Search Results (10)

The Segment Anything Model 2 (SAM2) has achieved state-of-the-art performance in pixel-level object segmentation for both static and dynamic visual content. Its streaming memory architecture maintains spatial context across video sequences, yet struggles with long-term tracking due to its static inference framework. SAM 2’s fixed temporal window approach indiscriminately retains historical frames, failing to account for frame quality or dynamic motion patterns. This leads to error propagation and tracking instability in challenging scenarios involving fast-moving objects, partial occlusions, or crowded environments. To overcome these limitations, this paper proposes SAM2Plus, a zero-shot enhancement framework that integrates Kalman filter prediction, dynamic quality thresholds, and adaptive memory management. The Kalman filter models object motion using physical constraints to predict trajectories and dynamically refine segmentation states, mitigating positional drift during occlusions or velocity changes. Dynamic thresholds, combined with multi-criteria evaluation metrics (e.g., motion coherence, appearance consistency), prioritize high-quality frames while adaptively balancing confidence scores and temporal smoothness. This reduces ambiguities among similar objects in complex scenes. SAM2Plus further employs an optimized memory system that prunes outdated or low-confidence entries and retains temporally coherent context, ensuring constant computational resources even for infinitely long videos. Extensive experiments on two video object segmentation (VOS) benchmarks demonstrate SAM2Plus’s superiority over SAM 2. It achieves an average improvement of 1.0 in J&F metrics across all 24 direct comparisons, with gains exceeding 2.3 points on SA-V and LVOS datasets for long-term tracking. The method delivers real-time performance and strong generalization without fine-tuning or additional parameters, effectively addressing occlusion recovery and viewpoint changes. By unifying motion-aware physics-based prediction with spatial segmentation, SAM2Plus bridges the gap between static and dynamic reasoning, offering a scalable solution for real-world applications such as autonomous driving and surveillance systems. Full article

As a multimodal fusion task, audio-visual segmentation (AVS) aims to locate sounding objects at the pixel level within a given image. This capability holds significant importance and practical value in applications such as intelligent surveillance, multimedia content analysis, and human–robot interaction. However, existing AVS models typically feature complex architectures, require a large number of parameters, and are challenging to deploy on embedded platforms. Furthermore, these models often lack integration with object tracking mechanisms and fail to address the issue of the mis-segmentation of unvoiced objects caused by environmental noise in real-world scenarios. To address these challenges, this research proposes a lightweight audio-visual segmentation framework incorporating an audio-guided space–time memory network (AG-STMNet). First, a mask generator with a scoring mechanism was developed to identify sounding objects from generated masks. This component integrates Fastsam, a lightweight, pre-trained, object-aware segmentation model, with WAV2CLIP, a parameter-efficient audio-visual alignment model. Subsequently, AG-STMNet, an audio-guided video object segmentation network, was introduced to track sounding objects using video object segmentation techniques while mitigating environmental noise. Finally, the mask generator and AG-STMNet were combined to form the complete framework. The experimental results demonstrate that the framework achieves a mean Intersection over Union (mIoU) score of 41.5, indicating its potential as a viable lightweight solution for practical applications. Full article

In this paper, we propose sketch-based video object segmentation (SKVOS), a novel task that segments objects consistently across video frames using human-drawn sketches as queries. Traditional reference-based methods, such as photo masks and language descriptions, are commonly used for segmentation. Photo masks provide high precision but are labor intensive, limiting scalability. While language descriptions are easy to provide, they often lack the specificity needed to distinguish visually similar objects within a frame. Despite their simplicity, sketches capture rich, fine-grained details of target objects and can be rapidly created, even by non-experts, making them an attractive alternative for segmentation tasks. We introduce a new approach that utilizes sketches as efficient and informative references for video object segmentation. To evaluate sketch-guided segmentation, we introduce a new benchmark consisting of three datasets: Sketch-DAVIS16, Sketch-DAVIS17, and Sketch-YouTube-VOS. Building on a memory-based framework for semi-supervised video object segmentation, we explore effective strategies for integrating sketch-based references. To ensure robust spatiotemporal coherence, we introduce two key innovations: the Temporal Relation Module and Sketch-Anchored Contrastive Learning. These modules enhance the model’s ability to maintain consistency both across time and across different object instances. Our method is evaluated on the Sketch-VOS benchmark, demonstrating superior performance with overall improvements of 1.9%, 3.3%, and 2.0% over state-of-the-art methods on the Sketch-YouTube-VOS, Sketch-DAVIS 2016, and Sketch-DAVIS 2017 validation sets, respectively. Additionally, on the YouTube-VOS validation set, our method outperforms the leading language-based VOS approach by 10.1%. Full article

