Image editing in our daily lives often requires models to first understand user’s intention and then proceed with the editing. Despite significant advancements in image editing technology, understanding and executing complex instructions remains a substantial challenge. Existing image editing models either fail to comprehend complex intentions or make errors when dealing with multiple objects. To address these challenges, we present an innovative image editing framework that employs Chain-of-Thought (CoT) reasoning and localizing capabilities of multimodal Large Language Models (LLMs) to assist diffusion models in generating more refined images. We meticulously design a CoT process comprising instruction decomposition, region localization, and detailed description. We train our model to learn the CoT process and the mask of the edited image. By providing diffusion models with generated prompts and generated masks, our model edits images with a superior understanding of instructions. Extensive experiments demonstrate that our model outperforms existing state-of-the-art models in image generation both qualitatively and quantitatively. Notably, our model exhibits an enhanced ability of understanding complex prompts and generating corresponding images.

Single object tracking aims to locate the target object in a video sequence according to the state specified by different modal references, including the initial bounding box (BBOX), natural language (NL), or both (NL+BBOX). Due to the gap between different modalities, most existing trackers are designed for single or partial of these reference settings and overspecialize on the specific modality. Differently, we present a unified tracker called UVLTrack, which can simultaneously handle all three reference settings (BBOX, NL, NL+BBOX) with the same parameters. The proposed UVLTrack enjoys several merits. First, we design a modality-unified feature extractor for joint visual and language feature learning and propose a multi-modal contrastive loss to align the visual and language features into a unified semantic space. Second, a modality-adaptive box head is proposed, which makes full use of the target reference to mine ever-changing scenario features dynamically from video contexts and distinguish the target in a contrastive way, enabling robust performance in different reference settings. Extensive experimental results demonstrate that UVLTrack achieves promising performance on seven visual tracking datasets, three vision-language tracking datasets, and three visual grounding datasets. Codes and models will be open-sourced at https://github.com/OpenSpaceAI/UVLTrack.

Incremental object detection (IOD) task requires a model to learn continually from newly added data. However, directly fine-tuning a well-trained detection model on a new task will sharply decrease the performance on old tasks, which is known as catastrophic forgetting. Knowledge distillation, including feature distillation and response distillation, has been proven to be an effective way to alleviate catastrophic forgetting. However, previous works on feature distillation heavily rely on low-level feature information, while under-exploring the importance of high-level semantic information. In this paper, we discuss the cause of catastrophic forgetting in IOD task as destruction of semantic feature space. We propose a method that dynamically distills both semantic and feature information with consideration of both between-class discriminativeness and within-class consistency on Transformer-based detector. Between-class discriminativeness is preserved by distilling class-level semantic distance and feature distance among various categories, while within-class consistency is preserved by distilling instance-level semantic information and feature information within each category. Extensive experiments are conducted on both Pascal VOC and MS COCO benchmarks. Our method outperforms all the previous CNN-based SOTA methods under various experimental scenarios, with a remarkable mAP improvement from 36.90% to 39.80% under one-step IOD task.