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Found 9 papers, 0 papers with code
Bridge 2D-3D: Uncertainty-aware Hierarchical Registration Network with Domain Alignment
In the AMAM, we design an adversarial approach to reduce the domain gap between image and point cloud.
ACEBench: Who Wins the Match Point in Tool Learning?
Normal evaluates function calls in basic scenarios; Special evaluates function calls in scenarios with vague or incomplete instructions; Agent introduces multi-agent interactions to simulate function calling evaluation in real-world multi-turn interactions.
Towards Precise Scaling Laws for Video Diffusion Transformers
Achieving optimal performance of video diffusion transformers within given data and compute budget is crucial due to their high training costs.
Beyond Sight: Towards Cognitive Alignment in LVLM via Enriched Visual Knowledge
Specifically, the VE's representation of visual information may not fully align with LLM's cognitive framework, leading to a mismatch where visual features exceed the language model's interpretive range.
SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs
Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable perceptual and reasoning abilities, typically comprising a Vision Encoder, an Adapter, and a Large Language Model (LLM).
Ranked #71 on
Visual Question Answering
on MM-Vet
CBQ: Cross-Block Quantization for Large Language Models
Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs.
Advanced Efficient Strategy for Detection of Dark Objects Based on Spiking Network with Multi-Box Detection
The whole study proposes a combination of spiked and normal convolution layers as an energy-efficient and reliable object detector model.
Boosting Mobile CNN Inference through Semantic Memory
Human brains are known to be capable of speeding up visual recognition of repeatedly presented objects through faster memory encoding and accessing procedures on activated neurons.
Weight-dependent Gates for Network Pruning
In this paper, a simple yet effective network pruning framework is proposed to simultaneously address the problems of pruning indicator, pruning ratio, and efficiency constraint.
