Autonomous driving systems have made significant advances in Q&A, perception, prediction, and planning based on local visual information, yet they struggle to incorporate broader navigational context that human drivers routinely utilize. We address this critical gap between local sensor data and global navigation information by proposing NavigScene, an auxiliary navigation-guided natural language dataset that simulates a human-like driving environment within autonomous driving systems. Moreover, we develop three complementary paradigms to leverage NavigScene: (1) Navigation-guided Reasoning, which enhances vision-language models by incorporating navigation context into the prompting approach; (2) Navigation-guided Preference Optimization, a reinforcement learning method that extends Direct Preference Optimization to improve vision-language model responses by establishing preferences for navigation-relevant summarized information; and (3) Navigation-guided Vision-Language-Action model, which integrates navigation guidance and vision-language models with conventional driving models through feature fusion. Extensive experiments demonstrate that our approaches significantly improve performance across perception, prediction, planning, and question-answering tasks by enabling reasoning capabilities beyond visual range and improving generalization to diverse driving scenarios. This work represents a significant step toward more comprehensive autonomous driving systems capable of navigating complex, unfamiliar environments with greater reliability and safety.

Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance. In this paper, we propose a novel framework named Privileged Prior Information Distillation for Image Matting (PPID-IM) that can effectively transfer privileged prior environment-aware information to improve the performance of trimap-free students in solving hard foregrounds. The prior information of trimap regulates only the teacher model during the training stage, while not being fed into the student network during actual inference. To achieve effective privileged cross-modality (i.e. trimap and RGB) information distillation, we introduce a Cross-Level Semantic Distillation (CLSD) module that reinforces the students with more knowledgeable semantic representations and environment-aware information. We also propose an Attention-Guided Local Distillation module that efficiently transfers privileged local attributes from the trimap-based teacher to trimap-free students for the guidance of local-region optimization. Extensive experiments demonstrate the effectiveness and superiority of our PPID on image matting. The code will be released soon.

Enhancing model prediction confidence on target data is an important objective in Unsupervised Domain Adaptation (UDA). In this paper, we explore adversarial training on penultimate activations, i.e., input features of the final linear classification layer. We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features, as used in previous works. Furthermore, with activation normalization commonly used in domain adaptation to reduce domain gap, we derive two variants and systematically analyze the effects of normalization on our adversarial training. This is illustrated both in theory and through empirical analysis on real adaptation tasks. Extensive experiments are conducted on popular UDA benchmarks under both standard setting and source-data free setting. The results validate that our method achieves the best scores against previous arts. Code is available at https://github.com/tsun/APA.

Most existing portrait matting models either require expensive auxiliary information or try to decompose the task into sub-tasks that are usually resource-hungry. These challenges limit its application on low-power computing devices. In this paper, we propose an ultra-light-weighted portrait matting network via parallel semantic guidance (PSGNet) for real-time portrait matting without any auxiliary inputs. PSGNet leverages parallel multi-level semantic information to efficiently guide the feature representations to replace traditional sequential semantic hints from objective decomposition. We also introduce an efficient fusion module to effectively combine parallel branches of PSGNet to minimize the representation redundancy. Comprehensive experiments demonstrate that our PSGNet can achieve remarkable performance on both synthetic and real-world images. Our PSGNet is capable to process at 100fps thanks to its ultra-small number of parameters, which makes it deployable on low-power computing devices without compromising on the performance of real-time portrait matting.

Adversarial contrastive learning (ACL) is considered an effective way to improve the robustness of pre-trained models. In contrastive learning, a projector which consists of multilayer perceptron (MLP) will project high dimension 3D point cloud feature into low dimension for calculating contrastive loss during contrastive pretraining.We propose a novel method for generating high-quality 3D adversarial examples for adversarial training, which leverages the virtual adversarial loss with the feature representations prior to projection in a contrastive learning framework. To train the self-supervised contrastive learning framework adversarially, we introduce our robust aware loss function. Additionally, we show that incorporating high difference points using the Difference of Normal (DoN) operator as an additional input for adversarial self-supervised contrastive learning can significantly enhance the adversarial robustness of the pre-trained model. Our proposed method, POINTACL, is evaluated on several downstream tasks, including 3D classification and 3D segmentation using multiple datasets. Our experimental results demonstrate that POINTACL achieves state-of-the-art performance in terms of robust accuracy when compared to other contrastive adversarial learning methods.

Active Domain Adaptation (ADA) queries the labels of a small number of selected target samples to help adapting a model from a source domain to a target domain. The local context of queried data is important, especially when the domain gap is large. However, this has not been fully explored by existing ADA works. In this paper, we propose a Local context-aware ADA framework, named LADA, to address this issue. To select informative target samples, we devise a novel criterion based on the local inconsistency of model predictions. Since the labeling budget is usually small, fine-tuning model on only queried data can be inefficient. We progressively augment labeled target data with the confident neighbors in a class-balanced manner. Experiments validate that the proposed criterion chooses more informative target samples than existing active selection strategies. Furthermore, our full method clearly surpasses recent ADA arts on various benchmarks. Code is available at https://github.com/tsun/LADA.

