In this paper, we propose a unified place recognition framework that handles both retrieval and reranking with a novel transformer model, named $R^{2}$Former. The proposed reranking module takes feature correlation, attention value, and xy coordinates into account, and learns to determine whether the image pair is from the same location. The whole pipeline is end-to-end trainable and the reranking module alone can also be adopted on other CNN or transformer backbones as a generic component. Remarkably, $R^{2}$Former significantly outperforms state-of-the-art methods on major VPR datasets with much less inference time and memory consumption. It also achieves the state-of-the-art on the hold-out MSLS challenge set and could serve as a simple yet strong solution for real-world large-scale applications. Experiments also show vision transformer tokens are comparable and sometimes better than CNN local features on local matching. The code is released at https://github.com/Jeff-Zilence/R2Former.

* CVPR 
Viaarxiv icon

Multimodal Video Adapter for Parameter Efficient Video Text Retrieval

Jan 19, 2023
Bowen Zhang, Xiaojie Jin, Weibo Gong, Kai Xu, Zhao Zhang, Peng Wang, Xiaohui Shen, Jiashi Feng

Figure 1 for Multimodal Video Adapter for Parameter Efficient Video Text Retrieval
Figure 2 for Multimodal Video Adapter for Parameter Efficient Video Text Retrieval
Figure 3 for Multimodal Video Adapter for Parameter Efficient Video Text Retrieval
Figure 4 for Multimodal Video Adapter for Parameter Efficient Video Text Retrieval

State-of-the-art video-text retrieval (VTR) methods usually fully fine-tune the pre-trained model (e.g. CLIP) on specific datasets, which may suffer from substantial storage costs in practical applications since a separate model per task needs to be stored. To overcome this issue, we present the premier work on performing parameter-efficient VTR from the pre-trained model, i.e., only a small number of parameters are tunable while freezing the backbone. Towards this goal, we propose a new method dubbed Multimodal Video Adapter (MV-Adapter) for efficiently transferring the knowledge in the pre-trained CLIP from image-text to video-text. Specifically, MV-Adapter adopts bottleneck structures in both video and text branches and introduces two novel components. The first is a Temporal Adaptation Module employed in the video branch to inject global and local temporal contexts. We also learn weights calibrations to adapt to the dynamic variations across frames. The second is a Cross-Modal Interaction Module that generates weights for video/text branches through a shared parameter space, for better aligning between modalities. Thanks to above innovations, MV-Adapter can achieve on-par or better performance than standard fine-tuning with negligible parameters overhead. Notably, on five widely used VTR benchmarks (MSR-VTT, MSVD, LSMDC, DiDemo, and ActivityNet), MV-Adapter consistently outperforms various competing methods in V2T/T2V tasks with large margins. Codes will be released.

Viaarxiv icon

Contrastive Masked Autoencoders are Stronger Vision Learners

Jul 27, 2022
Zhicheng Huang, Xiaojie Jin, Chengze Lu, Qibin Hou, Ming-Ming Cheng, Dongmei Fu, Xiaohui Shen, Jiashi Feng

Figure 1 for Contrastive Masked Autoencoders are Stronger Vision Learners
Figure 2 for Contrastive Masked Autoencoders are Stronger Vision Learners
Figure 3 for Contrastive Masked Autoencoders are Stronger Vision Learners
Figure 4 for Contrastive Masked Autoencoders are Stronger Vision Learners

Masked image modeling (MIM) has achieved promising results on various vision tasks. However, the limited discriminability of learned representation manifests there is still plenty to go for making a stronger vision learner. Towards this goal, we propose Contrastive Masked Autoencoders (CMAE), a new self-supervised pre-training method for learning more comprehensive and capable vision representations. By elaboratively unifying contrastive learning (CL) and masked image model (MIM) through novel designs, CMAE leverages their respective advantages and learns representations with both strong instance discriminability and local perceptibility. Specifically, CMAE consists of two branches where the online branch is an asymmetric encoder-decoder and the target branch is a momentum updated encoder. During training, the online encoder reconstructs original images from latent representations of masked images to learn holistic features. The target encoder, fed with the full images, enhances the feature discriminability via contrastive learning with its online counterpart. To make CL compatible with MIM, CMAE introduces two new components, i.e. pixel shift for generating plausible positive views and feature decoder for complementing features of contrastive pairs. Thanks to these novel designs, CMAE effectively improves the representation quality and transfer performance over its MIM counterpart. CMAE achieves the state-of-the-art performance on highly competitive benchmarks of image classification, semantic segmentation and object detection. Notably, CMAE-Base achieves $85.3\%$ top-1 accuracy on ImageNet and $52.5\%$ mIoU on ADE20k, surpassing previous best results by $0.7\%$ and $1.8\%$ respectively. Codes will be made publicly available.

