Search Results for author:
Found 12 papers, 8 papers with code
Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference
In recent years, the application of multimodal large language models (MLLM) in various fields has achieved remarkable success.
Prompt-based Distribution Alignment for Unsupervised Domain Adaptation
Therefore, in this paper, we first experimentally demonstrate that the unsupervised-trained VLMs can significantly reduce the distribution discrepancy between source and target domains, thereby improving the performance of UDA.
Learning Disentangled Identifiers for Action-Customized Text-to-Image Generation
Experimental results show that existing subject-driven customization methods fail to learn the representative characteristics of actions and struggle in decoupling actions from context features, including appearance.
Check, Locate, Rectify: A Training-Free Layout Calibration System for Text-to-Image Generation
To align the generated image with layout instructions, we present a training-free layout calibration system SimM that intervenes in the generative process on the fly during inference time.
VGDiffZero: Text-to-image Diffusion Models Can Be Zero-shot Visual Grounders
Large-scale text-to-image diffusion models have shown impressive capabilities for generative tasks by leveraging strong vision-language alignment from pre-training.
Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning
Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs.
VoP: Text-Video Co-operative Prompt Tuning for Cross-Modal Retrieval
Many recent studies leverage the pre-trained CLIP for text-video cross-modal retrieval by tuning the backbone with additional heavy modules, which not only brings huge computational burdens with much more parameters, but also leads to the knowledge forgetting from upstream models.
Reference-Limited Compositional Zero-Shot Learning
Compositional zero-shot learning (CZSL) refers to recognizing unseen compositions of known visual primitives, which is an essential ability for artificial intelligence systems to learn and understand the world.
Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation
To solve this problem, we present a plug-in Hierarchical Tree Structure-aware (HTS) method, which not only learns the relationship of FSL and pretext tasks, but more importantly, can adaptively select and aggregate feature representations generated by pretext tasks to maximize the performance of FSL tasks.
Reference-Limited Compositional Learning: A Realistic Assessment for Human-level Compositional Generalization
To narrow the considerable gap between artificial and human intelligence, we propose a new task, namely reference-limited compositional learning (RLCL), which reproduces three core challenges to mimic human perception: compositional learning, few-shot, and few referential compositions.
Pareto Self-Supervised Training for Few-Shot Learning
While few-shot learning (FSL) aims for rapid generalization to new concepts with little supervision, self-supervised learning (SSL) constructs supervisory signals directly computed from unlabeled data.
Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition
However, these approaches only augment the representations of samples with available semantics while ignoring the query set, which loses the potential for the improvement and may lead to a shift between the modalities combination and the pure-visual representation.
