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Found 24 papers, 12 papers with code
Integrating Clinical Knowledge into Concept Bottleneck Models
Concept bottleneck models (CBMs), which predict human-interpretable concepts (e. g., nucleus shapes in cell images) before predicting the final output (e. g., cell type), provide insights into the decision-making processes of the model.
Evolving Storytelling: Benchmarks and Methods for New Character Customization with Diffusion Models
Additionally, to mitigate the character confusion of generated results, we propose EpicEvo, a method that customizes a diffusion-based visual story generation model with a single story featuring the new characters seamlessly integrating them into established character dynamics.
Delocate: Detection and Localization for Deepfake Videos with Randomly-Located Tampered Traces
Deepfake videos are becoming increasingly realistic, showing few tampering traces on facial areasthat vary between frames.
Recap: Detecting Deepfake Video with Unpredictable Tampered Traces via Recovering Faces and Mapping Recovered Faces
In the recovering stage, the model focuses on randomly masking regions of interest (ROIs) and reconstructing real faces without unpredictable tampered traces, resulting in a relatively good recovery effect for real faces while a poor recovery effect for fake faces.
WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes
We then annotated ten thousand WBC images with these attributes.
Mover: Mask and Recovery based Facial Part Consistency Aware Method for Deepfake Video Detection
Specifically, given a real face image, we first pretrain a masked autoencoder to learn facial part consistency by dividing faces into three parts and randomly masking ROIs, which are then recovered based on the unmasked facial parts.
Benchmarking White Blood Cell Classification Under Domain Shift
Recognizing the types of white blood cells (WBCs) in microscopic images of human blood smears is a fundamental task in the fields of pathology and hematology.
Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization
Based on the single-stage instance segmentation framework, we propose a regularization model to predict foreground pixels and use its relation to instance segmentation to construct a cross-task consistency loss.
Action Recognition based on Cross-Situational Action-object Statistics
We set out to identify properties of training data that lead to action recognition models with greater generalization ability.
Novel View Synthesis for High-fidelity Headshot Scenes
Based on these observations, we propose a method to use both NeRF and 3DMM to synthesize a high-fidelity novel view of a scene with a face.
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition
One-shot fine-grained visual recognition often suffers from the problem of having few training examples for new fine-grained classes.
AVA-AVD: Audio-Visual Speaker Diarization in the Wild
Audio-visual speaker diarization aims at detecting "who spoke when" using both auditory and visual signals.
How You Move Your Head Tells What You Do: Self-supervised Video Representation Learning with Egocentric Cameras and IMU Sensors
We are particularly interested in learning egocentric video representations benefiting from the head-motion generated by users' daily activities, which can be easily obtained from IMU sensors embedded in AR/VR devices.
Reverse-engineer the Distributional Structure of Infant Egocentric Views for Training Generalizable Image Classifiers
We analyze egocentric views of attended objects from infants.
Whose hand is this? Person Identification from Egocentric Hand Gestures
But while one's own face is not frequently visible, their hands are: in fact, hands are among the most common objects in one's own field of view.
A Computational Model of Early Word Learning from the Infant's Point of View
Human infants have the remarkable ability to learn the associations between object names and visual objects from inherently ambiguous experiences.
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition
One-shot fine-grained visual recognition often suffers from the problem of training data scarcity for new fine-grained classes.
Fine-Grained Image Classification
Fine-Grained Visual Recognition
+2
Active Object Manipulation Facilitates Visual Object Learning: An Egocentric Vision Study
Inspired by the remarkable ability of the infant visual learning system, a recent study collected first-person images from children to analyze the `training data' that they receive.
edge2vec: Representation learning using edge semantics for biomedical knowledge discovery
We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.
Combining Pyramid Pooling and Attention Mechanism for Pelvic MR Image Semantic Segmentaion
One of the time-consuming routine work for a radiologist is to discern anatomical structures from tomographic images.
Minimizing Supervision for Free-space Segmentation
Our work demonstrates the potential for performing free-space segmentation without tedious and costly manual annotation, which will be important for adapting autonomous driving systems to different types of vehicles and environments
Distantly Supervised Road Segmentation
We present an approach for road segmentation that only requires image-level annotations at training time.
Using Artificial Tokens to Control Languages for Multilingual Image Caption Generation
Recent work in computer vision has yielded impressive results in automatically describing images with natural language.
A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks
CNNs eliminate the need for manually designing features and separation rules, but require a large amount of annotated training data.
