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Found 24 papers, 11 papers with code
Video SimpleQA: Towards Factuality Evaluation in Large Video Language Models
Recent advancements in Large Video Language Models (LVLMs) have highlighted their potential for multi-modal understanding, yet evaluating their factual grounding in video contexts remains a critical unsolved challenge.
Dual Mutual Learning Network with Global-local Awareness for RGB-D Salient Object Detection
RGB-D salient object detection (SOD), aiming to highlight prominent regions of a given scene by jointly modeling RGB and depth information, is one of the challenging pixel-level prediction tasks.
PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos
In this paper, we propose PhysGame as a pioneering benchmark to evaluate physical commonsense violations in gameplay videos.
PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance
In this paper, we identify the key issue as the redundant content in videos.
A Peaceman-Rachford Splitting Approach with Deep Equilibrium Network for Channel Estimation
The main idea is to construct a fixed-point equation for channel estimation, which can be implemented into the deep equilibrium (DEQ) model with a fixed network.
MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval
Text-Video Retrieval (TVR) aims to align and associate relevant video content with corresponding natural language queries.
Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions
This effect is especially visible for open questions and questions of high difficulty or novelty.
Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach
We first identify biased structural evolutions in a dynamic graph based on the evolving trend of vertex degree and then propose FairDGE, the first structurally Fair Dynamic Graph Embedding algorithm.
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter
Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries.
Expert-Guided Extinction of Toxic Tokens for Debiased Generation
Large language models (LLMs) can elicit social bias during generations, especially when inference with toxic prompts.
ST-LLM: Large Language Models Are Effective Temporal Learners
In this paper, we investigate a straightforward yet unexplored question: Can we feed all spatial-temporal tokens into the LLM, thus delegating the task of video sequence modeling to the LLMs?
Surgment: Segmentation-enabled Semantic Search and Creation of Visual Question and Feedback to Support Video-Based Surgery Learning
The segmentation pipeline enables functionalities to create visual questions and feedback desired by surgeons from a formative study.
Context Matters: Data-Efficient Augmentation of Large Language Models for Scientific Applications
In this paper, we explore the challenges inherent to Large Language Models (LLMs) like GPT-4, particularly their propensity for hallucinations, logic mistakes, and incorrect conclusions when tasked with answering complex questions.
RetriBooru: Leakage-Free Retrieval of Conditions from Reference Images for Subject-Driven Generation
Previous methods use the same reference image as the target.
Weighted Joint Maximum Mean Discrepancy Enabled Multi-Source-Multi-Target Unsupervised Domain Adaptation Fault Diagnosis
Despite the remarkable results that can be achieved by data-driven intelligent fault diagnosis techniques, they presuppose the same distribution of training and test data as well as sufficient labeled data.
Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study
Though self-supervised contrastive learning (CL) has shown its potential to achieve state-of-the-art accuracy without any supervision, its behavior still remains under investigated by academia.
HashEncoding: Autoencoding with Multiscale Coordinate Hashing
We present HashEncoding, a novel autoencoding architecture that leverages a non-parametric multiscale coordinate hash function to facilitate a per-pixel decoder without convolutions.
WEKA-Based: Key Features and Classifier for French of Five Countries
This paper describes a French dialect recognition system that will appropriately distinguish between different regional French dialects.
Is Self-Supervised Learning More Robust Than Supervised Learning?
Our insight has implications in improving the downstream robustness of supervised learning.
Shuffle Augmentation of Features from Unlabeled Data for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA), a branch of transfer learning where labels for target samples are unavailable, has been widely researched and developed in recent years with the help of adversarially trained models.
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
Hierarchical reinforcement learning has demonstrated significant success at solving difficult reinforcement learning (RL) tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Modular Architecture for StarCraft II with Deep Reinforcement Learning
We present a novel modular architecture for StarCraft II AI.
Reinforcement Learning with Deep Energy-Based Policies
We propose a method for learning expressive energy-based policies for continuous states and actions, which has been feasible only in tabular domains before.
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks.
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