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Claim this profileWorks276 from public data
- CCNet: Criss-Cross Attention for Semantic Segmentation3,111
For each pixel, a novel criss-cross attention module in CCNet harvests the contextual information of all the pixels on its criss-cross path by taking a further recurrent operation, each pixel can finally capture the full-image dependencies from all pixels.
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- Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach900
This work investigates a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems and proposes a new adversarial erasing approach for localizing and expanding object regions progressively.
- Perceptual Generative Adversarial Networks for Small Object Detection888
This work addresses the small object detection problem by developing a single architecture that internally lifts representations of small objects to super-resolved ones, achieving similar characteristics as large objects and thus more discriminative for detection.
- HCP: A Flexible CNN Framework for Multi-Label Image Classification827
Experimental results on Pascal VOC 2007 and VOC 2012 multi-label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state-of-the-arts, where an arbitrary number of object segment hypotheses are taken as the inputs.
- Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation648
It is found that varying dilation rates can effectively enlarge the receptive fields of convolutional kernels and more importantly transfer the surrounding discriminative information to non-discriminative object regions, promoting the emergence of these regions in the object localization maps.
- Adversarial Complementary Learning for Weakly Supervised Object Localization626
This work mathematically proves that class localization maps can be obtained by directly selecting the class-specific feature maps of the last convolutional layer, which paves a simple way to identify object regions and presents a simple network architecture including two parallel-classifiers for object localization.
- STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation617
A simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation, which demonstrates the superiority of the proposed STC framework compared with other state-of-the-arts frameworks.
- SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation599
This article proposes a simple yet effective similarity guidance network to tackle the one-shot (SG-One) segmentation problem, aiming at predicting the segmentation mask of a query image with the reference to one densely labeled support image of the same category.
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- Horizontal Pyramid Matching for Person Re-Identification512
A simple yet effective Horizontal Pyramid Matching (HPM) approach to fully exploit various partial information of a given person, so that correct person candidates can be still identified even even some key parts are missing.
- University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization505
It is argued that drones could serve as the third platform to deal with the geo-localization problem and propose a strong CNN baseline on this challenging dataset, named University-1652, which is the first drone-based geo- localization dataset and enables two new tasks, i.e., drone-view target localization and drone navigation.
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- Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning369
This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data with a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors.
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- PixelLM: Pixel Reasoning with Large Multimodal Model313
This work introduces PixelLM, an effective and efficient LMM for pixel-level reasoning and understanding, and proposes a target refinement loss to enhance the model's ability to differentiate between multiple targets, leading to substantially improved mask quality.
- Collaborative Video Object Segmentation by Foreground-Background Integration295
This paper proposes Collaborative video object segmentation by Foreground-Background Integration (CFBI) approach, which implicitly imposes the feature embedding from the target foreground object and its corresponding background to be contrastive, promoting the segmentation results accordingly.
- Revisiting RCNN: On Awakening the Classification Power of Faster RCNN285
The potential of detector classification power is demonstrated by a simple, effective, and widely-applicable Decoupled Classification Refinement (DCR) network and failure cases of state-of-the-art detectors are analyzed.
- Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection282
This paper develops a forgery-aware adapter to adapt image features to discern and inte-grate local forgery traces within image and frequency do-mains, and finds that considering the contrastive ob-jectives between adapted image features and text prompt embeddings results in a nontrivial generalization improvement.
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- Self-produced Guidance for Weakly-Supervised Object Localization269
Self-produced Guidance (SPG) masks which separate the foreground i.e., the object of interest, from the background to provide the classification networks with spatial correlation information of pixels are proposed.
- Attentive Contexts for Object Detection269
This work develops a novel attention to context convolution neural network (AC-CNN)-based object detection model that effectively incorporates global and local contextual information into the region-based CNN (e.g., fast R-CNN and faster R- CNN) detection framework and provides better object detection performance.
- Devil in the Details: Towards Accurate Single and Multiple Human Parsing266
This paper identifies several useful properties, including feature resolution, global context information and edge details, and performs rigorous analyses to reveal how to leverage them to benefit the human parsing task, resulting in a simple yet effective Context Embedding with Edge Perceiving (CE2P) framework for single human parsing.
- Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation256
The reason why the current adversarial loss is often unstable in minimizing the distribution discrepancy is revealed and it is shown that the method can help ease this issue by minimizing the most similar stuff and instance features between the source and the target domains.
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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