Referring video object segmentation (R-VOS) is a fundamental vision-language task which aims to segment the target referred by language expression in all video frames. Existing query-based R-VOS methods have conducted in-depth exploration of the interaction and alignment between visual and linguistic features but fail to transfer the information of the two modalities to the query vector with balanced intensities. Furthermore, most of the traditional approaches suffer from severe information loss in the process of multi-scale feature fusion, resulting in inaccurate segmentation. In this paper, we propose DCT, an end-to-end decoupled cross-modal transformer for referring video object segmentation, to better utilize multi-modal and multi-scale information. Specifically, we first design a Language-Guided Visual Enhancement Module (LGVE) to transmit discriminative linguistic information to visual features of all levels, performing an initial filtering of irrelevant background regions. Then, we propose a decoupled transformer decoder, using a set of object queries to gather entity-related information from both visual and linguistic features independently, mitigating the attention bias caused by feature size differences. Finally, the Cross-layer Feature Pyramid Network (CFPN) is introduced to preserve more visual details by establishing direct cross-layer communication. Extensive experiments have been carried out on A2D-Sentences, JHMDB-Sentences and Ref-Youtube-VOS. The results show that DCT achieves competitive segmentation accuracy compared with the state-of-the-art methods. Full article

Existing video object segmentation (VOS) methods based on matching techniques commonly employ a reference set comprising historical segmented frames, referred to as ‘memory frames’, to facilitate the segmentation process. However, these methods suffer from the following limitations: (i) Inherent segmentation errors in memory frames can propagate and accumulate errors when utilized as templates for subsequent segmentation. (ii) The non-local matching technique employed in top-leading solutions often fails to incorporate positional information, potentially leading to incorrect matching. In this paper, we introduce the Modulated Memory Network (MMN) for VOS. Our MMN enhances matching-based VOS methods in the following ways: (i) Introducing an Importance Modulator, which adjusts memory frames using adaptive weight maps generated based on the segmentation confidence associated with each frame. (ii) Incorporating a Position Modulator that encodes spatial and temporal positional information for both memory frames and the current frame. The proposed modulator improves matching accuracy by embedding positional information. Meanwhile, the Importance Modulator mitigates error propagation and accumulation by incorporating confidence-based modulation. Through extensive experimentation, we demonstrate the effectiveness of our proposed MMN, which also achieves promising performance on VOS benchmarks. Full article

The purpose of semi-supervised video object segmentation (VOS) is to predict and generate object masks in subsequent video frames after being provided with the initial frame’s object mask. Currently, mainstream methods leverage historical frame information for enhancing the network’s performance. However, this approach faces the following issues: (1) They often overlook important shape information, leading to decreased accuracy in segmenting object-edge areas. (2) They often use pixel-level motion estimation to guide the matching for addressing distractor objects. However, this brings heavy computation costs and struggle against occlusion or fast/blurry motion. For the first problem, this paper introduces an object shape extraction module that exploits both the high-level and low-level features to obtain object shape information, by which the shape information can be used to further refine the predicted masks. For the second problem, this paper introduces a novel object-level motion prediction module, in which it stores the representative motion features during the training stage, and predicts the object motion by retrieving them during the inference stage. We evaluate our method on benchmark datasets compared with recent state-of-the-art methods, and the results demonstrate the effectiveness of the proposed method. Full article

