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Found 11 papers, 5 papers with code
Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models
In this work, we adopt the intuition that the LVLM tends to respond logically consistently for existent objects but inconsistently for hallucinated objects.
Stealthy Attack on Large Language Model based Recommendation
Recently, the powerful large language models (LLMs) have been instrumental in propelling the progress of recommender systems (RS).
CPT: Competence-progressive Training Strategy for Few-shot Node Classification
This could lead the meta-learner to face complex tasks too soon, hindering proper learning.
Decomposition Ascribed Synergistic Learning for Unified Image Restoration
Learning to restore multiple image degradations within a single model is quite beneficial for real-world applications.
Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation
Even worse, the strong statistical correlation might mislead models to learn the spurious preference towards inconsequential modalities.
Ingredient-Oriented Multi-Degradation Learning for Image Restoration
Learning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsic ingredients behind the degradation.
Visual Recognition-Driven Image Restoration for Multiple Degradation With Intrinsic Semantics Recovery
Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images.
Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation
Although having access to multiple modalities might allow us to capture rich information, we argue that the simple coarse-grained fusion by linear combination or concatenation in previous work is insufficient to fully understand content information and item relationships. To this end, we propose a latent structure MIning with ContRastive mOdality fusion method (MICRO for brevity).
Mining Latent Structures for Multimedia Recommendation
To be specific, in the proposed LATTICE model, we devise a novel modality-aware structure learning layer, which learns item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs.
A Survey on Graph Structure Learning: Progress and Opportunities
Specifically, we first formulate a general pipeline of GSL and review state-of-the-art methods classified by the way of modeling graph structures, followed by applications of GSL across domains.
A Graph-based Relevance Matching Model for Ad-hoc Retrieval
To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial.
