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Shaoxiang Chen

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6 papers

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NeurIPS Conference 2024 Conference Paper

ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model

Yiming Sun
Fan Yu
Shaoxiang Chen
Yu Zhang
Junwei Huang
Yang Li
Chenhui Li
Changbo Wang

Visual object tracking aims to locate a targeted object in a video sequence based on an initial bounding box. Recently, Vision-Language~(VL) trackers have proposed to utilize additional natural language descriptions to enhance versatility in various applications. However, VL trackers are still inferior to State-of-The-Art (SoTA) visual trackers in terms of tracking performance. We found that this inferiority primarily results from their heavy reliance on manual textual annotations, which include the frequent provision of ambiguous language descriptions. In this paper, we propose ChatTracker to leverage the wealth of world knowledge in the Multimodal Large Language Model (MLLM) to generate high-quality language descriptions and enhance tracking performance. To this end, we propose a novel reflection-based prompt optimization module to iteratively refine the ambiguous and inaccurate descriptions of the target with tracking feedback. To further utilize semantic information produced by MLLM, a simple yet effective VL tracking framework is proposed and can be easily integrated as a plug-and-play module to boost the performance of both VL and visual trackers. Experimental results show that our proposed ChatTracker achieves a performance comparable to existing methods.

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AAAI Conference 2024 Conference Paper

Instance-Aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning

Yang Jiao
Zequn Jie
Shaoxiang Chen
Lechao Cheng
Jingjing Chen
Lin Ma
Yu-Gang Jiang

Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field. Under such a paradigm, accurate BEV representation construction relies on reliable depth estimation for multi-camera images. However, existing approaches exhaustively predict depths for every pixel without prioritizing objects, which are precisely the entities requiring detection in the 3D space. To this end, we propose IA-BEV, which integrates image-plane instance awareness into the depth estimation process within a BEV-based detector. First, a category-specific structural priors mining approach is proposed for enhancing the efficacy of monocular depth generation. Besides, a self-boosting learning strategy is further proposed to encourage the model to place more emphasis on challenging objects in computation-expensive temporal stereo matching. Together they provide advanced depth estimation results for high-quality BEV features construction, benefiting the ultimate 3D detection. The proposed method achieves state-of-the-art performances on the challenging nuScenes benchmark, and extensive experimental results demonstrate the effectiveness of our designs.

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NeurIPS Conference 2024 Conference Paper

Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models

Yang Jiao
Shaoxiang Chen
Zequn Jie
Jingjing Chen
Lin Ma
Yu-Gang Jiang

Large Multimodal Model (LMM) is a hot research topic in the computer vision area and has also demonstrated remarkable potential across multiple disciplinary fields. A recent trend is to further extend and enhance the perception capabilities of LMMs. The current methods follow the paradigm of adapting the visual task outputs to the format of the language model, which is the main component of a LMM. This adaptation leads to convenient development of such LMMs with minimal modifications, however, it overlooks the intrinsic characteristics of diverse visual tasks and hinders the learning of perception capabilities. To address this issue, we propose a novel LMM architecture named Lumen, a Large multimodal model with versatile vision-centric capability enhancement. We decouple the LMM's learning of perception capabilities into task-agnostic and task-specific stages. Lumen first promotes fine-grained vision-language concept alignment, which is the fundamental capability for various visual tasks. Thus the output of the task-agnostic stage is a shared representation for all the tasks we address in this paper. Then the task-specific decoding is carried out by flexibly routing the shared representation to lightweight task decoders with negligible training efforts. Comprehensive experimental results on a series of vision-centric and VQA benchmarks indicate that our Lumen model not only achieves or surpasses the performance of existing LMM-based approaches in a range of vision-centric tasks while maintaining general visual understanding and instruction following capabilities.

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IJCAI Conference 2019 Conference Paper

Deep Learning for Video Captioning: A Review

Shaoxiang Chen
Ting Yao
Yu-Gang Jiang

Deep learning has achieved great successes in solving specific artificial intelligence problems recently. Substantial progresses are made on Computer Vision (CV) and Natural Language Processing (NLP). As a connection between the two worlds of vision and language, video captioning is the task of producing a natural-language utterance (usually a sentence) that describes the visual content of a video. The task is naturally decomposed into two sub-tasks. One is to encode a video via a thorough understanding and learn visual representation. The other is caption generation, which decodes the learned representation into a sequential sentence, word by word. In this survey, we first formulate the problem of video captioning, then review state-of-the-art methods categorized by their emphasis on vision or language, and followed by a summary of standard datasets and representative approaches. Finally, we highlight the challenges which are not yet fully understood in this task and present future research directions.

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AAAI Conference 2019 Conference Paper

Motion Guided Spatial Attention for Video Captioning

Shaoxiang Chen
Yu-Gang Jiang

Sequence-to-sequence models incorporated with attention mechanism have shown promising improvements on video captioning. While there is rich information both inside and between frames, spatial attention is rarely explored and motion information is usually handled by 3D-CNNs as just another modality for fusion. On the other hand, researches about human perception suggest that apparent motion can attract attention. Motivated by this, we aim to learn spatial attention on video frames under the guidance of motion information for caption generation. We present a novel video captioning framework by utilizing Motion Guided Spatial Attention (MGSA). The proposed MGSA exploits the motion between video frames by learning spatial attention from stacked optical flow images with a custom CNN. To further relate the spatial attention maps of video frames, we designed a Gated Attention Recurrent Unit (GARU) to adaptively incorporate previous attention maps. The whole framework can be trained in an end-to-end manner. We evaluate our approach on two benchmark datasets, MSVD and MSR-VTT. The experiments show that our designed model can generate better video representation and state of the art results are obtained under popular evaluation metrics such as BLEU@4, CIDEr, and ME- TEOR.

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AAAI Conference 2019 Conference Paper

Semantic Proposal for Activity Localization in Videos via Sentence Query

Shaoxiang Chen
Yu-Gang Jiang

This paper presents an efficient algorithm to tackle temporal localization of activities in videos via sentence queries. The task differs from traditional action localization in three aspects: (1) Activities are combinations of various kinds of actions and may span a long period of time. (2) Sentence queries are not limited to a predefined list of classes. (3) The videos usually contain multiple different activity instances. Traditional proposal-based approaches for action localization that only consider the class-agnostic “actionness” of video snippets are insufficient to tackle this task. We propose a novel Semantic Activity Proposal (SAP) which integrates the semantic information of sentence queries into the proposal generation process to get discriminative activity proposals. Visual and semantic information are jointly utilized for proposal ranking and refinement. We evaluate our algorithm on the TACoS dataset and the Charades-STA dataset. Experimental results show that our algorithm outperforms existing methods on both datasets, and at the same time reduces the number of proposals by a factor of at least 10.

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