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Works190 from public data
- Salient Object Detection in the Deep Learning Era: An In-Depth Survey810
This paper reviews deep SOD algorithms from different perspectives, including network architecture, level of supervision, learning paradigm, and object-/instance-level detection, and looks into the generalization and difficulty of existing SOD datasets.
- Video Salient Object Detection via Fully Convolutional Networks642
A novel data augmentation technique that simulates video training data from existing annotated image data sets, which enables the deep video saliency network to learn diverse saliency information and prevents overfitting with the limited number of training videos.
- Exploring Cross-Image Pixel Contrast for Semantic Segmentation596
This work raises a pixel-wise metric learning paradigm for semantic segmentation, by explicitly exploring the structures of labeled pixels, which were rarely explored before, and hopes this work will encourage the community to rethink the current de facto training paradigm in semantic segmentsation.
- Learning Human-Object Interactions by Graph Parsing Neural Networks596
This paper addresses the task of detecting and recognizing human-object interactions (HOI) in images and videos with the Graph Parsing Neural Network (GPNN), a framework that incorporates structural knowledge while being differentiable end-to-end.
- Poly Kernel Inception Network for Remote Sensing Detection544
The Poly Kernel Inception Network (PKINet) is introduced, which employs multi-scale convolution kernels without dilation to extract object features of varying scales and capture local context in remote sensing images.
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- See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks537
This work introduces a novel network, called as CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view and proposes a unified and end-to-end trainable framework where different co-att attention variants can be derived for mining the rich context within videos.
- 535
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- Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection492
This paper proposes a fast video salient object detection model, based on a novel recurrent network architecture, named Pyramid Dilated Bidirectional ConvLSTM (PDB-ConvL STM), which achieves state-of-the-art results on two popular benchmarks, well demonstrating its superior performance and high applicability.
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- Human-Aware Motion Deblurring403
A human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG) humans based on a triple-branch encoder-decoder architecture that performs favorably against the state-of-the-art motion deblurred methods, especially in capturing semantic details.
- 395
- Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation392
This paper proposes a pose grammar to tackle the problem of 3D human pose estimation, which takes 2D pose as input and learns a generalized 2D-3D mapping function and enforces high-level constraints over human poses.
- 384
- A Survey on 3D Gaussian Splatting383
The first systematic overview of the recent developments and critical contributions in 3D GS is provided, with a detailed exploration of the underlying principles and the driving forces behind the emergence of 3D GS, laying the groundwork for understanding its significance.
- Rethinking Semantic Segmentation: A Prototype View377
This study uncovers several limitations of such parametric segmentation regime, and proposes a nonparametric alternative based on non-learnable prototypes, which allows the model to directly shape the pixel embedding space, by optimizing the arrangement between embedded pixels and anchored prototypes.
- Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation359
This paper addresses the value of cross-image semantic relations for comprehensive object pattern mining and provides a unified framework that handles well different WSSS settings, i.e., learning WSSD with (1) precise image-level supervision only, (2) extra simple single-label data, and (3) extra noisy web data.
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- A Survey on Deep Learning Technique for Video Segmentation316
This survey comprehensively review two basic lines of research — generic object segmentation in videos, and video semantic segmentation — by introducing their respective task settings, background concepts, perceived need, development history, and main challenges.
- Revisiting Video Saliency: A Large-Scale Benchmark and a New Model311
This work introduces a new benchmark for predicting human eye movements during dynamic scene free-viewing, and proposes a novel video saliency model that augments the CNN-LSTM network architecture with an attention mechanism to enable fast, end-to-end saliency learning.
- Video Object Segmentation with Episodic Graph Memory Networks303
This work exploits an episodic memory network, organized as a fully connected graph, to store frames as nodes and capture cross-frame correlations by edges and yields a neat yet principled framework, which can generalize well both one-shot and zero-shot video object segmentation tasks.
- Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks302
This work proposes a novel attentive graph neural network (AGNN) for zero-shot video object segmentation (ZVOS) and extends it to an additional task: image object co-segmentation (IOCS), demonstrating the generalizability of the framework.
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 2026-10-13. 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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