RGB-Thermal (RGB-T) pedestrian detection aims to locate pedestrians in RGB-T image pairs to exploit the complementation between the two modalities for improving detection robustness in extreme conditions. Most existing algorithms assume that the RGB-T image pairs are well registered, while in the real world, they are not ideally aligned due to parallax or different field-of-view of the cameras. The pedestrians in misaligned image pairs may be located at different positions in two images, which results in two challenges: 1) how to achieve inter-modality complementation using spatially misaligned RGB-T pedestrian patches and 2) how to recognize unpaired pedestrians at the boundary. To address these issues, we propose a new paradigm for unregistered RGB-T pedestrian detection, which predicts two separate pedestrian locations in RGB and thermal images. Specifically, we propose a cross-modality proposal-guided feature mining (CPFM) mechanism to extract two precise fusion features for representing a pedestrian in the two modalities, even if the given RGB-T image pair is unaligned. It enables us to effectively exploit the complementation between the two modalities. With the CPFM mechanism, we build a two-stream dense detector that predicts two pedestrian locations in the two modalities based on the corresponding fusion features mined by the CPFM mechanism. In addition, we design a data augmentation method, named Homography, to simulate the discrepancy in scales and views between images. We also investigate two non-maximum suppression (NMS) methods for post-processing purposes. Favorable experimental results demonstrate the effectiveness and robustness of our method in addressing unregistered pedestrians with different shifts.

Temporal and spatial contexts, characterizing target appearance variations and target-background differences, respectively, are crucial for improving the online adaptive ability and instance-level discriminative ability of object tracking. However, most existing trackers focus on either the temporal context or the spatial context during tracking and have not exploited these contexts simultaneously and effectively. In this paper, we propose a Spatial-TEmporal Memory (STEM) network to exploit these contexts jointly for object tracking. Specifically, we develop a key-value structured memory model equipped with a key-value index-based memory reading mechanism to model the spatial and temporal contexts simultaneously. To update the memory with new target states and ensure the diversity of the memory, we introduce a similarity-aware memory update scheme. In addition, we construct an entropy-guided ensemble strategy to fuse the prediction models based on these two contexts, such that these two contexts can be exploited to estimate the target state jointly. Extensive experimental results on eight challenging datasets, including OTB2015, TC128, UAV123, VOT2018, LaSOT, TrackingNet, GOT-10k, and OxUvA, demonstrate that the proposed method performs favorably against state-of-the-art trackers.

Many robotics and industry applications have a high demand for the capability to estimate the 6D pose of novel objects from the cluttered scene. However, existing classic pose estimation methods are object-specific, which can only handle the specific objects seen during training. When applied to a novel object, these methods necessitate a cumbersome onboarding process, which involves extensive dataset preparation and model retraining. The extensive duration and resource consumption of onboarding limit their practicality in real-world applications In this paper, we introduce ZeroPose, a novel zero-shot framework that performs pose estimation following a Discovery-Orientation-Registration (DOR) inference pipeline. This framework generalizes to novel objects without requiring model retraining. Given the CAD model of a novel object, ZeroPose enables in seconds onboarding time to extract visual and geometric embeddings from the CAD model as a prompt. With the prompting of the above embeddings, DOR can discover all related instances and estimate their 6D poses without additional human interaction or presupposing scene conditions. Compared with existing zero-shot methods solved by the render-and-compare paradigm, the DOR pipeline formulates the object pose estimation into a feature-matching problem, which avoids time-consuming online rendering and improves efficiency. Experimental results on the seven datasets show that ZeroPose as a zero-shot method achieves comparable performance with object-specific training methods and outperforms the state-of-the-art zero-shot method with 50x inference speed improvement.

Trackers based on the IoU prediction network (IoU-Net) have shown superior performance, which refines a coarse bounding box to an accurate one by maximizing the IoU between the target and the coarse box. However, the traditional IoU-Net is less effective in exploiting the limited but crucial supervision information contained in the initial frame, including the discriminative information between the target and backgrounds and the structure information of the initial target. Missing such information makes the IoU-Net less robust to background distractors and diverse variations of the target appearance. To address this issue, we propose a target-aware state estimation network for visual tracking. A gradient-guided feature adjustment module is built on an online discriminative model to generate target-aware features for constructing the state estimation network; it conveys the online learned discriminative information into the offline trained state estimation network. In addition, we propose a structure-aware integration module and embed it into the state estimation network, enabling the tracker to explicitly model the structure information of the initial target. Extensive experimental results on the VOT2018, OTB2015, UAV123, NFS30, TC128, TrackingNet, LaSOT, and VOT2018-LT datasets demonstrate that the proposed approach performs favorably against state-of-the-art trackers.

With the advancement of AIGC, video frame interpolation (VFI) has become a crucial component in existing video generation frameworks, attracting widespread research interest. For the VFI task, the motion estimation between neighboring frames plays a crucial role in avoiding motion ambiguity. However, existing VFI methods always struggle to accurately predict the motion information between consecutive frames, and this imprecise estimation leads to blurred and visually incoherent interpolated frames. In this paper, we propose a novel diffusion framework, Motion-Aware latent Diffusion models (MADiff), which is specifically designed for the VFI task. By incorporating motion priors between the conditional neighboring frames with the target interpolated frame predicted throughout the diffusion sampling procedure, MADiff progressively refines the intermediate outcomes, culminating in generating both visually smooth and realistic results. Extensive experiments conducted on benchmark datasets demonstrate that our method achieves state-of-the-art performance significantly outperforming existing approaches, especially under challenging scenarios involving dynamic textures with complex motion.