# BAM-DETR: Boundary-Aligned Moment Detection Transformer for Temporal Sentence Grounding in Videos

arXiv: https://arxiv.org/abs/2312.00083 · HTML: https://mlsanity.com/paper/2312.00083/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 60 of its 77 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 30 of 60)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Localizing Moments in Video with Natural Language](https://mlsanity.com/paper/1708.01641/) (2017)
- [TALL: Temporal Activity Localization via Language Query](https://mlsanity.com/paper/1705.02101/) (2017)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [Conditional DETR for Fast Training Convergence](https://mlsanity.com/paper/2108.06152/) (2021)
- [Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language](https://mlsanity.com/paper/1912.03590/) (2019)
- [BSN: Boundary Sensitive Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1806.02964/) (2018)
- [Temporal Action Detection with Structured Segment Networks](https://mlsanity.com/paper/1704.06228/) (2017)
- [Span-based Localizing Network for Natural Language Video Localization](https://mlsanity.com/paper/2004.13931/) (2020)
- [TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval](https://mlsanity.com/paper/2001.09099/) (2020)
- [UniVTG: Towards Unified Video-Language Temporal Grounding](https://mlsanity.com/paper/2307.16715/) (2023)
- [Local-Global Video-Text Interactions for Temporal Grounding](https://mlsanity.com/paper/2004.07514/) (2020)
- [Dense Regression Network for Video Grounding](https://mlsanity.com/paper/2004.03545/) (2020)
- [Fast Convergence of DETR with Spatially Modulated Co-Attention](https://mlsanity.com/paper/2101.07448/) (2021)
- [Query-Dependent Video Representation for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2303.13874/) (2023)
- [To Find Where You Talk: Temporal Sentence Localization in Video with Attention Based Location Regression](https://mlsanity.com/paper/1804.07014/) (2018)
- [Rethinking Transformer-based Set Prediction for Object Detection](https://mlsanity.com/paper/2011.10881/) (2020)
- [Learning Salient Boundary Feature for Anchor-free Temporal Action Localization](https://mlsanity.com/paper/2103.13137/) (2021)

## Built on by (most cited first, 29 of 29)

- [Saliency-Guided DETR for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2410.01615/) (2024)
- [Chrono: A Simple Blueprint for Representing Time in MLLMs](https://mlsanity.com/paper/2406.18113/) (2024)
- [Enrich and Detect: Video Temporal Grounding with Multimodal LLMs](https://mlsanity.com/paper/2510.17023/) (2025)
- [Moment of Untruth: Dealing with Negative Queries in Video Moment Retrieval](https://mlsanity.com/paper/2502.08544/) (2025)
- [LD-DETR: Loop Decoder DEtection TRansformer for Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2501.10787/) (2025)
- [Vid-Morp: Video Moment Retrieval Pretraining from Unlabeled Videos in the Wild](https://mlsanity.com/paper/2412.00811/) (2024)
- [Moment Sampling in Video LLMs for Long-Form Video QA](https://mlsanity.com/paper/2507.00033/) (2025)
- [Moment Quantization for Video Temporal Grounding](https://mlsanity.com/paper/2504.02286/) (2025)
- [Sim-DETR: Unlock DETR for Temporal Sentence Grounding](https://mlsanity.com/paper/2509.23867/) (2025)
- [Empower Words: DualGround for Structured Phrase and Sentence-Level Temporal Grounding](https://mlsanity.com/paper/2510.20244/) (2025)
- [CVA: Context-aware Video-text Alignment for Video Temporal Grounding](https://mlsanity.com/paper/2603.24934/) (2026)
- [The Devil is in the Spurious Correlations: Boosting Moment Retrieval with Dynamic Learning](https://mlsanity.com/paper/2501.07305/) (2025)
- [TimeLoc: A Unified End-to-End Framework for Precise Timestamp Localization in Long Videos](https://mlsanity.com/paper/2503.06526/) (2025)
- [Sparse-Dense Side-Tuner for efficient Video Temporal Grounding](https://mlsanity.com/paper/2507.07744/) (2025)
- [Audio Does Matter: Importance-Aware Multi-Granularity Fusion for Video Moment Retrieval](https://mlsanity.com/paper/2508.04273/) (2025)
- [Image-to-Video Transfer Learning based on Image-Language Foundation Models: A Comprehensive Survey](https://mlsanity.com/paper/2510.10671/) (2025)
- [Object-Centric Framework for Video Moment Retrieval](https://mlsanity.com/paper/2512.18448/) (2025)
- [Finding Optimal Video Moment without Training: Gaussian Boundary Optimization for Weakly Supervised Video Grounding](https://mlsanity.com/paper/2602.03071/) (2026)
- [MomentMix Augmentation with Length-Aware DETR for Temporally Robust Moment Retrieval](https://mlsanity.com/paper/2412.20816/) (2024)
- [Augmenting Moment Retrieval: Zero-Dependency Two-Stage Learning](https://mlsanity.com/paper/2510.19622/) (2025)
- [See More, Store Less: Memory-Efficient Resolution for Video Moment Retrieval](https://mlsanity.com/paper/2601.09350/) (2026)
- [Length Matters: Length-Aware Transformer for Temporal Sentence Grounding](https://mlsanity.com/paper/2508.04299/) (2025)
- [OVG-HQ: Online Video Grounding with Hybrid-modal Queries](https://mlsanity.com/paper/2508.11903/) (2025)
- [UniversalVTG: A Universal and Lightweight Foundation Model for Video Temporal Grounding](https://mlsanity.com/paper/2604.08522/) (2026)
- [MASRA: MLLM-Assisted Semantic-Relational Consistent Alignment for Video Temporal Grounding](https://mlsanity.com/paper/2605.03398/) (2026)
- [GIRL-DETR: Gradient-Isolated Reinforcement Learning for Video Moment Retrieval](https://mlsanity.com/paper/2606.00775/) (2026)
- [Conformal Coverage Guarantees for Any Video Temporal Grounder](https://mlsanity.com/paper/2608.07434/) (2026)
- [EviDETR: Preserving Query-Relevant Temporal Evidence for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2609.30724/) (2026)
- [Concept Driven Domain Adaptation: Finding an Abstract Needle in a Haystack](https://mlsanity.com/paper/2610.00973/) (2026)

## Abstract (arXiv)

Temporal sentence grounding aims to localize moments relevant to a language description. Recently, DETR-like approaches achieved notable progress by predicting the center and length of a target moment. However, they suffer from the issue of center misalignment raised by the inherent ambiguity of moment centers, leading to inaccurate predictions. To remedy this problem, we propose a novel boundary-oriented moment formulation. In our paradigm, the model no longer needs to find the precise center but instead suffices to predict any anchor point within the interval, from which the boundaries are directly estimated. Based on this idea, we design a boundary-aligned moment detection transformer, equipped with a dual-pathway decoding process. Specifically, it refines the anchor and boundaries within parallel pathways using global and boundary-focused attention, respectively. This separate design allows the model to focus on desirable regions, enabling precise refinement of moment predictions. Further, we propose a quality-based ranking method, ensuring that proposals with high localization qualities are prioritized over incomplete ones. Experiments on three benchmarks validate the effectiveness of the proposed methods. The code is available at https://github.com/Pilhyeon/BAM-DETR.