{"id":"https://openalex.org/W7154510748","doi":"https://doi.org/10.48550/arxiv.2604.12508","title":"From Attenuation to Attention: Variational Information Flow Manipulation for Fine-Grained Visual Perception","display_name":"From Attenuation to Attention: Variational Information Flow Manipulation for Fine-Grained Visual Perception","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154510748","doi":"https://doi.org/10.48550/arxiv.2604.12508"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.12508","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12508","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.12508","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114196482","display_name":"Jilong Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Jilong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133637934","display_name":"Yang Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8429999947547913,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8429999947547913,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.06719999760389328,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.019999999552965164,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7085999846458435},{"id":"https://openalex.org/keywords/information-flow","display_name":"Information flow","score":0.6029000282287598},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.5999000072479248},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5936999917030334},{"id":"https://openalex.org/keywords/visual-perception","display_name":"Visual perception","score":0.48010000586509705},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.3666999936103821},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.3433000147342682},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3386000096797943}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7085999846458435},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7045000195503235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6444000005722046},{"id":"https://openalex.org/C2779136372","wikidata":"https://www.wikidata.org/wiki/Q10283002","display_name":"Information flow","level":2,"score":0.6029000282287598},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5999000072479248},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5936999917030334},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.48010000586509705},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4546999931335449},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.3666999936103821},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.3239000141620636},{"id":"https://openalex.org/C184652730","wikidata":"https://www.wikidata.org/wiki/Q2357982","display_name":"Attenuation","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.32190001010894775},{"id":"https://openalex.org/C50335755","wikidata":"https://www.wikidata.org/wiki/Q483247","display_name":"Phenomenon","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C52622258","wikidata":"https://www.wikidata.org/wiki/Q131222","display_name":"Information theory","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2808000147342682},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.25290000438690186},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.12508","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12508","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.12508","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12508","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.7159518003463745,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"Multimodal":[1],"Large":[2],"Language":[3],"Models":[4],"(MLLMs)":[5],"have":[6],"demonstrated":[7],"impressive":[8],"capabilities":[9],"in":[10,17,58,154],"general":[11],"visual":[12,29,43,107,140],"understanding,":[13],"they":[14],"frequently":[15],"falter":[16],"fine-grained":[18,42,137,157],"perception":[19,158],"tasks":[20],"that":[21,143],"require":[22],"identifying":[23],"tiny":[24],"objects":[25],"or":[26,48],"discerning":[27],"subtle":[28],"relationships.":[30],"We":[31],"attribute":[32],"this":[33,75,83],"limitation":[34],"to":[35,72,104,110],"Visual":[36],"Attenuation:":[37],"a":[38,59,94,99,115,119],"phenomenon":[39],"where":[40],"sparse":[41],"signals":[44],"are":[45],"prematurely":[46],"suppressed":[47],"diluted":[49],"by":[50],"dominant":[51],"textual":[52],"tokens":[53],"during":[54,63],"network":[55],"propagation,":[56],"resulting":[57],"\"loss":[60],"of":[61,78,159],"focus\"":[62],"the":[64,87,106,111,156],"deep-level":[65],"decision-making":[66],"process.":[67],"Existing":[68],"input-centric":[69],"solutions":[70],"fail":[71],"fundamentally":[73],"reverse":[74],"intrinsic":[76],"mechanism":[77],"information":[79],"loss.":[80],"To":[81],"address":[82],"challenge,":[84],"we":[85],"propose":[86],"Variational":[88,101],"Information":[89],"Flow":[90],"(VIF)":[91],"framework.":[92],"Adopting":[93],"probabilistic":[95],"perspective,":[96],"VIF":[97,122,144],"leverages":[98],"Conditional":[100],"Autoencoder":[102],"(CVAE)":[103],"model":[105],"saliency":[108],"relevant":[109],"question-answer":[112],"pair":[113],"as":[114],"latent":[116],"distribution.":[117],"As":[118],"plug-and-play":[120],"module,":[121],"can":[123],"be":[124],"integrated":[125],"into":[126],"existing":[127],"architectures.":[128],"Extensive":[129],"evaluations":[130],"across":[131],"diverse":[132],"benchmarks,":[133],"covering":[134],"General":[135],"VQA,":[136],"perception,":[138],"and":[139],"grounding,":[141],"demonstrate":[142],"yields":[145],"competitive":[146],"improvements":[147],"over":[148],"previous":[149],"methods,":[150],"validating":[151],"its":[152],"effectiveness":[153],"enhancing":[155],"MLLMs.":[160]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-16T00:00:00"}