Viaarxiv icon

SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing

Dec 07, 2021
Yichun Shi, Xiao Yang, Yangyue Wan, Xiaohui Shen

Figure 1 for SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing
Figure 2 for SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing
Figure 3 for SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing
Figure 4 for SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing

Recent studies have shown that StyleGANs provide promising prior models for downstream tasks on image synthesis and editing. However, since the latent codes of StyleGANs are designed to control global styles, it is hard to achieve a fine-grained control over synthesized images. We present SemanticStyleGAN, where a generator is trained to model local semantic parts separately and synthesizes images in a compositional way. The structure and texture of different local parts are controlled by corresponding latent codes. Experimental results demonstrate that our model provides a strong disentanglement between different spatial areas. When combined with editing methods designed for StyleGANs, it can achieve a more fine-grained control to edit synthesized or real images. The model can also be extended to other domains via transfer learning. Thus, as a generic prior model with built-in disentanglement, it could facilitate the development of GAN-based applications and enable more potential downstream tasks.

* project page at https://SemanticStyleGAN.github.io 
Viaarxiv icon

Video Salient Object Detection via Contrastive Features and Attention Modules

Nov 03, 2021
Yi-Wen Chen, Xiaojie Jin, Xiaohui Shen, Ming-Hsuan Yang

Figure 1 for Video Salient Object Detection via Contrastive Features and Attention Modules
Figure 2 for Video Salient Object Detection via Contrastive Features and Attention Modules
Figure 3 for Video Salient Object Detection via Contrastive Features and Attention Modules
Figure 4 for Video Salient Object Detection via Contrastive Features and Attention Modules

Video salient object detection aims to find the most visually distinctive objects in a video. To explore the temporal dependencies, existing methods usually resort to recurrent neural networks or optical flow. However, these approaches require high computational cost, and tend to accumulate inaccuracies over time. In this paper, we propose a network with attention modules to learn contrastive features for video salient object detection without the high computational temporal modeling techniques. We develop a non-local self-attention scheme to capture the global information in the video frame. A co-attention formulation is utilized to combine the low-level and high-level features. We further apply the contrastive learning to improve the feature representations, where foreground region pairs from the same video are pulled together, and foreground-background region pairs are pushed away in the latent space. The intra-frame contrastive loss helps separate the foreground and background features, and the inter-frame contrastive loss improves the temporal consistency. We conduct extensive experiments on several benchmark datasets for video salient object detection and unsupervised video object segmentation, and show that the proposed method requires less computation, and performs favorably against the state-of-the-art approaches.

* Accepted in WACV 2022 
Viaarxiv icon

Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis

Apr 12, 2021
Xiaoyu Xiang, Ding Liu, Xiao Yang, Yiheng Zhu, Xiaohui Shen, Jan P. Allebach

Figure 1 for Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis
Figure 2 for Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis
Figure 3 for Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis
Figure 4 for Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis

In this paper, we explore the open-domain sketch-to-photo translation, which aims to synthesize a realistic photo from a freehand sketch with its class label, even if the sketches of that class are missing in the training data. It is challenging due to the lack of training supervision and the large geometry distortion between the freehand sketch and photo domains. To synthesize the absent freehand sketches from photos, we propose a framework that jointly learns sketch-to-photo and photo-to-sketch generation. However, the generator trained from fake sketches might lead to unsatisfying results when dealing with sketches of missing classes, due to the domain gap between synthesized sketches and real ones. To alleviate this issue, we further propose a simple yet effective open-domain sampling and optimization strategy to "fool" the generator into treating fake sketches as real ones. Our method takes advantage of the learned sketch-to-photo and photo-to-sketch mapping of in-domain data and generalizes them to the open-domain classes. We validate our method on the Scribble and SketchyCOCO datasets. Compared with the recent competing methods, our approach shows impressive results in synthesizing realistic color, texture, and maintaining the geometric composition for various categories of open-domain sketches.

* 19 pages, 17 figures 
Viaarxiv icon

DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues

Mar 30, 2021
Yifan Wang, Linjie Luo, Xiaohui Shen, Xing Mei

Figure 1 for DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues
Figure 2 for DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues
Figure 3 for DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues
Figure 4 for DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues

Recently, significant progress has been made in single-view depth estimation thanks to increasingly large and diverse depth datasets. However, these datasets are largely limited to specific application domains (e.g. indoor, autonomous driving) or static in-the-wild scenes due to hardware constraints or technical limitations of 3D reconstruction. In this paper, we introduce the first depth dataset DynOcc consisting of dynamic in-the-wild scenes. Our approach leverages the occlusion cues in these dynamic scenes to infer depth relationships between points of selected video frames. To achieve accurate occlusion detection and depth order estimation, we employ a novel occlusion boundary detection, filtering and thinning scheme followed by a robust foreground/background classification method. In total our DynOcc dataset contains 22M depth pairs out of 91K frames from a diverse set of videos. Using our dataset we achieved state-of-the-art results measured in weighted human disagreement rate (WHDR). We also show that the inferred depth maps trained with DynOcc can preserve sharper depth boundaries.

* 3DV 2020 
Viaarxiv icon