Different from most existing approaches that require trimap generation for each frame, we reformulate video object matting (VOM) by introducing improved semantic guidance propagation. The proposed approach can achieve a higher degree of temporal coherence between frames with only a single coarse mask as a reference. In this paper, we adapt the hierarchical memory matching mechanism into the space-time baseline to build an efficient and robust framework for semantic guidance propagation and alpha prediction. To enhance the temporal smoothness, we also propose a cross-frame attention refinement (CFAR) module that can refine the feature representations across multiple adjacent frames (both historical and current frames) based on the spatio-temporal correlation among the cross- frame pixels. Extensive experiments demonstrate the effectiveness of hierarchical spatio-temporal semantic guidance and the cross-video-frame attention refinement module, and our model outperforms the state-of-the-art VOM methods. We also analyze the significance of different components in our model.

Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain. It is a challenging problem especially when a large domain gap lies between the source and target domains. In this paper we propose a novel solution named SSRT (Safe Self-Refinement for Transformer-based domain adaptation), which brings improvement from two aspects. First, encouraged by the success of vision transformers in various vision tasks, we arm SSRT with a transformer backbone. We find that the combination of vision transformer with simple adversarial adaptation surpasses best reported Convolutional Neural Network (CNN)-based results on the challenging DomainNet benchmark, showing its strong transferable feature representation. Second, to reduce the risk of model collapse and improve the effectiveness of knowledge transfer between domains with large gaps, we propose a Safe Self-Refinement strategy. Specifically, SSRT utilizes predictions of perturbed target domain data to refine the model. Since the model capacity of vision transformer is large and predictions in such challenging tasks can be noisy, a safe training mechanism is designed to adaptively adjust learning configuration. Extensive evaluations are conducted on several widely tested UDA benchmarks and SSRT achieves consistently the best performances, including 85.43% on Office-Home, 88.76% on VisDA-2017 and 45.2% on DomainNet.

Most existing CNN-based salient object detection methods can identify fine-grained segmentation details like hair and animal fur, but often mispredict the salient object due to lack of global contextual information caused by locality convolution layers. The limited training data of the current SOD task adds additional difficulty to capture the saliency information. In this paper, we propose a two-stage predict-refine SDETR model to leverage both benefits of transformer and CNN layers that can produce results with accurate saliency prediction and fine-grained local details. We also propose a novel pre-train dataset annotation COCO SOD to erase the overfitting problem caused by insufficient training data. Comprehensive experiments on five benchmark datasets demonstrate that the SDETR outperforms state-of-the-art approaches on four evaluation metrics, and our COCO SOD can largely improve the model performance on DUTS, ECSSD, DUT, PASCAL-S datasets.

Most automatic matting methods try to separate the salient foreground from the background. However, the insufficient quantity and subjective bias of the current existing matting datasets make it difficult to fully explore the semantic association between object-to-object and object-to-environment in a given image. In this paper, we propose a Situational Perception Guided Image Matting (SPG-IM) method that mitigates subjective bias of matting annotations and captures sufficient situational perception information for better global saliency distilled from the visual-to-textual task. SPG-IM can better associate inter-objects and object-to-environment saliency, and compensate the subjective nature of image matting and its expensive annotation. We also introduce a textual Semantic Transformation (TST) module that can effectively transform and integrate the semantic feature stream to guide the visual representations. In addition, an Adaptive Focal Transformation (AFT) Refinement Network is proposed to adaptively switch multi-scale receptive fields and focal points to enhance both global and local details. Extensive experiments demonstrate the effectiveness of situational perception guidance from the visual-to-textual tasks on image matting, and our model outperforms the state-of-the-art methods. We also analyze the significance of different components in our model.

Several video-based 3D pose and shape estimation algorithms have been proposed to resolve the temporal inconsistency of single-image-based methods. However it still remains challenging to have stable and accurate reconstruction. In this paper, we propose a new framework Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation (DTS-VIBE), to generate 3D human pose and mesh from RGB videos. We reformulate the task as a multi-modality problem that fuses RGB and optical flow for more reliable estimation. In order to fully utilize both sensory modalities (RGB or optical flow), we train a two-stream temporal network based on transformer to predict SMPL parameters. The supplementary modality, optical flow, helps to maintain temporal consistency by leveraging motion knowledge between two consecutive frames. The proposed algorithm is extensively evaluated on the Human3.6 and 3DPW datasets. The experimental results show that it outperforms other state-of-the-art methods by a significant margin.

Most existing human matting algorithms tried to separate pure human-only foreground from the background. In this paper, we propose a Virtual Multi-modality Foreground Matting (VMFM) method to learn human-object interactive foreground (human and objects interacted with him or her) from a raw RGB image. The VMFM method requires no additional inputs, e.g. trimap or known background. We reformulate foreground matting as a self-supervised multi-modality problem: factor each input image into estimated depth map, segmentation mask, and interaction heatmap using three auto-encoders. In order to fully utilize the characteristics of each modality, we first train a dual encoder-to-decoder network to estimate the same alpha matte. Then we introduce a self-supervised method: Complementary Learning(CL) to predict deviation probability map and exchange reliable gradients across modalities without label. We conducted extensive experiments to analyze the effectiveness of each modality and the significance of different components in complementary learning. We demonstrate that our model outperforms the state-of-the-art methods.

