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Found 13 papers, 6 papers with code
LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion
Camouflaged vision perception is an important vision task with numerous practical applications.
Large Model Based Referring Camouflaged Object Detection
Our motivation is to make full use of the semantic intelligence and intrinsic knowledge of recent Multimodal Large Language Models (MLLMs) to decompose this complex task in a human-like way.
Prompt Switch: Efficient CLIP Adaptation for Text-Video Retrieval
In text-video retrieval, recent works have benefited from the powerful learning capabilities of pre-trained text-image foundation models (e. g., CLIP) by adapting them to the video domain.
Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering
Based on our formulation, we further propose MulTi-Modal PRompt LearnIng with DecouPLing bEfore InTeraction (TRIPLET), a novel approach that builds on a pre-trained vision-language model and consists of decoupled prompts and prompt interaction strategies to capture the complex interactions between modalities.
Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models
Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity.
Ranked #1 on
Story Visualization
on Pororo
InfoBehavior: Self-supervised Representation Learning for Ultra-long Behavior Sequence via Hierarchical Grouping
Typically, the risk can be identified by jointly considering product content (e. g., title and image) and seller behavior.
TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data
Among ubiquitous multimodal data in the real world, text is the modality generated by human, while image reflects the physical world honestly.
Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs
Large-scale knowledge graphs (KGs) are shown to become more important in current information systems.
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization
Deep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues.
Multi-Task Self-Supervised Learning for Disfluency Detection
The pre-trained network is then fine-tuned using human-annotated disfluency detection training data.
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention
Semantically controlled neural response generation on limited-domain has achieved great performance.
Ranked #5 on
Data-to-Text Generation
on MULTIWOZ 2.1
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning
The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction
Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem.
