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- Recent advances in convolutional neural networks6,348
This paper details the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation, and introduces various applications of convolutional neural networks in computer vision, speech and natural language processing.
- When Gaussian Process Meets Big Data: A Review of Scalable GPs920
This article is devoted to reviewing state-of-the-art scalable GPs involving two main categories: global approximations that distillate the entire data and local approximation that divide the data for subspace learning.
- Auto-Encoding Scene Graphs for Image Captioning857
This work proposes Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions and validates the effectiveness of SGAE on the challenging MS-COCO image captioned benchmark.
- MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-view Images656
MVSplat is introduced, an efficient model that, given sparse multi-view images as input, predicts clean feed-forward 3D Gaussians and builds a cost volume representation via plane sweeping, where the cross-view feature similarities stored in the cost volume can provide valuable geometry cues to the estimation of depth.
- GMFlow: Learning Optical Flow via Global Matching583
A GMFlow framework is proposed, which consists of three main components: a customized Transformer for feature enhancement, a correlation and softmax layer for global feature matching, and a self-attention layer for flow propagation, which outperforms 31-refinements RAFT on the challenging Sintel benchmark, while using only one refinement and running faster.
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- Pluralistic Image Completion539
This paper proposes a novel and probabilistically principled framework with two parallel paths for pluralistic image completion that utilizes the only one given ground truth to get prior distribution of missing parts and rebuild the original image from this distribution.
- 3D Hand Shape and Pose Estimation From a Single RGB Image517
This work proposes a Graph Convolutional Neural Network (Graph CNN) based method to reconstruct a full 3D mesh of hand surface that contains richer information of both 3D hand shape and pose and proposes a weakly-supervised approach by leveraging the depth map as a weak supervision in training.
- Mutual consistency learning for semi-supervised medical image segmentation457
A novel mutual consistency network (MC-Net+) is proposed to effectively exploit the unlabeled data for semi-supervised medical image segmentation to force the model to generate invariant results in such challenging regions, aiming at regularizing the model training.
- A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design448
This article categorizes, reviews, and analyzes the state-of-the-art single−/multi-response adaptive sampling approaches for global metamodeling in support of simulation-based engineering design and discusses some important issues that affect the success of an adaptive sampling approach.
- Unifying Flow, Stereo and Depth Estimation433
It is demonstrated that cross-attention enables integration of knowledge from another image via cross-view interactions, which greatly improves the quality of the extracted features and enables cross-task transfer.
- Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models420
This work proposes to incorporate generative processes into the cross-modal feature embedding, through which it is able to learn not only the global abstract features but also the local grounded features of image-text pairs.
- Joint source channel rate-distortion analysis for adaptive mode selection and rate control in wireless video coding386
An analytic solution for adaptive intra mode selection and joint source-channel rate control under time-varying wireless channel conditions is derived and significantly improves the end-to-end video quality in wireless video coding and transmission.
- Semi-supervised Left Atrium Segmentation with Mutual Consistency Training363
A novel Mutual Consistency Network (MC-Net) for semi-supervised left atrium segmentation from 3D MR images that consists of one encoder and two slightly different decoders, and the prediction discrepancies of two decoder are transformed as an unsupervised loss by a designed cycled pseudo label scheme to encourage mutual consistency.
- Scene Graph Generation With External Knowledge and Image Reconstruction330
This paper proposes a novel scene graph generation algorithm with external knowledge and image reconstruction loss to overcome dataset issues, and extracts commonsense knowledge from the external knowledge base to refine object and phrase features for improving generalizability inscene graph generation.
- Fast Vision Transformers with HiLo Attention323
This work introduces LITv2, a simple and effective ViT which performs favourably against the existing state-of-the-art methods across a spectrum of different model sizes with faster speed and shows that HiLo is superior to the existing attention mechanisms by comprehensively benchmarking FLOPs, speed and memory consumption on GPUs and CPUs.
- Weakly-Supervised 3D Hand Pose Estimation from Monocular RGB Images320
A weakly-supervised method, adaptating from fully-annotated synthetic dataset toWeakly-labeled real-world dataset with the aid of a depth regularizer, which generates depth maps from predicted 3D pose and serves as weak supervision for3D pose regression.
- Remarks on multi-output Gaussian process regression313
This article investigates the state-of-the-art multi-output Gaussian processes (MOGPs) that can transfer the knowledge across related outputs in order to improve prediction quality and gives some recommendations regarding the usage of MOGPs.
- Portable Food‐Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks295
Cross‐reactive colorimetric barcode combinatorics and deep convolutional neural networks (DCNNs) are combined to form a system for monitoring meat freshness that concurrently provides scent fingerprint and fingerprint recognition.
- Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation240
A comprehensive review of the recent progress in image segmentation, covering 190 publications, gives an overview of broad segmentation topics including not only the classic unsupervised methods, but also the recent weakly-/semi- supervised methods and the fully-super supervised methods.
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- Causal Attention for Vision-Language Tasks228
CATT improves various popular attention-based vision-language models by considerable margins and has great potential in large-scale pre-training, e.g., it can promote the lighter LXMERT, which uses fewer data and less computational power, comparable to the heavier UNITER.
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- T$$^2$$Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks204
A framework that comprises an image translation network for enhancing realism of input images, followed by a depth prediction network that can be trained end-to-end, leading to good results, even surpassing early deep-learning methods that use real paired data.
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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 2026-10-12. 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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