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Found 32 papers, 21 papers with code
SOS-1K: A Fine-grained Suicide Risk Classification Dataset for Chinese Social Media Analysis
Seven pre-trained models were evaluated in two tasks: high and low suicide risk, and fine-grained suicide risk classification on a level of 0 to 10.
MambaPupil: Bidirectional Selective Recurrent model for Event-based Eye tracking
Event-based eye tracking has shown great promise with the high temporal resolution and low redundancy provided by the event camera.
AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts
Cognitive Behavioral Therapy (CBT) is an effective technique for addressing the irrational thoughts stemming from mental illnesses, but it necessitates precise identification of cognitive pathways to be successfully implemented in patient care.
Event-Based Eye Tracking. AIS 2024 Challenge Survey
This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Intention-driven Ego-to-Exo Video Generation
Finally, the inferred human movement and high-level action descriptions jointly guide the generation of exocentric motion and interaction content (i. e., corresponding optical flow and occlusion maps) in the backward process of the diffusion model, ultimately warping them into the corresponding exocentric video.
Event-based Asynchronous HDR Imaging by Temporal Incident Light Modulation
In this paper, we propose AsynHDR, a Pixel-Asynchronous HDR imaging system, based on key insights into the challenges in HDR imaging and the unique event-generating mechanism of Dynamic Vision Sensors (DVS).
Chinese MentalBERT: Domain-Adaptive Pre-training on Social Media for Chinese Mental Health Text Analysis
To address this, we have collected a huge dataset from Chinese social media platforms and enriched it with publicly available datasets to create a comprehensive database encompassing 3. 36 million text entries.
LEMON: Learning 3D Human-Object Interaction Relation from 2D Images
Which underexploit certain correlations between the interaction counterparts (human and object), and struggle to address the uncertainty in interactions.
Likelihood-Aware Semantic Alignment for Full-Spectrum Out-of-Distribution Detection
Full-spectrum out-of-distribution (F-OOD) detection aims to accurately recognize in-distribution (ID) samples while encountering semantic and covariate shifts simultaneously.
Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation
In addition, our method also achieves state-of-the-art weakly supervised semantic segmentation performance on the PASCAL VOC 2012 and MS COCO 2014 datasets.
Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media
In response, we introduce two novel annotated datasets from Chinese social media, focused on cognitive distortions and suicidal risk classification.
Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionals
In the contemporary landscape of social media, an alarming number of users express negative emotions, some of which manifest as strong suicidal intentions.
Spatial-Aware Token for Weakly Supervised Object Localization
Specifically, a spatial token is first introduced in the input space to aggregate representations for localization task.
Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection
Semantically coherent out-of-distribution (SCOOD) detection aims to discern outliers from the intended data distribution with access to unlabeled extra set.
Grounding 3D Object Affordance from 2D Interactions in Images
Comprehensive experiments on PIAD demonstrate the reliability of the proposed task and the superiority of our method.
Leverage Interactive Affinity for Affordance Learning
Perceiving potential "action possibilities" (i. e., affordance) regions of images and learning interactive functionalities of objects from human demonstration is a challenging task due to the diversity of human-object interactions.
Grounded Affordance from Exocentric View
Due to the diversity of interactive affordance, the uniqueness of different individuals leads to diverse interactions, which makes it difficult to establish an explicit link between object parts and affordance labels.
Learning Affordance Grounding from Exocentric Images
To empower an agent with such ability, this paper proposes a task of affordance grounding from exocentric view, i. e., given exocentric human-object interaction and egocentric object images, learning the affordance knowledge of the object and transferring it to the egocentric image using only the affordance label as supervision.
Location-Free Camouflage Generation Network
However, these methods struggle in 1) efficiently generating camouflage images using foreground and background with arbitrary structure; 2) camouflaging foreground objects to regions with multiple appearances (e. g. the junction of the vegetation and the mountains), which limit their practical application.
Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning
Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved.
Phrase-Based Affordance Detection via Cyclic Bilateral Interaction
In this paper, we explore to perceive affordance from a vision-language perspective and consider the challenging phrase-based affordance detection problem, i. e., given a set of phrases describing the action purposes, all the object regions in a scene with the same affordance should be detected.
Background Activation Suppression for Weakly Supervised Object Localization
Existing FPM-based methods use cross-entropy (CE) to evaluate the foreground prediction map and to guide the learning of generator.
On Exploring and Improving Robustness of Scene Text Detection Models
It is crucial to understand the robustness of text detection models with regard to extensive corruptions, since scene text detection techniques have many practical applications.
Learning Visual Affordance Grounding from Demonstration Videos
For the object branch, we introduce a semantic enhancement module (SEM) to make the network focus on different parts of the object according to the action classes and utilize a distillation loss to align the output features of the object branch with that of the video branch and transfer the knowledge in the video branch to the object branch.
Ranked #2 on
Video-to-image Affordance Grounding
on EPIC-Hotspot
One-Shot Object Affordance Detection in the Wild
To empower robots with this ability in unseen scenarios, we first study the challenging one-shot affordance detection problem in this paper, i. e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected.
Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes.
One-Shot Affordance Detection
To empower robots with this ability in unseen scenarios, we consider the challenging one-shot affordance detection problem in this paper, i. e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected.
Deep Structure-Revealed Network for Texture Recognition
Next, these primitives are associated with a dependence learning module (DLM) to generate structural representation, in which a two-way collaborative relationship strategy is introduced to perceive the spatial dependencies among multiple primitives.
Self-Supervised Tuning for Few-Shot Segmentation
Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples.
Deep Multiple-Attribute-Perceived Network for Real-World Texture Recognition
Texture recognition is a challenging visual task as multiple perceptual attributes may be perceived from the same texture image when combined with different spatial context.
One-Shot Texture Retrieval with Global Context Metric
In this paper, we tackle one-shot texture retrieval: given an example of a new reference texture, detect and segment all the pixels of the same texture category within an arbitrary image.
