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Found 16 papers, 4 papers with code
Hammer: Robust Function-Calling for On-Device Language Models via Function Masking
Large language models have demonstrated impressive value in performing as autonomous agents when equipped with external tools and API calls.
Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model
To generate reconstructed images with the desired bits per pixel and assess the BD-rate performance of both the JPEG-AI verification model and VVC intra, bit rate matching is employed.
Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization
Currently, there is a high demand for neural network-based image compression codecs.
Triple Disentangled Representation Learning for Multimodal Affective Analysis
We revisit the disentanglement issue, and propose a novel triple disentanglement approach, TriDiRA, which disentangles the modality-invariant, effective modality-specific and ineffective modality-specific representations from input data.
Learning Subjective Time-Series Data via Utopia Label Distribution Approximation
In this work, we propose Utopia Label Distribution Approximation (ULDA) for time-series data, which makes the training label distribution closer to real-world but unknown (utopia) label distribution.
Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment
Eliminating the covariate shift cross domains is one of the common methods to deal with the issue of domain shift in visual unsupervised domain adaptation.
Pairwise Emotional Relationship Recognition in Drama Videos: Dataset and Benchmark
We expect the ERATO as well as our proposed SMTA to open up a new way for PERR task in video understanding and further improve the research of multi-modal fusion methodology.
Subjective evaluation of traditional and learning-based image coding methods
Additionally, we utilize some typical and frequently used objective quality metrics to evaluate the coding methods in the experiment as comparison.
Mirror Sample Based Distribution Alignment for Unsupervised Domain Adaption
Unsupervised Domain Adaption has great value in both machine learning theory and applications.
ESA-ReID: Entropy-Based Semantic Feature Alignment for Person re-ID
Person re-identification (re-ID) is a challenging task in real-world.
RNE: A Scalable Network Embedding for Billion-scale Recommendation
However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods.
VIDEO AFFECTIVE IMPACT PREDICTION WITH MULTIMODAL FUSION AND LONG-SHORT TEMPORAL CONTEXT
Feature extraction, multi-modal fusion and temporal context fusion are crucial stages for predicting valence and arousal values in the emotional impact, but have not been successfully exploited.
Video Affective Effects Prediction with Multi-modal Fusion and Shot-Long Temporal Context
We select the most suitable modalities for valence and arousal tasks respectively and each modal feature is extracted using the modality-specific pre-trained deep model on large generic dataset.
PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation
This paper proposes Personalized Diversity-promoting GAN (PD-GAN), a novel recommendation model to generate diverse, yet relevant recommendations.
Recent Advances in Diversified Recommendation
With the rapid development of recommender systems, accuracy is no longer the only golden criterion for evaluating whether the recommendation results are satisfying or not.
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation
Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years.
