Camouflaged object detection (COD) aims to identify the objects that seamlessly blend into the surrounding backgrounds. Due to the intrinsic similarity between the camouflaged objects and the background region, it is extremely challenging to precisely distinguish the camouflaged objects by existing approaches. In this paper, we propose a hierarchical graph interaction network termed HGINet for camouflaged object detection, which is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Specifically, we first design a region-aware token focusing attention (RTFA) with dynamic token clustering to excavate the potentially distinguishable tokens in the local region. Afterwards, a hierarchical graph interaction transformer (HGIT) is proposed to construct bi-directional aligned communication between hierarchical features in the latent interaction space for visual semantics enhancement. Furthermore, we propose a decoder network with confidence aggregated feature fusion (CAFF) modules, which progressively fuses the hierarchical interacted features to refine the local detail in ambiguous regions. Extensive experiments conducted on the prevalent datasets, i.e . COD10K, CAMO, NC4K and CHAMELEON demonstrate the superior performance of HGINet compared to existing state-of-the-art methods. Our code is available at https://github.com/Garyson1204/HGINet.

Objects with different orientations are ubiquitous in the real world ( e.g. , texts/hands in the scene image, objects in the aerial image, etc. ), and the widely-used axis-aligned bounding box does not compactly enclose the oriented objects. Thus arbitrarily-oriented object detection has attracted rising attention in recent years. In this paper, we propose a novel and effective model to detect arbitrarily-oriented objects. Instead of directly predicting the angles of oriented bounding boxes like most existing methods, we evolve the axis-aligned bounding box to the oriented quadrilateral box with the assistance of dynamically gathering contour information. More specifically, we first obtain the axis-aligned bounding box in an anchor-free manner. After that, we set the key points based on the sampled contour points of the axis-aligned bounding box. To improve the localization performance, we enrich the feature representations of these key points by exploiting a dynamic information gathering mechanism. This technique propagates the geometrical and semantic information along the sampled contour points, and fuses the information from the semantic neighbors of each sampled point, which varies for different locations. Finally, we estimate the offsets between the axis-aligned bounding box key points and the oriented quadrilateral box corner points. Extensive experiments on two frequently-used aerial image benchmarks HRSC2016 and DOTA, as well as scene text/hand datasets ICDAR2015, TD500, and Oxford-Hand, demonstrate the effectiveness and advantage of our proposed model.

In most recent years, Siamese trackers have drawn great attention because of their well-balanced accuracy and efficiency. Although these approaches have achieved great success, the discriminative power of the conventional Siamese trackers is still limited by the insufficient template-candidate representation. Most of the existing approaches take non-aligned features to learn a similarity function for template-candidate matching, while the target object's geometrical transformation is seldom explored. To address this problem, we propose a novel Siamese tracking framework, which enables to dynamically transform the template-candidate features to a more discriminative viewpoint for similarity matching. Specifically, we reformulate the template-candidate matching problem of the conventional Siamese tracker from the perspective of Lucas-Kanade (LK) image alignment approach. A Lucas-Kanade network (LKNet) is proposed and incorporated to the Siamese architecture to learn aligned feature representations in data-driven trainable manner, which is able to enhance the model adaptability in challenging scenarios. Within this framework, we propose two Siamese trackers named LK-Siam and LK-SiamRPN to validate the effectiveness. Extensive experiments conducted on the prevalent datasets show that the proposed method is more competitive over a number of state-of-the-art methods.

Existing tracking-by-detection approaches using deep features have achieved promising results in recent years. However, these methods mainly exploit feature representations learned from individual static frames, thus paying little attention to the temporal smoothness between frames. This easily leads trackers to drift in the presence of large appearance variations and occlusions. To address this issue, we propose a two-stream network to learn discriminative spatio-temporal feature representations to represent the target objects. The proposed network consists of a Spatial ConvNet module and a Temporal ConvNet module. Specifically, the Spatial ConvNet adopts 2D convolutions to encode the target-specific appearance in static frames, while the Temporal ConvNet models the temporal appearance variations using 3D convolutions and learns consistent temporal patterns in a short video clip. Then we propose a proposal refinement module to adjust the predicted bounding box, which can make the target localizing outputs to be more consistent in video sequences. In addition, to improve the model adaptation during online update, we propose a contrastive online hard example mining (OHEM) strategy, which selects hard negative samples and enforces them to be embedded in a more discriminative feature space. Extensive experiments conducted on the OTB, Temple Color and VOT benchmarks demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.

Transformer-based trackers have achieved promising success and become the dominant tracking paradigm because of their accuracy and efficiency. Despite the substantial progress, most of the existing approaches handle object tracking as a deterministic coordinate regression problem, while the target localization uncertainty has been largely overlooked, which hampers trackers’ ability to maintain reliable target state prediction in challenging scenarios. To address this issue, we propose UncTrack, a novel uncertainty-aware transformer-based tracker that predicts the target localization uncertainty and incorporates this uncertainty information for accurate target state inference. Specifically, UncTrack uses a transformer encoder to perform feature interactions between the template and search images. The output features are passed into an uncertainty-aware localization decoder (ULD) to coarsely predict the corner-based localization and the corresponding localization uncertainty. Then, the localization uncertainty is sent into a prototype memory network (PMN) to excavate valuable historical information to identify whether the target state prediction is reliable. To enhance the template representation, the samples with high confidence are fed back into the prototype memory bank for memory updating, which makes the tracker more robust to challenging appearance variations. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods. Our code is available at https://github.com/ManOfStory/UncTrack.

Building extraction from remote sensing images is a challenging task due to the complex structure variations of the buildings. Existing methods employ convolutional or self-attention blocks to capture the multi-scale features in the segmentation models, while the inherent gap of the feature pyramids and insufficient global-local feature integration leads to inaccurate, ambiguous extraction results. To address this issue, in this paper, we present an Uncertainty-Aggregated Global-Local Fusion Network (UAGLNet), which is capable to exploit high-quality global-local visual semantics under the guidance of uncertainty modeling. Specifically, we propose a novel cooperative encoder, which adopts hybrid CNN and transformer layers at different stages to capture the local and global visual semantics, respectively. An intermediate cooperative interaction block (CIB) is designed to narrow the gap between the local and global features when the network becomes deeper. Afterwards, we propose a Global-Local Fusion (GLF) module to complementarily fuse the global and local representations. Moreover, to mitigate the segmentation ambiguity in uncertain regions, we propose an Uncertainty-Aggregated Decoder (UAD) to explicitly estimate the pixel-wise uncertainty to enhance the segmentation accuracy. Extensive experiments demonstrate that our method achieves superior performance to other state-of-the-art methods. Our code is available at https://github.com/Dstate/UAGLNet.