Visual Grounding with Attention-Driven Constraint Balancing

• URL: http://arxiv.org/abs/2407.03243v2
• Date: Sat, 6 Jul 2024 15:22:24 GMT
• Title: Visual Grounding with Attention-Driven Constraint Balancing
• Authors: Weitai Kang, Luowei Zhou, Junyi Wu, Changchang Sun, Yan Yan,
• Abstract summary: We propose Attention-Driven Constraint Balancing (AttBalance) to optimize the behavior of visual features within language-relevant regions. We achieve constant improvements over five different models evaluated on four different benchmarks. We attain a new state-of-the-art performance by integrating our method into QRNet.
• Score: 19.30650183073788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unlike Object Detection, Visual Grounding task necessitates the detection of an object described by complex free-form language. To simultaneously model such complex semantic and visual representations, recent state-of-the-art studies adopt transformer-based models to fuse features from both modalities, further introducing various modules that modulate visual features to align with the language expressions and eliminate the irrelevant redundant information. However, their loss function, still adopting common Object Detection losses, solely governs the bounding box regression output, failing to fully optimize for the above objectives. To tackle this problem, in this paper, we first analyze the attention mechanisms of transformer-based models. Building upon this, we further propose a novel framework named Attention-Driven Constraint Balancing (AttBalance) to optimize the behavior of visual features within language-relevant regions. Extensive experimental results show that our method brings impressive improvements. Specifically, we achieve constant improvements over five different models evaluated on four different benchmarks. Moreover, we attain a new state-of-the-art performance by integrating our method into QRNet.

Related papers

• A Lightweight Modular Framework for Low-Cost Open-Vocabulary Object Detection Training [0.07499722271664146]
  We introduce a lightweight framework that significantly reduces the number of parameters while preserving, or even improving, performance. Our solution is applied to MDETR, resulting in the development of Lightweight MDETR (LightMDETR), an optimized version of MDETR. LightMDETR not only reduces computational costs but also outperforms several state-of-the-art methods in terms of accuracy.
  arXiv  Detail & Related papers  (2024-08-20T12:27:53Z)
• A Simple Background Augmentation Method for Object Detection with Diffusion Model [53.32935683257045]
  In computer vision, it is well-known that a lack of data diversity will impair model performance. We propose a simple yet effective data augmentation approach by leveraging advancements in generative models. Background augmentation, in particular, significantly improves the models' robustness and generalization capabilities.
  arXiv  Detail & Related papers  (2024-08-01T07:40:00Z)
• Semantic Object-level Modeling for Robust Visual Camera Relocalization [14.998133272060695]
  We propose a novel method of automatic object-level voxel modeling for accurate ellipsoidal representations of objects. All of these modules are entirely intergrated into visual SLAM system.
  arXiv  Detail & Related papers  (2024-02-10T13:39:44Z)
• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• Bidirectional Correlation-Driven Inter-Frame Interaction Transformer for Referring Video Object Segmentation [44.952526831843386]
  We propose a correlation-driven inter-frame interaction Transformer, dubbed BIFIT, to address these issues in RVOS. Specifically, we design a lightweight plug-and-play inter-frame interaction module in the decoder. A vision-ferring interaction is implemented before the Transformer to facilitate the correlation between the visual and linguistic features.
  arXiv  Detail & Related papers  (2023-07-02T10:29:35Z)
• Part-guided Relational Transformers for Fine-grained Visual Recognition [59.20531172172135]
  We propose a framework to learn the discriminative part features and explore correlations with a feature transformation module. Our proposed approach does not rely on additional part branches and reaches state-the-of-art performance on 3-of-the-level object recognition.
  arXiv  Detail & Related papers  (2022-12-28T03:45:56Z)
• Weakly Supervised Video Salient Object Detection [79.51227350937721]
  We present the first weakly supervised video salient object detection model based on relabeled "fixation guided scribble annotations" An "Appearance-motion fusion module" and bidirectional ConvLSTM based framework are proposed to achieve effective multi-modal learning and long-term temporal context modeling.
  arXiv  Detail & Related papers  (2021-04-06T09:48:38Z)
• Progressive Self-Guided Loss for Salient Object Detection [102.35488902433896]
  We present a progressive self-guided loss function to facilitate deep learning-based salient object detection in images. Our framework takes advantage of adaptively aggregated multi-scale features to locate and detect salient objects effectively.
  arXiv  Detail & Related papers  (2021-01-07T07:33:38Z)
• Benchmarking Unsupervised Object Representations for Video Sequences [111.81492107649889]
  We compare the perceptual abilities of four object-centric approaches: ViMON, OP3, TBA and SCALOR. Our results suggest that the architectures with unconstrained latent representations learn more powerful representations in terms of object detection, segmentation and tracking. Our benchmark may provide fruitful guidance towards learning more robust object-centric video representations.
  arXiv  Detail & Related papers  (2020-06-12T09:37:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.