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Found 6 papers, 0 papers with code
ExACT: Language-guided Conceptual Reasoning and Uncertainty Estimation for Event-based Action Recognition and More
Then, we propose a conceptual reasoning-based uncertainty estimation module, which simulates the recognition process to enrich the semantic representation.
UniBind: LLM-Augmented Unified and Balanced Representation Space to Bind Them All
To make this possible, we 1) construct a knowledge base of text embeddings with the help of LLMs and multi-modal LLMs; 2) adaptively build LLM-augmented class-wise embedding center on top of the knowledge base and encoded visual embeddings; 3) align all the embeddings to the LLM-augmented embedding center via contrastive learning to achieve a unified and balanced representation space.
Image Anything: Towards Reasoning-coherent and Training-free Multi-modal Image Generation
It extracts entity features from the multi-modal representations powered by our specially constructed entity knowledge graph; 2) Attribute Fusion Branch adeptly preserves and processes the attributes.
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event Camera
The ability to detect objects in all lighting (i. e., normal-, over-, and under-exposed) conditions is crucial for real-world applications, such as self-driving. Traditional RGB-based detectors often fail under such varying lighting conditions. Therefore, recent works utilize novel event cameras to supplement or guide the RGB modality; however, these methods typically adopt asymmetric network structures that rely predominantly on the RGB modality, resulting in limited robustness for all-day detection.
EventBind: Learning a Unified Representation to Bind Them All for Event-based Open-world Understanding
Accordingly, we first introduce a novel event encoder that subtly models the temporal information from events and meanwhile, generates event prompts for modality bridging.
CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout
To tackle the issue of 'guidance collapse' and enhance consistency, we propose a novel framework, dubbed CompoNeRF, by integrating an editable 3D scene layout with object specific and scene-wide guidance mechanisms.
