Beyond Referring Expressions: Scenario Comprehension Visual Grounding

Dimensions overall score 7.0

• Existing visual grounding benchmarks primarily evaluate alignment between image regions and literal referring expressions, where models can often succeed by matching a prominent named category.limitation 95%
  Evidencepartial

  Existing visual grounding benchmarks primarily evaluate alignment between image regions and literal referring expressions, where models can often succeed by matching a prominent named category.

  Implicationpartial

  Directly stated in the abstract as the motivation for the work

  Verificationpartial

  partial

• Scenario-based visual grounding is more challenging than standard referring expression grounding because targets must be inferred from roles, intentions, and relational context rather than explicit naming.technical 90%
  Evidencepartial

  We explore a complementary and more challenging setting of scenario-based visual grounding, where the target must be inferred from roles, intentions, and relational context rather than explicit naming.

  Implicationpartial

  Directly stated in the abstract as the core problem definition

  Verificationpartial

  partial

• The Referring Scenario Comprehension (RSC) benchmark contains approximately 31k training examples, 4k in-domain test examples, and a 3k out-of-distribution split with unseen object categories.method 95%
  Evidencepartial

  RSC contains approximately 31k training examples, 4k in-domain test examples, and a 3k out-of-distribution split with unseen object categories.

  Implicationpartial

  Direct numeric evidence provided in the abstract

  Verificationpartial

  partial

• The ScenGround method combines supervised warm-starting with difficulty-aware reinforcement learning for scenario-based visual grounding.method 90%
  Evidencepartial

  We further propose ScenGround, a curriculum reasoning method serving as a reference point for this setting, combining supervised warm-starting with difficulty-aware reinforcement learning.

  Implicationpartial

  Directly stated in the abstract as the proposed method

  Verificationpartial

  partial

• Scenario-based queries expose systematic failures in current models that standard benchmarks do not reveal.result 90%
  Evidencepartial

  Experiments show that scenario-based queries expose systematic failures in current models that standard benchmarks do not reveal

  Implicationpartial

  Directly stated in the abstract as an experimental finding

  Verificationpartial

  partial

• Curriculum training improves performance on challenging slices and transfers to standard benchmarks.result 85%
  Evidencepartial

  curriculum training improves performance on challenging slices and transfers to standard benchmarks.

  Implicationpartial

  Directly stated in the abstract as an experimental result

  Verificationpartial

  partial

• RSC benchmark queries are paragraph-length texts that describe object roles, user goals, and contextual cues, including deliberate references to distractor objects.method 90%
  Evidencepartial

  The queries in this benchmark are paragraph-length texts that describe object roles, user goals, and contextual cues, including deliberate references to distractor objects that often require deep understanding to resolve.

  Implicationpartial

  Direct description of the benchmark characteristics in the abstract

  Verificationpartial

  partial

• Each RSC instance is annotated with interpretable difficulty tags for uniqueness, clutter, size, overlap, and position which expose distinct failure modes.method 90%
  Evidencepartial

  Each instance is annotated with interpretable difficulty tags for uniqueness, clutter, size, overlap, and position which expose distinct failure modes and support fine-grained analysis.

  Implicationpartial

  Direct description of benchmark features in the abstract

  Verificationpartial

  partial

Competitive landscape

A new benchmark and curriculum learning method for visual grounding that understands object roles and context, going beyond literal descriptions to enable more robust AI perception.