Synthetic long-context LLM benchmarks (e.g., "needle-in-the-haystack") test
only surface-level retrieval capabilities, but how well can long-context LLMs
retrieve, synthesize, and reason over information across book-length inputs? We
address this question by creating NoCha, a dataset of 1,001 minimally different
pairs of true and false claims about 67 recently-published English fictional
books, written by human readers of those books. In contrast to existing
long-context benchmarks, our annotators confirm that the largest share of pairs
in NoCha require global reasoning over the entire book to verify. Our
experiments show that while human readers easily perform this task, it is
enormously challenging for all ten long-context LLMs that we evaluate: no
open-weight model performs above random chance (despite their strong
performance on synthetic benchmarks), while GPT-4o achieves the highest
accuracy at 55.8%. Further analysis reveals that (1) on average, models perform
much better on pairs that require only sentence-level retrieval vs. global
reasoning; (2) model-generated explanations for their decisions are often
inaccurate even for correctly-labeled claims; and (3) models perform
substantially worse on speculative fiction books that contain extensive
world-building. The methodology proposed in NoCha allows for the evolution of
the benchmark dataset and the easy analysis of future models.

通过创建 NoCha 数据集，我们评估了长文本 LLMs 在检索、综合和推理书籍等长篇输入上的能力，并发现其在全局推理方面普遍具有巨大挑战，并提出了一种能够演化基准数据集并分析未来模型的方法。

一千零一对：对长文本语言模型的 “新” 挑战

One Thousand and One Pairs: A "novel" challenge for long-context  language models

Semantic segmentation aims to classify every pixel of an input image.
Considering the difficulty of acquiring dense labels, researchers have recently
been resorting to weak labels to alleviate the annotation burden of
segmentation. However, existing works mainly concentrate on expanding the seed
of pseudo labels within the image's salient region. In this work, we propose a
non-salient region object mining approach for weakly supervised semantic
segmentation. We introduce a graph-based global reasoning unit to strengthen
the classification network's ability to capture global relations among disjoint
and distant regions. This helps the network activate the object features
outside the salient area. To further mine the non-salient region objects, we
propose to exert the segmentation network's self-correction ability.
Specifically, a potential object mining module is proposed to reduce the
false-negative rate in pseudo labels. Moreover, we propose a non-salient region
masking module for complex images to generate masked pseudo labels. Our
non-salient region masking module helps further discover the objects in the
non-salient region. Extensive experiments on the PASCAL VOC dataset demonstrate
state-of-the-art results compared to current methods.

本文提出一种基于对象挖掘的弱监督语义分割方法，该方法采用图形全局推理单元和自我修正机制，可以在非显著区域激活对象特征，进一步挖掘非显著区域的对象，并通过使用假标签的潜在对象挖掘模块减少虚假负面率。实验结果表明，在 PASCAL VOC 数据集上与当前方法相比有最先进的效果。

基于弱监督语义分割的非显著区域对象挖掘

Non-Salient Region Object Mining for Weakly Supervised Semantic  Segmentation

Globally modeling and reasoning over relations between regions can be
beneficial for many computer vision tasks on both images and videos.
Convolutional Neural Networks (CNNs) excel at modeling local relations by
convolution operations, but they are typically inefficient at capturing global
relations between distant regions and require stacking multiple convolution
layers. In this work, we propose a new approach for reasoning globally in which
a set of features are globally aggregated over the coordinate space and then
projected to an interaction space where relational reasoning can be efficiently
computed. After reasoning, relation-aware features are distributed back to the
original coordinate space for down-stream tasks. We further present a highly
efficient instantiation of the proposed approach and introduce the Global
Reasoning unit (GloRe unit) that implements the coordinate-interaction space
mapping by weighted global pooling and weighted broadcasting, and the relation
reasoning via graph convolution on a small graph in interaction space. The
proposed GloRe unit is lightweight, end-to-end trainable and can be easily
plugged into existing CNNs for a wide range of tasks. Extensive experiments
show our GloRe unit can consistently boost the performance of state-of-the-art
backbone architectures, including ResNet, ResNeXt, SE-Net and DPN, for both 2D
and 3D CNNs, on image classification, semantic segmentation and video action
recognition task.

该研究提出了一种名为 GloRe 的全局推理单元，实现了卷积神经网络对于远距离区域之间的全局关系的建模，能够有效提升图像识别、语义分割和视频行为识别等计算机视觉任务的性能。

基于图的全局推理网络

Graph-Based Global Reasoning Networks

This paper addresses the task of estimating the 6D pose of a known 3D object
from a single RGB-D image. Most modern approaches solve this task in three
steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii)
Select and refine a pose from the pool. This work focuses on the second step.
While all existing approaches generate the hypotheses pool via local reasoning,
e.g. RANSAC or Hough-voting, we are the first to show that global reasoning is
beneficial at this stage. In particular, we formulate a novel fully-connected
Conditional Random Field (CRF) that outputs a very small number of
pose-hypotheses. Despite the potential functions of the CRF being non-Gaussian,
we give a new and efficient two-step optimization procedure, with some
guarantees for optimality. We utilize our global hypotheses generation
procedure to produce results that exceed state-of-the-art for the challenging
"Occluded Object Dataset".

本论文提出了一种使用全局推理的新型全连接 CRF 方法来生成位姿假设池，相较于基于局部推理的方法，该方法提供了超过当前难度较大的 “被遮挡物体数据集” 位姿估计最新技术的结果。