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- 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.
- 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.
- 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.
- 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.
- Visual Recognition with Deep Nearest Centroids217
DNC is devised, a conceptually elegant yet surprisingly effective network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most classic and simple classifiers, and brings fundamental insights into related fields.
- MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation212
A novel end-to-end learning neural network, i.e., MATNet, for zero-shot video object segmentation (ZVOS), motivated by the human visual attention behavior, which leverages motion cues as a bottom-up signal to guide the perception of object appearance.
- Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation206
This work proposes regional semantic contrast and aggregation (RCA), equipped with a regional memory bank to store massive, diverse object patterns appearing in training data, which acts as strong support for exploration of dataset-level semantic structure.
- Deep Hierarchical Semantic Segmentation198
HSSN is devised, a general HSS framework that tackles two critical issues in this task: how to efficiently adapt existing hierarchy-agnostic segmentation networks to the HSS setting, and how to leverage the hierarchy information to regularize HSS network learning.
- Face Forensics in the Wild195
This work constructs a novel large-scale dataset, called FFIW10K, which comprises 10,000 high-quality forgery videos, with an average of three human faces in each frame, and proposes a novel algorithm to tackle the task of multi-person face forgery detection.
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- Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation147
This work designs a graph neural network (GNN) for group-wise semantic segmentation (WSSS), wherein input images are represented as graph nodes, and the underlying relations between a pair of images are char- acterized by an efficient co-attention mechanism.
- Detail-Preserving Transformer for Light Field Image Super-resolution140
This paper proposes a novel formulation built upon Transformers, by treating LFSR as a sequence-to-sequence reconstruction task, and proposes a detail-preserving Transformer (termed as DPT), by leveraging gradient maps of light field to guide the sequence learning.
- Cascaded Human-Object Interaction Recognition130
This work introduces a cascade architecture for a multi-stage, coarse-to-fine HOI understanding, and makes the framework flexible to perform fine-grained pixel-wise relation segmentation; this provides a new glimpse into better relation modeling.
- Weakly Supervised 3D Object Detection from Lidar Point Cloud127
This work proposes a weakly supervised approach for 3D object detection, only requiring a small set of weakly annotated scenes, associated with a few precisely labeled object instances, achieved by a two-stage architecture design.
- Local-Global Context Aware Transformer for Language-Guided Video Segmentation120
This work presents Locater, which augments the Transformer architecture with a finite memory so as to query the entire video with the language expression in an efficient manner, and outperforms previous state-of-the-arts solutions.
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- Volumetric memory network for interactive medical image segmentation109
A novel Volumetric Memory Network is proposed, dubbed as VMN, to enable segmentation of 3D medical images in an interactive manner and leads to a robust interactive segmentation engine, which can generalize well to various types of user annotations.
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- Core: Cooperative Reconstruction for Multi-Agent Perception96
Core is presented, a conceptually simple, effective and communication-efficient model for multi-agent cooperative perception that addresses the task from a novel perspective of cooperative reconstruction, based on two key insights: cooperating agents together provide a more holistic observation of the environment and the holistic observation can serve as valuable supervision to explicitly guide the model learning how to reconstruct the ideal observation based on collaboration.
- CLUSTSEG: Clustering for Universal Segmentation92
These innovations closely link CLUSTSEG to EM clustering and make it a transparent and powerful framework that yields superior results across the above segmentation tasks.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. 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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