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Found 19 papers, 6 papers with code
VideoReasonBench: Can MLLMs Perform Vision-Centric Complex Video Reasoning?
Recent studies have shown that long chain-of-thought (CoT) reasoning can significantly enhance the performance of large language models (LLMs) on complex tasks.
Twin Co-Adaptive Dialogue for Progressive Image Generation
Modern text-to-image generation systems have enabled the creation of remarkably realistic and high-quality visuals, yet they often falter when handling the inherent ambiguities in user prompts.
Kimi-VL Technical Report
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities - all while activating only 2. 8B parameters in its language decoder (Kimi-VL-A3B).
Advanced Deep Learning Methods for Protein Structure Prediction and Design
After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic.
Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy
While some methods focus on enhancing prompts to make the generated images fit user needs, the model is still hard to understand users' real needs, especially for non-expert users.
RaSeRec: Retrieval-Augmented Sequential Recommendation
Although prevailing supervised and self-supervised learning (SSL)-augmented sequential recommendation (SeRec) models have achieved improved performance with powerful neural network architectures, we argue that they still suffer from two limitations: (1) Preference Drift, where models trained on past data can hardly accommodate evolving user preference; and (2) Implicit Memory, where head patterns dominate parametric learning, making it harder to recall long tails.
Ranked #2 on
Sequential Recommendation
on Amazon-Beauty
SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation
Recent studies in Retrieval-Augmented Generation (RAG) have investigated extracting evidence from retrieved passages to reduce computational costs and enhance the final RAG performance, yet it remains challenging.
SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning
However, applying the highly capable SSMs to SNNs for long sequences learning poses three major challenges: (1) The membrane potential is determined by the past spiking history of the neuron, leading to reduced efficiency for sequence modeling in parallel computing scenarios.
MSSDA: Multi-Sub-Source Adaptation for Diabetic Foot Neuropathy Recognition
To advance DFN research, we have collected a novel dataset comprising continuous plantar pressure data to recognize diabetic foot neuropathy.
SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models
Known as low energy consumption networks, spiking neural networks (SNNs) have gained a lot of attention within the past decades.
PhysMamba: State Space Duality Model for Remote Physiological Measurement
Remote Photoplethysmography (rPPG) enables non-contact physiological signal extraction from facial videos, offering applications in psychological state analysis, medical assistance, and anti-face spoofing.
Towards Detecting LLMs Hallucination via Markov Chain-based Multi-agent Debate Framework
The advent of large language models (LLMs) has facilitated the development of natural language text generation.
Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection
In the algorithm selection research, the discussion surrounding algorithm features has been significantly overshadowed by the emphasis on problem features.
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives
Meanwhile, it is important to note that the correlation, like ranking correlation, plays a significant role in NR-IQA tasks.
Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation
The high-dimensional algorithm representation extracted by LLM, after undergoing a feature selection module, is combined with the problem representation and passed to the similarity calculation module.
RCP-RF: A Comprehensive Road-car-pedestrian Risk Management Framework based on Driving Risk Potential Field
Recent years have witnessed the proliferation of traffic accidents, which led wide researches on Automated Vehicle (AV) technologies to reduce vehicle accidents, especially on risk assessment framework of AV technologies.
TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene
In our experiments, the performance gain brought by GridMask is stronger than spectrum augmentation in ASCs.
Multi-label Causal Variable Discovery: Learning Common Causal Variables and Label-specific Causal Variables
Analyzing these mechanisms, we provide the theoretical property of common causal variables, based on which the discovery and distinguishing algorithm is designed to identify these two types of variables.
