Differential Contrastive Training for Gaze Estimation

• URL: http://arxiv.org/abs/2502.20128v2
• Date: Fri, 18 Apr 2025 06:28:11 GMT
• Title: Differential Contrastive Training for Gaze Estimation
• Authors: Lin Zhang, Yi Tian, XiYun Wang, Wanru Xu, Yi Jin, Yaping Huang,
• Abstract summary: We propose a novel Differential Contrastive Training strategy, which boosts gaze estimation performance with the help of the CLIP.<n>A Differential Contrastive Gaze Estimation network (DCGaze) composed of a Visual Appearance-aware branch and a Semantic Differential-aware branch is introduced.
• Score: 24.53837441433775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The complex application scenarios have raised critical requirements for precise and generalizable gaze estimation methods. Recently, the pre-trained CLIP has achieved remarkable performance on various vision tasks, but its potentials have not been fully exploited in gaze estimation. In this paper, we propose a novel Differential Contrastive Training strategy, which boosts gaze estimation performance with the help of the CLIP. Accordingly, a Differential Contrastive Gaze Estimation network (DCGaze) composed of a Visual Appearance-aware branch and a Semantic Differential-aware branch is introduced. The Visual Appearance-aware branch is essentially a primary gaze estimation network and it incorporates an Adaptive Feature-refinement Unit (AFU) and a Double-head Gaze Regressor (DGR), which both help the primary network to extract informative and gaze-related appearance features. Moreover, the Semantic Difference-aware branch is designed on the basis of the CLIP's text encoder to reveal the semantic difference of gazes. This branch could further empower the Visual Appearance-aware branch with the capability of characterizing the gaze-related semantic information. Extensive experimental results on four challenging datasets over within and cross-domain tasks demonstrate the effectiveness of our DCGaze. Code will be available upon acceptance.

