Search Results for author:
Found 24 papers, 7 papers with code
Let Storytelling Tell Vivid Stories: An Expressive and Fluent Multimodal Storyteller
Storytelling aims to generate reasonable and vivid narratives based on an ordered image stream.
A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment
In this paper, we introduce a substantial crowdsourcing annotation dataset collected from a real-world crowdsourcing platform.
Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation
Previous in-context learning (ICL) research has focused on tasks such as classification, machine translation, text2table, etc., while studies on whether ICL can improve human-like dialogue generation are scarce.
Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation
We devise a novel diffusion model that can undertake the task of simultaneously face swapping and reenactment.
Towards Long-term Annotators: A Supervised Label Aggregation Baseline
These annotators could leave substantial historical annotation records on the crowdsourcing platforms, which can benefit label aggregation, but are ignored by previous works.
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model
Aligning agent behaviors with diverse human preferences remains a challenging problem in reinforcement learning (RL), owing to the inherent abstractness and mutability of human preferences.
Examining the Effect of Pre-training on Time Series Classification
(iv) Adding more pre-training data does not improve generalization, but it can strengthen the advantage of pre-training on the original data volume, such as faster convergence.
Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation
Insufficient synergy neglects the idea that a robust 3D representation should align with the joint vision-language space, rather than independently aligning with each modality.
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective
In this paper, we investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise.
Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning
In summary, our research emphasizes the significance of trajectory-based data sampling techniques in enhancing the efficiency and performance of offline RL algorithms.
FlowFace++: Explicit Semantic Flow-supervised End-to-End Face Swapping
The discriminator is shape-aware and relies on a semantic flow-guided operation to explicitly calculate the shape discrepancies between the target and source faces, thus optimizing the face swapping network to generate highly realistic results.
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
In this paper, we present an end-to-end framework Structure-CLIP, which integrates Scene Graph Knowledge (SGK) to enhance multi-modal structured representations.
TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles
In this work, we propose an expression-controllable one-shot talking head method, dubbed TalkCLIP, where the expression in a speech is specified by the natural language.
2D Semantic Segmentation task 3 (25 classes)
Talking Head Generation
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 2022.
DINet: Deformation Inpainting Network for Realistic Face Visually Dubbing on High Resolution Video
Different from previous works relying on multiple up-sample layers to directly generate pixels from latent embeddings, DINet performs spatial deformation on feature maps of reference images to better preserve high-frequency textural details.
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning
Despite its success, ACL's applicability is limited by (1) the lack of a general student framework for dealing with the varying number of agents across tasks and the sparse reward problem, and (2) the non-stationarity of the teacher's task due to ever-changing student strategies.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
StyleTalk: One-shot Talking Head Generation with Controllable Speaking Styles
In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio.
TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective
TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions which further improves estimation accuracy.
FlowFace: Semantic Flow-guided Shape-aware Face Swapping
Unlike most previous methods that focus on transferring the source inner facial features but neglect facial contours, our FlowFace can transfer both of them to a target face, thus leading to more realistic face swapping.
Facial Action Unit Detection and Intensity Estimation from Self-supervised Representation
As a fine-grained and local expression behavior measurement, facial action unit (FAU) analysis (e. g., detection and intensity estimation) has been documented for its time-consuming, labor-intensive, and error-prone annotation.
Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering
While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?"
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning
We introduce the Fever Basketball game, a novel reinforcement learning environment where agents are trained to play basketball game.
Reinforcement Learning Experience Reuse with Policy Residual Representation
Experience reuse is key to sample-efficient reinforcement learning.
Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction
Besides, we propose a new experience replay mechanism to alleviate the issue of the sparse transitions at the high level of abstraction and the non-stationarity of multiagent learning.
