With　the　rapid　development　of　artificial　intelligence,　deep　neural　networks　have　achieved　great　performance　in　many　tasks.　However,　traditional　deep　learning　methods　require　a　large　amount　of　training　data,　which　may　not　be　available　in　certain　practical　scenarios.　In　contrast,　few-shot　learning　aims　to　learn　a　model　that　can　be　readily　adapted　to　new　unseen　classes　from　only　one　or　a　few　labeled　examples.　Despite　this　success,　most　existing　methods　rely　on　pre-trained　feature　extractor　networks　trained　with　global　features,　ignoring　the　discrimination　of　local　features,　and　weak　generalization　capabilities　limit　their　performance.　To　address　the　problem,　according　to　the　human＇s　coarse-to-fine　cognition　paradigm,　we　propose　an　Inverted　Pyramid　Network　with　Spatial-adapted　and　Task-oriented　Tuning　(TIPN)　for　few-shot　learning.　Specifically,　the　proposed　framework　represents　local　features　for　categories　that　are　difficult　to　distinguish　by　global　features　and　recognizes　objects　from　both　global　and　local　perspectives.　Moreover,　to　ensure　the　calibration　validity　of　the　proposed　model　at　the　local　stage,　we　introduce　the　Spatial-adapted　Layer　to　preserve　the　discriminative　global　representation　ability　of　the　pre-trained　backbone　network.　Meanwhile,　as　the　representations　extracted　from　the　past　categories　are　not　applicable　to　the　current　new　tasks,　we　further　propose　the　Task-oriented　Tuning　strategy　to　adjust　the　parameters　of　the　Batch　Normalization　layer　in　the　pre-trained　feature　extractor　network,　to　explicitly　transfer　knowledge　from　base　classes　to　novel　classes　according　to　the　support　samples　of　each　task.　Extensive　experiments　conducted　on　multiple　benchmark　datasets　demonstrate　that　our　method　can　significantly　outperform　many　state-of-the-art　few-shot　learning　methods.