Efficient and accurate camouflaged object detection (COD) poses a challenge
in the field of computer vision. Recent approaches explored the utility of edge
information for network co-supervision, achieving notable advancements.
However, these approaches introduce an extra branch for complex edge
extraction, complicate the model architecture and increases computational
demands. Addressing this issue, our work replicates the effect that animal's
camouflage can be easily revealed under a shifting spotlight, and leverages it
for network co-supervision to form a compact yet efficient single-branch
network, the Co-Supervised Spotlight Shifting Network (CS$^3$Net). The
spotlight shifting strategy allows CS$^3$Net to learn additional prior within a
single-branch framework, obviating the need for resource demanding multi-branch
design. To leverage the prior of spotlight shifting co-supervision, we propose
Shadow Refinement Module (SRM) and Projection Aware Attention (PAA) for feature
refinement and enhancement. To ensure the continuity of multi-scale features
aggregation, we utilize the Extended Neighbor Connection Decoder (ENCD) for
generating the final predictions. Empirical evaluations on public datasets
confirm that our CS$^3$Net offers an optimal balance between efficiency and
performance: it accomplishes a 32.13% reduction in Multiply-Accumulate (MACs)
operations compared to leading efficient COD models, while also delivering
superior performance.

基于移动聚光灯的单支网络，在效率和性能之间取得了最佳平衡，比领先的高效 COD 模型减少了 32.13% 的 MAC 运算，并且在性能上表现出色。

移动的聚光灯：一种简单但高效的单支网络用于穿透伪装

Shifting Spotlight for Co-supervision: A Simple yet Efficient  Single-branch Network to See Through Camouflage

Gait recognition holds the promise to robustly identify subjects based on
walking patterns instead of appearance information. In recent years, this field
has been dominated by learning methods based on two principal input
representations: dense silhouette masks or sparse pose keypoints. In this work,
we propose a novel, point-based Contour-Pose representation, which compactly
expresses both body shape and body parts information. We further propose a
local-to-global architecture, called GaitContour, to leverage this novel
representation and efficiently compute subject embedding in two stages. The
first stage consists of a local transformer that extracts features from five
different body regions. The second stage then aggregates the regional features
to estimate a global human gait representation. Such a design significantly
reduces the complexity of the attention operation and improves efficiency and
performance simultaneously. Through large scale experiments, GaitContour is
shown to perform significantly better than previous point-based methods, while
also being significantly more efficient than silhouette-based methods. On
challenging datasets with significant distractors, GaitContour can even
outperform silhouette-based methods.

通过提出一种新的基于点的 Contour-Pose 表示方法和一种局部到全局的架构，本文在步态识别领域表现出更好的性能和更高的效率，进一步证明在具有显著干扰的数据集上超过轮廓点方法的成果。

GaitContour: 基于轮廓姿势表示的高效步态识别

GaitContour: Efficient Gait Recognition based on a Contour-Pose  Representation

There emerges a promising trend of using large language models (LLMs) to
generate code-like plans for complex inference tasks such as visual reasoning.
This paradigm, known as LLM-based planning, provides flexibility in problem
solving and endows better interpretability. However, current research is mostly
limited to basic scenarios of simple questions that can be straightforward
answered in a few inference steps. Planning for the more challenging multi-hop
visual reasoning tasks remains under-explored. Specifically, under multi-hop
reasoning situations, the trade-off between accuracy and the complexity of
plan-searching becomes prominent. The prevailing algorithms either address the
efficiency issue by employing the fast one-stop generation or adopt a complex
iterative generation method to improve accuracy. Both fail to balance the need
for efficiency and performance. Drawing inspiration from the dual system of
cognition in the human brain, the fast and the slow think processes, we propose
a hierarchical plan-searching algorithm that integrates the one-stop reasoning
(fast) and the Tree-of-thought (slow). Our approach succeeds in performance
while significantly saving inference steps. Moreover, we repurpose the PTR and
the CLEVER datasets, developing a systematic framework for evaluating the
performance and efficiency of LLMs-based plan-search algorithms under reasoning
tasks at different levels of difficulty. Extensive experiments demonstrate the
superiority of our proposed algorithm in terms of performance and efficiency.
The dataset and code will be release soon.

利用大型语言模型（LLM）生成类似代码的计划来进行复杂推理任务，提出一种集成单次推理和思维树的层次化计划搜索算法，平衡效率和性能需求，在多跳视觉推理任务方面表现出优越性能和效率。