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- Visual saliency based on multiscale deep features1,361
This paper discovers that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks.
- Proceedings of the 29th ACM International Conference on Multimedia922
A robust deep learning framework for real-world SR that randomly erases potential adversarial noises in the frequency domain of input images or features and is more insensitive to adversarial attacks and presents more stable SR results than existing models and defenses is proposed.
- Deep Contrast Learning for Salient Object Detection774
This paper proposes an end-to-end deep contrast network that significantly improves the state of the art in salient object detection and extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries.
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- Visual Saliency Detection Based on Multiscale Deep CNN Features394
This paper discovers that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks.
- Aligning Cyber Space With Physical World: A Comprehensive Survey on Embodied AI384
This survey gives a comprehensive exploration of the latest advancements in Embodied AI, exploring the complexities of MLMs in virtual and real embodied agents, highlighting their significance in facilitating interactions in digital and physical environments.
- Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering348
Extensive experiments demonstrate the superiority of the CMCIR in discovering visual-linguistic causal structures and achieving robust event-level visual question answering.
- Adaptive Context Selection for Polyp Segmentation344
This work proposes an adaptive context selection based encoder-decoder framework which is composed of Local Context Attention (LCA) module, Global Context Module (GCM) and Adaptive Selection Module (ASM), which shows outstanding performance compared with other state-of-the-art methods.
- Multi-label Image Recognition by Recurrently Discovering Attentional Regions338
This work achieves the interpretable and contextualized multi-label image classification by developing a recurrent memorized-attention module that demonstrates superior performances over other existing state-of-the-arts in both accuracy and efficiency.
- Instance-Level Salient Object Segmentation297
This paper presents a salient instance segmentation method that produces a saliency mask with distinct object instance labels for an input image, and proposes a multiscale saliency refinement network, which generates high-quality salient region masks and salient object contours.
- Crowd Counting With Deep Structured Scale Integration Network289
A novel Deep Structured Scale Integration Network (DSSINet) for crowd counting is proposed, which addresses the scale variation of people by using structured feature representation learning and hierarchically structured loss function optimization.
- Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction279
A novel contextualized spatial–temporal network (CSTN) for taxi origin-destination demand prediction, which consists of three components for the modeling of local spatial context (LSC), temporal evolution context (TEC), and global correlation context (GCC), respectively.
- Dynamic Graph Attention for Referring Expression Comprehension263
This paper proposes a dynamic graph attention network to perform multi-step reasoning by modeling both the relationships among the objects in the image and the linguistic structure of the expression, and constructs a graph for the image with the nodes and edges corresponding to the objects and their relationships respectively.
- Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining262
A non-locally enhanced encoder-decoder network framework that can effectively remove rain-streaks on rainy image of various densities while well preserving the image details, which achieves significant improvements over the recent state-of-the-art methods.
- A Real-Time Cross-Modality Correlation Filtering Method for Referring Expression Comprehension253
A novel Realtime Cross-modality Correlation Filtering method (RCCF) that reformulates the referring expression comprehension as a correlation ï¬ltering process and achieves leading performance in RefClef, RefCOCO, Ref COCO+ and RefC OCOg benchmarks.
- Referring Image Segmentation via Cross-Modal Progressive Comprehension250
A Cross-Modal Progressive Comprehension (CMPC) module and a Text-Guided Feature Exchange (TGFE) module to effectively address the challenging task of referring image segmentation.
- Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules247
A thyroid region prior guided feature enhancement network (TRFE+) is designed for accurate thyroid nodule segmentation and a normalization approach with respect to the channel dimension is applied to alleviate the domain gap during the training process.
- Attention-Aware Face Hallucination via Deep Reinforcement Learning235
A novel Attention-aware Face Hallucination (Attention-FH) framework which resorts to deep reinforcement learning for sequentially discovering attended patches and then performing the facial part enhancement by fully exploiting the global interdependency of the image.
- Motion Guided Attention for Video Salient Object Detection216
A multi-task motion guided video salient object detection network, which learns to accomplish two sub-tasks using twoSub-networks, one sub-network for salient object Detection in still images and the other for motion saliency detection in optical flow images, which significantly outperforms existing state-of-the-art algorithms on a wide range of benchmarks.
- DreamEditor: Text-Driven 3D Scene Editing with Neural Fields213
This paper proposes DreamEditor, a novel framework that enables users to perform controlled editing of neural fields using text prompts by representing scenes as mesh-based neural fields, and generates highly realistic textures and geometry.
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- Linguistic Structure Guided Context Modeling for Referring Image Segmentation211
A "gather-propagate-distribute" scheme to model multimodal context by cross-modal interaction and implement this scheme as a novel Linguistic Structure guided Context Modeling (LSCM) module.
- Cross-Modal Progressive Comprehension for Referring Segmentation202
This paper proposes a cross-modal progressive comprehension (CMPC) scheme to effectively mimic human behaviors and implements it as a CMPC-I (Image) module and aCMPC-V (Video) module to improve referring image and video segmentation models.
- SAM2-UNet: segment anything 2 makes strong encoder for natural and medical image segmentation198
It is proved that the Segment Anything Model 2 (SAM2) can be a strong encoder for U-shaped segmentation models, and a simple but effective framework is proposed, termed SAM2-UNet, for versatile image segmentation.
- Crowd Counting using Deep Recurrent Spatial-Aware Network198
A unified neural network framework, named Deep Recurrent Spatial-Aware Network, which adaptively addresses the two issues in a learnable spatial transform module with a region-wise refinement process, and achieves an improvement on the largest dataset WorldExpo’10 and the most challenging dataset UCF-CC.
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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