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Bowei Zhang

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5 papers

1 author row

AAAI Conference 2026 Conference Paper

ModalSyncSum: Synchronizing Image and Text for Reliable Summary Generation

Xuanqi Chen
Ziying Rong
Xinfeng Liao
Yiqian Wu
Bowei Zhang
Pengfei Fu
Shengyi Jiang

Multimodal summarization with multimodal output (MSMO) aims to generate coherent textual summaries while selecting the most semantically relevant images to enhance expressiveness. Despite the advancements of large multimodal models like GPT-4o, LLaMA-3, and Grok-3, these models often exhibit hallucination and weak visual-text alignment when applied to MSMO tasks. To address these challenges, we propose ModalSyncSum, a unified framework that enhances semantic consistency and visual faithfulness. It incorporates image-aware information extraction to mitigate visual-text misalignment, QA-based description verification to detect and correct hallucinated image descriptions, and named entity-guided refinement to ensure factual accuracy and entity alignment across modalities. Furthermore, we introduce a new evaluation metric M3AS, which jointly considers image content coverage, text-image alignment, and summary consistency, filling the gap in evaluating multimodal summary quality. Experimental results show that our model outperforms prompt-based baselines across multiple datasets, achieving significant gains on ROUGE, BLEU, and BERTScore, with BLEU improving by 21.95%. In human evaluation, M3AS exhibits stronger correlation with human judgments in consistency, image-summary relevance, and focus, surpassing existing automatic metrics.

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NeurIPS Conference 2025 Conference Paper

Depth-Supervised Fusion Network for Seamless-Free Image Stitching

Zhiying Jiang
Ruhao Yan
Zengxi Zhang
Bowei Zhang
Jinyuan Liu

Image stitching synthesizes images captured from multiple perspectives into a single image with a broader field of view. The significant variations in object depth often lead to large parallax, resulting in ghosting and misalignment in the stitched results. To address this, we propose a depth-consistency-constrained seamless-free image stitching method. First, to tackle the multi-view alignment difficulties caused by parallax, a multi-stage mechanism combined with global depth regularization constraints is developed to enhance the alignment accuracy of the same apparent target across different depth ranges. Second, during the multi-view image fusion process, an optimal stitching seam is determined through graph-based low-cost computation, and a soft-seam region is diffused to precisely locate transition areas, thereby effectively mitigating alignment errors induced by parallax and achieving natural and seamless stitching results. Furthermore, considering the computational overhead in the shift regression process, a reparameterization strategy is incorporated to optimize the structural design, significantly improving algorithm efficiency while maintaining optimal performance. Extensive experiments demonstrate the superior performance of the proposed method against the existing methods. Code is available at https: //github. com/DLUT-YRH/DSFN.

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NeurIPS Conference 2025 Conference Paper

FEEDBACK FRICTION: LLMs Struggle to Fully Incorporate External Feedback

Dongwei Jiang
Bowei Zhang
Andrew Wang
Nicholas Andrews
Daniel Khashabi

Recent studies have shown LLMs possess some ability to improve their responses when given external feedback. However, it remains unclear how effectively and thoroughly these models can incorporate extrinsic feedback. In an ideal scenario, if LLMs receive near-perfect and complete feedback, we would expect them to fully integrate the feedback and reach correct solutions. In this paper, we systematically investigate LLMs’ ability to incorporate feedback by designing a controlled experimental environment. For each problem, a solver model attempts a solution, then a feedback generator with access to near-complete ground-truth answers produces targeted feedback, after which the solver tries again. We evaluate this pipeline across a diverse range of tasks, including math reasoning, knowledge reasoning, scientific reasoning, and general multi-domain evaluations with state-of-the-art language models including Claude 3. 7 with extended thinking. Surprisingly, even under these near-ideal conditions, solver models consistently show resistance to feedback, a limitation that we term FEEDBACK FRICTION. To mitigate this limitation, we experiment with sampling-based strategies like progressive temperature increases and explicit rejection of previously attempted incorrect answers, which yield improvements but still fail to help models achieve target performance. We analyze FEEDBACK FRICTION and find that models’ confidence on specific questions, measured by semantic entropy, predicts feedback resistance: high-confidence predictions remain resistant to external correction. We hope that highlighting this issue in LLMs will help future research in self-improvement.

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NeurIPS Conference 2025 Conference Paper

TAPIP3D: Tracking Any Point in Persistent 3D Geometry

Bowei Zhang
Lei Ke
Adam Harley
Katerina Fragkiadaki

We introduce TAPIP3D, a novel approach for long-term 3D point tracking in monocular RGB and RGB-D videos. TAPIP3D represents videos as camera-stabilized spatio-temporal feature clouds, leveraging depth and camera motion information to lift 2D video features into a 3D world space where camera movement is effectively canceled out. Within this stabilized 3D representation, TAPIP3D iteratively refines multi-frame motion estimates, enabling robust point tracking over long time horizons. To handle the irregular structure of 3D point distributions, we propose a 3D Neighborhood-to-Neighborhood (N2N) attention mechanism—a 3D-aware contextualization strategy that builds informative, spatially coherent feature neighborhoods to support precise trajectory estimation. Our 3D-centric formulation significantly improves performance over existing 3D point tracking methods and even surpasses state-of-the-art 2D pixel trackers in accuracy when reliable depth is available. The model supports inference in both camera-centric (unstabilized) and world-centric (stabilized) coordinates, with experiments showing that compensating for camera motion leads to substantial gains in tracking robustness. By replacing the conventional 2D square correlation windows used in prior 2D and 3D trackers with a spatially grounded 3D attention mechanism, TAPIP3D achieves strong and consistent results across multiple 3D point tracking benchmarks. Our code and trained checkpoints will be public.

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TCS Journal 2018 Journal Article

Near-linear time approximation schemes for geometric maximum coverage

Kai Jin
Jian Li
Haitao Wang
Bowei Zhang
Ningye Zhang

We study approximation algorithms for the following geometric version of the maximum coverage problem: Let P be a set of n weighted points in the plane. Let D represent a planar object, such as a rectangle, or a disk. We want to place m copies of D such that the sum of the weights of the points in P covered by these copies is maximized. For any fixed ε > 0, we present efficient approximation schemes that can find a ( 1 − ε ) -approximation to the optimal solution. In particular, for m = 1 and for the special case where D is a rectangle, our algorithm runs in time O ( n log ⁡ ( 1 ε ) ), improving on the previous result. For m > 1 and the rectangular case, our algorithm runs in O ( n ε log ⁡ ( 1 ε ) + m ε log ⁡ m + m ( 1 ε ) O ( min ⁡ ( m, 1 ε ) ) ) time. For a more general class of shapes (including disks, polygons with O ( 1 ) edges), our algorithm runs in O ( n ( 1 ε ) O ( 1 ) + m ϵ log ⁡ m + m ( 1 ε ) O ( min ⁡ ( m, 1 ε 2 ) ) ) time.

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