Tracking and segmenting small targets in remote sensing videos on edge devices carries significant engineering implications. However, many semi-supervised video object segmentation (S-VOS) methods heavily rely on extensive video random-access memory (VRAM) resources, making deployment on edge devices challenging. Our goal is to develop an edge-deployable S-VOS method that can achieve high-precision tracking and segmentation by selecting a bounding box for the target object. First, a tracker is introduced to pinpoint the position of the tracked object in different frames, thereby eliminating the need to save the results of the split as other S-VOS methods do, thus avoiding an increase in VRAM usage. Second, we use two key lightweight components, correlation filters (CFs) and the Mobile Segment Anything Model (MobileSAM), to ensure the inference speed of our model. Third, a mask diffusion module is proposed that improves the accuracy and robustness of segmentation without increasing VRAM usage. We use our self-built dataset containing airplanes and vehicles to evaluate our method. The results show that on the GTX 1080 Ti, our model achieves a J&F score of 66.4% under the condition that the VRAM usage is less than 500 MB, while maintaining a processing speed of 12 frames per second (FPS). The model we propose exhibits good performance in tracking and segmenting small targets on edge devices, providing a solution for fields such as aircraft monitoring and vehicle tracking that require executing S-VOS tasks on edge devices. Full article

A novel video object segmentation algorithm, which segments out multiple objects in a video sequence in unsupervised or weakly supervised manners, is proposed in this work. First, we match visually important object instances to construct salient object tracks through a video sequence without any user supervision. We formulate this matching process as the problem to find maximal weight cliques in a complete k-partite graph and develop the sequential clique optimization algorithm to determine the cliques efficiently. Then, we convert the resultant salient object tracks into object segmentation results and refine them based on Markov random field optimization. Second, we adapt the sequential clique optimization algorithm to perform weakly supervised video object segmentation. To this end, we develop a sparse-to-dense network to convert the point cliques into segmentation results. The experimental results demonstrate that the proposed algorithm provides comparable or better performances than recent state-of-the-art VOS algorithms. Full article

This paper describes a post-evaluation analysis of the system developed by ViVoLAB research group for the IberSPEECH-RTVE 2020 Multimodal Diarization (MD) Challenge. This challenge focuses on the study of multimodal systems for the diarization of audiovisual files and the assignment of an identity to each segment where a person is detected. In this work, we implemented two different subsystems to address this task using the audio and the video from audiovisual files separately. To develop our subsystems, we used the state-of-the-art speaker and face verification embeddings extracted from publicly available deep neural networks (DNN). Different clustering techniques were also employed in combination with the tracking and identity assignment process. Furthermore, we included a novel back-end approach in the face verification subsystem to train an enrollment model for each identity, which we have previously shown to improve the results compared to the average of the enrollment data. Using this approach, we trained a learnable vector to represent each enrollment character. The loss function employed to train this vector was an approximated version of the detection cost function (aDCF) which is inspired by the DCF widely used metric to measure performance in verification tasks. In this paper, we also focused on exploring and analyzing the effect of training this vector with several configurations of this objective loss function. This analysis allows us to assess the impact of the configuration parameters of the loss in the amount and type of errors produced by the system. Full article

Semi-supervised video object segmentation (VOS) has obtained significant progress in recent years. The general purpose of VOS methods is to segment objects in video sequences provided with a single annotation in the first frame. However, many of the recent successful methods heavily fine-tune the object mask in the first frame, which decreases their efficiency. In this work, to address this issue, we propose a symmetry encoder-decoder network with the attention mechanism for video object segmentation (SAVOS) requiring only one forward pass to segment the target object in a video. Specifically, the encoder generates a low-resolution mask with smoothed boundaries, while the decoder further refines the details of the segmentation mask and integrates lower level features progressively. Besides, to obtain accurate segmentation results, we sequentially apply the attention module on multi-scale feature maps for refinement. We conduct several experiments on three challenging datasets (i.e., DAVIS 2016, DAVIS 2017, and SegTrack v2) to show that SAVOS achieves competitive performance against the state-of-the-art. Full article