Recent multi-camera multi-object tracking (MCMOT) algorithms are primarily trained using per-detection identity annotations, which are complicated to obtain. In contrast, labeling a language description per-object is a more natural and human-friendly way. In this paper, we explore MCMOT in a language-supervised manner (LS-MCMOT) and propose a novel approach LaVST, which performs language-to-vision weakly-supervised learning based on reliable pseudo-labels generated via tracklet-level cross-modality matching. In addition, we design an ID-aware projection self-correction mechanism to correct inaccurate image-to-ground projection in a self-supervised manner. The models trained with our approach exhibit promising performance in LS-MCMOT. Surprisingly, they perform favorably against state-of-the-art identity-supervised methods, especially in cross-dataset evaluation (with an average gain by 20.0% in IDF1), underscoring the potential of language annotations in MCMOT. Codes and language annotations will be available here.

Concept Bottleneck Models (CBMs) enhance the interpretability of deep neural networks by mapping images to human-understandable concepts and then using the concepts to make predictions. While they improve transparency, existing CBMs primarily explain only the final layer's features, limiting the interpretability of intermediate layers. Additionally, constructing a comprehensive concept set remains a challenging task, further constraining model performance. In this paper, we investigate the assignment of concept granularity across model layers and propose theHierarchicalConceptBottleneckModel (HCBM) to enhance interpretability. HCBM introduces a Hybrid Concept Bottleneck Layer (HCBL) at each layer, consisting of a Predefined Concept Bottleneck (PCB) that maps visual features to concepts of corresponding granularity and a Compensation Concept Bottleneck (CCB) which incorporates the concept frequency loss and the concept semantic loss to capture compensation concepts for improving performance. Extensive experiments demonstrate that HCBM outperforms state-of-the-art methods. It is worth noting that the HCBM with CLIP RN50 as the backbone outperforms the black-box model.

Tracking by natural language specification requires trackers to jointly perform grounding and tracking tasks. Existing methods either use separate models or a single shared network, failing to account for the link and diversity between tasks jointly. In this paper, we propose a novel framework that performs dynamic task switching to customize its network path routing for each task within a unified model. For this purpose, we design a task-switchable attention module, which enables the acquisition of modal relation patterns with different dominant modalities for each task via dynamic task switching. In addition, to alleviate the inconsistency between the static language description and the dynamic target appearance during tracking, we propose a language renovation mechanism that renovates the initial language online via visual-context-aware linguistic prompting. Extensive experimental results on five datasets demonstrate that the proposed method performs favorably against state-of-the-art approaches for both grounding and tracking. Our project will be available at: https://github.com/mkg1204/SAKTrack.

This article aims to solve the video object segmentation (VOS) task in a scribble-supervised manner, in which VOS models are not only initialized with sparse target scribbles for inference but also trained by sparse scribble annotations. Thus, the annotation burdens for both initialization and training can be substantially lightened. The difficulties of scribble-supervised VOS lie in two aspects: 1) it demands a strong reasoning ability to carefully segment the target given only a sparse initial target scribble and 2) it necessitates learning dense prediction from sparse scribble annotations during training, requiring powerful learning capability. In this work, we propose a reliability-guided hierarchical memory network (RHMNet) for this task, which segments the target in a stepwise expanding strategy w.r.t. the memory reliability level. To be specific, RHMNet maintains a reliability-guided memory bank. It first uses the high-reliability memory to locate the region with high reliability belonging to the target, i.e., highly similar to the initial target scribble. Then, it expands the located high-reliability region to the entire target conditioned on the region itself and all existing memories. In addition, we propose a scribble-supervised learning mechanism to facilitate the model learning for dense prediction. It exploits the pixel-level relations within a single frame and the instance-level variations across multiple frames to take full advantage of the scribble annotations in sequence training samples. The favorable performance on four popular benchmarks demonstrates that our method is promising. Our project is available at: https://github.com/mkg1204/RHMNet-for-SSVOS.

Tracking by natural language specification aims to locate the referred target in a sequence based on the natural language description. Existing algorithms solve this issue in two steps, visual grounding and tracking, and accordingly deploy the separated grounding model and tracking model to implement these two steps, respectively. Such a separated framework overlooks the link between visual grounding and tracking, which is that the natural language descriptions provide global semantic cues for localizing the target for both two steps. Besides, the separated framework can hardly be trained end-to-end. To handle these issues, we propose a joint visual grounding and tracking framework, which reformulates grounding and tracking as a unified task: localizing the referred target based on the given visual-language references. Specifically, we propose a multi-source relation modeling module to effectively build the relation between the visual-language references and the test image. In addition, we design a temporal modeling module to provide a temporal clue with the guidance of the global semantic information for our model, which effectively improves the adaptability to the appearance variations of the target. Extensive experimental results on TNL2K, LaSOT, OTB99, and RefCOCOg demonstrate that our method performs favorably against state-of-the-art algorithms for both tracking and grounding. Code is available at https://github.com/lizhou-cs/JointNLT.

The crux of long-term tracking lies in the difficulty of tracking the target with discontinuous moving caused by out-of-view or occlusion. Existing long-term tracking methods follow two typical strategies. The first strategy employs a local tracker to perform smooth tracking and uses another re-detector to detect the target when the target is lost. While it can exploit the temporal context like historical appearances and locations of the target, a potential limitation of such strategy is that the local tracker tends to misidentify a nearby distractor as the target instead of activating the re-detector when the real target is out of view. The other long-term tracking strategy tracks the target in the entire image globally instead of local tracking based on the previous tracking results. Unfortunately, such global tracking strategy cannot leverage the temporal context effectively. In this work, we combine the advantages of both strategies: tracking the target in a global view while exploiting the temporal context. Specifically, we perform global tracking via ensemble of local trackers spreading the full image. The smooth moving of the target can be handled steadily by one local tracker. When the local tracker accidentally loses the target due to suddenly discontinuous moving, another local tracker close to the target is then activated and can readily take over the tracking to locate the target. While the activated local tracker performs tracking locally by leveraging the temporal context, the ensemble of local trackers renders our model the global view for tracking. Extensive experiments on six datasets demonstrate that our method performs favorably against state-of-the-art algorithms.