The performance of existing unsupervised video object segmentation methods typically suffers from severe performance degradation on test videos when tested in out-of-distribution scenarios. The primary reason is that the test data in real- world may not follow the independent and identically distribution (i.i.d.) assumption, leading to domain shift. In this paper, we propose a generalizable fourier augmentation method during training to improve the generalization ability of the model. To achieve this, we perform Fast Fourier Transform (FFT) over the intermediate spatial domain features in each layer to yield corresponding frequency representations, including amplitude components (encoding scene-aware styles such as texture, color, contrast of the scene) and phase components (encoding rich semantics). We produce a variety of style features via Gaussian sampling to augment the training data, thereby improving the generalization capability of the model. To further improve the cross-domain generalization performance of the model, we design a phase feature update strategy via exponential moving average using phase features from past frames in an online update manner, which could help the model to learn cross-domain-invariant features. Extensive experiments show that our proposed method achieves the state-of-the-art performance on popular benchmarks.

The existing unsupervised video object segmentation methods depend heavily on the segmentation model trained offline on a labeled training video set, and cannot well generalize to the test videos from a different domain with possible distribution shifts. We propose to perform online fine-tuning on the pre-trained segmentation model to adapt to any ad-hoc videos at the test time. To achieve this, we design an offline semi-supervised adversarial training process, which leverages the unlabeled video frames to improve the model generalizability while aligning the features of the labeled video frames with the features of the unlabeled video frames. With the trained segmentation model, we further conduct an online self-supervised adversarial finetuning, in which a teacher model and a student model are first initialized with the pre-trained segmentation model weights, and the pseudo label produced by the teacher model is used to supervise the student model in an adversarial learning framework. Through online finetuning, the student model is progressively updated according to the emerging patterns in each test video, which significantly reduces the test-time domain gap. We integrate our offline training and online fine-tuning in a unified framework for unsupervised video object segmentation and dub our method Online Adversarial Self-Tuning (OAST). The experiments show that our method outperforms the state-of-the-arts with significant gains on the popular video object segmentation datasets.

Spatial-temporal structural details of targets in video (e.g. varying edges, textures over time) are essential to accurate Unsupervised Video Object Segmentation (UVOS). The vanilla multi-head self-attention in the Transformer-based UVOS methods usually concentrates on learning the general low-frequency information (e.g. illumination, color), while neglecting the high-frequency texture details, leading to unsatisfying segmentation results. To address this issue, this paper presents a Temporally efficient Gabor Transformer (TGFormer) for UVOS. The TGFormer jointly models the spatial dependencies and temporal coherence intra- and inter-frames, which can fully capture the rich structural details for accurate UVOS. Concretely, we first propose an effective learnable Gabor filtering Transformer to mine the structural texture details of the object for accurate UVOS. Then, to adaptively store the redundant neighboring historical information, we present an efficient dynamic neighboring frame selection module to automatically choose the useful temporal information, which simultaneously relieves the blurry frame and reduces the computation burden. Finally, we make the UVOS model be a fully Transformer architecture, meanwhile aggregating the information from space, Gabor and time domains, yielding a strong representation with rich structure details. Extensive experiments on five mainstream UVOS benchmarks (DAVIS2016, FBMS, DAVSOD, ViSal, and MCL) demonstrate the superiority of the presented solution to sate-of-the-art methods.

Spatio-temporal feature representation is essential for accurate unsupervised video object segmentation, which needs an effective feature propagation paradigm for both appearance and motion features that can fully interchange information across frames. However, existing solutions mainly focus on the forward feature propagation from the preceding frame to the current one, either using the former segmentation mask or motion propagation in a frame-by-frame manner. This ignores the bi-directional temporal feature interactions (including the backward propagation from the future to the current frame) across all frames that can help to enhance the spatiotemporal feature representation for segmentation prediction. To this end, this paper presents a novel Dense Bidirectional Spatio-temporal feature propagation Network (DBSNet) to fully integrate the forward and the backward propagations across all frames. Specifically, a dense bi-ConvLSTM module is first developed to propagate the features across all frames in a forward and backward manner. This can fully capture the multi-level spatio-temporal contextual information across all frames, producing an effective feature representation that has a strong discriminative capability to tell from noisy backgrounds. Following it, a spatio-temporal Transformer refinement module is designed to further enhance the propagated features, which can effectively capture the spatio-temporal long-range dependencies among all frames. Afterwards, a Co-operative Direction-aware Graph Attention (Co-DGA) module is designed to integrate the propagated appearancemotion cues, yielding a strong spatio-temporal feature representation for segmentation mask prediction. The Co-DGA assigns proper attentional weights to neighboring points along the coordinate axis, making the segmentation model to selectively focus on the most relevant neighbors. Extensive evaluations on four mainstream challenging benchmarks including DAVIS16, FBMS, DAVSOD, and MCL demonstrate that the proposed DBSNet achieves favorable performance against state-of-the-art methods in terms of all evaluation metrics.