Xiaokun Feng, Shiyu Hu, Xuchen Li, Dailing Zhang, Meiqi Wu, Jing Zhang, Xiaotang Chen, Kaiqi Huang

This is an official pytorch implementation of the paper ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking.

• [8/2025] ATCTrack's code is available!
• [6/2025] ATCTrack is accepted by ICCV25 Highlight!

Vision-language tracking aims to locate the target object in the video sequence using a template patch and a language description provided in the initial frame. To achieve robust tracking, especially in complex long-term scenarios that reflect real-world conditions as recently highlighted by MGIT, it is essential not only to characterize the target features but also to utilize the context features related to the target. However, the visual and textual target-context cues derived from the initial prompts generally align only with the initial target state. Due to their dynamic nature, target states are constantly changing, particularly in complex long-term sequences. It is intractable for these cues to continuously guide Vision-Language Trackers (VLTs). Furthermore, for the text prompts with diverse expressions, our experiments reveal that existing VLTs struggle to discern which words pertain to the target or the context, complicating the utilization of textual cues.

In this work, we present a novel tracker named ATCTrack, which can obtain multimodal cues Aligned with the dynamic target states through comprehensive Target-Context feature modeling, thereby achieving robust tracking. Specifically, (1) for the visual modality, we propose an effective temporal visual target-context modeling approach that provides the tracker with timely visual cues. (2) For the textual modality, we achieve precise target words identification solely based on textual content, and design an innovative context words calibration method to adaptively utilize auxiliary context words. (3) We conduct extensive experiments on mainstream benchmarks and ATCTrack achieves a new SOTA performance

conda create -n atctrack python=3.8
conda activate atctrack
bash install.sh

Our ATCTrack is trained on LaSOT, TNL2K, RefCOCOg, OTB99-Lang, VastTrack, GOT-10k, and TrackingNet datasets.
Put these tracking datasets in ./data. It should look like:

${ATCTrack_ROOT}
 -- data
     -- lasot
         |-- airplane
         |-- basketball
         |-- bear
         ...
     -- got10k
         |-- test
         |-- train
         |-- val
     -- coco
         |-- annotations
         |-- images
     -- trackingnet
         |-- TRAIN_0
         |-- TRAIN_1
         ...
         |-- TRAIN_11
         |-- TEST
     -- VastTrack
         |-- unisot_train_final_backup
             |-- Aardwolf
             ...
             |-- Zither
         |-- unisot_final_test
             |-- Aardwolf
             ...
             |-- Zither
     -- tnl2k
         -- train
             |-- Arrow_Video_ZZ04_done
             |-- Assassin_video_1-Done
             |-- Assassin_video_2-Done
             ...
         -- test
             |-- advSamp_Baseball_game_002-Done
             |-- advSamp_Baseball_video_01-Done
             |-- advSamp_Baseball_video_02-Done
             ...

Run the following command to set paths for this project

python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir .

After running this command, you can also modify paths by editing these two files

lib/train/admin/local.py  # paths about training
lib/test/evaluation/local.py  # paths about testing

The backbone and patch embedding of ATCTrack are initialized with pre-trained weights from Fast-iTPN, and we adopt RoBERTa-Base as our text encoder.
Please download the fast_itpn_base_clipl_e1600.pt, fast_itpn_large_1600e_1k.pt and roberta-base checkpoints and place them in ./resource/pretrained_models.

You can run the following command to train the ATCTrack-B:

python -m torch.distributed.launch --nproc_per_node 3 lib/train/run_training.py --script atctrack --config atctrack_base --save_dir .

Besides, you can run the following command to train the ATCTrack-L:

python -m torch.distributed.launch --nproc_per_node 3 lib/train/run_training.py --script atctrack --config atctrack_large --save_dir .

First, you need to set the paths for the various evaluation benchmarks in ./lib/test/evaluation/local.py, and prepare the model weights for evaluation. Then, run the following command to perform evaluation on different benchmarks (taking atctrack_base as an example).

• LaSOT

python tracking/test.py --tracker_name atctrack --tracker_param atctrack_base --dataset lasot_lang --threads 4 --num_gpus 2 --ckpt_path '{your_dir_saved_model_ckpt}/ATCTrack_b.pth.tar'
python tracking/analysis_results.py --dataset_name lasot_lang --tracker_param atctrack_base

• LaSOT_ext

python tracking/test.py --tracker_name atctrack --tracker_param atctrack_base --dataset lasot_extension_subset_lang --threads 4 --num_gpus 2 --ckpt_path '{your_dir_saved_model_ckpt}/ATCTrack_b.pth.tar'
python tracking/analysis_results.py --dataset_name lasot_extension_subset_lang --tracker_param atctrack_base

• TNL2K

python tracking/test.py --tracker_name atctrack --tracker_param atctrack_base --dataset tnl2k --threads 4 --num_gpus 2 --ckpt_path '{your_dir_saved_model_ckpt}/ATCTrack_b.pth.tar'
python tracking/analysis_results.py --dataset_name lasot_extension_subset_lang --tracker_param atctrack_base

• MGIT

Please refer to the official MGIT testing platform and tools to complete the corresponding evaluation.

The trained models, and the raw tracking results are provided in the .

We would like to express our gratitude to the following open-source repositories that our work is based on: SeqtrackV2, AQATrack, Fast-iTPN. Their contributions have been invaluable to this project.