Visual Alignment Constraint for Continuous Sign Language Recognition

• URL: http://arxiv.org/abs/2104.02330v1
• Date: Tue, 6 Apr 2021 07:24:58 GMT
• Title: Visual Alignment Constraint for Continuous Sign Language Recognition
• Authors: Yuecong Min, Aiming Hao, Xiujuan Chai, Xilin Chen
• Abstract summary: Vision-based Continuous Sign Language Recognition aims to recognize unsegmented gestures from image sequences. In this work, we revisit the overfitting problem in recent CTC-based CSLR works and attribute it to the insufficient training of the feature extractor. We propose a Visual Alignment Constraint (VAC) to enhance the feature extractor with more alignment supervision.
• Score: 74.26707067455837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-based Continuous Sign Language Recognition (CSLR) aims to recognize unsegmented gestures from image sequences. To better train CSLR models, the iterative training scheme is widely adopted to alleviate the overfitting of the alignment model. Although the iterative training scheme can improve performance, it will also increase the training time. In this work, we revisit the overfitting problem in recent CTC-based CSLR works and attribute it to the insufficient training of the feature extractor. To solve this problem, we propose a Visual Alignment Constraint (VAC) to enhance the feature extractor with more alignment supervision. Specifically, the proposed VAC is composed of two auxiliary losses: one makes predictions based on visual features only, and the other aligns short-term visual and long-term contextual features. Moreover, we further propose two metrics to evaluate the contributions of the feature extractor and the alignment model, which provide evidence for the overfitting problem. The proposed VAC achieves competitive performance on two challenging CSLR datasets and experimental results show its effectiveness.

Related papers

• Continual Panoptic Perception: Towards Multi-modal Incremental Interpretation of Remote Sensing Images [16.0258685984844]
  Continual learning (CL) breaks off the one-way training manner and enables a model to adapt to new data, semantics and tasks continuously. We propose a unified continual learning model that leverages multi-task joint learning covering pixel-level classification, instance-level segmentation and image-level perception.
  arXiv  Detail & Related papers  (2024-07-19T12:22:32Z)
• Visual Prompt Tuning in Null Space for Continual Learning [51.96411454304625]
  Existing prompt-tuning methods have demonstrated impressive performances in continual learning (CL) This paper aims to learn each task by tuning the prompts in the direction orthogonal to the subspace spanned by previous tasks' features. In practice, an effective null-space-based approximation solution has been proposed to implement the prompt gradient projection.
  arXiv  Detail & Related papers  (2024-06-09T05:57:40Z)
• Calibrated Self-Rewarding Vision Language Models [27.686545023186852]
  Large Vision-Language Models (LVLMs) have made substantial progress by integrating pre-trained large language models (LLMs) and vision models through instruction tuning. LVLMs often exhibit the hallucination phenomenon, where generated text responses appear linguistically plausible but contradict the input image. We propose the Calibrated Self-Rewarding (CSR) approach, which enables the model to self-improve by iteratively generating candidate responses, evaluating the reward for each response, and curating preference data for fine-tuning.
  arXiv  Detail & Related papers  (2024-05-23T14:30:33Z)
• CLIP Brings Better Features to Visual Aesthetics Learners [12.0962117940694]
  Image aesthetics assessment (IAA) is one of the ideal application scenarios for such methods due to subjective and expensive labeling procedure. In this work, an unified and flexible two-phase textbfCLIP-based textbfSemi-supervised textbfKnowledge textbfDistillation paradigm is proposed, namely textbftextitCSKD.
  arXiv  Detail & Related papers  (2023-07-28T16:00:21Z)
• VIBR: Learning View-Invariant Value Functions for Robust Visual Control [3.2307366446033945]
  VIBR (View-Invariant Bellman Residuals) is a method that combines multi-view training and invariant prediction to reduce out-of-distribution gap for RL based visuomotor control. We show that VIBR outperforms existing methods on complex visuo-motor control environment with high visual perturbation.
  arXiv  Detail & Related papers  (2023-06-14T14:37:34Z)
• CRC-RL: A Novel Visual Feature Representation Architecture for Unsupervised Reinforcement Learning [7.4010632660248765]
  A novel architecture is proposed that uses a heterogeneous loss function, called CRC loss, to learn improved visual features. The proposed architecture, called CRC-RL, is shown to outperform the existing state-of-the-art methods on the challenging Deep mind control suite environments.
  arXiv  Detail & Related papers  (2023-01-31T08:41:18Z)
• Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models [108.13378788663196]
  We propose Subspace Prompt Tuning (SubPT) to project the gradients in back-propagation onto the low-rank subspace spanned by the early-stage gradient flow eigenvectors during the entire training process. We equip CoOp with Novel Learner Feature (NFL) to enhance the generalization ability of the learned prompts onto novel categories beyond the training set.
  arXiv  Detail & Related papers  (2022-11-04T02:06:22Z)
• Visual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problem [60.0878532426877]
  We propose a novel collaborative learning scheme from the viewpoint of visual perturbation calibration. Specifically, we devise a visual controller to construct two sorts of curated images with different perturbation extents. The experimental results on two diagnostic VQA-CP benchmark datasets evidently demonstrate its effectiveness.
  arXiv  Detail & Related papers  (2022-07-24T23:50:52Z)
• Prompt-based Learning for Unpaired Image Captioning [86.44188293709307]
  Unpaired Image Captioning (UIC) has been developed to learn image descriptions from unaligned vision-language sample pairs. Recent successes of Vision-Language Pre-Trained Models (VL-PTMs) have triggered the development of prompt-based learning. We present in this paper a novel scheme based on prompt to train the UIC model, making best use of the powerful generalization ability.
  arXiv  Detail & Related papers  (2022-05-26T03:13:43Z)
• Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation [87.1188556802942]
  We present an approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting. We propose a novel Cross-Task Relation Layer (CTRL), which encodes task dependencies between the semantic and depth predictions. Furthermore, we propose an Iterative Self-Learning (ISL) training scheme, which exploits semantic pseudo-labels to provide extra supervision on the target domain.
  arXiv  Detail & Related papers  (2021-05-17T13:42:09Z)

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.