Related papers

• "Principal Components" Enable A New Language of Images [79.45806370905775]
  We introduce a novel visual tokenization framework that embeds a provable PCA-like structure into the latent token space. Our approach achieves state-of-the-art reconstruction performance and enables better interpretability to align with the human vision system.
  arXiv  Detail & Related papers  (2025-03-11T17:59:41Z)
• Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding [94.64781599202882]
  Vision Language Models (VLMs) have achieved remarkable progress in multimodal tasks. They often struggle with visual arithmetic, seemingly simple capabilities like object counting or length comparison. We propose CogAlign, a novel post-training strategy inspired by Piaget's theory of cognitive development.
  arXiv  Detail & Related papers  (2025-02-17T06:54:49Z)
• Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models [50.98559225639266]
  We investigate the contributions of visual features from different encoder layers using 18 benchmarks spanning 6 task categories.<n>Our findings reveal that multilayer features provide complementary strengths with varying task dependencies, and uniform fusion leads to suboptimal performance.<n>We propose the instruction-guided vision aggregator, a module that dynamically integrates multi-layer visual features based on textual instructions.
  arXiv  Detail & Related papers  (2024-12-26T05:41:31Z)
• Gaze-LLE: Gaze Target Estimation via Large-Scale Learned Encoders [33.26237143983192]
  We address the problem of gaze target estimation, which aims to predict where a person is looking in a scene. We propose Gaze-LLE, a novel transformer framework that streamlines gaze target estimation by leveraging features from a frozen DINOv2 encoder.
  arXiv  Detail & Related papers  (2024-12-12T18:55:30Z)
• CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing [66.6712018832575]
  Domain generalization (DG) based Face Anti-Spoofing (FAS) aims to improve the model's performance on unseen domains. We make use of large-scale VLMs like CLIP and leverage the textual feature to dynamically adjust the classifier's weights for exploring generalizable visual features.
  arXiv  Detail & Related papers  (2024-03-21T11:58:50Z)
• CLIP-Gaze: Towards General Gaze Estimation via Visual-Linguistic Model [13.890404285565225]
  We propose a novel framework called CLIP-Gaze that utilizes a pre-trained vision-language model to leverage its transferable knowledge. Our framework is the first to leverage the vision-and-language cross-modality approach for gaze estimation task.
  arXiv  Detail & Related papers  (2024-03-08T07:37:21Z)
• UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding [90.74967596080982]
  This paper extends Contrastive Language-Image Pre-training (CLIP) with multi-granularity alignment. We develop a Unified Multi-Granularity learning framework, termed UMG-CLIP, which simultaneously empowers the model with versatile perception abilities. With parameter efficient tuning, UMG-CLIP surpasses current widely used CLIP variants and achieves state-of-the-art performance on diverse image understanding benchmarks.
  arXiv  Detail & Related papers  (2024-01-12T06:35:09Z)
• GazeCLIP: Towards Enhancing Gaze Estimation via Text Guidance [9.639618473371083]
  Existing gaze estimation approaches overlook the rich semantic cues conveyed by linguistic signals and the priors embedded in CLIP feature space. Specifically, we intricately design a linguistic description generator to produce text signals with coarse directional cues. This is followed by the implementation of a fine-grained multi-modal fusion module aimed at modeling in image estimations between heterogeneous inputs.
  arXiv  Detail & Related papers  (2023-12-30T15:24:50Z)
• Harnessing Diffusion Models for Visual Perception with Meta Prompts [68.78938846041767]
  We propose a simple yet effective scheme to harness a diffusion model for visual perception tasks. We introduce learnable embeddings (meta prompts) to the pre-trained diffusion models to extract proper features for perception. Our approach achieves new performance records in depth estimation tasks on NYU depth V2 and KITTI, and in semantic segmentation task on CityScapes.
  arXiv  Detail & Related papers  (2023-12-22T14:40:55Z)
• MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition [41.78245303513613]
  We introduce MA-FSAR, a framework that employs the Fine-Tuning (PEFT) technique to enhance the CLIP visual encoder in terms of action-related temporal and semantic representations. In addition to these token-level designs, we propose a prototype-level text-guided construction module to further enrich the temporal and semantic characteristics of video prototypes.
  arXiv  Detail & Related papers  (2023-08-03T04:17:25Z)
• ComPtr: Towards Diverse Bi-source Dense Prediction Tasks via A Simple yet General Complementary Transformer [91.43066633305662]
  We propose a novel underlineComPlementary underlinetransformer, textbfComPtr, for diverse bi-source dense prediction tasks. ComPtr treats different inputs equally and builds an efficient dense interaction model in the form of sequence-to-sequence on top of the transformer.
  arXiv  Detail & Related papers  (2023-07-23T15:17:45Z)
• Contrastive Representation Learning for Gaze Estimation [8.121462458089143]
  We propose a contrastive representation learning framework for gaze estimation, named Gaze Contrastive Learning (GazeCLR) Our results show that GazeCLR improves the performance of cross-domain gaze estimation and yields as high as 17.2% relative improvement. The GazeCLR framework is competitive with state-of-the-art representation learning methods for few-shot evaluation.
  arXiv  Detail & Related papers  (2022-10-24T17:01:18Z)
• LatentGaze: Cross-Domain Gaze Estimation through Gaze-Aware Analytic Latent Code Manipulation [0.0]
  We propose a gaze-aware analytic manipulation method, based on a data-driven approach with generative adversarial network inversion's disentanglement characteristics. By utilizing GAN-based encoder-generator process, we shift the input image from the target domain to the source domain image, which a gaze estimator is sufficiently aware.
  arXiv  Detail & Related papers  (2022-09-21T08:05:53Z)
• Consistency Regularization for Deep Face Anti-Spoofing [69.70647782777051]
  Face anti-spoofing (FAS) plays a crucial role in securing face recognition systems. Motivated by this exciting observation, we conjecture that encouraging feature consistency of different views may be a promising way to boost FAS models. We enhance both Embedding-level and Prediction-level Consistency Regularization (EPCR) in FAS.
  arXiv  Detail & Related papers  (2021-11-24T08:03:48Z)
• Deep Attention-guided Graph Clustering with Dual Self-supervision [49.040136530379094]
  We propose a novel method, namely deep attention-guided graph clustering with dual self-supervision (DAGC) We develop a dual self-supervision solution consisting of a soft self-supervision strategy with a triplet Kullback-Leibler divergence loss and a hard self-supervision strategy with a pseudo supervision loss. Our method consistently outperforms state-of-the-art methods on six benchmark datasets.
  arXiv  Detail & Related papers  (2021-11-10T06:53:03Z)
• Weakly-Supervised Physically Unconstrained Gaze Estimation [80.66438763587904]
  We tackle the previously unexplored problem of weakly-supervised gaze estimation from videos of human interactions. We propose a training algorithm along with several novel loss functions especially designed for the task. We show significant improvements in (a) the accuracy of semi-supervised gaze estimation and (b) cross-domain generalization on the state-of-the-art physically unconstrained in-the-wild Gaze360 gaze estimation benchmark.
  arXiv  Detail & Related papers  (2021-05-20T14:58:52Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)

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.