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Works88 from public data
- Self-supervised Video Representation Learning by Pace Prediction268
This paper addresses the problem of self-supervised video representation learning from a new perspective -- by video pace prediction -- by introducing contrastive learning to push the model towards discriminating different paces by maximizing the agreement on similar video content.
- Self-Supervised Spatio-Temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics226
This paper proposes to learn visual features by regressing both motion and appearance statistics along spatial and temporal dimensions, given only the input video data, and shows that the approach can significantly improve the performance of C3D when applied to video classification tasks.
- Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss224
The long tail property is investigated and an attention-driven loss for the network supervision is proposed and a synergy network is proposed to automatically learn the information sharing strategies between the two tasks to better leverage the semantic information for monocular depth estimation.
- Connecting Image Denoising and High-Level Vision Tasks via Deep Learning204
A convolutional neural network in which convolutions are conducted in various spatial resolutions via downsampling and upsampling operations in order to fuse and exploit contextual information on different scales and the benefit of exploiting image semantics simultaneously for image denoising and high-level vision tasks via deep learning is demonstrated.
- When AWGN-Based Denoiser Meets Real Noises176
This paper proposes a novel approach to boost the performance of a real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN, and investigates Pixel-shuffle Down-sampling strategy to adapt the trained model to real noises.
- Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation156
This paper proposes affinity attention graph neural network, in which an affinity attention layer is designed to acquire the short- and long- distance information from soft graph edges to accurately propagate semantic labels from the confident seeds to the unlabeled pixels.
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- A hand pose tracking benchmark from stereo matching99
A novel stereo-based hand segmentation and depth estimation algorithm specially tailored for hand tracking here and the experiments indicate the effectiveness of the proposed algorithm by demonstrating that its tracking performance is comparable to the use of an active depth sensor under various of challenging scenarios.
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- Self-Supervised Representation Learning for Ultrasound Video67
This paper proposes a self-supervised learning approach to learn meaningful and transferable representations from medical imaging video without any type of human annotation, and forces the model to address anatomy-aware tasks with free supervision from the data itself.
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- FreMIM: Fourier Transform Meets Masked Image Modeling for Medical Image Segmentation61
A new MIM-based framework named FreMIM is presented for self-supervised pre-training to better accomplish medical image segmentation tasks and incorporates multi-stage supervision to guide the representation learning during the pre-training phase.
- Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics61
This paper proposes a novel pretext task to address the self-supervised video representation learning problem, inspired by the observation that human visual system is sensitive to rapidly changing contents in the visual field, and only needs impressions about rough spatial locations to understand the visual contents.
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- Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation55
The proposed MicroSeg is based on the assumption that background regions with strong objectness possibly belong to those concepts in the historical or future stages, and first splits the given image into hundreds of segment proposals with a proposal generator, to avoid forgetting old knowledge at the current training stage.
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- Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis52
The proposed model is end-to-end trainable and self-supervised without any external annotations, and is evaluated quantitatively and qualitatively with comparison to real fetal MR images and other approaches to synthesis, demonstrating its feasibility of synthesising realistic MR images.
- Self-Supervised Contrastive Video-Speech Representation Learning for Ultrasound46
This paper designed a framework to model the correspondence between video and audio without any kind of human annotations, and introduces cross-modal contrastive learning and an affinity-aware self-paced learning scheme to enhance correlation modelling.
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- Task-Driven Webpage Saliency44
An end-to-end learning framework for predicting task-driven visual saliency on webpages using a convolutional neural network to predict where people look at it under different task conditions, achieving state-of-the-art performance on a newly collected benchmark dataset.
- $360+x$: A Panoptic Multi-modal Scene Understanding Dataset43
This dataset offers a panoptic perspective that encapsulate third-person panoramic and front views, as well as egocentric monocular/binocular views with rich modalities including video, multi-channel audio, directional binaural delay, location data and textual scene descriptions within each scene captured, presenting comprehensive observation of the world.
- Frame Interpolation with Consecutive Brownian Bridge Diffusion31
This work proposes consecutive Brownian Bridge diffusion that takes a deterministic initial value as input, resulting in a much smaller cumulative variance of generated latent representations, leaving strong potential for further enhancement in VFI.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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