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Found 14 papers, 5 papers with code
What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities
To evaluate the diverse capabilities of virtual agents on the graph, we further present OmniEval, a multidimensional evaluation framework that includes subtask-level evaluation, graph-based metrics, and comprehensive tests across 10 capabilities.
Boosting Virtual Agent Learning and Reasoning: A Step-wise, Multi-dimensional, and Generalist Reward Model with Benchmark
The development of Generalist Virtual Agents (GVAs) powered by Multimodal Large Language Models (MLLMs) has shown significant promise in autonomous task execution.
SOYO: A Tuning-Free Approach for Video Style Morphing via Style-Adaptive Interpolation in Diffusion Models
Diffusion models have achieved remarkable progress in image and video stylization.
Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning
Specifically, we align the steps of Chain-of-Thought (CoT) reasoning with loop iterations and apply intermediate supervision during the training of Looped Transformers.
Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness
Instruction tuning fine-tunes pre-trained Multi-modal Large Language Models (MLLMs) to handle real-world tasks.
STEP: Enhancing Video-LLMs' Compositional Reasoning by Spatio-Temporal Graph-guided Self-Training
Video Large Language Models (Video-LLMs) have recently shown strong performance in basic video understanding tasks, such as captioning and coarse-grained question answering, but struggle with compositional reasoning that requires multi-step spatio-temporal inference across object relations, interactions, and events.
AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea
Instruction-based image editing aims to modify specific image elements with natural language instructions.
Unified Generative and Discriminative Training for Multi-modal Large Language Models
Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval, yet struggles with complex scenarios requiring fine-grained semantic differentiation.
Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration
In this paper, we propose UniKE, a novel multimodal editing method that establishes a unified perspective and paradigm for intrinsic knowledge editing and external knowledge resorting.
HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data
Multi-modal Large Language Models (MLLMs) tuned on machine-generated instruction-following data have demonstrated remarkable performance in various multi-modal understanding and generation tasks.
Dancing Avatar: Pose and Text-Guided Human Motion Videos Synthesis with Image Diffusion Model
Our approach employs a pretrained T2I diffusion model to generate each video frame in an autoregressive fashion.
Interactive Data Synthesis for Systematic Vision Adaptation via LLMs-AIGCs Collaboration
Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images.
Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World
Based on that, we further introduce a novel Entangled cross-modal prompt approach for open-world predicate scene graph generation (Epic), where models can generalize to unseen predicates in a zero-shot manner.
