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Found 14 papers, 5 papers with code
Harmonizing Visual Representations for Unified Multimodal Understanding and Generation
Unifying visual understanding and generation within a single multimodal framework remains a significant challenge, as the two inherently heterogeneous tasks require representations at different levels of granularity.
MatAnyone: Stable Video Matting with Consistent Memory Propagation
For robust training, we present a larger, high-quality, and diverse dataset for video matting.
Towards Robust and Expressive Whole-body Human Pose and Shape Estimation
Whole-body pose and shape estimation aims to jointly predict different behaviors (e. g., pose, hand gesture, facial expression) of the entire human body from a monocular image.
PGDiff: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidance
When combined with a diffusion prior, this partial guidance can deliver appealing results across a range of restoration tasks.
Modeling Continuous Motion for 3D Point Cloud Object Tracking
The task of 3D single object tracking (SOT) with LiDAR point clouds is crucial for various applications, such as autonomous driving and robotics.
ReCasNet: Improving consistency within the two-stage mitosis detection framework
Existing approaches utilize a two-stage pipeline: the detection stage for identifying the locations of potential mitotic cells and the classification stage for refining prediction confidences.
Medical Visual Question Answering: A Survey
Medical Visual Question Answering~(VQA) is a combination of medical artificial intelligence and popular VQA challenges.
Unsupervised Domain Adaptation for Retinal Vessel Segmentation with Adversarial Learning and Transfer Normalization
In this work, we explore unsupervised domain adaptation in retinal vessel segmentation by using entropy-based adversarial learning and transfer normalization layer to train a segmentation network, which generalizes well across domains and requires no annotation of the target domain.
Exploring Bottom-up and Top-down Cues with Attentive Learning for Webly Supervised Object Detection
To reduce the human labeling effort, we propose a novel webly supervised object detection (WebSOD) method for novel classes which only requires the web images without further annotations.
Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
Secondly, in CT scans, the lesions are often indistinguishable from the background since the lesion and non-lesion areas may have very similar appearances.
M2E-Try On Net: Fashion from Model to Everyone
Instead, we present a novel virtual Try-On network, M2E-Try On Net, which transfers the clothes from a model image to a person image without the need of any clean product images.
VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions
Most existing works in visual question answering (VQA) are dedicated to improving the accuracy of predicted answers, while disregarding the explanations.
Ranked #4 on
Explanatory Visual Question Answering
on GQA-REX
Explanatory Visual Question Answering
Multi-Task Learning
+1
Zero-Annotation Object Detection with Web Knowledge Transfer
Object detection is one of the major problems in computer vision, and has been extensively studied.
Exploiting Web Images for Weakly Supervised Object Detection
Object detection without bounding box annotations, i. e, weakly supervised detection methods, are still lagging far behind.
Ranked #23 on
Weakly Supervised Object Detection
on PASCAL VOC 2012 test
(using extra training data)