Vision is often used as a complementary modality for audio speech recognition (ASR), especially in the noisy environment where performance of solo audio modality significantly deteriorates. After combining visual modality, ASR is upgraded to the multi-modality speech recognition (MSR). In this paper, we propose a two-stage speech recognition model. In the first stage, the target voice is separated from background noises with help from the corresponding visual information of lip movements, making the model ‘listen' clearly. At the second stage, the audio modality combines visual modality again to better understand the speech by a MSR sub-network, further improving the recognition rate. There are some other key contributions: we introduce a pseudo-3D residual convolution (P3D)-based visual front-end to extract more discriminative features; we upgrade the temporal convolution block from 1D ResNet with the temporal convolutional network (TCN), which is more suitable for the temporal tasks; the MSR sub-network is built on the top of Element-wise-Attention Gated Recurrent Unit (EleAtt-GRU), which is more effective than Transformer in long sequences. We conducted extensive experiments on the LRS3-TED and the LRW datasets. Our two-stage model (audio enhanced multi-modality speech recognition, AE-MSR) consistently achieves the state-of-the-art performance by a significant margin, which demonstrates the necessity and effectiveness of AE-MSR.

Unconstrained remote gaze estimation remains challenging mostly due to its vulnerability to the large variability in head-pose. Prior solutions struggle to maintain reliable accuracy in unconstrained remote gaze tracking. Among them, appearance-based solutions demonstrate tremendous potential in improving gaze accuracy. However, existing works still suffer from head movement and are not robust enough to handle real-world scenarios. Especially most of them study gaze estimation under controlled scenarios where the collected datasets often cover limited ranges of both head-pose and gaze which introduces further bias. In this paper, we propose novel end-to-end appearance-based gaze estimation methods that could more robustly incorporate different levels of head-pose representations into gaze estimation. Our method could generalize to real-world scenarios with low image quality, different lightings and scenarios where direct head-pose information is not available. To better demonstrate the advantage of our methods, we further propose a new benchmark dataset with the most rich distribution of head-gaze combination reflecting real-world scenarios. Extensive evaluations on several public datasets and our own dataset demonstrate that our method consistently outperforms the state-of-the-art by a significant margin.

Multi-modality (talking face video and audio) information helps improve speech recognition performance compared to the single modality. In noisy environments, the effect of audio modality is weakened, which further affects the performance of multi-modality speech recognition (MSR). Most of the MSR methods use noisy audio signal as input of the audio modality without any enhancement (filtering the noisy components in the audio signal). In this paper, we propose an audio-enhanced multi-modality speech recognition model. In particular, the proposed model consists of two sub-networks, one is the visual speech enhancement (VE) sub-network and the other is the multi-modality speech recognition (MSR) sub-network. The VE sub-network is able to separate a speaker’s voice from background noises when given the corresponding talking face to enhance audio modality. Then the audio modality together with video modality are fed into the MSR sub-network to produce characters. We introduce a pseudo-3D residual network (P3D)based visual front-end to extract more advantageous visual features. The MSR sub-network is built on top of the Element-wise-Attention Gated Recurrent Unit (EleAttGRU) architecture which is more effective than Transformer in long sequences. We demonstrate the effectiveness of audio enhancement for MSR by extensive experiments. The proposed method surpasses the state-of-the-art MSR models on the LRS3-TED dataset and the LRW dataset.

The inherent noise in the observed (e.g., scanned) binary document image degrades the image quality and harms the compression ratio through breaking the pattern repentance and adding entropy to the document images. In this paper, we design a cost function in Bayesian framework with dictionary learning. Minimizing our cost function produces a restored image which has better quality than that of the observed noisy image, and a dictionary for representing and encoding the image. After the restoration, we use this dictionary (from the same cost function) to encode the restored image following the symbol-dictionary framework by JBIG2 standard with the lossless mode. Experimental results with a variety of document images demonstrate that our method improves the image quality compared with the observed image, and simultaneously improves the compression ratio. For the test images with synthetic noise, our method reduces the number of flipped pixels by 48.2% and improves the compression ratio by 36.36% as compared with the best encoding methods. For the test images with real noise, our method visually improves the image quality, and outperforms the cutting-edge method by 28.27% in terms of the compression ratio.

Low-rank matrix factorization serves as a key technique in learning latent factor models for many applications in machine learning. However, in many applications, observed data often exhibits different levels of noise. To address this issue, we propose a Robust Probabilistic Discriminative Matrix Factorization (RPDMF) method for binary matrix factorization on noise polluted data. We illustrate the benefits of our approach in real examples, and show how our method significantly outperforms Probabilistic Discriminative Matrix Factorization (PDMF) and classical method Weighted Nonnegative Matrix Factorization (WNMF) in the application of image tag completion.